U.S. patent application number 13/854002 was filed with the patent office on 2014-01-30 for systems and methods for antibody engineering.
This patent application is currently assigned to DNA Twopointo, Inc.. Invention is credited to Sridhar GOVINDARAJAN, Claes GUSTAFSSON, Jeremy Minshull.
Application Number | 20140032186 13/854002 |
Document ID | / |
Family ID | 34119818 |
Filed Date | 2014-01-30 |
United States Patent
Application |
20140032186 |
Kind Code |
A1 |
GUSTAFSSON; Claes ; et
al. |
January 30, 2014 |
SYSTEMS AND METHODS FOR ANTIBODY ENGINEERING
Abstract
Methods, computer systems, and computer program products for
biopolymer engineering. A variant set for a biopolymer of interest
is constructed by identifying, using a plurality of rules, a
plurality of positions in the biopolymer of interest and, for each
respective position in the plurality of positions, substitutions
for the respective position. The plurality of positions, and the
substitutions for each respective position in the plurality of
positions collectively defined a biopolymer sequence space. A
variant set comprising a plurality of variants of the biopolymer of
interest is selected. A property of all or a portion of the
variants in the variant set is measured. A sequence-actively
relationship is modeled between (i) one or more substitutions at
one or more positions of the biopolymer of interest represented by
the variant set and (ii) the property measured for all or the
portion of the variants in the variant set. The variant set is
redefined to comprise variants that include substitutions in the
plurality of positions that are selected based on function of the
sequence-activity relationship.
Inventors: |
GUSTAFSSON; Claes; (Belmont,
CA) ; GOVINDARAJAN; Sridhar; (Redwood City, CA)
; Minshull; Jeremy; (Los Altos, CA) |
Assignee: |
DNA Twopointo, Inc.
Menlo Park
CA
|
Family ID: |
34119818 |
Appl. No.: |
13/854002 |
Filed: |
March 29, 2013 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
12726843 |
Mar 18, 2010 |
8412461 |
|
|
13854002 |
|
|
|
|
10566954 |
Jan 31, 2006 |
|
|
|
PCT/US04/24751 |
Jul 30, 2004 |
|
|
|
12726843 |
|
|
|
|
60536862 |
Jan 15, 2004 |
|
|
|
60536357 |
Jan 14, 2004 |
|
|
|
60491815 |
Aug 1, 2003 |
|
|
|
Current U.S.
Class: |
703/2 ;
703/11 |
Current CPC
Class: |
C07K 16/461 20130101;
G16B 20/00 20190201; G16B 40/00 20190201; C07K 2317/76 20130101;
G16B 15/00 20190201; C07K 16/1027 20130101 |
Class at
Publication: |
703/2 ;
703/11 |
International
Class: |
G06F 19/18 20060101
G06F019/18 |
Claims
1. A method for constructing a variant set for an antibody of
interest, the method comprising: a) identifying, using a plurality
of rules, a plurality of positions in said antibody of interest
and, for each respective position in said plurality of positions,
one or more substitutions for the respective position, wherein the
plurality of positions and the one or more substitutions for each
respective position in the plurality of positions collectively
define an antibody sequence space; b) selecting a variant set,
wherein said variant set comprises a plurality of variants of said
antibody of interest and wherein said variant set is a subset of
said antibody sequence space; c) measuring a property of all or a
portion of the variants in said variant set; d) modeling a
sequence-activity relationship between (i) one or more
substitutions at one or more positions of the antibody of interest
represented by the variant set and (ii) the property measured for
all or said portion of the variants in the variant set; and e)
redefining said variant set to comprise variants that include
substitutions in said plurality of positions that are selected
based on a function of said sequence-activity relationship.
2. The method of claim 1, the method further comprising repeating
said measuring, modeling, and, optionally, said redefining, until a
variant in said variant set exhibits a value for said property that
exceeds a predetermined value.
3. The method of claim 2 wherein said predetermined value is a
value that is greater than the value for the property that is
exhibited by said antibody of interest
4. The method of claim 1, the method further comprising repeating
said measuring, modeling, and, optionally, said redefining, until a
variant in said variant set exhibits a value for said property that
is less than a predetermined value.
5. The method of claim 4 wherein said predetermined value is a
value that is less than the value for the property that is
exhibited by said antibody of interest.
6. The method of claim 1, the method further comprising repeating
said measuring, modeling, and, optionally, said redefining, a
predetermined number of times.
7. The method of claim 6 wherein said predetermined number of times
is two, three, four, or five
8. The method of claim 1 wherein said sequence-activity,
relationship comprises a plurality of values and wherein each value
in said plurality of values describes a relationship between (i) a
substitution at a position in said plurality of positions
represented by said all or said portion of the variants in said
variant set and said property, (ii) a plurality of substitutions at
a position in said plurality of positions represented by said all
or said portion of the variants in said variant set and said
property, or (iii) one or more substitutions in one or more
positions in said plurality of positions represented by said all or
said portion of the variants in said variant set and said
property.
9. The method of claim 8 wherein said modeling comprises
regressing:
V.sub.measured=W.sub.11P.sub.1S.sub.1+W.sub.12P.sub.1S.sub.2++W.sub.1NP.s-
ub.1S.sub.N++W.sub.M1P.sub.MS.sub.1+W.sub.M2P.sub.MS.sub.2+W.sub.MNP.sub.M-
S.sub.N wherein, V.sub.measured represents the property measured in
variants in said variant set; W.sub.MN=is a value in said plurality
of values; P.sub.M=is a position in said antibody of interest in
said plurality of positions in said antibody of interest; and
S.sub.N=is a substitution in the one or more positions for a
position in the plurality of positions in said antibody of
interest.
10. The method of claim 9 wherein said regressing comprises linear
regression, nonlinear regression, logistic regression, multivariate
data analysis, or partial least squares projection to latent
variables.
11. The method of claim 1 wherein said modeling comprises
computation of a neural network, computation of a bayesian model, a
generalized additive model, a support vector machine, or
classification using a regression tree.
12. The method of claim 1 wherein said modeling comprises boosting
or adaptive boosting.
13. The method of claim 1 wherein said redefining further
comprises: computing a predicted score for a population of variants
of said antibody of interest using said sequence-activity
relationship, wherein each variant in said population of variants
includes a substitution at one or more positions in said plurality
of positions in said antibody of interest; and selecting said
variant set from among said population of variants as a function of
the predicted score received by each variant in said set of
variants.
14. The method of claim 13, the method further comprising ranking
said population of variants, wherein each variant in said
population of variants is ranked based on the predicted score
received by the variant based upon the sequence-activity
relationship; and said selecting comprising accepting a
predetermined percentage of the top ranked variants in said
population of variants for said variant set.
15. The method of claim 13, wherein a respective variant in said
population of variants is selected for said variant set when the
predicted score of the respective variant exceeds a predetermined
value.
16. The method of claim 1 wherein said redefining step (e) further
comprises redefining said variant set to comprise one or more
variants each having a substitution in a position in said plurality
of positions not present in any variant in the variant set selected
by said selecting step (b).
17. The method of claim 1 wherein said modeling a sequence-activity
relationship (d) further comprises modeling a plurality of
sequence-activity relationships, wherein each respective
sequence-activity relationship in said plurality of
sequence-activity relationships describes the relationship between
(i) one or more substitutions at one or more positions of the
antibody of interest represented by the variant set and (ii) the
property measured for all or said portion of the variants in the
variant set; and said redefining said variant set (e) comprises
redefining said variant set to comprise variants that include
substitutions in said plurality of positions that are selected
based on a combination of said plurality of sequence-activity
relationships.
18. The method of claim 17, the method further comprising:
repeating said measuring based upon said redefined variant set,
wherein a property of all or a portion of the variants in the
redefined variant set is measured; and weighting each respective
sequence-activity relationship in said plurality of sequence
activity relationships based on an agreement between (i) measured
values for the property of variants in said redefined variant set
and (ii) values for the property of variants in said redefined
variant set that were predicted by said respective
sequence-activity relationship, wherein a first sequence-activity
relationship that achieves better agreement between measured and
predicted values than a second sequence-activity relationship
receives a higher weight than said second sequence-activity
relationship.
19. The method of claim 17 wherein said redefining step (e) further
comprises redefining said variant set to comprise one or more
variants each having a substitution in a position in said plurality
of positions not present in any variant in the variant set selected
by said selecting step (b).
20. The method of claim 18 wherein said redefining step (e) further
comprises redefining said variant set to comprise one or more
variants each having a substitution in a position in said plurality
of positions not present in any variant in the variant set selected
by said selecting step (b).
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims benefit, under 35 U.S.C.
.sctn.119(e), of U.S. Provisional Patent Application No. 60/491,815
filed on Aug. 1, 2003 which is incorporated herein, by reference,
in its entirety. This application also claims benefit, under 35
U.S.C. .sctn.119(e), of U.S. Provisional Patent Application No.
60/536,357 filed on Jan. 14, 2004 which is incorporated herein, by
reference, in its entirety. This application also claims benefit,
under 35 U.S.C. .sctn.119(e), of U.S. Provisional Patent
Application No. 60/536,862 filed on Jan. 15, 2004 which is
incorporated herein, by reference, in its entirety. This
application is a continuation of U.S. patent application Ser. No.
12/726,843, filed on Mar. 18, 2010, which is a continuation of U.S.
patent application Ser. No. 10/566, 954, now U.S. Pat. No.
8,005,620, issued on Aug. 23, 2011, which is a 35 U.S.C. .sctn.371
national stage filing of PCT/US2004/024751, filed Jul. 30,
2004.
1. FIELD OF THE INVENTION
[0002] The field of this invention relates to computer systems and
methods for designing sets of antibody variants and tools for
relating the functional properties of such antibodies to their
sequences. These relationships can then be used to determine the
relationship between an antibody's sequence and commercially
relevant properties of that antibody. Such sequence-function
relationships may be used to design and synthesize commercially
useful antibody compositions.
2. BACKGROUND OF THE INVENTION
[0003] Because of the immense size of sequence space, there is no
effective way to systematically screen all possible permutations of
an antibody for a desired property. To test each possible amino
acid at each position in an antibody, rapidly leads to such a large
number of molecules to be tested such that no available methods of
synthesis or testing are feasible. Furthermore, most molecules
generated in such a way would lack any measurable level of the
desired property. Total sequence space is very large and the
functional solutions in this space are sparsely distributed.
[0004] Two primary approaches have to date been used to identify
antibody molecules with desired properties: mechanistic and
empirical. There are significant limitations to both of these
approaches. The mechanistic approach is often hampered by
insufficient knowledge of the system to be improved, meaning either
that considerable resources must be devoted to characterizing the
system (for example by obtaining high quality protein crystal
structures and relating these to the properties of interest), or
that meaningful predictions cannot be made. In contrast, the
empirical approach requires no mechanistic understanding, but
relies upon direct measurements of an antibody's properties to
select those variants that are improved. This strength is also its
weakness; large numbers of variants cannot typically be tested
under conditions that are identical to those of the final
application. High throughput screens are widely used to provide
surrogate measurements of the properties of interest, but these are
often inadequate: binding of an antibody to an antigen is often an
inadequate predictor of clinical or diagnostic function.
[0005] Empirical engineering of antibodies relies upon creating and
testing sets of variants, then using this information to design and
synthesize subsequent sets of variants that are enriched for
components that contribute to the desired activity. A key
limitation for any empirical antibody engineering is in developing
a good assay for antibody function. The assay must measure antibody
properties that are relevant to the final application, but must
also be capable of testing a sufficient number of variants to
identify what may be only a small fraction that are actually
improved. The difficulty of creating such an assay is particularly
relevant when optimizing antibodies for complex functions that are
difficult to measure in high throughput. Examples include reduction
of viral titer or the killing of tumor cells.
[0006] Large numbers of variants cannot typically be tested under
conditions that are identical to those of the final application.
High throughput screens are widely used to provide surrogate
measurements of the properties of interest, but these are often
inadequate. As examples, binding of an antibody to an antigen in a
phage display assay can have little bearing on its ultimate
usefulness as a therapeutic protein.
[0007] Limitations in current methods for searching through
antibody sequences for specific commercially relevant
functionalities creates a need in the art for methods that can
design and synthesize small numbers of variants for functional
testing and that can use the resulting sequence and functional
information to design and synthesize small numbers of variants
improved for a desired commercially useful activity. Limitations in
current methods for choosing surrogate screens appropriate for
empirical antibody engineering creates a need in the art for
methods that can design and create small numbers of variants that
can then be tested for specific commercially relevant
functionalities.
3. SUMMARY OF THE INVENTION
[0008] The systems and methods described here apply novel
computational biology and data mining techniques to important
molecular design problems. In particular, novel ways to map
antibody sequence space are described. Such maps are used to direct
perturbations or modifications of the antibody sequences in order
to perturb or modify the activity of the antibodies in a controlled
fashion.
[0009] Methods are disclosed for biological engineering using the
design and synthesis of a set of sequences containing designed
substitutions that are statistically representative of a sequence
space, and that contain a high fraction of antibodies possessing
desired properties. In addition to its functionality, each antibody
is also designed to maximize the information that the set of
antibodies contains regarding the contribution of substitutions to
the desired antibody properties and to the contributions resulting
from interactions between substitutions. This in essence is a map
of the sequence space that can also be used to design perturbations
to modify the functionality of the antibody as desired.
[0010] The information used to create the substitutions that define
the sequence space can be derived from one or more of (i) multiple
sequence alignments, (ii) phylogenetic reconstructions of ancestral
sequences, (iii) analysis of families or superfamilies of
antibodies related by sequence, structure, function or partial
function, (iv) analysis of monomer substitution probabilities
within classes of antibody, (v) three dimensional structures (e.g.,
molecular models, X-ray crystallographic structures, nuclear
magnetic resonance models, molecular dynamic simulations), (vi)
immunogenic constraints, (vii) prior knowledge about the structure
and/or function of the sequences upon which design of the antibody
set is to be based, or (viii) any similar information pertaining to
a related or homologous antibody. In one embodiment of the
invention, this process is automated by use of an expert system
that acquires domain knowledge and captures it is a knowledge
database. This process can provide a score or rank order of
substitutions to be incorporated, and a reasoning based on user
specified constraints and domain specific data.
[0011] Generally speaking, the first step in the design and
manufacture of the statistically representative sequence sets of
this invention is the definition of the initial sequence space to
be searched. This involves defining one or more reference
sequences, identifying positions that are likely to tolerate
alteration, and identifying substitutions at these positions that
are likely to be acceptable or to produce desired changes in the
properties of the antibody. All possible combinatorial strings of
polymeric biological molecules define the total defined sequence
space to be searched. Each substitution at each position is
typically enumerated in silico and the acceptability defined
computationally. Desirability or acceptability of each possible
substitution is calculated according to one or more criteria. Such
calculations can be performed by a computational system using the
knowledge database, user specified constraints, and/or domain and
antibody specific data.
[0012] The present invention also provides a more formal systematic
method for selecting substitution positions. The use of a formal
system involves quantitative scores and/or filters for assessing
the favorability of substitution positions and the substitutions
possible at those positions. Formalizing the system for
substitution selection allows for the development of an automated
system for antibody optimization or humanization. The parameters,
filters and scores can be adjusted based on data from the
scientific literature and data from experiments designed or
interpreted by the automated system. By adjusting the scores and
filters, substitutions that are predicted to be favorable can be
aligned with those found experimentally to be favorable. Continuous
refinement of these scores and filters based on experimental or
computational data provides a way for the antibody optimization
system to learn and improve. This formalization and learning
capability are an aspect of the invention.
[0013] The second step in the design and manufacture of the
statistically representative sequence sets of this invention is to
define a subspace of the total sequence space to be searched in
each iteration of the synthesis testing and correlating process.
Typically the total allowed space matrix contains
10.sup.5-10.sup.50 antibodies, many orders of magnitude larger than
can be synthesized and measured under commercially relevant
conditions. Such commercially relevant conditions are presently
limited to numbers in the range of 10.sup.1-10.sup.3. The number of
antibody variants that can be synthesized and tested under
appropriate conditions is defined by the availability of resources.
The number of variant positions and the number of substitutions
that can be tested at each of those positions is then calculated,
such that each substitution will be present in a statistically
representative fraction of the set of antibodies to be synthesized.
Additionally, when using search methods like Tabu, Ant optimization
or similar techniques, the space can be searched on a sequence by
sequence basis by using a memory of the space that has been visited
previously and the properties encountered.
[0014] Typical experimental design methods can introduce more
changes in an antibody than the antibody can tolerate to remain
functional. Adaptations of these methods, for example by using
covering algorithms to reduce the total number of substitutions in
each antibody variant, while maximizing the number of different
combinations of pairs of substitutions is another aspect of the
invention.
[0015] The third step in the design and manufacture of the
statistically representative sequence sets (or sequence sets
relevant for specific optimzation techniques) of this invention is
to create a set of variant antibodies. This can be performed by
synthesizing the antibody sequences defined and designed in the
first two steps. The systematic design of such variants is one
aspect of the present invention. The antibodies can be synthesized
individually, or in a multiplexed set that is subsequently
deconvoluted by sequencing or some other appropriate method.
Alternatively, the antibodies can be created as a library of
variants. Many methods have been described in the art for creating
such libraries. See, for example, Stemmer (1994) Proc Natl Acad Sci
USA 91: 10747-51; Stemmer (1994) Nature 370: 389-91; Crameri et al.
(1996) Nat Med 2: 100-2; Crameri et al. (1998) Nature 391: 288-291;
Ness et al. (1999) Nat Biotechnol 17: 893-896; Volkov et al. (1999)
Nucleic Acids Res 27: e18; Volkov et al. (2000) Methods Enzymol
328: 447-56; Volkov et al. (2000) Methods Enzymol 328: 456-63; Coco
et al. (2001) Nat Biotechnol 19: 354-9; Gibbs et al. (2001) Gene
271: 13-20; Ninkovic et al. (2001) Biotechniques 30: 530-4, 536;
Coco et al. (2002) Nat Biotechnol 20: 1246-50; Ness et al. (2002)
Nat Biotechnol 20: 1251-5; Aguinaldo et al. (2003) Methods Mol Biol
231: 105-10; Coco (2003) Methods Mol Biol 231: 111-27; and Sun et
al. (2003) Biotechniques 34: 278-80, 282, 284 passim.
Alternatively, specifically designed antibodies can be synthesized
individually.
[0016] After synthesis, the designed set(s) of antibodies are
characterized functionally to measure the properties of interest.
This requires the development of an assay or surrogate assay
faithful to the property or properties of ultimate interest and to
test some members of the set of variants for more than one
property, including the property of ultimate interest. Data mining
techniques are then employed to characterize the functions of the
variants and to derive a relationship between antibody sequences
and properties. Optionally, the characterization data can be used
to provide information in a subsequent iteration of the method,
aiding in the design of a subsequent set of statistically
representative variants that can be synthesized and tested to
obtain a molecule with even more desirable properties. The data
from additional iterations of this process can also be used to
refine the data mining algorithms and models produced from the
first set of data. The knowledge created about the sequence space
can in turn be incorporated into the knowledge database for
evaluating the substitutions in the light of this data and
recalculating the scores or rank order of the substitutions. These
processes are aspects of the present invention.
[0017] Additionally, combinations of the methods described herein
can be made with other techniques such as directed evolution, DNA
shuffling, family shuffling and/or systematic scanning approaches.
These can be performed in any order and for any number of
iterations to produce the products described herein. All such
combinations are within the scope of the invention.
4. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] FIG. 1 illustrates an overview of the architecture of an
Expert System in accordance with an embodiment of the present
invention.
[0019] FIG. 2 illustrates a flowchart for an antibody engineering
method using integrated information sources to choose initial
substitutions, and sequence-activity relationships to assess them
in accordance with an embodiment of the present invention.
[0020] FIG. 3 is a schematic representation of a method for
selecting amino acid substitutions for the optimization or
humanization of antibodies in accordance with an embodiment of the
present invention.
[0021] FIG. 4 illustrates a method for calculation of weights (e.g.
contributions to activity) for each amino acid substitution in
accordance with an embodiment of the present invention.
[0022] FIG. 5 illustrates a method for calculation of weights
(e.g., contributions to activity) for each substitution in
accordance with an embodiment of the present invention. This method
provides information about the confidence of each weight by
comparison with weights obtained from randomized data.
[0023] FIG. 6 illustrates the amino acid sequence of wild type
proteinase K, reported by Gunkel et al. (1989) Eur J Biochem 179:
185-194, modified by (i) replacement of the fungal leader peptide
with an E. coli leader peptide, amino acids -20 to -1 (SEQ ID No.
1), and (ii) addition of a histidine tag to the C terminus (amino
acids 372-377), together with a ValAsp preceding the tag (amino
acids 370 and 371) to accommodate cloning sites in the nucleic acid
sequence.
[0024] FIGS. 7A and 7B illustrates the nucleotide sequence of
proteinase K optimized for expression in E coli. The E coli leader
peptide (amino acids -20 to -1 in FIG. 6) are encoded by
nucleotides -60 to -1 in FIGS. 7A and 7B. The proteinase K
sequence, beginning with Ala at amino acid 1 and ending with Ala at
amino acid 369, is encoded by nucleotides 1-1107. The histidine
tag, the two additional amino acids described in FIG. 6 and the
termination codon are encoded by nucleotides 1108-1133.
[0025] FIG. 8 shows the accession numbers of 49 proteinase K
homologs obtained by BLAST searching of Genbank.
[0026] FIG. 9 illustrates a distribution of proteinase K homolog
sequences (listed in FIG. 8) in the first two principal components
of the sequence space. Sequences 46-49 are derived from
thermostable organisms.
[0027] FIG. 10 illustrates a corresponding plot of all loads
describing the influence of each variable on the sample
distribution of FIG. 9
[0028] FIG. 11 provides magnified detail of the bottom left
quadrant from FIG. 10.
[0029] FIG. 12 provides principal component analysis-derived loads
for individual amino acids responsible for clustering of
thermostable proteinase K homologs.
[0030] FIG. 13 illustrates sample output from an Expert System
defining the 24 most highly scoring substitutions to be
incorporated into a set of variants for initial mapping of
proteinase K sequence-function space in accordance with an
embodiment of the present invention.
[0031] FIG. 14 illustrates a first designed set of 24 variants for
proteinase K. Each variant contains six substitutions from the wild
type sequence. The numbers refer to the substitutions identified in
FIG. 13.
[0032] FIG. 15 illustrates a second designed set of variants for
proteinase K.
[0033] FIGS. 16A-16F illustrate amino acid changes in a set of
synthesized proteinase K variants. Each column shows the changes
from the wild type sequence present in one variant. A blank cell
indicates the wild type sequence at that position. Amino acid
numbering is shown in FIG. 6.
[0034] FIGS. 17A and 17B provide activity measurements of
proteinase K variants. Proteinase K variants were assessed for six
different hydrolytic activities. All activities are normalized to
the average performance of the wild type proteinase K. In FIGS. 17A
and 17B, y1: hydrolysis of a modified tetrapeptide,
N-succinyl-Ala-Ala-Pro-Leu-p-nitroanilide (AAPL-p-NA) by purified
proteinase K variants at pH 7.5; y2: thermostability ratio:
activity after heat/activity without heat treatment, y6/y1; y4:
hydrolysis of a modified tetrapeptide,
N-succinyl-Ala-Ala-Pro-Leu-p-nitroanilide (AAPL-p-NA) by purified
proteinase K variants at pH 4.5; y5: hydrolysis of a modified
tetrapeptide, N-succinyl-Ala-Ala-Pro-Leu-p-nitroanilide (AAPL-p-NA)
by purified proteinase K variants at pH 5.5; y6: hydrolysis of a
modified tetrapeptide, N-succinyl-Ala-Ala-Pro-Leu-p-nitroanilide
(AAPL-p-NA) at pH 7.5 by purified proteinase K variants which have
been exposed to a heat treatment of 65.degree. C. for 5 minutes;
and y7: hydrolysis of casein measured as clearing zones, in an LB
agar plate containing 2% skimmed milk, around a bacterial colony
expressing the variant. Duplicate values indicate that a variant's
activity was measured on two separate occasions.
[0035] FIG. 18 illustrates a comparison between values predicted
and values measured for a protein sequence-activity model derived
from sequences shown in FIG. 16 and activity data (y6) shown in
FIG. 17. Measured activities of proteinase K variant activities
towards AAPL-p-NA following a five minute 65.degree. C. heat
treatment on the y-axis are compared with those predicted by the
model on the x-axis. All activities were measured at 37.degree. C.
and pH 7.0 using purified protein.
[0036] FIG. 19 illustrates the identification of amino acids
contributing to a specific function from a sequence-activity model.
Regression coefficients (squares, left axis) of variant amino acids
were derived from the sequence-activity model relating the
sequences of proteinase K sequence variants (with numbers lower
than 49) to activity y6. The number of occurrences of each amino
acid substitution are also shown (diamonds, right axis). Changes
from the wild type sequence are circled.
[0037] FIG. 20 illustrates the use of sequence-activity modeling to
design a new variant with improved activity. Four amino acid
substitutions were found to have positive regression coefficients
in their contribution to activity following heat-treatment (y6).
The variant test set contained one variant with one of these
changes (#19) and one with three of these changes (#40). A new
variant (#56) was synthesized to contain all four changes. The
graph shows the activity of these variants towards AAPL-p-NA
following five minute 65.degree. C. heat treatment. Purified
proteins were heated to 65.degree. C. then incubated with AAPL-p-NA
at pH 7.5. The reaction was followed by measuring the absorbance at
405 nm. Alterations from the wild type sequence are: #19, K208H
(filled triangles); #S40, V267I, G293A, K332R (open circles); #56,
K208H, V267I, G293A, K332R (filled squares).
[0038] FIG. 21 illustrates how different amino acids are important
for different functions in proteinase K. Beneficial amino acid
substitutions were calculated by sequence-activity modeling for
three different proteinase K properties. Changes from the wild type
sequence are underlined.
[0039] FIG. 22 is a schematic representation of a method for
selecting amino acid substitutions for the optimization of
antiviral activity of an antibody in accordance with an embodiment
of the present invention.
[0040] FIG. 23 is a schematic representation of a method for
selecting amino acid substitutions for the humanization of an
antibody in accordance with an embodiment of the present
invention.
[0041] FIG. 24 is a list of germline sequence locus identification
numbers obtained from VBase (http://www.mrc-cpe.cam.ac.uk)
[0042] FIG. 25 illustrates a distribution of RSV antibody and
antibody sequences (listed in FIG. 24) in the first two principal
components of the sequence space.
[0043] FIG. 26 illustrates a corresponding plot of all loads
describing the influence of each variable on the sample
distribution of FIG. 25
[0044] FIG. 27 provides magnified detail of the right center from
FIG. 26
[0045] FIG. 28 provides principal component analysis-derived loads
for individual amino acids responsible for clustering of sequences
in group containing the sequence 4-28.
[0046] FIG. 29 is a list of germline sequence locus identification
numbers obtained from VBase (http://www.mrc-cpe.cam.ac.uk)
[0047] FIG. 30 illustrates a distribution of AAF21612 antibody and
antibody sequences (listed in FIG. 29) in the first two principal
components of the sequence space.
[0048] FIG. 31 illustrates a corresponding plot of all loads
describing the influence of each variable on the sample
distribution of FIG. 30
[0049] FIG. 32 provides magnified detail of the bottom center from
FIG. 31
[0050] FIG. 33 provides principal component analysis-derived loads
for individual amino acids responsible for clustering of sequences
in group containing the sequence 5-a
5. DETAILED DESCRIPTION OF THE INVENTION
[0051] A general antibody humanization and/or maturation scheme is
shown in FIG. 2. These steps found in FIG. 2 will be briefly
introduced here and described in more detail below.
[0052] Step 01. An antibody or a plurality of antibodies, that
partially or fully achieves the desired property (e.g., function,
being humanized and/or matured) is used as a starting point (step
01).
[0053] Step 02. Substitutions to a sequence of step 01 are
identified using a combination of changes to the antibody sequence.
Such changes are either in monomer identity or in monomer
physico-chemical properties. These changes span either the CDR
and/or the framework region of heavy chain and/or the light chain
of the antibody. For example, consider the case in which the heavy
chain of the antibody is being humanized. In step 02, a
determination can be made that the 21.sup.st and 49.sup.th
positions of the heavy chain (based on the kabat numbering scheme)
can be changed. Moreover, in some embodiments, a determination is
made as to which substitutions can be made at such positions in
step 02. For instance, step 02 may not only determine that the
21.sup.st position of the antibody can be changed, but may also
determine that this position should be changed to a glycine,
alanine, or leucine.
[0054] In typical embodiments, several independent rules are used
to determine which positions of the antibodies of step 01 can be
changed. Each such rule scores or ranks individual substitutions
based on different methods and based on the nature of optimization
(i.e) humanization or maturation. Representative rules include, but
are not limited to, rules based on (i) changes found in functional,
structural or sequence classes, (ii) changes predicted to be
favorable using substitution matrices, (iii) changes predicted
using evolutionary analysis of the antibody structural and sequence
classes, (iv) changes seen in random mutagenesis screening, (v)
changes predicted by structural modeling, (vi) changes proposed by
an expert on the antibody and (vii) changes predicted to be
favorable using structural information (vii) changes derived from
comparing the framework region of the antibodies with human
germline sequences (viii) changes derived from comparing the
framework regions of human antibodies (ix) changes derived from
substitution matrices constructed from the positional frequencies
of amino acids in the CDR regions of all antibodies. Any number of
rules can be applied to the one or more antibodies of step 01.
[0055] In some embodiments of the present invention, each
independent rule assigns a score for each possible substitution
position (e.g. residue) in the antibody of step 01. The scores
generated by each of the rules are then combined by methods and/or
filters to determine the positions in the antibody that are
suitable for change. These scores generated by each of the rules
are specific to nature of the optimization process, (i.e) scores
are independently derived for humanization of antibodies and for
maturation of antibodies.
[0056] Step 03. Step 02 identified a set of candidate substitution
positions in the antibodies of step 01. In step 03, a variant set
incorporating such candidate substitutions is designed such that
each candidate substitution is tested in combination with many
different other candidate substitutions in order to cover the
possible search space as evenly as possible (step 03).
[0057] To illustrate, consider the case in which the antibody of
step 01 is a murine antibody and the 2.sup.nd, 5.sup.th, and
15.sup.th kabat positions of the heavy chain have been identified
as candidate substitution positions in step 03. Assuming that each
of these three positions can be independently substituted with any
of the twenty naturally occurring amino acids, there are 20.sup.3-1
different variant antibodies that could be constructed. In some
instances, step 02 will constrain the types of amino acids that can
be substituted at these positions based on the rules described
above. Nevertheless, the full antibody sequence space proposed in
step 02 even after filtering can be large. Step 03 seeks to
minimize the number of variants that are constructed in order to
evenly search and sample this large sequence space.
[0058] Step 04. Variant antibodies selected in step 03 are
individually synthesized and tested for function(s) of interest in
step 04. When the variant antibodies are synthesized individually
it is easier to keep the number of changes and the number of
variants synthesized and tested in each iteration of the process
relatively small. In some embodiments, between 5 and 200, more
preferably between 10 and 100, and even more preferably between 15
and 50 variants are synthesized and tested in step 04. By
minimizing the number of variants synthesized and tested,
relatively inaccurate high throughput assay screens can be avoided
in step 04.
[0059] Step 05. Various machine-learning methods or other
data-mining techniques are used to model the relationship between
the sequences and activities of the variant antibodies in step
05.
[0060] Step 06. The assessments of the affect of each substitution
upon the properties (functions) of the antibodies by each model
tested in step 05 are combined in step 06.
[0061] Step 07. The assessments of the affect of each substitution
upon the properties (functions) of the antibodies by each tested
model that was made in step 06 is used in step 07 to design a new
set of variant antibodies for synthesis and testing
[0062] Repeating steps 04-07. Steps 04 through 07 are repeated a
number of times. Each iteration of steps 04-07 seeks to design a
set of high scoring and diverse antibodies for synthesis and
functional testing. Each new set of measurements from an iteration
of step 04 is used to refine the sequence-activity model until an
end point is reach, at which point the method progresses to step
08.
[0063] Step 08. The performance of the methods used to select
substitution positions in step 02 and to model the
sequence-activity relationships in instances of step 05 are
assessed by analyzing the sequences of the best performing
variants. In general, the best performing variants are any variants
in any iteration of the cycle defined by steps 04-07 that score
best in one or more functional assays for the target antibody. Step
08 provides a method for tuning the adjustable parameters of the
system. Once these parameters have been adjusted, steps 02 through
07, including multiple iterations of the cycle defined by steps
04-07, are repeated. Advantageously, one of the adjustable
parameters of the system is the individual weights for each of the
methods applied in step 02. For example, those step 02 methods that
were good at identifying substitution positions associated with
high scoring antibody variants are up-weighted in the next instance
of steps 02 through 07. The modification of weights applied to
methods in step 02 based on the results of cycles of steps 04-07
allows the system to learn from previous results thereby improving
the accuracy with which the system can identify beneficial
substitutions (in step 02) and assess the contribution of
substitutions to antibody activity (in steps 05 and 06).
5.1 Expert Systems for Defining a Sequence Space
[0064] FIG. 1 details an exemplary system that supports the
functionality described above. The system is preferably a computer
system 10 having: [0065] a central processing unit 22; [0066] a
main non-volatile storage unit 14, for example a hard disk drive,
for storing software and data, the storage unit 14 controlled by
storage controller 12; [0067] a system memory 36, preferably high
speed random-access memory (RAM), for storing system control
programs, data, and application programs, comprising programs and
data loaded from non-volatile storage unit 14; system memory 36 may
also include read-only memory (ROM); [0068] a user interface 32,
comprising one or more input devices (e.g., keyboard 28) and a
display 26 or other output device; [0069] a network interface card
20 for connecting to any wired or wireless communication network 34
(e.g., a wide area network such as the Internet); [0070] an
internal bus 30 for interconnecting the aforementioned elements of
the system; and [0071] a power source 24 to power the
aforementioned elements.
[0072] Operation of computer 10 is controlled primarily by
operating system 40, which is executed by central processing unit
22. Operating system 40 can be stored in system memory 36. In a
typical implementation, system memory 36 includes: [0073] operating
system 40; [0074] file system 42 for controlling access to the
various files and data structures used by the present invention;
[0075] a user interface 104; [0076] an expert system 100; [0077]
case-specific data 110; and [0078] knowledge base 108.
[0079] As illustrated in FIG. 1, computer 10 comprises
case-specific data 110 and knowledge base 108. Case-specific data
110 and knowledge base 108 each independently comprise any form of
data storage system including, but not limited to, a flat file, a
relational database (SQL), and an on-line analytical processing
(OLAP) database (MDX and/or variants thereof). In some specific
embodiments, case-specific data 110 and/or knowledge base 108 is a
hierarchical OLAP cube. In some specific embodiments, case-specific
data 110 and/or knowledge base 108 comprises a star schema that is
not stored as a cube but has dimension tables that define
hierarchy. In some embodiments, case-specific data 110 and/or
knowledge base 108 is respectively a single database. In other
embodiments, case-specific data 110 and/or knowledge base 108 in
fact comprises a plurality of databases that may or may not all be
hosted by the same computer 10. In such embodiments, some component
databases of case-specific data 110 and/or knowledge base 108 are
stored on one or more computer systems that are not illustrated by
FIG. 1 but that are addressable by wide area network 34.
[0080] It will be appreciated that many of the modules illustrated
in FIG. 1 can be located on one or more remote computers. For
example, some embodiments of the present application are accessible
in web service-type implementations. In such embodiments, user
interface module 104 and other modules can reside on a client
computer that is in communication with computer 10 via network 34.
In some embodiments, for example, user interface 104 can be an
interactive web page.
[0081] In some embodiments, the case-specific data 110 and/or
knowledge base 108 and modules (e.g. modules 100, 104, 112, 106,
116, 114, 118, 130, 132) illustrated in FIG. 1 are on a single
computer (computer 10) and in other embodiments such data is hosted
by several computers (not shown). Any arrangement of case-specific
data 110 and knowledge base 108 and the modules illustrated in FIG.
1 on one or more computers is within the scope of the present
invention so long as these components are addressable with respect
to each other across network 34 or by other electronic means. Thus,
the present invention fully encompasses a broad array of computer
systems.
[0082] Now that an overview of a computer system and the data
structures stored in such a computer system has been presented,
more details on the inventive data structures and software modules
of the present invention will be described.
[0083] Expert system 100 is a software module that includes stored
knowledge and solves problems in a specific field (for example
antibody engineering) by emulating some of the decision processes
of a human expert(s). The first set of algorithms that chooses the
substitutions and the sequence space to explore for antibody
engineering (steps 02 and 03 of FIG. 2) may require expertise in
the domains of polynucleotide structure and function, antibody
structure and function, protein structural analysis and
interpretation, protein structure and function, protein and nucleic
acid phylogeny and evolution, chemical and enzymatic mechanisms,
bioinformatics and related fields. Expert system 100 applies the
knowledge to problems specified by a user who is not necessarily an
expert in the domain(s). This invention describes the construction
and use of expert system 100 for selecting substitutions useful for
mapping and engineering antibody functions.
[0084] Two functions expert system 100 provides in order to define
a sequence space to search are (i) the identification of one or
more positions in the antibody at which substitution is likely to
be accepted and where at least some substitutions, insertions,
deletions or modifications are likely to result in a functional
antibody and (ii) the identification of residues or modifications
that are likely to result in a functional antibody when used to
substitute or insert at each of the one or more positions
identified in (i). An additional or alternative purpose of expert
system 100 is the identification of residues or modifications that
are likely to affect the desired properties or functions of the
antibody. These functions are represented as step 02 in FIG. 2.
[0085] One aspect of this invention is the use of methods to
identify positions that can be varied, then to synthesize a set of
antibody variants containing these substitutions and to test the
antibodies for one or more property or function, with the aim of
deriving relationships between antibody sequence and function.
[0086] A user can interact with expert system 100 using user
interface 104. In some embodiments, user interface 104 comprises
menus, natural language or any other style of interaction. Expert
system 100 uses inference engine 106 to reason using the expert
knowledge stored in knowledge database 108 together with
case-specific data 110 relating to the specific antibody or class
of antibodies to be mapped and/or engineered. Case-specific data
110 can be acquired as input from the user of expert system 100,
presented in knowledge base 108, or acquired from case-specific
knowledge generated by the results of experimentation and the
analysis facilitated by sequence-activity correlating methods of
this invention described in further detail below. These
sequence-activity correlating methods are performed in step 05 of
FIG. 2, for example. The data from these sequence-activity
correlating methods can additionally be used to add to or alter the
information contained within knowledge base 108.
[0087] Expert knowledge will typically be stored in knowledge base
108 in the form of a set of rules 120. An exemplary rule 120
is:
TABLE-US-00001 IF (an antibody protein has known variants that
possess some activity) THEN { assign probabilities for
incorporating the variant residues based on their occurrence in
some set of other naturally occurring antibodies and/or
synthetically derived antibodies using a substitution matrix to
determine the likelihood of such a substitution occurring in nature
}
[0088] Another exemplary rule 120 is:
TABLE-US-00002 IF (desired activity is binding affinity) THEN{
Change weights used to score/ rank the substitutions found in known
antibody classes that bind to the desired target }
Additional examples of rules 120 are each of the filters described
in FIGS. 4 and 5.
[0089] Case-specific data 110 can be precompiled by experts. It can
also be obtained as user response to questions contained in a
component of expert system 100, for example user interface 104,
knowledge base 108 or inference engine 106.
[0090] The functionality relied on by rules 120 of expert system
100 can also be obtained, in part, by a set of automatic actions
executed using one or more computational processes 118. An example
of a computational process 118 is: [0091] Upon input of a target
sequence (from Step 01) { [0092] 202 Search one or more sequence
databases for homologs of the target antibody sequence. Store any
such sequences in knowledge base 108 [0093] 204 Identify any
functional information provided for any of these target antibody
sequences by any of these databases. Store any such functional
information in knowledge base 108 [0094] 206 Search one or more
structure databases for homologs of the target antibody sequence.
Store any such homolog structural information in knowledge base
108. [0095] 208 Search one or more databases for known variants of
the target antibody sequence. Store any sequence and functional
information in knowledge base 108. [0096] 210 Compute the scores
for every enumerated substitution found in steps 202 through 208
using select rules 120.
[0097] Computational processes 118 can be stored in knowledge base
108 as illustrated in FIG. 1, in expert system 100, or in any data
structure that is accessible by expert system 100. Some embodiments
of expert system 100 include explanation subsystem 112. Explanation
subsystem 112 provides reasons to the user for why particular
substitutions are selected by rules 120. Some embodiments of expert
system 100 include knowledge base editor 114 to allow an
administrator to add, delete, or modify components of knowledge
base 108 including, but not limited to, rules 120.
[0098] In some embodiments, expert system 100 provides scores for
each substitution enumerated along with the contribution to that
score from various methods 130 used to evaluate the desirability of
each substitution. The weights 132 for the various methods 130 are
derived from knowledge base 108 and can be updated by an expert
using knowledge base editor 108 and can also be updated
automatically using rules in knowledge base 108.
[0099] Inference engine 106 is a software module that reasons using
information stored in knowledge base 108. One embodiment of
inference engine 106 is a rule-based system. Rule-based systems
typically implement forward or backward chaining strategies.
Inference engine 106 can be goal driven using backward chaining to
test whether some hypothesis is true, or data driven, using forward
chaining to draw new conclusions from existing data. Various
embodiments of expert system 100 can use either or both strategies.
For example, some topics that can be posed by expert system 100 in
a goal driven/backward chaining strategy can include: (i) how
conservative should an approach be, (ii) how many iterations of the
process are likely to achieve the activity of interest, (iii) by
what factor should the desired activity increase, and (iv)
descriptions of any prior experiments that have failed and why they
have failed. Answers to these topics allows expert system 100 to
access information from experiments and data from the scientific
literature or from personal communications that can be relevant for
the design of the sequence space of interest.
[0100] Inference engine 106 can calculate a probability that a
variant residue will provide a desired activity in an antibody of
interest. The antibody can be an Fab fragment, (Fab).sub.2
fragment, a scFv, fragment, a polynucleotide having its own
activity of interest, a polynucleotide that encodes an antibody
having an activity of interest, or a polynucleotide that encodes a
polypeptide that is responsible for synthesis of an antibody having
an activity of interest.
[0101] A profile 116 can be created by inference engine 106 based
on probability scores and weighting factors. In some embodiments,
inference engine 106 calculates the probability that defined
substitutions will result in an antibody having the desired
function, for any variant of the reference antibody. For example,
in some instances, knowledge base 108 can contain information
describing residue positions in the reference sequence that exhibit
a high degree of variance in homologs or among sequences in the
same structural or sequence class. Inference engine 106 may thus
give a high probability that substitutions at such positions will
be active. One method of calculating the degree of amino acid
variance is described by Gribskov, 1987, Proc Natl Acad Sci USA 84,
4355. As another example, in some instances, a sequence alignment
can be available in knowledge base 108 to serve as the basis of a
Hidden Markov model that can be used to calculate the probability
that one specific residue will be followed by a second specific
residue. These models also include probabilities for gaps and
insertions. See, Krogh, "An introduction to Hidden Markov models
for biological sequences," in Computational Methods in Molecular
Biology, Salzberg et al., eds, Elsevier, Amsterdam. Such models can
be used by inference engine 106 to calculate the probability that a
particular substitution will possess a desired function.
[0102] In some embodiments of the present invention, a variety of
different substitution matrices 122 stored in knowledge base 108
can be used by expert system 100 to identify suitable replacement
residues for positions likely to accept substitutions.
Specifically, substitutions specific for antibody framework regions
and antibody CDR regions can be generated using the sequences in
the database.
[0103] Additionally, substitutions based on the amino acid
frequencies compiled for every CDR position for every antibody
class in the kabat database can be derived. In addition, the
availability of a replacement residue that is likely to be
functional can itself determine whether or not a position is likely
to accept substitutions. This can be generated from functional
sequences that are naturally occurring and/or generated
synthetically whose properties have been measured. Substitution
matrix 122 choices will impact the probability calculated for
likely functionality of a variant. Thus, if mutations based on
sequence alignment are desired, a substitution matrix 122 derived
from the set of sequences should be chosen. Alternatively, if
mutations that depend on general mutability are desired, a
substitution matrix 122 reflecting this need should be chosen.
Substitution matrices 122 can be calculated based on the
environment of a residue, e.g., inside or accessible, in coil or in
beta-sheet. See, for example, Overington et al., 1992, Protein Sci
1:216.
[0104] Methods to identify solvent accessible residues and to
compute their solvent availability are known in the art. See, for
example, Hubbard, Protein Eng 1:159 (1987). Such calculated solvent
availability can be used to determine which substitution matrix 122
is used. More complex substitution matrices 122 that consider
secondary structure, solvent accessibility, and the residue
chemistry are also suitable for use in probability matrices. See,
for example, Bowie & Eisenberg, Nature 356:83 (1992).
[0105] Conservation indices 124 stored in knowledge base 108 can be
used by inference engine 106 to calculate probabilities that a
substitution will result in an antibody with desired properties. In
this capacity, one can avoid mutating residues that are highly
conserved, or conversely, focus mutations on conserved regions of
the antibody. Algorithms for calculating conservation indices 124
at each position in a multiple sequence alignment are known in the
art. See, for example, Novere et al., 1999 Biophys. Journal
76:2329-2345.
[0106] Inference engine 106 can also use knowledge of the effects
of single mutations as a factor in calculating the probability that
a substitution will possess a desired function when mutation effect
data 126 is stored in knowledge base 108. Mutation effect data 126
can originate, for example, from mutagenesis scans or from those
substitutions found in naturally occurring variants that affect the
function of interest.
[0107] Inference engine 106 can also use structural information 128
(e.g., crystal structure, in silico models of antibodies, de novo
modeled antibody, etc.) stored in knowledge base 108. For example,
inference engine 106 can assign higher probabilities to amino acid
residues in framework regions that are close to the CDR of an
antibody, as will affect activity and/or specificity than more
distant residues. Similarly, proximity to an epitope, proximity to
an area of structural conflict, proximity to a conserved sequence,
proximity to a binding site, proximity to a cleft in the protein,
proximity to a modification site, etc. can be calculated from
structural information 128 and used to calculate the probability
that a substitution will result in a functional antibody. To
calculate the distance of a residue from a region of functional
interest, physical distances obtained using a known crystal
structure of the reference sequence can be used. Alternatively,
molecular modeling approaches can be used. For example, the
structure of the reference sequence can be predicted based on its
homology to a known structure, and then used to calculate
distances. Or the entire structure of the reference sequence can be
predicted and distances then calculated from the predicted
structure.
[0108] In some embodiments structural information 128 is energy
minimized. For example, the behavior of an antibody can be modeled
using molecular dynamic simulations. In a specific example, a
crystal structure or a predicted structure can be subjected to
molecular dynamic simulation in order to model the effect of
various external conditions such as the presence of solvent, the
effect of temperature and ionic strength, upon the determined or
predicted structure.
[0109] In addition to the examples of elements of information that
can be used as a part of a knowledge base 108 described above,
other information that can contribute to an antibody knowledge base
108 that can then be used by inference engine 106 of an expert
system 100 to calculate the probability that a substitution will
possess a desired function include, but is not limited to,
individual sequence analysis (including sequence complexity,
sequence content and composition, internal base-pairing and
secondary structure predictions) sequence comparisons (including
structure-based sequence alignments, homology-based sequence
alignments, phylogenetic comparisons based on multiple pairwise
comparisons, phylogenetic comparisons based on principal component
analysis of sequence alignments, Hidden Markov models),
evolutionary molecular analysis, structural analysis (including
those using X-ray crystallographic data, nuclear magnetic resonance
studies, structure threading algorithms, molecular dynamic
simulations, active site geometry, determination of surface,
internal and active site residues), known or predicted data
relating sequence or structure to functional mechanisms, chemical
and biophysical properties of functional groups, known or predicted
functional effects of changes (for example information derived from
the Protein Mutant Resource database, from an evolutionary
comparison of sequence and activity data or from a comparison
binding pockets and residues for the antibody with binding pockets
and residues for other antibodies or sets of antibodies),
substitution matrices derived from sequence comparisons, mutations
that are known or that can be predicted to affect physical
properties of proteins (including stability, thermostability),
known or predicted properties (including plasticity and tolerance
to substitutions) of homologous or related antibodies (including
other members of sequence, structurally or functionally related
classes of antibodies), known or predicted immunological effects
and constraints for specific sequence residues or motifs, known or
predicted sequence effects on in vivo or in vitro
post-translational or post-transcriptional modifications, known or
predicted effects of the functional environment (including other
proteins, nucleic acids or other molecules contained within a
cell), measured or predicted biochemical or biophysical properties
(including crystallization), effects of sequences on the expression
of nucleic acids or proteins (including known or predicted RNA
splice sites, protein splice sites, promoter sequences,
transcriptional enhancer sequences, transcription and translation
terminator sequences, sequences that affect the stability of a
protein or nucleic acid, codon usage tables, nucleic acid GC
content). Sources of this information can include, without
limitation, text mined from scientific literature, data mined from
genomic sequences, expressed sequences, structural databases and in
second and subsequent iterations of the process, case specific data
from the first points of the sequence space mapped.
[0110] In some embodiments of the present invention, knowledge base
108 is optionally preprocessed for information by knowledge base
editor 114. For example, knowledge base 108 can contain all
available antibody sequences. During preprocessing by knowledge
base editor 114, such sequences can be, for example, (i) aligned
and distributed on a phylogenetic tree, (ii) grouped by principal
component analysis (PCA), (iii) grouped by nonlinear component
analysis (NLCA) (iv) grouped by independent component analysis
(ICA), used to create sequence profiles (see, for example Gribskov,
1987, Proc Natl Acad Sci USA 84, 4355), (v) used to create Hidden
Markov models or (vi) used to calculate structures prior to
interrogation by the user (vii) classified into canonical
structural classes as defined by Chothis and lesk (REF). PCA, NLCA,
and ICA is described in, for example, Duda et al., Pattern
Classification, Second Edition, John Wiley & Sons, Section
10.13, which is hereby incorporated by reference.
[0111] In one embodiment, the output from an expert system 100 will
describe the various substitutions recommended by methods 130 based
on assignment of scores, confidences, ranks, or probabilities
(hereinafter "scores") using rules 120 in knowledge base 108. In
preferred embodiments, these scores are cumulative. That is, every
rule 120 used by a method 130 will assign a score to the
substitution under consideration and these scores can be higher if
more rules are satisfied.
[0112] For example, FIG. 3 shows a series of steps that can be
executed by expert system 100 in order to identify substitutions
that are likely to increase the ability of an antibody to bind to a
specific target antigen. Five independent methods 130 are shown for
assessing the suitability of a substitution in the framework and
CDRs: (i) substitutions from antibody sequences derived from other
species and/or from synthetically derived antibodies and/or
germline sequences from human and/or other species (ii)
substitutions from homologous and modeled structures, (iii)
substitutions from substitution matrices, (iv) substitutions from
principal component analysis (PCA) and (v) substitutions from
binding pocket analysis. For each method 130, one or more rules
(filters) 120 defined in knowledge base 108 are used. For example,
method (ii), substitutions from homologous structures, uses two
rules 120. The first rule 120 is an estimate of the mean root mean
square deviation (RMSD) from the target structure for every five
residue window of the homolog structure, and select framework sites
that deviate from the target structure by more than three A. The
second rule 120 identifies amino acid substitutions that are found
in homologous sequences and select framework sites that are within
five A of the complementarity determining region. In FIG. 3 rules
120 are applied as filters: a substitution that satisfies one of
the rules is considered to have passed through that filter and
receives a score. For example, in FIG. 3, this score is 1. The
rules 120 used (applied) by the four other methods 130 for
assessing the suitability of substitutions shown in FIG. 3 are also
applied as filters. The score for each method can then be combined,
for example by summing them. All possible substitutions can then be
ranked in order of their cumulative scores. Although there are many
variants, in some embodiments of the present invention, a component
of step 02 of FIG. 2 uses the following algorithm in order to
identify suitable substitutions:
TABLE-US-00003 for each residue position j of the antibody
identified in step 01 { for each possible substitution k of residue
j { initialize score.sub.jk; for each method m (method 130) in a
suite of methods { initialize score.sub.m for each filter n (rule
120) in method m { compute filter n based on substitution k at
position j; score.sub.m = score.sub.m + result of filter n; }
score.sub.jk = score.sub.jk + score.sub.m } } rank all
scores.sub.jk
[0113] Those substitutions that have satisfied more of the rules
will have been assigned higher cumulative scores (score.sub.m), and
those with the highest scores will be selected for incorporation
into a set of antibody variants.
[0114] There are many variations of ways to combine scores produced
by two or more rules 120. Variations are possible (i) in the
methods of assigning scores, (ii) in the methods of combining
scores, and (iii) in the methods of assigning different weights to
scores produced by different rules 120. Rules 120 can also be
combined on a case by case basis, using expert knowledge. These
rules 120 can be stored in a knowledge base 108 and can be executed
by inference engine 106 using user input acquired by questioning
the user for requirements and knowledge via the user interface
104.
5.1.1 Variations in the Method of Assigning Scores
[0115] In preferred embodiments, each rule 120 produces a
reproducible quantitative value that can be used as a measure of
the suitability of a substitution. However, there are many
different ways in which quantitative scores can be obtained, and
these ways can differ between different rules 120. A rule 120 can
be used to produce an absolute quantitative score. This absolute
quantitative score can be used directly, or it can be used to
create a rank order list or a filter. As an example consider rule
1b of FIG. 3. Rule 1b calculates the difference in free energy
between a target antibody and an antibody containing a
substitution. This value can then be used in several different ways
to compare the favorability of different substitutions. For
example, (i) the absolute value of the free energy difference
(caused by the substitution) can be used, (ii) the free energy
differences of all possible substitutions can be ranked in order of
favorability, then a subset of substitutions that are predicted to
be the most favorable can be selected and assigned a score, (iii)
the score can be a single value assigned to all of the
substitutions belonging to the subset of the most favorable, (iv)
the score can be a measure of the rank order of the substitution,
so that the most favorable substitutions receive a higher score
than those that are calculated to be less favorable, (v) a rule can
also be used to rank all possible substitutions in order of
predicted favorability and then eliminate a subset of these
substitutions that are predicted to be the least favorable. In
option (v), substitutions that were eliminated would receive a
score of zero.
[0116] A way in which the predicted free energy change of a
substitution can be used as a rule to obtain quantitative measures
of the favorability of a substitution has been described. An
absolute quantitative value obtained by any method for favorability
can also be transformed by use of a function. In the case of free
energy change, instead of using the free energy change itself the
exp(free energy change) or step functions that can reflect (iii)
above can be used. One of skill in the art will appreciate that
there are other rules that can be applied to assess the effect of a
substitution in order to produce absolute quantitative scores and
all such other rules are included within the scope of the present
invention.
5.1.2 Variations in the Method of Combining Scores
[0117] The scores produced by individual rules can be combined in a
variety of ways. In some embodiments they are added together in the
manner illustrated in the algorithm illustrated in Section 5.1
above. In some embodiments, the scores are multiplied together. For
example,
TABLE-US-00004 for each residue position j of the antibody
identified in step 01 { for each possible substitution k of residue
j { initialize score.sub.jk; for each method m (method 130) in a
suite of methods { initialize score.sub.m for each filter n (rule
120) in method m { compute filter n based on substitution k at
position j; score.sub.m = score.sub.m .times. result of filter n; }
score.sub.jk = score.sub.jk + score.sub.m } } rank all
scores.sub.jk
[0118] In some embodiments, one or more rules 120 can be used as a
filter, so that only substitutions passing the one or more filter
are used, regardless of their scores from the other rules. For
example,
TABLE-US-00005 for each residue position j of the antibody
identified in step 01 { for each possible substitution k of residue
j { initialize score.sub.jk; set abort false for each method m
(method 130) in a suite of methods { initialize score.sub.m for
each filter n (rule 120) in method m { compute filter n based on
substitution k at position j; if result of filter n is negative {
set abort true break; } score.sub.m = score.sub.m + result of
filter n; } if abort { set score.sub.jk = 0 break; } else {
score.sub.jk = score.sub.jk + score.sub.m /* or score =
f.sup.1(score.sub.jk)* f.sup.2(score.sub.m)*/ /* scores can be
functionally transformed or normalized */ } } } rank all
scores.sub.jk
[0119] In some embodiments, a cumulative score can be produced by
any mathematical function of the scores produced by two or more
individual rules. For example,
TABLE-US-00006 for each residue positiony of the antibody
identified in step 01 { for each possible substitution k of residue
j { initialize score.sub.jk; for each method m (method 130) in a
suite of methods { initialize score.sub.m for each filter n (rule
120) in method m { compute filter n based on substitution k at
position j; score.sub.m = score.sub.m + weight.sub.n .times.
(result of filter n); } score.sub.jk = score.sub.jk + score.sub.m }
} rank all scores.sub.jk
[0120] In the exemplary algorithm above weight, is some rule 120
specific weight that is independently assigned to a rule. Such
weights can be stored in knowledge base 108 and adjusted by an
expert using knowledge-base editor 114 (FIG. 1).
[0121] In addition, prior to combination, scores produced by
individual rules can be scaled or normalized and/or transformed by
a mathematical function to facilitate their combination. For
example, in the case humanization of RSV-19, the mutation I46V was
identified as the most favorable substitution in the framework
region by combining the scores from methods 130. The distance,
expressed in fraction of amino acid differences, was transformed
and a Poisson correction (-log [1-fraction]) applied and multiplied
by the product of the absolute scores obtained from the other
methods 130. The resulting scores for all substitutions were ranked
and I46V (combination score 126) was ranked 1. In this example
different criteria were used to compute the scores for the
framework and CDR regions.
5.1.3 Variations in the Method of Assigning Weights to Scores From
Different Rules
[0122] As indicated in Section 5.1.2, the scores produced by
individual rules 120 can be assigned different weights prior to
being combined. For example, if the total score for a substituting
monomer x at position i (S.sub.ix) is obtained by adding the scores
obtained by applying n different rules, the score can be expressed
by Equations (1) or (2):
S.sub.ix=W.sub.1i.sub.xR.sub.1+W.sub.2i.sub.xR.sub.2+W.sub.3i.sub.xR.sub-
.3+W.sub.4i.sub.xR.sub.4+W.sub.5i.sub.xR.sub.5+ . . .
+w.sub.ni.sub.xR.sub.n (Eq. 1)
[0123] where, [0124] i.sub.xR.sub.n is the score given by rule n
for substituting monomer x at position i; and [0125] W.sub.n is a
weight applied to the score given by rule n
[0125] S.sub.ix=f(W.sub.1R.sub.1(i.sub.x),W.sub.2R.sub.2(i.sub.x),
. . . ,W.sub.jR.sub.j(i.sub.x)) (Eq. 2)
[0126] where, [0127] R.sub.j(i.sub.x) is the score given by rule j
for substituting monomer x at position i; [0128] W.sub.j is a
weight applied to scores given by rule j; and [0129] f is some
mathematical function.
[0130] Rules (and weights) can be (i) specific for a substitution
of monomer x at a specific location, (ii) specific for position for
any and/or a group of monomer substitution(s), (iii) specific for
any and/or a group of positions for a specific monomer x, (iv)
specific for any substitutions derived from a particular and/or a
group of homologs, (v) or specific for any position derived from a
particular and/or a group of homologs.
[0131] The use of weights to modify scores obtained using different
rules has a number of benefits.
[0132] Firstly, the use of weights to modify scores obtained using
different rules 120 allows different rules 120 to have different
degrees of influence over the final score for a substitution. For
example if Rule 4 is the most important in determining the
suitability of a substitution in a particular antibody, then this
rule can be made to dominate the total score for the substitutions
by making W.sub.4 much higher than the other weights.
[0133] Secondly, the use of weights to modify scores obtained using
different rules 120 allows different rules 120 to have different
degrees of influence over the final score for a substitution
depending upon the class or subclass of antibodies being
considered. For example a rule 120 considering the structural
effect of a substitution can be most important for engineering an
antibody, while a rule 120 considering the statistical likelihood
of a substitution using a substitution matrix can be most important
for engineering a protease. In this case, by first determining to
which class of antibody the target antibody belongs, expert system
100 can then be used to assign weights to the scores from different
rules 120 that will result in the most accurate assessment of the
favorability of substitutions. Moreover, as previously described,
expert system 100 can assign different weights to different
methods, to produce more control over how substitutions scores are
computed.
[0134] Thirdly, the use of weights to modify scores obtained using
different rules 120 allows expert system 100 to incorporate
information obtained from previous experiments. For example,
another aspect of the invention involves the use of
sequence-activity relationships to empirically measure the
contribution of substitutions to one or more activity of an
antibody. This aspect of the invention is described more fully in
Section 5.5. This sequence-activity determination effectively
creates a feedback loop by which weights assigned to the scores
from different rules 120 applied by expert system 100 can be
adjusted. As an example, consider the case in which 20
substitutions within an antibody (represented by S.sub.1--S.sub.20)
receive final combined scores C.sub.1-C.sub.20 from expert system
100. A set of antibodies that contain these substitutions are
synthesized, and a sequence-activity relationships derived using
wet lab assays. The sequence-activity relationships are used to
determine actual scores that measure the fitness of each
substitution for the desired activity of the antibody
(F.sub.1-F.sub.20). The weights applied to each rule 120 and/or
method 130 can then be adjusted so that the observed fitness of
each substitution, F.sub.1-F.sub.20, correlate more closely with
scores C.sub.1-C.sub.20 produced by expert system 100. In some
embodiments, this correlation is the correlation between the
absolute values of the scores for each substitution from expert
system and the observed fitness of each substitution derived from
the sequence-activity relationship. In some embodiments, the
correlation can be a correlation between the rank order of effect
of substitutions predicted by expert system 100 and the rank order
of substitutions observed or derived from the sequence-activity
relationship. The weights applied to each rule 120 can also be
adjusted so that the correlation between the observed fitness of
substitutions and the scores produced by expert system 100 is
maximized for more than one set of substitutions, in one or more
different target antibodies.
[0135] Different classes of antibodies can optionally be used to
provide different sets of substitutions for comparing observed
fitness and scores produced by expert system 100. This allows
different weights to be calculated to apply to the scores produced
by different rules 120 as a function of antibody class. One skilled
in the art will appreciate that there are many possible variations
of using experimental results to adjust weights applied to rule 120
scores. All such variants, whose predictive scoring functions can
be adjusted based upon experimental data, are within the scope of
the expert systems 100 of the present invention and can thus be
considered systems capable of learning.
[0136] Because of the capacity for expert systems 100 of the
present invention to learn by, for example, adjustment of rule 120
weights, in some instances it can be desirable to select
substitutions that are favored strongly by different rules 120.
Such selection can facilitate the establishment of the appropriate
weights to be applied to different rules 120 used by expert system
100.
[0137] The score for a substitution based on two or more rules can
be calculated independently or using conditional probabilities. An
expert system 100 can produce scores for at least 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40,
45, 50, 60, 70, 80, 90 or 100 positions in the reference sequence
up to the entire sequence, and can include contiguous residues or
noncontiguous residues or mixtures thereof. The expert system 100
can include at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45 or 50 different
residues. Naturally occurring residues can be included in the
expert system, as well as unnatural residues for synthetic methods,
and combinations thereof.
[0138] In another embodiment of the invention, the above
calculations can be performed by an expert with access to the
relevant knowledge base 108, for example, by using user interface
104.
[0139] Examples of the ways in which such expert system 100 can be
used to automatically select substitutions to make in an antibody
will now be described in the following sections with reference to
FIGS. 1 and 2. The following exemplary process is intended to
illustrate one possible embodiment of the invention. One skilled in
the art will recognize that there are many possible variations on
this theme, and the following is not intended to limit the present
invention. The selection process refers to the scheme shown in FIG.
3.
[0140] FIG. 3 shows a series of independent rules 120, each of
which can be used to produce a score for any possible amino acid
substitution in an antibody. In one embodiment of the invention,
all possible single substitutions can be enumerated computationally
and then scored according to one or more of the rules executed by
expert system 100.
5.1.4 Rules Based on Substitutions from Related Antibody
Sequences
[0141] One source of information that can be used to construct
rules 120 that assess the likely effect of amino acid substitutions
upon one or more activities of an antibody is the sequence of one
or more homologous or related antibodies. See, for example, FIG. 3,
rule 3a. Homologous sequences are generally analogous functionally
and structurally, although having been subjected separately to
different selective pressures they are also likely to be optimized
differently. Antibody sequences variants can also be generated in
the lab using many techniques and sequence properties of several
such antibodies are available in the database and literature. Amino
acids that differ between homologous sequences thus provide a guide
to substitutions that are likely to yield functional though
different antibody sequences. For humanization of antibodies,
alignment of the target antibody with human germline sequences
available in the databases is used to identify residues in the
human framework. The sequences can be grouped into classes as
defined by Chothia and Lesk (Chothia C, Lesk A M, "Canonical
structures for the hypervariable regions of immunoglobulins." J
Mol. Biol. 1987 Aug. 20; 196(4):901-17). Alignment of homologous
sequences can therefore be used to identify candidate substitution
positions.
[0142] In one approach, homologous antibody sequences or sequence
classes are aligned (e.g., by using clustalw; Thompson et al.,
1994, Nucleic Acids Res 22: 4673-80) and then a phylogenetic tree
is reconstructed. Conservation indices can then be calculated for
each site (e.g., Dopazo, 1997, Comput Appl Biosci 13: 313-7) and
the information content calculated for each site (e.g., Zhang,
2002, J Comput Biol 9: 487-503). These scores can be exhaustively
calculated for every position in the antibody. The scores reflect
the extent of tolerance to substitutions in the antibody at each
position. The scores can be normalized using the phylogenetic tree
to eliminate bias in the homolog sequences found in databases (for
e.g. ease of access to certain template DNAs results in sequences
from certain class of organisms dominates the database.) Scores for
a given alignment can also be normalized to have an average value
of 0.0 and a standard deviation of 1.0, or other standard
procedures can be used to compare and combine scores from multiple
methods. These values can then be used directly as a score, as
outlined above and in Equation (1) or Equation (2). In some
embodiments, all sites with a score above a certain threshold value
can be selected. For example, a cutoff (threshold) of 0.0 can be
chosen (which is set to be the average score). In still other
embodiments, all sites with a score below a certain threshold value
can be eliminated. In some embodiments, the most variable (e.g.,
least conserved) sites can be selected by ranking the sites in
order of these scores. For example the most highly scoring site can
be selected, or the 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,
33, 34, 35, 36, 37, 38, 39, 40, 50, 60, 70, 80, 90 or 100 most
highly scoring sites can be selected. In some embodiments the least
variable (e.g., most conserved) sites can be eliminated by ranking
the sites in order of these scores. For example, the least highly
scoring site can be eliminated, or the 10, 20, 30, 40, 50, 60, 70,
80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210,
220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340,
350, 360, 370, 380, 390, 400, 500, 600, 700, 800, 900 or 1000 least
highly scoring sites can be eliminated (FIG. 3, Rule 1a).
[0143] Amino acid diversity and tolerance at each site can be
measured as a fitness property of each amino acid at every
location. In this approach all related antibody sequences available
can be considered. The most fit residue for that position carries a
higher value (e.g., Koshi et al., 2001, Pac Symp Biocomput 191-202;
O. Soyer, M. W. Dimmic, R. R. Neubig, and R. A. Goldstein; Pacific
Symposium on Biocomputing 7:625-636 (2002). Sites can be grouped
into site-classes or treated independently. Sites and site classes
most fit to change based on the substitution rate and the
substitutions most favorable based on the fitness can be selected
(FIG. 3, Rule 2a). In some embodiments, these values of fitness can
then be used directly as a score, as outlined above and in Equation
(1) or Equation (2). In some embodiments all sites with a score
above a certain threshold value can be selected. For example, a
cutoff (threshold) of 0.0 can be chosen (when the normalization of
scores sets the wild type residue found in the reference to be 0.0.
In some embodiments, all sites with a score below a certain
threshold value can be eliminated. Threshold values of 0.0 or below
can be eliminated, thereby only including amino changes that have a
higher fitness value that the reference wild type amino acid found
in that position. In some embodiments, the sites most tolerant to
change can be selected by ranking the sites in order of these
scores. For example, the most highly scoring site can be selected,
or the 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 50, 60, 70, 80, 90 or 100 most highly scoring
sites may be selected. In some embodiments, the sites least
tolerant to change can be eliminated by ranking the sites in order
of these scores. For example, the least highly scoring site can be
eliminated, or the 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110,
120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240,
250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370,
380, 390, 400, 500, 600, 700, 800, 900 or 1000 least highly scoring
sites can be eliminated.
[0144] For example, in the study of G-protein coupled receptors
(GPCR) by Soyer et. al. (O. Soyer, M. W. Dimmic, R. R. Neubig, and
R. A. Goldstein; Pacific Symposium on Biocomputing 7:625-636
(2002)), using the 8-site class model the class #8 was identified
to have the highest substitution rate and the property correlating
with fitness of amino acids at these positions was identified to be
"charge transfer" propensity of the amino acid. In the present
invention, amino acids in the sites that carry a higher relative
fitness compared to the wild type amino acid found in that position
are identified as suitable for substitution. The scores for these
residues will be higher and can be combined with other methods
130.
[0145] Scores can also be assigned to residues from related
sequences that are classified into the same canonical class as the
target antibody. In this approach substitutions that are derived
from sequences that are part of the same Chothia-Lesk canonical
class can be scored (FIG. 3 Rule 3a).
5.1.5 Rules Based on Substitutions from Related Antibody
Structures
[0146] The structures of many antibodies and their variants are
also available in the RCSB protein data bank ((2002) Acta Cryst. D
58 (6:1), pp. 899-907); and Structural Bioinformatics (2003); P. E.
Bourne and H. Weissig, Hoboken, N.J., John Wiley & Sons, Inc.
pp. 181-198. The availability of structures can help identify amino
acid changes that affect protein function. One way in which they
can be used to do so is to avoid changes to the antibody of
interest that will not be structurally tolerated by the antibody.
Changes computed in-silico using energy functions and force fields
correlate with experimentally measured free energy changes in the
stabilities of proteins. See, for example, Privalov et al., 1988,
Adv Protein Chem 39: 191-234; Lee, 1993, Protein Sci 2: 733-8;
Freire, 2001, Methods Mol Biol 168: 37-68; and Guerois et al.,
2002, J Mol Biol 320: 369-87). Therefore, candidate amino acid
changes can be modeled into the structure(s) computationally and
changes in the free energy computed. These computationally
calculated changes in free energies resulting from the
substitutions can then be used directly as a score, as outlined
above and in Equation (1) or Equation (2). Alternatively, all
changes can be selected that increase the free energy of the
antibody by less than a certain value. For example, all changes
that would increase the free energy by less than 1 kCal/mol can be
selected, all changes that would increase the free energy by less
than 1.5 kCal/mol can be selected, all changes that would increase
the free energy by less than 2 kCal/mol can be selected, or all
changes that would increase the free energy by less than 2.5
kCal/mol can be selected. In some embodiments, all changes can be
eliminated that increase the free energy of the antibody by more
than a certain value. For example, all changes that would increase
the free energy by more than 1 kCal/mol can be eliminated, all
changes that would increase the free energy by more than 1.5
kCal/mol can be eliminated, all changes that would increase the
free energy by more than 2 kCal/mol can be eliminated, all changes
that would increase the free energy by more than 2.5 kCal/mol can
be eliminated. In some embodiments, the best tolerated
substitutions can be selected by ranking the sites in order of the
predicted increase in free energy. For example, the substitution
with the lowest increase in free energy can be selected, or the 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
38, 39, 40, 50, 60, 70, 80, 90 or 100 substitutions with the lowest
increase in free energy may be selected. In some embodiments, the
substitutions with the greatest increases in free energy can be
eliminated by ranking the sites in order of these scores. For
example, the 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120,
130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250,
260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380,
390, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000,
6000, 7000, 8000, 9000, 10000, 12000, 14000, 16000, 18000 or 20000
substitutions with the greatest increases in free energy can be
eliminated (FIG. 3, Rule 1b).
[0147] In alternative embodiments, multiple changes can be modeled
into the structure(s) computationally and changes in the free
energies resulting from the substitutions computed. These free
energy values can be used to identify changes that are "valid"
independently, but not together. Amino acid changes that are
independent can be selected preferentially. Amino acid clashes that
yield a higher free energy when compared to the free energies
produced by modeling changes separately can be eliminated.
[0148] Regions of the antibody that differ structurally between
antibodies are more likely to tolerate change, while those regions
that are structurally conserved are likely to be less tolerant.
Structures can be directly obtained from the database or predicted
using various structure modeling software packages. Structures of
homologs and mutants can be superposed on the wild type structure.
See, for example, May et al., 1994, Protein Eng 7: 475-85; and
Ochagavia et al., 2002, Bioinformatics 18: 637-40). Structural
conservation can be calculated as the root mean squared (RMS)
deviations of the backbones of the superposed chains. This can be
computed as the deviations of individual residues, or more
preferably as the deviations of a running average over a between
two and ten residue stretch of the backbone between the target
antibody and one or more homologous antibodies. These
computationally calculated RMS deviations for every position
between homologous structures can then be used directly as a score,
as outlined above and in Equation (1) or Equation (2). In some
embodiments, RMS deviations between the alpha carbons (or backbone
atoms) in the structure of the target antibody and one or more
homologous or related antibodies that are greater than a threshold
value can be considered structurally labile and these sites can be
selected. This threshold RMS deviation between homologous
structures can be greater than 2 .ANG., 2.5 .ANG., 3 .ANG., 3.5
.ANG., 4 .ANG., 4.5 .ANG., 5 .ANG..
[0149] In some embodiments, RMS deviations between the alpha
carbons in the structure of the target antibody and one or more
homologous or related antibodies that are less than a threshold
value can be considered structurally conserved and these sites can
be eliminated. This threshold RMS deviation between homologous
structures can be less than 2 .ANG., 2.5 .ANG., 3 .ANG., 3.5 .ANG.,
4 .ANG., 4.5 .ANG., or 5 .ANG..
[0150] In some embodiments sites can be ranked in order of the
calculated RMS deviations between the alpha carbons in the
structure of the target antibody and one or more homologous or
related antibodies and those with the highest calculated RMS
deviations selected. For example, the site with the highest
calculated RMS deviations between homologous structure can be
selected, or the 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,
33, 34, 35, 36, 37, 38, 39, 40, 50, 60, 70, 80, 90 or 100 sites
with the highest calculated RMS deviations between homologous
structure may be selected.
[0151] In some embodiments, sites can be ranked in order of the
calculated RMS deviations between the alpha carbons in the
structure of the target antibody and one or more homologous or
related antibodies and those with the lowest calculated RMS
deviations eliminated. For example, the site with the lowest
calculated RMS deviations between homologous structures can be
eliminated or the 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,
33, 34, 35, 36, 37, 38, 39, 40, 50, 60, 70, 80, 90 or 100 sites
with the lowest calculated RMS deviations between homologous
structure can be eliminated (FIG. 3, Rule 2b). Changes near binding
sites (and CDRs) are highly likely to influence the activity of the
antibody and are good candidates for substitution. All amino acid
substitutions that are found in one or more variants can be tested
for proximity to a binding or regulatory site of the antibody. In
some embodiments, the distance between an amino acid substitution
that is found in one or more homologs from a binding or catalytic
or regulatory site can be used directly as a score, as outlined
above and in Equation (1) or Equation (2). Alternatively, in some
embodiments, all amino acid substitutions that are found in one or
more homologs and that are within a threshold distance of a binding
or regulatory site in the antibody can be selected. This threshold
distance can be less than 2 .ANG., 2.5 .ANG., 3 .ANG., 3.5 .ANG., 4
.ANG., 4.5 .ANG., 5 .ANG., 5.5 .ANG., 6 .ANG., 6.5 .ANG., 7 .ANG..
In still other embodiments, all amino acid substitutions that are
found in one or more homologs and that are beyond a threshold
distance of a binding or regulatory site in the antibody can be
eliminated. This threshold distance can be more than 2 .ANG., 2.5
.ANG., 3 .ANG., 3.5 .ANG., 4 .ANG., 4.5 .ANG., 5 .ANG., 5.5 .ANG.,
6 .ANG., 6.5 .ANG., or 7 .ANG.. In still other alternative
embodiments, all amino acid substitutions that are found in one or
more homologs can be ranked in order of proximity to a binding or
regulatory site in the protein and those that are closest to the
binding or regulatory site selected by a rule 120. For example, the
substitution closest to the binding or catalytic or regulatory site
can be selected, or between 2 and 20, between 10 and 100, or the
top 200 substitutions closest to the binding or catalytic or
regulatory site can be selected. In still other alternative
embodiments, all amino acid substitutions that are found in one or
more homologs can be ranked in order of proximity to a binding or
regulatory site in the antibody and those that are farthest from
the binding or catalytic or regulatory site eliminated. For
example, the substitution farthest from the binding or regulatory
site can be eliminated. In some embodiments, between 2 and 20,
between 10 and 100, or the top 200 substitutions farthest from the
binding or regulatory site can be eliminated.
5.1.6 Rules Based on Substitutions from Substitution Matrices
[0152] Another source of information that can be used to construct
rules 120 that assess the likely effect of amino acid substitutions
upon one or more activities of an antibody is the frequency with
which one amino acid is observed to substitute for another amino
acid in different proteins. The matrix can be expressed in terms of
probabilities or values derived from probabilities by mathematical
transformation involving probabilities of transitions or
substitutions (Pij) and observed frequencies of amino acids (Fi).
Matrices using such transformation include scoring matrices like
PAM100, PAM250, and BLOSUUM etc. See, for example, FIG. 3, rule 1c.
Substitution matrices are derived from pairwise alignments of
protein homologs from sequence databases. They constitute estimates
of the probability that one amino acid will be changed to another
while conserving function. Different substitution matrices are
calculated from different sets of sequences. For example, they can
be based on the structural environment of a residue (Overington,
1992, Genet Eng (NY) 14: 231-49; and Overington et al., 1992,
Protein Sci 1: 216-26) or on additional factors including secondary
structure, solvent accessibility, and residue chemistry (Luthy et
al., 1992, Nature 356: 83-5. Substitution matrices can be derived
for specific sites or group of sites in the antibody. Specifically,
substitutions specific for antibody framework regions and antibody
CDR regions can be generated using the sequences in the database.
Additionally, substitutions can be derived based on the amino acid
frequencies compiled for every CDR position for every antibody
class in the kabat database.
[0153] A substitution matrix that best captures the observed
sequences in the antibody family of interest can be calculated
using the Bayesian method developed by Goldstein et al. (Koshi et
al., 1995, Protein Eng 8: 641-645) and used to score all candidate
substitutions.
[0154] In some embodiments these values can then be used directly
as a score, as outlined above and in Equation (1) or Equation (2).
The scores can expressed as Pij: the probability of substituting
residue i with j. Any transformations of Pij can also be used. Pij
can be computed for a specified evolutionary distance. In
alternative embodiments, all substitutions with a probability above
a certain threshold value may be selected. Threshold values of
0.00001, 0.00001, 0.0001, 0.01 or 0.1 can be used for probabilities
and/or threshold values of -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5 for
any PAM matrix. In still other embodiments, all substitutions with
a probability below a certain threshold value may be eliminated.
Threshold values of 0.00001, 0.00001, 0.0001, 0.01 or 0.1 can be
used for probabilities and/or threshold values of -5, -4, -3, -2,
-1, 0, 1, 2, 3, 4, 5 for any PAM matrix In still other embodiments,
the most favorable substitutions can be selected by ranking
substitutions in order of their substitution matrix probability
scores. For example, the most highly scoring substitution can be
selected, or the top 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
32, 33, 34, 35, 36, 37, 38, 39, 40, up to 50, up to 60, up to 70,
up to 80, up to 90, up to 100, up to 110, up to 120, up to 130, up
to 140, up to 150, up to 160, up to 170, up to 180, up to 190, up
to 200, up to 210, up to 220, up to 230, up to 240, up to 250, up
to 260, up to 270, up to 280, up to 290, up to 300, up to 310, up
to 320, up to 330, up to 340, up to 350, up to 360, up to 370, up
to 380, up to 390, up to 400, up to 500, up to 600, up to 700, up
to 800, up to 900, up to 1000, up to 2000, up to 3000, up to 4000,
up to 5000, up to 6000, up to 7000, up to 8000, up to 9000, up to
10000, up to 12000, up to 14000, up to 16000, up to 18000 or up to
20000 most highly scoring substitutions can be selected. In still
other embodiments, the least favorable substitutions can be
eliminated by ranking substitutions in order of their substitution
matrix probability scores. For example, the least substitution with
the lowest substitution matrix probability may be eliminated, or
the 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
37, 38, 39, 40, up to 50, up to 60, up to 70, up to 80, up to 90,
up to 100, up to 110, up to 120, up to 130, up to 140, up to 150,
up to 160, up to 170, up to 180, up to 190, up to 200, up to 210,
up to 220, up to 230, up to 240, up to 250, up to 260, up to 270,
up to 280, up to 290, up to 300, up to 310, up to 320, up to 330,
up to 340, up to 350, up to 360, up to 370, up to 380, up to 390,
up to 400, up to 500, up to 600, up to 700, up to 800, up to 900,
up to 1000, up to 2000, up to 3000, up to 4000, up to 5000, up to
6000, up to 7000, up to 8000, up to 9000, up to 10000, up to 12000,
up to 14000, up to 16000, up to 18000 or up to 20000 substitutions
with the lowest substitution matrix probability can be
eliminated.
[0155] A substitution or a scoring matrix can be calculated by
considering homologous and/or related antibodies from many
different antibody classes (e.g., Benner et al., 1994, Protein Eng
7: 1323-1332; and Tomii et al., 1996, Protein Eng 9: 27-36) can be
used to score all candidate substitutions. In some embodiments,
these values can then be used directly as a score, as outlined
above and in Equation (1) or Equation (2). In some embodiments, all
substitutions with a probability above a certain threshold value
can be selected. Threshold values of 0.00001, 0.00001, 0.0001, 0.01
or 0.1 can be used for probabilities and/or threshold values of -5,
-4, -3, -2, -1, 0, 1, 2, 3, 4, 5 for any PAM matrix can be used. In
still other embodiments, all substitutions with a probability below
a certain threshold value can be eliminated. Threshold values of
0.00001, 0.00001, 0.0001, 0.01 or 0.1 can be used for probabilities
and/or threshold values of -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5 can
be used for any PAM matrix. In still other embodiments, the most
favorable substitutions can be selected by ranking substitutions in
order of their substitution matrix probability scores. For example
the most highly scoring substitution may be selected, or the 2, 3,
4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,
39, 40, up to 50, up to 60, up to 70, up to 80, up to 90, up to
100, up to 110, up to 120, up to 130, up to 140, up to 150, up to
160, up to 170, up to 180, up to 190, up to 200, up to 210, up to
220, up to 230, up to 240, up to 250, up to 260, up to 270, up to
280, up to 290, up to 300, up to 310, up to 320, up to 330, up to
340, up to 350, up to 360, up to 370, up to 380, up to 390, up to
400, up to 500, up to 600, up to 700, up to 800, up to 900, up to
1000, up to 2000, up to 3000, up to 4000, up to 5000, up to 6000,
up to 7000, up to 8000, up to 9000, up to 10000, up to 12000, up to
14000, up to 16000, up to 18000 or up to 20000 most highly scoring
substitutions can be selected. In still other embodiments, the
least favorable substitutions can be eliminated by ranking
substitutions in order of their substitution matrix probability
scores. For example, the least substitution with the lowest
substitution matrix probability may be eliminated, or the 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,
23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
40, up to 50, up to 60, up to 70, up to 80, up to 90, up to 100, up
to 110, up to 120, up to 130, up to 140, up to 150, up to 160, up
to 170, up to 180, up to 190, up to 200, up to 210, up to 220, up
to 230, up to 240, up to 250, up to 260, up to 270, up to 280, up
to 290, up to 300, up to 310, up to 320, up to 330, up to 340, up
to 350, up to 360, up to 370, up to 380, up to 390, up to 400, up
to 500, up to 600, up to 700, up to 800, up to 900, up to 1000, up
to 2000, up to 3000, up to 4000, up to 5000, up to 6000, up to
7000, up to 8000, up to 9000, up to 10000, up to 12000, up to
14000, up to 16000, up to 18000 or up to 20000 substitutions with
the lowest substitution matrix probability can be eliminated.
5.1.7 Rules Based on Substitutions from Principal Component
Analysis of Sequence Alignments
[0156] Antibody sequences can be mathematically represented in
terms many variables, each variable representing the type of amino
acid at a specific location. For example the sequence AGWRY can be
represented by 5 variables, where variable 1 assumes a value of "A"
corresponding to position 1, variable 2 is "G" corresponding to
position 2 and so on. Each variable can assume 1 of 20
possibilities. Alternatively each variable can also represent
multiple positions (say 2) and assume 1 of 400 values (for 2
positions) corresponding to 20.times.20=400 combination of possible
amino acid pairs. Alternatively, each position can assume a value
corresponding to a physico-chemical property of the amino acid
instead of amino acid identity. Alternatively, each variable can be
a combination of variables representing properties of amino acids.
Alternatively, each variable can be represented in a binary form
corresponding to presence or absence of a particular amino acid.
Alternatively, each variable can be represented in a binary form
corresponding to presence or absence of a defined group of amino
acids.
[0157] Typical antibodies contain many hundred variables. A set of
antibodies are various points in the variables space, and
relationships between various antibodies can be represented in
terms of the values of the variables corresponding to those
antibodies. In such a high-dimension space (due to high degree of
variables) antibodies can be clustered and classified using
statistical techniques like the principal components analysis,
k-means clustering, SVM etc.
[0158] Using such methods, particularly but not limiting to
Principal Component Analysis (PCA), we can classify sequences and
identify residues that differentiate various related antibody
sequences and their functions. Typical antibody sequence alignments
contain many amino acid positions at which differences occur,
leading to a high number of dimensions required to represent the
sequence space. A sequence alignment can be subjected to principal
component analysis to identify new composite dimensions that
describe and visualize a significant fraction of the variation
between a set of sequences. The new dimensions (the principal
components) can also be described in terms of the contributions of
each monomer variation within the original sequence alignment to
that dimension (the "loads"). Typically a single principal
component contains contributions from tens or hundreds of different
monomer differences within a set of antibody sequences. One
powerful application of principal component analysis is that it can
be used to suggest a relationship between antibody sequence and
function. Antibody sequence can be represented in terms of the
principal components of that sequence. Principal components can
then be identified in which antibodies are grouped functionally.
The loads of those principal components can then be used to
identify the monomers that are most responsible for the grouping of
the antibodies within sequence space. These monomers are thus good
candidates for substitutions likely to affect function.
[0159] Thus for proteins, amino acid substitutions that are most
important in differentiating and grouping sequences are often also
those that functionally differentiate the proteins. Identification
of such amino acids using dimension-reducing techniques such as
principal component analysis has been described (e.g., Casari et
al., 1995, Nat Struct Biol 2: 171-178; Gogos et al., 2000, Proteins
40: 98-105; and del Sol Mesa et al., 2003, J Mol Biol 326:
1289-1302). PCA can identify sequence features and substitutions
corresponding to the desired phenotype of the protein and scores
"loads" for these features in the direction of desired phenotype
are used as absolute scores or as filters to identify
substitutions.
[0160] An example of the use of principal component analysis for
identification of favorable substitutions is also shown in FIGS.
8-12. FIG. 8 shows the accession number of the list of 49 proteases
whose sequences are homologous to proteinase K. A property of
interest in this example is activity during or after exposure of
the protein to heat. The 49 sequences were subjected to principal
component analysis, and the distribution of the sequences in the
first two principal components is shown in FIG. 9. Proteases 46,
47, 48 and 49 were all obtained from thermostable organisms and can
thus be expected to possess desirable thermostability properties.
As shown in FIG. 9, these four proteases are grouped together in
the first two principal components of the sequence space,
characterized by strongly negative scores in both principal
components 1 and 2. FIG. 10 shows the contributions (the "loads")
of all amino acid differences within the alignment of the 49
proteases, to the new dimensions principal components 1 and 2. FIG.
11 shows an expanded detail of the lower left corner of FIG. 10 in
which the identities of each amino acid contributing to the
principal components are now shown. These amino acids are those
most responsible for giving a protein sequence a strong negative
score in principal component 1 and principal component 2. These
contributions are quantitated in FIG. 12. Because these scores are
also those seen for proteases from thermophilic organsisms, the
amino acids that are primarily responsible for conferring these
scores upon proteins are very good candidates for amino acids that
may confer desirable properties, in this case thermostability.
[0161] An example of the use of principal component analysis
related to antibody humanization for identification of favorable
substitutions is also shown in FIGS. 24-28. FIG. 24 shows the
sequence identification number listed by locus of germline sequence
from VBase (available at http://www.mrc-cpe.cam.ac.uk/). A
properties of interest in this example are characteristics of
sequence 4-28. The heavy chain of sequences listed in FIG. 24 along
with the heavy chain of the murine antibody RSV19 were subjected to
principal component analysis, and the distribution of the sequences
in the first two principal components is shown in FIG. 25. Sequence
4-28 is the sequence cluster containing sequences close in locus
id. As shown in FIG. 25, these are grouped together in the first
principal components of the sequence space, characterized by
strongly positive scores in principal component 1. FIG. 26 shows
the contributions (the "loads") of all amino acid differences
within the alignment, to the new dimensions principal components 1
and 2. FIG. 27 shows an expanded detail of the right center of FIG.
26 in which the identities of each amino acid contributing to the
principal components are now shown. These amino acids are those
most responsible for giving a protein sequence a strong positive
score in principal component 1. Some of these contributions are
quantitated in FIG. 28. The amino acids that are primarily
responsible for conferring these scores upon proteins are serve as
candidates for amino acids that may confer desirable properties, in
this case characteristics of germline sequence 4-28.
[0162] An example of the use of principal component analysis
related to antibody maturation for identification of favorable
substitutions is also shown in FIGS. 29-33. FIG. 29 shows the
sequence identification number listed by locus of germline sequence
from VBase (available at http://www.mrc-cpe.cam.ac.uk/). A property
of interest in this example are characteristics of sequence 5-a.
The heavy chains of germline sequences along with the heavy chain
of AAF21612 were subjected to principal component analysis, and the
distribution of the sequences in the first two principal components
is shown in FIG. 30. Sequence 5-a is in the sequence cluster
containing sequences close to locus id 1-x. As shown in FIG. 30,
these sequences are grouped together in the second principal
component of the sequence space, characterized by strongly negative
scores in principal component 2. FIG. 31 shows the contributions
(the "loads") of all amino acid differences within the alignment,
to the new dimensions principal components 1 and 2. FIG. 32 shows
an expanded detail of the lower center of FIG. 31 in which the
identities of each amino acid contributing to the principal
components are now shown. These amino acids are those most
responsible for giving a protein sequence a strong negative score
in principal component 2. Some of these contributions are
quantitated in FIG. 33. The amino acids that are primarily
responsible for conferring these scores upon proteins are very good
candidates for amino acids that may confer desirable properties, in
this case characteristics of germline sequence 5-a.
[0163] Any sequence principal component can be used that
contributes to differentiating between two sets of antibodies and
that is likely to reflect some functional differences of interest.
In some embodiments, the "load" contributed by a substitution to
one or more such principal component of sequence can be used
directly as a score, as outlined above and in Equation (1) or
Equation (2). In some embodiments, all substitutions with a "load"
above a certain threshold value can be selected. Threshold values
can be determined from the distribution of load values. For
example, select top ten percent positive loads in principal
component 1. In some embodiments, all substitutions with a "load"
below a certain threshold value can be eliminated. For example,
eliminate the top ten percent of the negative loads in principal
component 1. In still other embodiments, the substitutions with the
highest loads can be selected by ranking substitutions in order of
their loads. For example, the substitution with the highest "load"
can be selected, or the 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,
32, 33, 34, 35, 36, 37, 38, 39, 40, 50, 60, 70, 80, 90 or 100
substitutions with the highest "loads" can be selected. In still
other embodiments, the substitutions with the lowest loads can be
eliminated by ranking substitutions in order of their loads. For
example, the substitution with the lowest "load" can be eliminated,
or the 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40 up to 50, up to 60, up to 70, up to 80, up to
90, up to 100, up to 110, up to 120, up to 130, up to 140, up to
150, up to 160, up to 170, up to 180, up to 190, up to 200, up to
210, up to 220, up to 230, up to 240, up to 250, up to 260, up to
270, up to 280, up to 290, up to 300, up to 310, up to 320, up to
330, up to 340, up to 350, up to 360, up to 370, up to 380, up to
390, up to 400, up to 500, up to 600, up to 700, up to 800, up to
900, up to 1000, up to 2000, up to 3000, up to 4000, up to 5000, up
to 6000, up to 7000, up to 8000, up to 9000, up to 10000, up to
12000, up to 14000, up to 16000, up to 18000 or up to 20000
substitutions with the lowest "loads" may be eliminated.
5.1.8 Other Exemplary Rules Based Upon Principal Component Analysis
of Sequence Alignments
[0164] All of the scores obtained as described in subsections 5.1.4
through 5.1.7 are just examples of ways in which such values can be
calculated. These values can then be combined in one of the ways
described in Section 5.1. One skilled in the art will readily
appreciate that there are many variations on methods for obtaining
quantitative measures of the predicted fitness of a substitution in
a antibody in such a way that these values may subsequently be
combined. All such variations are included as aspects of the
invention.
[0165] By combining the scores obtained from the rules used in
methods 132 of expert system 100, a set of substitutions can be
identified for testing. These may be the substitutions with the
highest aggregate scores, they may be the substitutions with the
highest score for each individual rule 120, or they may be derived
in some other way using the scores produced by the rules 120 used
by methods 130 of expert system 100. In some embodiments, the
number of substitutions selected by step 03 of FIG. 2 in one cycle
of the optimization process is less than 1000 substitutions, more
preferably less than 250 substitutions, more preferably less than
100 substitutions and more preferably less than 50
substitutions.
5.2 Design of an Antibody Variant Set
[0166] The rules discussed in Section 5.1 above and shown in FIG. 3
are one example of the way in which an initial sequence space can
be defined. The sequence space is defined in terms of an initial
target antibody sequence, and substitutions to be made in that
target sequence. Each substitution is defined in terms of a
position in the target antibody, and the identity of a monomer with
which the monomer at that position in the target antibody is to be
replaced. Selection of the target antibody corresponds to step 01
in FIG. 2. Definition of the sequence space corresponds to step 02
in FIG. 2. This section is directed to step 03 of FIG. 2.
[0167] Once an initial set of substitutions has been selected in
accordance with Section 5.1, a set of variants incorporating these
changes can be designed (the designed antibody variant set). This
process corresponds to step 03 in FIG. 2. In preferred embodiments,
this designed antibody variant set includes only a subset of the
total number of possible variants that could be generated. For
example, the total number of possible variant proteins in a
sequence space defined by a target antibody containing all possible
combinations of 24 substitutions is 2.sup.24>16,000,000. However
the methods of the present invention allow the interrogation of
this sequence space by designing and synthesizing only a very small
fraction of the total number of antibodies that are included in the
sequence space defined by the initial target antibody and the
substitutions. In some embodiments, the number of variants in the
designed antibody variant set is less than 1000 variants, more
preferably less than 250 variants and more preferably less than 100
variants. This is possible because, although the designed antibody
variant set includes only a subset of the total number of possible
variants (e.g. the possible combinations of substitutions), care is
taken to test all antibody substitutions in many different sequence
contexts. An example is shown in FIG. 14, where a set of 24
variants were designed to interrogate the sequence space defined by
a target antibody sequence and 24 substitutions in FIG. 13. Here,
each variant contains six substitutions, each substitution occurs
six times within the designed antibody variant set, and each
occurrence of each substitution takes place within a quite
different context, that is it is combined with a different set of
other substitutions each time.
[0168] The aim when designing a set of antibody variants to
interrogate a sequence space defined by a target antibody sequence
and a set of substitutions is to obtain a designed antibody variant
set where the substitutions are distributed in such a way that a
large amount of information can subsequently be extracted from
sequence-activity relationships. In this respect the design of
antibody variant sets has common elements with the design of
experimental datasets from a diverse range of other disciplines
including agriculture and engineering. Methods to optimize
experimental datasets (experimental design or design of experiment:
DOE) are described by Sir R. A. Fisher in 1920 (Fisher, The Design
of Experiments, MacMillan Publishing Company; 9th edition, 1971).
Plackett and Burman developed the idea further with the
introduction of screening designs (e.g., Plackett et al., 1946,
Biometrika 33: 305-325), and Taguchi subsequently introduced the
orthogonal matrix (Taguchi, 1986, Introduction to Quality
Engineering, Asian Productivity Organization, Distributed by
[0169] American Supplier Institute Inc., Dearborn, Mich.). Any
number of experimental design techniques can be used to maximize
the information content of the designed antibody variant set
including, but not limited to, complete factorial design, 2.sup.k
factorial design, 2.sup.k fractional factorial design, central
composite, latin squares, greco-latin squares, Plackett-Burmann
designs, Taguchi design, and combinations thereof. See, for
example, Box et al., 1978, Statistics for Experimenters. New York,
Wiley, for examples of such techniques that can be used to
construct a designed antibody variant set from the initial set of
antibody substitutions selected in accordance with Section 5.1 that
tests a maximum number of combinations in a minimal number of
antibody variants.
[0170] The methods described above were designed to maximize the
amount of information that could be obtained from a specified
limited number of experiments that could be performed. This is
conceptually comparable to the resource limitation seen in antibody
optimization, where functional tests are complex and time, cost or
other resource-limited. However, a significant difference between
antibody optimization and other applications of experimental design
is that for antibody optimization there is an additional
constraint. In designing antibody variants, the simultaneous
introduction of many changes can adversely affect functional
properties of the antibody. In contrast to traditional experimental
design strategies, it is advantageous in the present invention to
reduce the number of previously untested substitutions present in
each variant to ten or less, preferably to five or less, more
preferably to between 3 and 10. For instance, in some specific
embodiments, the number of previously untested substitutions
present in each variant is 10, 9, 8, 7, 6, 5, 4 or 3. In other
words, in subsequent cycles of steps 02 through 07, less than 10,
9, 8, 7, 6, 5, 4 or 3 new variants are chosen. Here, a variant
references to an antibody that has a sequence that is identical to
the sequence of the antibody selected in step 01 of FIG. 2 with the
exception that there are one or more substitutions in the sequence.
Here, a substitution refers to a mutation at a particular position
in the antibody from the residue found at that position in the
antibody selected in step 01 of FIG. 2 to some other residue.
[0171] To design an antibody variant set that will yield useful
sequence-activity information upon analysis of the functional
properties and sequences of the antibody variants, any method can
be appropriate provided that the number of substitutions in each
variant set is relatively small so that the majority of antibodies
are active. For instance, in preferred embodiments, the number of
previously untested substitutions present in each variant is
preferably 9, 8, 7, 6, 5, 4, 3 or 2. Furthermore, it is desirable
that each selected substitution be tried an approximately equal
number of times in the designed antibody variant set. It is further
desirable that each substitution be tested in many different
sequence contexts. In other words each substitution appears in a
number of different antibody variants, in each case being combined
with a different set of other substitutions. In FIG. 14 the
substitution L180I appears in variant 3 with P97S, E138A, Y194S,
A236V, V267I and in variant 18 with N95C, S107D, V1671, G293A,
I310K.
[0172] A variation of the above method is to require (i) that each
substitution identified be tried an approximately equal number of
times in the designed antibody variant set, and (ii) that as many
different combinations of two substitutions (e.g. substitution
pairs) as possible be tested. For example, to test forty
substitutions in an antibody it may be desirable to incorporate a
maximum of five changes per variant. For forty substitutions there
are (40.times.39/2) 780 possible pairs of substitutions. In one
variant with five substitutions there are ten pairs of
substitutions. So in forty variants there will be a maximum of 400
substitution pairs. The aim is then to maximize the number of
different substitution pairs that are tested and to try to
represent each substitution five times. The substitution pairs can
be scored with the initial selection algorithm, and the top scoring
400 substitution pairs tested. The solution to such a problem of
finding variants with the constraints mentioned here is known as a
coverage problem. The coverage problem is NP-hard. Therefore greedy
and other forms of approximate solutions are used to solve the
NP-hard problems in the present invention. For instance, in some
embodiments, the algorithms described in Gandhi et al., 2001,
Lecture Notes in Computer Science 2076: 225 are used. In some
embodiments, the desired set of sequences can be evolved using
monte carlo algorithms and genetic algorithms to maximize the
number of pairs in the variant set. Genetic algorithms are
described in Section 7.5.1 of Duda et al., 2001, Pattern
Classification, Second Edition, John Wiley & Sons, Inc., New
York, which is hereby incorporated by reference in its entirety.
Further, similar algorithms can be used to expand the coverage
problem to maximize the number of triplets, quadruplets and so
on.
[0173] An exemplary code for maximizing the substitution pairs
using an evolutionary coverage algorithm is shown below:
[0174] Let m be the number of identified substitutions, n be the
number of variants to be synthesized, and k be the number of
substitutions per variant.
[0175] Create n initial variants with k substitutions, each
occurring n.times.k/m times among variants. This can be done
randomly or sequentially. This set is not optimal. Then,
TABLE-US-00007 for 10000 iterations { i. Choose two random
variants; ii. Choose two random positions; iii. Count the number of
distinct substitution pairs seen among variants; iv. Swap the
substitutions (if any) at the two positions between the two chosen
variants; v. Check if the number of substitutions per variant is k;
vi. Check if number of times a given substitution occurs among all
variants equals n .times. k/m; vii. Count the number of distinct
substitution pairs seen among variants; viii. If the count from
vii) is greater than count from iii) and v) and vi) are true,
accept the changes to the variants from step iv), else, dismiss the
changes and retain original values. }
[0176] Alternatively, the set of substitutions can be divided into
two or more groups and be used to design variants where each
variant contains substitutions from a particular group, for example
by dividing the antibody into functional domains such as different
complementarity determining regions (CDRs) or framework regions.
The substitutions in such a variant can be subject the coverage
algorithms with constraints described above. Each group can also be
combined with other groups of substitutions to design initial
variants and coverage algorithm can be applied to combination of
substitution groups. Groups of substitutions can be arrived at
using knowledge of antibody domain and/or functional and structural
properties of amino acid residues in the antibody. For example we
can identify all substitutions based on Section 5.1 and select top
scoring ones, and classify them into groups of substitutions based
on which domain of the antibody they are present in such as
different complementarity determining regions (CDRs) or framework
regions. Alternatively, we can also classify substitutions based on
the their special location in the protein structure (e.g surface
position versus interior positions) based on experimentally
determined structure or using prediction algorithms. Alternatively,
substitutions can be classified based on their proximity to the
binding sites (e.g residues <5 .ANG. from the binding site
belong to one class and residues >5 .ANG. from the binding site
to another). Constraints to number of substitutions to be designed
in a variant from each substitution group can also be added (e.g.,
no more than two variants from each substitution group). For
example, two substitutions can be chosen from the group close to
the binding site and three from the group on the surface of the
antibody. Such methods differ from typical experimental design or
design of experiment (DOE) methods in the fact that no more than
five changes in a variant are allowed and the occurrence of the
selected pairs is maximized by scoring. Other DOE methods for
distributing 40 substitutions would require as many as between 18
and 22 changes in an antibody, which would have a high likelihood
of being detrimental to antibody function.
[0177] Alternatively or additionally, an antibody variant set can
be created stochastically by library synthesis methods such as
parallel site-directed mutagenesis, DNA shuffling or other methods
for incorporating defined substitutions into an antibody such as
those described in Section 5.8. In these instances the variant set
contains substitutions distributed at random, so precisely defined
variants are not synthesized. Instead, the introduction of
substitutions is controlled so that the average number of
substitutions incorporated into each variant is between 1 and 10,
more preferably the average number of substitutions incorporated
into each variant is between 1 and 5. Variants can then be selected
at random and the distribution of substitutions can be determined
by determining the sequence of the antibody. In some embodiments of
the invention less than 1000 variants created by library synthesis
methods are synthesized and sequenced, preferably less than 500
variants created by library synthesis methods are synthesized and
sequenced, more preferably less than 250 variants created by
library synthesis methods are synthesized and sequenced, even more
preferably less than 100 variants created by library synthesis
methods are synthesized and sequenced. In some embodiments that use
libraries, the creation of a library can be simulated using
computational modeling of shuffling and other methods. See, for
example, Moore, 2001, Proc Natl Acad Sci USA 13, 3226-3231; Moore
and Maranas, 2000, J Theor Biol. 205, pp. 483-503.
[0178] Once the antibody variant set has been designed, the
variants are synthesized using methods known in the art.
Representative, but nonlimiting synthetic methods are described in
Section 5.8, below. Then the antibodies are tested for relevant
biological properties. Such relevant biological properties include,
but are not limited to antibody solubility and activity.
Nonlimiting examples of how such antibody activity can be tested
are described in Section 5.9 below. Together the synthesis and
testing of the antibody variants represent step 04 in FIG. 2.
5.3 Methods for Mapping a Sequence Space to a Function Space
[0179] Once substitutions have been selected using expert system
100 (FIG. 2, step 04), and variants have been designed, synthesized
and tested for one or more activity or function, it is desirable to
use the sequence and activity information from the designed
antibody variant set to assess the contributions of substitutions
to the one or more antibody activity or function. This process is
represented as step 05 in FIG. 2. Assessment of the contributions
of substitutions to one or more antibody function can be performed
by deriving a sequence-activity relationship. Such a relationship
can be expressed very generally, for example as shown in Equation
3
Y=f(x.sub.1,x.sub.2, . . . ,x.sub.i) (Eq 3)
[0180] where, [0181] Y is a quantitative measure of a property of
the antibody (e.g., activity), [0182] x.sub.i is a descriptor of a
substitution, a combination of substitutions, or a component of one
or more substitutions in the sequence of the antibody, and [0183]
f( ) is a mathematical function that can take several forms.
[0184] A model of the sequence-activity relationship can be
described as a functional form whose parameters have been trained
for the input data (Y and x.sub.i). Protein sequences can be
mathematically represented in terms of many variables (descriptors,
predictors), each variable representing the type of amino acid at a
specific location (linear form in terms of the position of the
amino acid). For example, the sequence AGWRY can be represented by
five variables, where variable one assumes a value of "A"
corresponding to position 1, variable two is "G" corresponding to
position two and so on. Each variable can assume 1 of 20
possibilities. Alternatively, each variable can also represent
multiple positions (say two) and assume 1 of 400 values (for 2
positions) corresponding to 20.times.20=400 combination of possible
amino acid pairs. For example, a variable can describe position one
and two and assume a value of "AG" (thereby creating a variable
that in non-linear in terms of position of the amino acid).
Alternatively, each position can assume a value corresponding to a
physico-chemical property of the amino acid instead of amino acid
identity. For example, the position can be described in terms of
the mass of the amino acid at that location. For the sequence
AGWRY, a variable for position one can assume the value 71.09 and
position two 57.052 and so on. Alternatively, each position can be
described by one or several principal components derived to
represent many physico-chemical properties of the amino acid
present in that position. Alternatively, each variable can be a
combination of variables representing properties of amino acids.
Alternatively, each variable can be represented in a binary form
corresponding to presence or absence of a particular amino acid.
For example, consider two variants AGWRY and AKWRY, Position two
can be "1" if G is present at that position and "0" if it is absent
and the descriptor for that position can have the value "0" or "1."
Alternatively, each variable can be represented in a binary form
corresponding to presence or absence of a defined group of amino
acids.
[0185] In equation 3, the functional form f( )) correlates
descriptors of an antibody sequence (x.sub.i) to its activity. In a
simple embodiment of the invention, the function f can be a linear
combination of x.sub.i:
Y=w.sub.1x.sub.1+w.sub.2x.sub.2,+ . . . +w.sub.ix.sub.i
[0186] where w.sub.i is a weight (or coefficients of x.sub.i).
[0187] In some embodiments, to derive a sequence-activity
relationship, a set of descriptors (x.sub.i) that can describe all
of the substitutions within the antibody variant set is identified.
Values of Y for each member of the antibody variant set are
measured. Values for each weight (w.sub.i) are then calculated such
that the differences between values predicted for each value of Y
by Equation 3 and those observed experimentally are minimized for
the antibody variants set, or for a selected subset of such
antibody variants.
[0188] The minimization step above can also use weights for
different activity predictions and, in general, can use a loss
function. In one embodiment this loss function can be squared error
loss, where weights that minimize the sum of squares of the
differences between predicted and measured values for the dataset
are computed.
[0189] In some embodiments statistical regression methods are used
to identify relationships between dependent (x.sub.i) and
independent variables (Y). Such techniques include, but are not
limited to, linear regression, non-linear regression, logistic
regression, multivariate data analysis, and partial least squares
regression. See, for example, Hastie, The Elements of Statistical
Learning, 2001, Springer, New York; Smith, Statistical Reasoning,
1985, Allyn and Bacon, Boston. In one embodiment, regression
techniques like the PLS (Partial Least Square) can be used to solve
for the weights (w.sub.i) in the equation X. Partial Least Squares
(PLS) is a tool for modeling linear relationships between
descriptors. The method is used to compress the data matrix
composed of descriptors (variables) of variant sequences being
modeled into a set of latent variable called factors. The number of
latent variable is much smaller than the number of variables
(descriptors) in the input sequence data. For example, if the
number of input variable is 100, the number of latent variables can
be less than 10. The factors are determined using the nonlinear
iterative partial least squares algorithm. The orthogonal factor
scores are used to fit a set of activities to the dependent
variables. Even when the predictors are highly collinear or
linearly dependent, the method finds a good model. Alternative PLS
algorithms like the SIMPLS can also be used for regression. In such
methods, the contribution to the activities from every variable can
be deconvoluted to study the effect of sequence on the function of
the antibody.
[0190] In some embodiments, modeling techniques are used to derive
sequence-activity relationships. Such modeling techniques include
linear and non-linear approaches. Linear and non-linear approaches
are differentiated from each other based on the algebraic
relationships used between variables and responses in such
approaches. In the system being modeled, the input data (e.g.,
variables that serve as descriptors of the antibody sequence), in
turn, can be linearly related to the variables provided or
non-linear combinations of the variables. It is therefore possible
to perform different combinations of models and data-types: linear
input variables can be incorporated into a linear model, non-linear
input variables can be incorporated into a linear model and
non-linear variables can be incorporated into a non-linear
model.
[0191] Many functional forms of f( ) (Eqn. 3) can be used and the
functional form can be combined using weights defined in the
knowledge base 108 for analysis. For example, Function f( ) can
assume non-linear form. An example of non-linear functional form
is:
Y=w.sub.12*x.sub.1*x.sub.2+w.sub.123*x.sub.1*x.sub.3+ . . .
+w.sub.nn*x.sub.n*x.sub.n.
[0192] Non-linear functions can also be derived using modeling
techniques such as machine learning methods. For example, the
sequence (x.sub.i)-activity (Y) data to predict the activities of
any sequence given the descriptors for a sequence can be determined
using neural networks, Bayesian models, generalized additive
models, support vector machines, classification using regression
trees.
[0193] The data describing variants of the initial antibody can be
represented in many forms. In some embodiments, all or a portion of
the data is represented in a binary format. For example,
representing the presence or absence of a specified residue at a
particular position by a "1" or a "0" constitutes a linear binary
variable. In another example, representing the presence of a
specified residue at one position AND a second specified residue at
a second position by a "1" constitutes a non-linear binary
variable. In some embodiments, all or a portion of the data is
represented as Boolean operators. In some embodiments, all or a
portion of the data is represented as principal component
descriptors derived from a set of properties. See, for example,
Sandberg et al., 1998, J Med. Chem. 41, 2481-91. Antibody input
sequence data can also use descriptors based on comparison with a
sequence profile (e.g., a hidden Markov model, or principal
component analysis of a set of sequences). For example in FIG. 9,
PC1 and PC2 values of the sequences can be used as descriptors for
the sequences in that figure. In addition, any number of principle
components can be used as descriptors. See, for example, Casari et
al., 1995, Nat Struct Biol. 2:171-8; and Gogos et al., 2000,
Proteins 40:98-105.
[0194] To initiate step 05 (FIG. 2), the antibody sequence data in
the designed set and the results of the assays performed on the
designed set are converted to a form that can be used in pattern
classification and/or statistical techniques in order to identify
relationships between the results of the assays and the
substitutions present in the designed set. In general, such
conversion involves a step in which independent variables and
dependent variables are enumerated. Here, the independent variables
are the various substitutions (mutations) that are present in the
designed set. The dependent variables are the results of assays,
such as those described in Section 5.9.
[0195] Each substitution can be considered independently. The
presence or absence of a substitution or residue at a specific
position can be used to describe one or more of the independent
variables. The presence or absence of two or more substitutions or
residues at two or more specific positions can be used to describe
one or more of the independent variables. One or more
physico-chemical descriptors of a substitution or residue at a
specific position can be used to describe one or more of the
independent variables. One or more physico-chemical descriptors of
two or more substitutions or residues at two or more specific
positions can be used to describe one or more of the independent
variables. Then, pattern classification and/or statistical
techniques are used to identify relationships between particular
substitutions, or combinations of substitutions, and the assay
data.
[0196] In some embodiments, supervised learning techniques are used
to identify relationships between mutations in the designed set and
antibody properties identified in assays results such as assays
performed in Section 5.9. Such supervised learning techniques
include, but are not limited to, Bayesian modeling, nonparametric
techniques (e.g., Parzen windows, k.sub.n-Nearest-Neighbor
algorithms, and fuzzy classification), neural networks (e.g.,
hopfield network, multilayer neural networks and support vector
machines), and machine learning algorithms (e.g.,
algorithm-independent machine learning). See, for example, Duda et
al., Pattern Classification, 2.sup.nd edition, 2001, John Wiley
& Sons, Inc. New York; and Pearl, Probabilistic Reasoning in
Intelligent Systems: Networks of Plausible Inference, Revised
Second Printing, 1988, Morgan Kaufmann, San Francisco. For example,
the sequence (x.sub.i)-activity (Y) data can be sed to predict the
activities of any sequence given the descriptors for a sequence
using a neural network. The input for the network is the
descriptors and the output is the predicted value of Y. The weights
and the activation function can be trained using supervised
decision based learning rules. The learning is performed on a
subset of variants called the training set and performance of the
network is evaluated on a test set.
[0197] In some embodiments, unsupervised learning techniques are
used to identify relationships between mutations in the designed
set and antibody properties identified in assays results such as
assays performed in Section 5.9. Such unsupervised learning
techniques include, but are not limited to stochastic searches
(e.g., simulated annealing, Boltzmann learning, evolutionary
methods, principal component analysis, and clustering methods).
See, for example, Duda et al., Pattern Classification, 2.sup.nd
edition, 2001, John Wiley & Sons, Inc. New York. For example,
the weights in equation 5 can be adjusted by using monte carlo and
genetic algorithms. The optimization of weights for non-linear
functions can be complicated and no simple analytical method can
provide a good solution in closed form. Genetic algorithms have
been successfully used in search spaces of such magnitude. Genetic
algorithms and genetic programming techniques can also be used to
optimize the function form to best fit the data. For instance, many
recombinations of functional forms applied on descriptors of the
sequence variants can be applied.
[0198] In some embodiments, boosting techniques are used to
construct and/or improve models developed using any of the other
techniques described herein. A model of the sequence-activity
relationship can be described as a functional form whose parameters
have been trained for the input data (Y and x.sub.i). Many
algorithms/techniques to build models have been described.
Algorithms applied on a specific dataset can be weak in that the
predictions can be less accurate or "weak" (yielding poor models).
Models can be improved using boosting techniques. See, for example,
Hastie et al., The Elements of Statistical Learning, 2001,
Springer, New York. The purpose of boosting is to combine the
outputs of many "weak" predictors into a powerful "committee." In
one embodiment of the invention, boosting is applied using the
AdaBoost algorithm. Here, the prediction algorithm is sequentially
applied to repeatedly modified versions of the data thereby
producing a sequence of models. The predictions from all of these
models are combined through a weighted majority vote to produce the
final prediction. The data modification at each step consists of
applying weights (W.sup.b.sub.i) to each of the i training
observations. Initially weights are set to 1/N, where N is the
number of training observation (sequence-activity data). The
weights are modified individually in each successive iteration.
Training observations that were predicted poorly by a particular
model have their weights increased and training observations that
were predicted more accurately have their weights decreased. This
forces each successive model to concentrate on those training
observations that are issued by the previous model. The step of
combining the models to produce a "committee" assigns a weight to
each model based on the overall prediction error of that model.
[0199] The various modeling techniques and algorithms described
herein can be adapted to derive relationships between one or more
desired properties or functions of an antibody and therefore to
make multiple predictions from the same model. Modeling techniques
that have been adapted to derive sequence-activity relationships
for antibodies are within the scope of the present invention. Some
of these methods derive linear relationships (for example partial
least squares projection to latent structures) and others derive
non-linear relationships (for example neural networks). Algorithms
that are specialized for mining associations in the data are also
useful for designing sequences to be used in the next iteration of
sequence space exploration. These modeling techniques can robustly
deal with experimental noise in the activity measured for each
variant. Often experiments are performed in replicates and for each
variant there will be multiple measurements of the same activity.
These multiple measurements (replicate values) can be averaged and
treated as a single number for every variant while modeling the
sequence-activity relationship. The average can be a simple mean or
another form of an average such as a geometric or a harmonic mean.
In the case of multiple measurements, outliers can be eliminated.
In addition, the error estimation for a model derived using any
algorithm can incorporate the multiple measurements through
calculating the standard deviation of the measurement and comparing
the predicted activity from the model with the average and estimate
the confidence interval within which the prediction lies. Weights
for observations to be used in models can also be derived from the
accuracy of measurement, for example, through estimating standard
deviation and confidence intervals. This procedure can put less
emphasis on variants whose measurements are not accurate.
Alternatively, these replicate values can be treated independently.
This will result in duplicating the sequences in the dataset. For
example, if sequence variant i represented by descriptor values
{x.sub.j}.sup.i1 has been measured in triplicates (Y.sub.i1,
Y.sub.i2, Y.sub.i3), the training set for modeling will include
descriptor value {x.sub.j}.sup.i2 with activity Y.sub.i2 and
{x.sub.j}.sup.i3 with activity Y.sub.i3 in addition to
{x.sub.j}.sup.i1 with activity Y.sub.i1, where
{x.sub.j}.sup.i1={x.sub.j}.sup.j2={x.sub.j}.sup.i3.
[0200] A representative modeling routine in accordance with one
embodiment of the invention comprises the following steps.
[0201] Step 302. Relevant descriptors of the monomeric variables
are identified. These descriptors can convey physico-chemical
properties relevant to the interaction between biomolecules or
classify the monomers (residues) as discreet entities represented
in binary form as described earlier. The former is preferred for
residue positions in the antibody sequence where the number of
different amino acid substitutions is four or more or where the
variables can assume one of four possible values for those
positions and the physico-chemical properties values are well
distributed (e.g.) different from each other. The latter is
preferred for positions that have four or less possible values for
the relevant variable, and/or the values are clustered (e.g.) are
not very different from each other. To create non-linear variables,
new variables are formed that are a combination of monomeric
variables. For example, consider two variants AGWRY and AKYRY. The
linear binary form of the variable (descriptor) for position 2
assumes a value of "1" if G is present at that position and "0" if
it is absent. Alternatively, a non-linear variable can be created
in addition to the linear variables describing each position. In
the above example, a new non-linear variable representing position
"2" and "3" can assume four values in numeric form. In one form,
the variable can assume a value of 11 for "GW", 10 for "GY", 01 for
"KW" and 00 for "KY". In other representations of binary non-linear
variable, four variables can describe position 2 and 3, where
variable one assumes a value of "1" if the sequence at position 2
and 3 is "GW" and "0" otherwise and the second variable takes the
values of "1" or "0" if the sequence is "GY" or otherwise and so
on.
[0202] In some embodiments it is advantageous to identify regions
and thereby variables based on factors including, but not limited
to, structures, domains, motifs and exons, optionally using expert
system 100 to do so, in order to weigh different variables and
their contribution to the model or to build sequence activity
models based on these factors. For example, a weight of "1" can be
assigned to variables in the heavy chain of the antibody and "0"
for variables in light chain of the antibody when modeling activity
Y.sub.1 and a weight of "0" can be assigned to variables in heavy
chain and "1" for light chain when modeling activity Y.sub.2. This
weighting can also incorporate constraints such as immunogenicity
and other functional considerations that may or may not be measured
in experiments, but which can be fully or partially predicted using
computational techniques. For example, a negative weight can be
assigned to appearance of a T-cell epitope in a variant, or removal
of glycosylation sites.
[0203] Step 304. In step 304 the parameters for the functional form
of the sequence-activity relationship are optimized to obtain a
model by minimizing the difference between the predicted values and
real values of the activity of the antibody. Such optimization
adjusts the individual weights for each of the descriptors
identified in preceding steps using a refinement algorithm such as
least squares regression techniques. Other methods that use
alternative loss functions for minimization can be used to analyze
any particular dataset. For example, in some antibody
sequence-activity data sets, the activities may not be distributed
evenly throughout the measured range. This will skew the model
towards data points in the activity space that are clustered. This
can be disadvantageous because datasets often contain more data for
antibody variants with low levels of activity, so the model or map
will be biased towards accuracy for these antibodies that are of
lower interest. This skewed distribution can be compensated for by
modeling using a probability factor or a cost function based on
expert knowledge. This function can be modeled for the activity
value or can be used to assign weights to data points based on
their activity. As an example, for a set of activities in the range
of 0 to 10, transforming the data with a sigmoidal function
centered at five will give more weight to sequences with activity
above five. Such a function can optionally also be altered with
subsequent iterations, thereby focusing the modeling on the part of
the dataset with the most desired functional characteristics. This
approach can also be coupled with exploring techniques like a Tabu
search, where undesired space is explored with lower
probabilities.
[0204] In some embodiments, algorithms that optimize the
sequence-activity model for the dataset by randomly starting with a
solution (e.g., randomly assigning weights w) and using methods
like hill-descent and/or monte-carlo and/or genetic algorithm
approaches to identify optimal solutions.
[0205] In embodiments directed to antibody engineering, robustness
of the models used is a significant criterion. Thus, obtaining
several sub-optimal solutions from various initial conditions and
looking at all the models for common features can be a desirable
methodology for ensuring the robustness of the solution. Another
way to obtain robust solutions is to create bootstrap data sets
based on the input data, than estimate a p-value or confidence on
the various coefficients of the model. In addition boosting
techniques like AdaBoost can be used to obtain a "committee" based
solution.
[0206] Step 306. Many mathematical modeling techniques for deriving
a sequence-activity correlation are evaluated. Preferred
mathematical modeling techniques used to identify and capture the
sequence-activity correlation handle (i) very large numbers of
variables (e.g 20 or more) and correlations between variables, (ii)
linear and non-linear interactions between variables, and (iii) are
able to extract the variables responsible for a given functional
perturbation for subsequent testing of the mathematical model
(e.g., models should be easily de-convoluted to assign the effect
of variables describing the amino acids substitution with
activities).
[0207] Step 308. In step 308 the coefficients (parameters) of the
model(s) are deconvoluted to see which amino acid substitutions
(variables/descriptors of the variants) influence the activity of
the antibody. It can be important to identify which descriptors of
the antibody are important for the activity of interest. Some of
the techniques, such as partial least squares regression (SIMPLS)
that uses projection to latent structures (compression of data
matrix into orthogonal factors) may be good at directly addressing
this point because contributions of variables to any particular
latent factors can be directly calculated. See, for example, Bucht
et al., 1999, Biochim Biophys Acta. 1431:471-82; and Norinder et
al., 1997, J Pept Res 49:155-62. Other methods such as neural
networks can learn from the data very well and make predictions
about the activity of entire antibodies, but it may be difficult to
extract information, such as individual contributing features of
the antibody from the model. Modeling techniques/methods that
directly correlate the amino acid variations to the activity are
preferred because we can derive the sequence-activity map
(relationship) to construct new variants not in dataset that have
preferentially higher activities. These methods can be adapted to
provide a direct answer and output in desired forms.
[0208] Step 310. In step 310 the models developed using various
algorithms and methods in the previous step can be evaluated by
cross validation methods. For example, by randomly leaving data out
to build a model and making predictions of data not incorporated
into the model is a standard technique for cross validation. In
some instances of antibody engineering, data may be generated over
a period of months. The data can be added incrementally to the
modeling procedure as and when such data becomes available. This
can allow for validation of the model with partial or additional
datasets, as well as predictions for the properties of antibody
sequences for which activities are still not available. This
information may then be used to validate the model.
[0209] An example of internal model validation methods is shown in
FIGS. 4 and 5. In these schemes a confidence score for each
regression coefficient or weight vector can be generated for any
antibody sequence-activity model.
[0210] For example, in one embodiment of the present invention,
average values for weight functions can be obtained by omitting a
part of the available data. Either individual sequences and their
associated activities or individual substitution positions can be
left out. A sequence-activity relationship can then be constructed
from this partial data. This process can be repeated many times,
each time the data to leave out is selected randomly. Finally an
average and range of values for each weight function is calculated.
The weight functions can then also be ranked in order of their
importance to activity.
[0211] To assess the probability that a substitution is associated
with an activity by random chance, the same weight function
calculations can be performed when the sequences and activities are
randomly associated (FIG. 5). In this case there should be no
relationship between sequence and function, so weight functions
arise only by chance. A measure of the confidence for the weight
function can then be calculated. It is related to the number of
standard deviations by which the value calculated when sequences
and activities are correctly associated exceeds the value
calculated when they are randomly associated. The above methods on
model assessment, model inference and averaging are discussed in
detail by Hastie et al., 2001, Springer Verlag, series in
statistics.
[0212] Step 312. In step 312 new antibody sequences that are
predicted to possess one or more desired property are derived.
Alternatively it can be desirable to rank order the input variables
for detailed sequence-activity correlation measures. The model can
be used to propose sequences that have high probabilities of being
improved. Such sequences can then be synthesized and tested. In one
embodiment, this can be achieved if the effects of various sequence
features of the antibodies on their functions are known based on
the modeling. Alternatively, for methods like neural networks,
10.sup.3 or 10.sup.6 or 10.sup.9 or 10.sup.12 or 10.sup.15 or
10.sup.18 or as many as 10.sup.80 sequences can be evaluated in
silico. Then those predicted by the model to possess one or more
desired properties are selected.
[0213] Step 314. The statistical quality of the model fit to the
input data is evaluated in step 314. Validation of
sequence-activity correlation can be internal, using
cross-validation of the data, or preferably external, by
forecasting the functional perturbation of a set of new sequences
derived from the model. Sequences with predicted values of their
functional perturbations are then physically made and tested in the
same experimental system used to quantify the training set. If the
sequence-activity relationship of the dataset is satisfactory
quantified using internal and external validation, the model can be
applied to a) predict the functional value of other related
sequences not present in the training set, and b) design new
sequences within the described space that are likely to have a
function value that is outside or within the range of function
given by the training set.
[0214] The initial set of data can be small, so models built from
it can be inaccurate. Initial models may not contain terms to
account for amino acid interactions. Others have found that amino
acid changes within an antibody are approximately additive and few
interaction terms are required to describe the effects of mutations
on protein function. See, for example, Aita et al. (2000)
Biopolymers 54: 64-79; Aita et al. (2001) Protein Eng 14: 633-8;
Choulier et al. (2002) Protein Eng 15: 373-82; and Prusis et al.
(2002) Protein Eng 15: 305-11. However such interactions can be
important and can result in a variant that incorporates all
beneficial changes having low activity (Aita et al. (2002)
Antibodies 64: 95-105). Improving the modeled relationship further
depends upon obtaining better values for weights whose confidence
scores are low. To obtain this data, additional variants designed
as described in Section 5.4 below will provide additional data
useful in establishing more precise sequence-activity
relationships.
[0215] The output from each method for modeling a sequence-activity
relationship can be one or more of: (i) a regression coefficient,
weight or other value describing the relative or absolute
contribution of each substitution or combination of substitutions
to one or more activity of the antibody, (ii) a standard deviation,
variance or other measure of the confidence with which the value
describing the contribution of the substitution or combination of
substitutions to one or more activity of the antibody can be
assigned, (iii) a rank order of preferred substitutions, (iv) the
additive & non-additive components of each substitution or
combination of substitutions, (v) a mathematical model that can be
used for analysis and prediction of the functions of in silico
generated sequences, (vi) a modification of one or more inputs or
weights used by an expert system 100 to select substitutions or
(vii) a modification of the methods used by expert system 100 to
design an antibody variant set.
5.3.1 Methods for Combining the Results from Two or More
Sequence-activity Relationship Modeling Methods
[0216] It will be appreciated by one skilled in the art that each
different method for deriving relationships between antibody
sequences and activities can differ in the precise values of their
outputs. In some embodiments of the invention it is therefore
desirable to combine the outputs from two or more such methods for
subsequent uses. This corresponds to step 06 in FIG. 2. There are a
variety of ways in which such outputs can be combined. In some
embodiments, each output can be independently applied to the
subsequent design of antibody variants (FIG. 2, step 07) or the
modification of parameters or weights used by expert system 100 for
the selection of substitutions (FIG. 2 step 02) or the design of
antibody variant sets (FIG. 2 step 03). In some embodiments,
average values (or some other mathematical function of two or more
values derived by two or more sequence-activity models) can be
calculated for the regression coefficient, weight or other value
describing the relative or absolute contribution of each
substitution or combination of substitutions to one or more
activity of the antibody (e.g., as defined in Equation 4 below). In
some embodiments, the standard deviation, variance or other measure
of the confidence with which the value describing the contribution
of the substitution or combination of substitutions to one or more
activity of the antibody can be assigned (e.g., as defined in
Equation 4 below). In some embodiments, the rank order of preferred
substitutions is used to combine the methods. In some embodiments,
the additive (linear variables) and non-additive components
(non-linear variables) of each substitution or combination of
substitutions is combined:
V.sub.ix=f(M.sub.1(i.sub.x),M.sub.2(i.sub.x), . . .
,M.sub.j(i.sub.x)) (Eq. 6)
[0217] where, [0218] V.sub.ix is a combined measure of one of the
descriptors measuring the performance of an antibody in which
monomer x is substituted at position i; [0219] M.sub.j(i.sub.x) is
a measure of one of descriptors measuring the performance of an
antibody in which monomer x is substituted at position i,
determined by sequence-activity correlating method
j(M.sub.j(i.sub.x) is the contribution of i.sub.x as determined by
Model j); and [0220] f( ) is some mathematical function.
[0221] The methods used to derive sequence-activity relationships
can be chosen or modified such that they better predict the
performance of individual substitutions within a combination of
other substitutions in an antibody, as described in more detail in
Subsection 5.4.4.
5.4 Use of Sequence-Activity Relationships to Design Optimized
Variants or Additional Variant Sets
[0222] There are many ways to use the results of sequence-activity
correlations described in Section 5.3 in the design of a subsequent
set of variants. This corresponds to step 07 of FIG. 2.
Conceptually, this step is similar to the processes corresponding
to steps 02 and 03 in FIG. 2. It involves defining a sequence space
in terms of an antibody sequence and a set of substitutions, then
designing a set of antibody variants that incorporate those
substitutions in different combinations.
5.4.1 Definition of the Sequence Space to Represent Additional
Variant Sets
[0223] A few methods for defining a sequence space for an optimized
variant or additional variant set, using an antibody sequence and a
set of substitutions are enumerated here by way of examples not
intended to limit the scope of the present invention.
[0224] In one embodiment the sequence space can be defined in terms
of the original target antibody sequence and substitutions that
have already been tested. In preferred embodiments of the
invention, this method for defining the sequence space is used if
the desired degree of further increase in one or more activity of
the antibody is less than 10-fold, preferably less than 5-fold,
more preferably less than 2-fold.
[0225] In another embodiment, the sequence space can be defined in
terms of the original target antibody sequence and a combination of
substitutions that have already been tested and those that have not
yet been tested. In preferred embodiments of the invention, this
method for defining the sequence space is used if the desired
degree of further increase in one or more activity of the antibody
is greater than 2-fold, preferably greater than 5-fold, and more
preferably greater than 10-fold.
[0226] In still another embodiment, the sequence space can be
defined purely in terms of the original target antibody sequence
and substitutions that have not yet been tested. This method for
defining the sequence space is generally most appropriate for the
initial variant set as represented in FIG. 2 step 02.
5.4.2 Assessment of Previously Tested Substitutions for
Incorporation into Optimized Variants or Additional Variant
Sets
[0227] The methods for selecting substitutions that have not
previously been tested have been described in Section 5.1. Methods
for selecting or eliminating substitutions that have previously
been tested use one or more of the outputs from the methods for
correlating antibody sequences with their activities. A few methods
for defining a sequence space for an optimized variant or
additional variant set, using an antibody sequence and a set of
substitutions are enumerated here by way of examples. In the
following examples, the term "substitution" can also mean a pair or
larger group of substitutions (for example, when the descriptors of
antibodies are represented in non-linear form as described in
section 5.3), since sequence-activity relationships can produce
regression coefficients, weights or other measurements of
contribution to function and confidences for these measurements
that apply not to individual substitutions but to specific
combinations of these substitutions.
[0228] (i) A substitution can be selected if it has a positive
regression coefficient, weight or other value describing its
relative or absolute contribution to one or more activity of the
antibody.
[0229] (ii) A substitution can be selected if it has a positive
regression coefficient, weight or other value describing its
relative or absolute contribution to one or more activity of the
antibody, and it is at least one standard deviation, preferably two
standard deviations or more preferably three standard deviations
above neutrality.
[0230] (iii) A substitution can be selected if it has a positive
regression coefficient, weight or other value describing its
relative or absolute contribution to one or more activity of the
antibody, and it has also been tested at least once, preferably at
least twice, more preferably at least 3 times, more preferably at
least 4 times, even more preferably at least 5 times.
[0231] (iv) A substitution can be selected if it has a positive
regression coefficient, weight or other value describing its
relative or absolute contribution to one or more activity of the
antibody, and it is at least one standard deviation, preferably two
standard deviations or more preferably three standard deviations
above neutrality, and it has also been tested at least once,
preferably at least twice, more preferably at least 3 times, more
preferably at least 4 times, even more preferably at least 5
times.
[0232] (v) A substitution can be selected from a rank ordered list
of substitutions. For example the most favorable substitution may
be selected, or the 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19 or 20 most favorable substitutions can be
selected.
[0233] (vi) A substitution can be selected from a rank ordered list
of substitutions. For example, the most favorable substitution can
be selected, or the 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19 or 20 most favorable substitutions can be selected,
and it is at least one standard deviation, preferably two standard
deviations or more preferably three standard deviations above
neutrality.
[0234] (vii) A substitution can be selected from a rank ordered
list of substitutions. For example, the most favorable substitution
can be selected, or the 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19 or 20 most favorable substitutions can be
selected, and it has also been tested at least once, preferably at
least twice, more preferably at least 3 times, more preferably at
least 4 times, even more preferably at least 5 times.
[0235] (viii) A substitution can be selected from a rank ordered
list of substitutions. For example, the most favorable substitution
may be selected, or the 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19 or 20 most favorable substitutions can be
selected, and it is at least one standard deviation, preferably two
standard deviations or more preferably three standard deviations
above neutrality, and it has also been tested at least once,
preferably at least twice, more preferably at least 3 times, more
preferably at least 4 times, even more preferably at least 5
times.
[0236] (ix) A substitution can be selected if it has a negative
regression coefficient, weight or other value describing its
relative or absolute contribution to one or more activity of the
antibody, and it is less than three standard deviations, preferably
less than two standard deviations or more preferably less than one
standard deviation below neutrality.
[0237] (x) A substitution can be selected if it has a negative
regression coefficient, weight or other value describing its
relative or absolute contribution to one or more activity of the
antibody, and it has also been tested no more than 5 times,
preferably no more than 4 times, more preferably no more than 3
times, more preferably no more than twice, even more preferably no
more than once.
[0238] (xi) A substitution can be selected if it has a negative
regression coefficient, weight or other value describing its
relative or absolute contribution to one or more activity of the
antibody, and it is less than three standard deviations, preferably
less than two standard deviations or more preferably less than one
standard deviation below neutrality, and it has also been tested no
more than 5 times, preferably no more than 4 times, more preferably
no more than 3 times, more preferably no more than twice, even more
preferably no more than once.
[0239] (xii) A substitution can be eliminated if it has a negative
regression coefficient, weight or other value describing its
relative or absolute contribution to one or more activity of the
antibody.
[0240] (xiii) A substitution can be eliminated if it has a negative
regression coefficient, weight or other value describing its
relative or absolute contribution to one or more activity of the
antibody, and it is at least one standard deviation, preferably two
standard deviations or more preferably three standard deviations
above neutrality.
[0241] (xiv) A substitution can be eliminated if it has a negative
regression coefficient, weight or other value describing its
relative or absolute contribution to one or more activity of the
antibody, and it has also been tested at least once, preferably at
least twice, more preferably at least 3 times, more preferably at
least 4 times, even more preferably at least 5 times.
[0242] (xv) A substitution can be eliminated if it has a negative
regression coefficient, weight or other value describing its
relative or absolute contribution to one or more activity of the
antibody, and it is at least one standard deviation, preferably two
standard deviations or more preferably three standard deviations
above neutrality, and it has also been tested at least once,
preferably at least twice, more preferably at least 3 times, more
preferably at least 4 times, even more preferably at least 5
times.
5.4.3 Methods for Designing Antibody Variant Sets Incorporating
Previously Tested Substitutions
[0243] Antibody variants that combine or eliminate previously
tested substitutions can serve at least two purposes. First, they
can be used to obtain antibody variants that are improved for one
or more property, activity or function of interest. Generally,
though not exclusively, substitutions selected according to
criteria (i)-(viii) in subsection 5.4.2 are most likely to be
appropriate for this purpose. Second, they can be used to obtain
additional information relating the sequence to the activity of an
antibody, thereby improving the accuracy with which predictions can
be made concerning the effect of substitutions upon one or more
property, activity or function of an antibody. Generally, though
not exclusively, substitutions selected according to criteria
(i)-(xi) in subsection 5.4.2 are most likely to be appropriate for
this purpose.
[0244] The following methods can be used to design antibody
variants containing combinations of substitutions selected by one
or more of the methods described in subsection 5.4.2.
[0245] Method 1.
[0246] An antibody that has previously been tested for the one or
more property, activity or function of interest is selected. In
preferred embodiments the selected antibody has one of the 100
highest experimentally measured scores for the property, activity
or function of interest, more preferably one of the 50 highest
experimentally measured scores, even more preferably one of the 25
highest experimentally measured scores, even more preferably one of
the 10 highest experimentally measured scores.
[0247] The substitutions in the selected antibody are combined with
one or more substitutions selected by one or more of the methods
described in subsection 5.4.2. In preferred embodiments less than
10 selected substitutions are used, more preferably less than 5
selected substitutions are used, even more preferably less than 3
selected substitutions are used.
[0248] Method 2.
[0249] An antibody that has previously been tested for the one or
more property, activity or function of interest is selected. In
preferred embodiments the selected antibody has one of the 100
highest experimentally measured scores for the property, activity
or function of interest, more preferably one of the 50 highest
experimentally measured scores, even more preferably one of the 25
highest experimentally measured scores, even more preferably one of
the 10 highest experimentally measured scores.
[0250] The substitutions in the selected antibody are combined with
one or more substitutions selected by one or more of the methods
described in subsection 5.4.2. In preferred embodiments less than
10 selected substitutions are used, more preferably less than 5
selected substitutions are used, even more preferably less than 3
selected substitutions are used. In addition, these substitutions
are combined with one or more substitutions selected by one or more
method described in Section 5.1 (i.e., by the methods used in step
03 of FIG. 2). In preferred embodiments, less than 10 of these last
selected substitutions are used, more preferably less than 5 of
these last selected substitutions are used, even more preferably
less than 3 of these last selected substitutions are used.
[0251] Method 3.
[0252] Two or more substitutions identified by one or more of the
methods described in subsection 5.4.2 are selected. In preferred
embodiments less than 100 selected substitutions, more preferably
less than 50, and even more preferably less than 25 are used. One
or more antibody variants containing these substitutions are
designed using the methods described in Section 5.2.
[0253] Method 4.
[0254] One or more substitutions selected by one or more of the
methods described in subsection 5.4.2 are selected. In preferred
embodiments less than 100 selected substitutions, more preferably
less than 50, and even more preferably less than 25 are used. One
or more substitutions are selected using one or more of the methods
described in Section 5.1. In preferred embodiments, less than 100,
and more preferably less than 50 of these selected substitutions
are used. Then, one or more antibody variants are designed using
the methods described in Section 5.2.
[0255] Method 5.
[0256] One or more substitutions selected by one or more of the
methods described in subsection 5.4.2 that contribute most
positively to the property (e.g., function, activity of interest)
are selected. In preferred embodiments, between 1 and 20 most
positive substitutions are selected. One or more antibody variant
that has already been tested for the property is selected. In
preferred embodiments, the between 1 and 20 most active antibodies
are selected. One or more of the selected substitutions is added to
each of the one or more selected antibodies. In preferred
embodiments, the number of substitution positions to be added to
each antibody variant sequence is between 1 and 10, more preferably
between 1 and 6, and even more preferably between 1 and 3.
[0257] Method 6.
[0258] Substitutions whose regression coefficients, weights or
other values describing the relative or absolute contribution to
one or more activity of the antibody are positive are selected.
Those substitutions whose regression coefficients, weights or other
values describing the relative or absolute contribution to one or
more activity of the antibody have confidences within a threshold
distance from the randomized average weight for that substitution
are eliminated. In preferred embodiments, this threshold distance
is within 1 standard deviation, more preferably within 2 standard
deviations. The substitutions with positive weights and high
confidences are combined into a single variant. Alternatively, the
selected substitutions are used to design a set of antibody
variants as described in Section 5.2.
[0259] Method 7.
[0260] Substitutions are ranked in the order in which confidences
can be assigned to regression coefficients, weights or other values
describing the relative or absolute contribution to one or more
activity of the antibody. The substitutions with lowest confidence
scores are selected. From the sequences of antibody variants whose
activities have already been measured, those that have high values
for the property of interest are selected. In preferred
embodiments, between 1 and 20 tested antibody variant sequences
with highest activities are selected. One or more of the selected
substitutions is added to each selected variant. In preferred
embodiments, the number of substitutions to be added to each
antibody variant sequence is between 1 and 10, more preferably
between 1 and 6, and even more preferably between 1 and 3.
[0261] Method 8.
[0262] One or more antibody variants that have already been tested
for the property of interest are selected. In preferred
embodiments, between 1 and 20 most active antibodies are selected.
One or more substitutions for which a contribution to the property
has been calculated are selected. For each of the one or more
selected antibodies, the following process is performed. One of the
selected substitutions is added or removed and the predicted
activity of the resultant antibody is calculated using one or more
models for sequence-activity relationship as described in the
section 5.3. Exemplary models include, but are not limited to (i)
regression techniques that provide regression coefficients for the
descriptors, (ii) models that generate weights or other value
describing the relative or absolute contribution of each
substitution or combination of substitutions to one or more
activity of the antibody, (iii) models that provide standard
deviation, variance or other measures of the confidence with which
the value describing the contribution of the substitution or
combination of substitutions to one or more activity of the
antibody can be assigned, (iv) models that rank order preferred
substitutions, (v) models that provide additive and non-additive
components of each substitution or combination of substitutions,
(vi) analytical mathematical models that can be used for analysis
and prediction of the functions of in silico generated sequences
(vii) supervised and unsupervised machine learning techniques like
neural networks that can predict the activity of new antibody
sequences expressed in terms of the descriptors that are used in
modeling.
[0263] If the predicted activity of the new antibody is greater
than the predicted value of the antibody before the change, the
change is incorporated. Otherwise, the process reverts to the
sequence of the antibody before the change. This process continues
for a certain number of steps (preferably more than 10 steps, more
preferably more than 100 steps, even more preferably more than 1000
steps) or until the predicted activity of the antibody converges to
a value. Either the final antibody sequence in the series of
iterations of the method, or the antibody sequence in the series
with the highest predicted activity is selected. This process can
optionally be performed more than once starting from each initial
antibody sequence.
[0264] Method 9.
[0265] As an optional addition to any of the design methods
including methods 1, 2, 5, and 7, one or more substitutions
determined to be detrimental to the desired property (e.g., by any
of the criteria described in subsection 5.4.2 including criteria
(xii)-(xv)) are eliminated.
[0266] Method 10.
[0267] As an optional addition to any design method, newly designed
variants that can be reached by making a certain number of
substitutions to an antibody sequence whose activity has already
been measured are discarded and not synthesized. In preferred
embodiments newly designed variants that can be reached by making
10 or fewer substitutions to an antibody sequence whose activity
has already been measured are not synthesized. More preferably,
newly designed variants that can be reached by making 5 or fewer
substitutions to an antibody sequence whose activity has already
been measured are not synthesized. More preferably, newly designed
variants that can be reached by making 3 or fewer substitutions to
an antibody sequence whose activity has already been measured are
not synthesized. Even more preferably, newly designed variants that
can be reached by making 2 or fewer substitutions to an antibody
sequence whose activity has already been measured are not
synthesized. Most preferably, newly designed variants that can be
reached by making 1 to an antibody sequence whose activity has
already been measured are not synthesized.
[0268] One skilled in the art will appreciate that there are many
possible ways of using sequence-activity information to design
improved antibody variants. The schemes outlined above are intended
to illustrate a few of the design possibilities.
5.4.4 Methods for Modifying the Choice and Combinations of Methods
used to Determine Sequence-Activity Relationships
[0269] The performances of different sequence-activity modeling
methods can be quantitatively compared. Such comparisons can be
used to modify variable parameters within each method, or to select
methods of combining the results of two or more sequence-activity
correlating methods as outlined in Subsection 5.3.1.
[0270] The outputs of methods that determine sequence-activity
relationship are outlined in Section 5.3. These outputs can be
combined to calculate the predicted activity of an antibody and the
confidence with which that activity can be predicted. These
predictions can be compared with activity values obtained
experimentally for newly designed and synthesized antibody
variants, and the method or methods of deriving sequence-activity
relationships may be chosen or modified in one or more of the
following ways.
[0271] 1. The weights applied to the scores produced by the one or
more sequence-activity correlating methods, for example as shown in
Equation 4 or as described in Subsection 5.3.1 can be modified such
that one or more of the following are true. [0272] (i) The activity
value predicted for the most active newly designed and synthesized
antibody variant most closely matches the experimentally determined
activity for that variant. [0273] (ii) The rank order of activity
values predicted for some number of the most active newly designed
and synthesized antibody variants most closely match the
experimentally determined rank order of activity for those
variants. In preferred embodiments the rank order of activity
values predicted for the 5 most active newly designed and
synthesized antibody variants most closely matches the
experimentally determined rank order of activity for those
variants, more preferably the rank order of activity values
predicted for the 10 most active newly designed and synthesized
antibody variants most closely matches the experimentally
determined rank order of activity for those variants, even more
preferably the rank order of activity values predicted for the 15
most active newly designed and synthesized antibody variants most
closely matches the experimentally determined rank order of
activity for those variants. [0274] (iii) The fewest newly designed
and synthesized antibody variants predicted to be more active than
the initial target antibody possess experimentally determined
activity that is lower than the initial target antibody. [0275]
(iv) The fewest newly designed and synthesized antibody variants
predicted to be more active than the most active previously tested
antibody possess experimentally determined activities that are
lower than the most active previously tested antibody.
[0276] 2. The sequence-activity correlating method is chosen such
that one or more of the following are true. [0277] (i) The activity
value predicted for the most active newly designed and synthesized
antibody variant most closely matches the experimentally determined
activity for that variant. [0278] (ii) The rank order of activity
values predicted for some number of the most active newly designed
and synthesized antibody variants most closely match the
experimentally determined rank order of activity for those
variants. In preferred embodiments the rank order of activity
values predicted for the 5 most active newly designed and
synthesized antibody variants most closely matches the
experimentally determined rank order of activity for those
variants, more preferably the rank order of activity values
predicted for the 10 most active newly designed and synthesized
antibody variants most closely matches the experimentally
determined rank order of activity for those variants, even more
preferably the rank order of activity values predicted for the 15
most active newly designed and synthesized antibody variants most
closely matches the experimentally determined rank order of
activity for those variants. [0279] (iii) The fewest newly designed
and synthesized antibody variants predicted to be more active than
the initial target antibody possess experimentally determined
activities that are lower than the initial target antibody. [0280]
(iv) The fewest newly designed and synthesized antibody variants
predicted to be more active than the most active previously tested
antibody possess experimentally determined activities that are
lower than the most active previously tested antibody.
[0281] 3. In some embodiments, the process of steps 1 or 2 can be
performed using regression techniques, machine learning or other
multivariate data analysis tools to calculate or minimize the
differences between the values predicted by the sequence-activity
relationship, and those observed experimentally.
[0282] 4. In some embodiments, the process of steps 1 or 2 can be
performed using values predicted by the sequence-activity
relationship, and those observed experimentally for more than one
set of antibodies.
[0283] 5. In some embodiments the process of step 4 can be
performed using two or more datasets from antibodies that fall into
the same class and subclass. For example, two or more sets of IgG
antibodies, two or more sets of IgE antibodies, two or more sets of
single chain antibodies, two or more sets of Fab fragments. Weights
for expert system rules 120 that are modified using two or more
datasets from antibodies of the same class and subclass can be
stored, for example in knowledge base 108 or case-specific data
110. These weights or choices for sequence-activity determining
methods can then be used by expert system 100 when a subsequent
target antibody sequence and activity dataset of that class and
subclass is presented.
[0284] 6. In some embodiments the process of step 4 can be
performed using two or more datasets from antibodies that fall into
the same class. For example two or more sets of human antibodies,
two or more sets of murine antibodies. Weights for expert system
100 rules 120 that are modified using two or more datasets from
antibodies of the same class can be stored, for example in
knowledge base 108 or case-specific data 110. These weights for
expert system 100 rules 120 can then be used by expert system 100
when a subsequent target antibody sequence and activity dataset of
that class and subclass is presented.
5.5 Use of Sequence-Activity Relationships to Train an Expert
System for Substitution Identification
[0285] The endpoint of a process of antibody optimization is
reached when one or more antibodies are obtained with one or more
properties at the levels defined by a user, these activity levels
being appropriate to allow the use of the antibody in performing a
specific task. This corresponds to FIG. 2 step 08.
[0286] In addition to designing improved antibody variants,
information from sequence-activity relationships can be used to
provide information to improve the initial selection of
substitutions, for example by modifying the weights applied to the
scores produced by the expert system 100 as described in Section
5.1. As an example, the weights can be modified according to the
following process.
[0287] 1. As described in Section 5.3, the sequence-activity
relationship can be used to calculate (i) a regression coefficient,
weight or other value describing the relative or absolute
contribution of each substitution or combination of substitutions
to one or more activity of the antibody, (ii) a standard deviation,
variance or other measure of the confidence with which the value
describing the contribution of the substitution or combination of
substitutions to one or more activity of the antibody can be
assigned, and/or (iii) a rank order of preferred substitutions.
[0288] 2. The results of applying two or more rules 120 of expert
system 100 are combined and can be used to obtain (i) a score
describing the predicted effect of a substitution upon one or more
antibody property, (ii) a probability or confidence describing the
predicted effect of a substitution upon one or more antibody
property, activity or function, or (iii) a predicted rank order of
preferred substitutions. Different values for each of these
predictions can result from modifications of the weights applied to
the scores produced by expert system 100 as described in Section
5.1, for example as shown in equations (1) or (2).
[0289] 3. The weights applied to the scores produced by expert
system 100 can be modified such that one or more of the following
are true. [0290] (i) The regression coefficient, weight or other
value describing the relative or absolute contribution of each
substitution or combination of substitutions to one or more
activity of the antibody that is derived from the sequence-activity
relationship more closely corresponds with the score describing the
predicted effect of a substitution upon one or more antibody
property, activity or function that is derived from expert system
100. [0291] (ii) The standard deviation, variance or other measure
of the confidence with which the value describing the contribution
of the substitution or the combination of substitutions to one or
more activity of the antibody can be assigned that is derived from
the sequence-activity relationship more closely corresponds with
the probability or confidence describing the predicted effect of a
substitution upon one or more antibody property, activity or
function that is derived from expert system 100. [0292] (iii) The
rank order of preferred substitutions that is derived from the
sequence-activity relationship more closely corresponds with the
predicted rank order of preferred substitutions that is derived
from expert system 100.
[0293] 4. In some embodiments, the process of steps 1 to 3 can be
performed using regression techniques, machine learning or other
multivariate data analysis tools to minimize the differences
between the values obtained from the sequence-activity
relationship, and those predicted by expert system 100.
[0294] 5. In some embodiments, the process of steps 1 to 3 can be
performed using expert system 100 predictions and sequence-activity
relationships for more than one set of antibodies.
[0295] 6. In some embodiments the process of step 5 can be
performed using two or more datasets from antibodies that fall into
the same class and subclass. For example, two or more sets of, two
or more sets of IgG antibodies, two or more sets of IgE antibodies,
two or more sets of single chain antibodies, two or more sets of
Fab fragments. Weights for expert system 100 rules 120 that are
modified using two or more datasets from antibodies of the same
class and subclass can be stored, for example in knowledge base 108
or case-specific data 110. These weights for expert system rules
120 can then be used by expert system 100 when a subsequent target
antibody of that class and subclass is presented.
[0296] 7. In some embodiments the process of step 5 can be
performed using two or more datasets from antibodies that fall into
the same class. For example, two or more sets of human antibodies,
two or more sets of murine antibodies. Weights for expert system
100 rules 120 that are modified using two or more datasets from
antibodies of the same class can be stored, for example in
knowledge base 108 or case-specific data 110. These weights for
rules 120 can then be used by expert system 100 when a subsequent
target antibody of that class is presented.
[0297] By using a formal system for substitution selection,
predictions made by expert system 100 can be improved so that
preferences (e.g. higher weights) are given to selection methods
130 that have performed well in previous iterations.
[0298] Different algorithms and methods for identifying productive
substitutions and for deriving sequence activity relationships may
be better suited to different types of antibody, including
different animal origins, different antibody fragments,
optimization compared with humanization.
[0299] By using feedback loops of this nature, where quantitative
scoring or ranking protocols are developed, a learning, automated
computational system for antibody optimization can be developed.
This system could include generic information applicable to all
antibody classes and specific information applicable to a more
limited subset of antibodies.
[0300] Such a computational system could be made available
directly, via the internet and/or on a subscription basis.
5.6 Utility of the Variants of this Invention
[0301] Other useful products produced by the method of the
invention include antibodies incorporating substitutions identified
through construction and characterizing sets of variant antibodies.
Where the antibody is encoded by a polynucleotide this also
includes vectors (including expression vectors) comprising such
polynucleotides, host cells comprising such polynucleotides and/or
vectors, and libraries of antibodies, and libraries of host cells
comprising and/or expressing such libraries of antibodies.
[0302] The antibodies developed using the methods of the invention
can be used alone or in combination with other prophylactic or
therapeutic agents for treating, managing, preventing or
ameliorating a disorder or one or more symptoms thereof.
[0303] The present invention provides methods for preventing,
managing, treating, or ameliorating a disorder comprising
administering to a subject in need thereof one or more antibodies
of the invention alone or in combination with one or more therapies
(e.g., one or more prophylactic or therapeutic agents) other than
an antibody of the invention. The present invention also provides
compositions comprising one or more antibodies of the invention and
one or more prophylactic or therapeutic agents other than
antibodies of the invention and methods of preventing, managing,
treating, or ameliorating a disorder or one or more symptoms
thereof utilizing said compositions. Therapeutic or prophylactic
agents include, but are not limited to, small molecules, synthetic
drugs, peptides, polypeptides, proteins, nucleic acids (e.g., DNA
and RNA nucleotides including, but not limited to, antisense
nucleotide sequences, triple helices, RNAi, and nucleotide
sequences encoding biologically active proteins, polypeptides or
peptides) antibodies, synthetic or natural inorganic molecules,
mimetic agents, and synthetic or natural organic molecules.
[0304] Any therapy that is known to be useful, or that has been
used or is currently being used for the prevention, management,
treatment, or amelioration of a disorder or one or more symptoms
thereof can be used in combination with an antibody of the
invention in accordance with the invention described herein. See,
e.g., Gilman et al., Goodman and Gilman's: The Pharmacological
Basis of Therapeutics, 10th ed., McGraw-Hill, New York, 2001; The
Merck Manual of Diagnosis and Therapy, Berkow, M. D. et al. (eds.),
17th Ed., Merck Sharp & Dohme Research Laboratories, Rahway,
N.J., 1999; Cecil Textbook of Medicine, 20th Ed., Bennett and Plum
(eds.), W.B. Saunders, Philadelphia, 1996 for information regarding
therapies (e.g., prophylactic or therapeutic agents) that have been
or are currently being used for preventing, treating, managing, or
ameliorating a disorder or one or more symptoms thereof. Examples
of such agents include, but are not limited to, immunomodulatory
agents, anti-inflammatory agents (e.g., adrenocorticoids,
corticosteroids (e.g., beclomethasone, budesonide, flunisolide,
fluticasone, triamcinolone, methlyprednisolone, prednisolone,
prednisone, hydrocortisone), glucocorticoids, steroids,
non-steriodal anti-inflammatory drugs (e.g., aspirin, ibuprofen,
diclofenac, and COX-2 inhibitors), pain relievers, leukotreine
antagonists (e.g., montelukast, methyl xanthines, zafirlukast, and
zileuton), beta2-agonists (e.g., albuterol, biterol, fenoterol,
isoetharie, metaproterenol, pirbuterol, salbutamol, terbutalin
formoterol, salmeterol, and salbutamol terbutaline),
anticholinergic agents (e.g., ipratropium bromide and oxitropium
bromide), sulphasalazine, penicillamine, dapsone, antihistamines,
anti-malarial agents (e.g., hydroxychloroquine), anti-viral agents,
and antibiotics (e.g., dactinomycin (formerly actinomycin),
bleomycin, erythomycin, penicillin, mithramycin, and anthramycin
(AMC)).
[0305] The antibodies of the invention can be used directly against
a particular antigen. In some embodiments, antibodies of the
invention belong to a subclass or isotype that is capable of
mediating the lysis of cells to which the antibody binds. In a
specific embodiment, the antibodies of the invention belong to a
subclass or isotype that, upon complexing with cell surface
proteins, activates serum complement and/or mediates antibody
dependent cellular cytotoxicity (ADCC) by activating effector cells
such as natural killer cells or macrophages.
[0306] The biological activities of antibodies are known to be
determined, to a large extent, by the constant domains or Fc region
of the antibody molecule (Uananue and Benacerraf, Textbook of
Immunology, 2nd Edition, Williams & Wilkins, p. 218 (1984)).
This includes their ability to activate complement and to mediate
antibody-dependent cellular cytotoxicity (ADCC) as effected by
leukocytes. Antibodies of different classes and subclasses differ
in this respect, as do antibodies from the same subclass but
different species; according to the present invention, antibodies
of those classes having the desired biological activity are
prepared.
[0307] In general, mouse antibodies of the IgG2a and IgG3 subclass
and occasionally IgG1 can mediate ADCC, and antibodies of the IgG3,
IgG2a, and IgM subclasses bind and activate serum complement.
Complement activation generally requires the binding of at least
two IgG molecules in close proximity on the target cell. However,
the binding of only one IgM molecule activates serum
complement.
[0308] The ability of any particular antibody to mediate lysis of
the target cell by complement activation and/or ADCC can be
assayed. The cells of interest are grown and labeled in vitro; the
antibody is added to the cell culture in combination with either
serum complement or immune cells which may be activated by the
antigen antibody complexes. Cytolysis of the target cells is
detected by the release of label from the lysed cells. In fact,
antibodies can be screened using the patient's own serum as a
source of complement and/or immune cells. The antibody that is
capable of activating complement or mediating ADCC in the in vitro
test can then be used therapeutically in that particular
patient.
[0309] Use of IgM antibodies may be preferred for certain
applications, however IgG molecules by being smaller may be more
able than IgM molecules to localize to certain types of infected
cells.
[0310] In some embodiments, the antibodies of this invention are
useful in passively immunizing patients.
[0311] The antibodies of the invention can also be used in
diagnostic assays either in vivo or in vitro for
detection/identification of the expression of an antigen in a
subject or a biological sample (e.g., cells or tissues).
Non-limiting examples of using an antibody, a fragment thereof, or
a composition comprising an antibody or a fragment thereof in a
diagnostic assay are given in U.S. Pat. Nos. 6,392,020; 6,156,498;
6,136,526; 6,048,528; 6,015,555; 5,833,988; 5,811,310; 8 5,652,114;
5,604,126; 5,484,704; 5,346,687; 5,318,892; 5,273,743; 5,182,107;
5,122,447; 5,080,883; 5,057,313; 4,910,133; 4,816,402; 4,742,000;
4,724,213; 4,724,212; 4,624,846; 4,623,627; 4,618,486; 4,176,174
(all of which are incorporated herein by reference). Suitable
diagnostic assays for the antigen and its antibodies depend on the
particular antibody used. Non-limiting examples are an ELISA,
sandwich assay, and steric inhibition assays. For in vivo
diagnostic assays using the antibodies of the invention, the
antibodies may be conjugated to a label that can be detected by
imaging techniques, such as X-ray, computed tomography (CT),
ultrasound, or magnetic resonance imaging (MRI). The antibodies of
the invention can also be used for the affinity purification of the
antigen from recombinant cell culture or natural sources.
5.7 Definitions
[0312] It is to be understood that this invention is not limited to
the particular methodology, devices, solutions or apparatuses
described, as such methods, devices, solutions or apparatuses can,
of course, vary. It is also to be understood that the terminology
used herein is for the purpose of describing particular embodiments
only, and is not intended to limit the scope of the present
invention.
[0313] Unless defined otherwise herein, all technical and
scientific terms used herein have the same meaning as commonly
understood by one of ordinary skill in the art to which this
invention belongs. Singleton et al., Dictionary Of Microbiology And
Molecular Biology, 2.sup.nd ed., John Wiley and Sons, New York
(1994), and Hale & Marham, The Harper Collins Dictionary Of
Biology, Harper Perennial, N.Y. (1991) provide one of skill with a
general dictionary of many of the terms used in this invention.
Bioinformatic terms referring to expert systems are used in the
same sense that they appear in Jackson, Introduction To Expert
Systems, 3.sup.rd ed., Addison-Wesley, NY (1999). Although any
methods and materials similar or equivalent to those described
herein can be used in the practice or testing of the present
invention, the preferred methods and materials are described.
Unless otherwise indicated, nucleic acids are written left to right
in 5' to 3' orientation; amino acid sequences are written left to
right in amino to carboxy orientation, respectively. The headings
provided herein are not limitations on the invention, but exemplify
the various aspects of the invention. Accordingly, the terms
defined immediately below are more fully defined by reference to
the specification as a whole.
[0314] The terms "polynucleotide," "oligonucleotide," "nucleic
acid" and "nucleic acid molecule" and "gene" are used
interchangeably herein to refer to a polymeric form of nucleotides
of any length, and may comprise ribonucleotides,
deoxyribonucleotides, analogs thereof, or mixtures thereof. This
term refers only to the primary structure of the molecule. Thus,
the term includes triple-, double- and single-stranded
deoxyribonucleic acid ("DNA"), as well as triple-, double- and
single-stranded ribonucleic acid ("RNA"). It also includes
modified, for example by alkylation, and/or by capping, and
unmodified forms of the polynucleotide. More particularly, the
terms "polynucleotide," "oligonucleotide," "nucleic acid" and
"nucleic acid molecule" include polydeoxyribonucleotides
(containing 2-deoxy-D-ribose), polyribonucleotides (containing
D-ribose), including tRNA, rRNA, hRNA, siRNA and mRNA, whether
spliced or unspliced, any other type of polynucleotide which is an
N- or C-glycoside of a purine or pyrimidine base, and other
polymers containing normucleotidic backbones, for example,
polyamide (e.g., peptide nucleic acids ("PNAs")) and polymorpholino
(commercially available from the Anti-Virals, Inc., Corvallis,
Oreg., as Neugene) polymers, and other synthetic sequence-specific
nucleic acid polymers providing that the polymers contain
nucleobases in a configuration which allows for base pairing and
base stacking, such as is found in DNA and RNA. There is no
intended distinction in length between the terms "polynucleotide,"
"oligonucleotide," "nucleic acid" and "nucleic acid molecule," and
these terms are used interchangeably herein. These terms refer only
to the primary structure of the molecule. Thus, these terms
include, for example, 3'-deoxy-2',5'-DNA, oligodeoxyribonucleotide
N3' P5' phosphoramidates, 2'-O-alkyl-substituted RNA, double- and
single-stranded DNA, as well as double- and single-stranded RNA,
and hybrids thereof including for example hybrids between DNA and
RNA or between PNAs and DNA or RNA, and also include known types of
modifications, for example, labels, alkylation, "caps,"
substitution of one or more of the nucleotides with an analog,
internucleotide modifications such as, for example, those with
uncharged linkages (e.g., methyl phosphonates, phosphotriesters,
phosphoramidates, carbamates, etc.), with negatively charged
linkages (e.g., phosphorothioates, phosphorodithioates, etc.), and
with positively charged linkages (e.g., aminoalkylphosphoramidates,
aminoalkylphosphotriesters), those containing pendant moieties,
such as, for example, proteins (including enzymes (e.g. nucleases),
toxins, antibodies, signal peptides, poly-L-lysine, etc.), those
with intercalators (e.g., acridine, psoralen, etc.), those
containing chelates (of, e.g., metals, radioactive metals, boron,
oxidative metals, etc.), those containing alkylators, those with
modified linkages (e.g., alpha anomeric nucleic acids, etc.), as
well as unmodified forms of the polynucleotide or
oligonucleotide.
[0315] Where the polynucleotides are to be used to express encoded
proteins, nucleotides which can perform that function or which can
be modified (e.g., reverse transcribed) to perform that function
are used. Where the polynucleotides are to be used in a scheme
which requires that a complementary strand be formed to a given
polynucleotide, nucleotides are used which permit such
formation.
[0316] It will be appreciated that, as used herein, the terms
"nucleoside" and "nucleotide" will include those moieties which
contain not only the known purine and pyrimidine bases, but also
other heterocyclic bases which have been modified. Such
modifications include methylated purines or pyrimidines, acylated
purines or pyrimidines, or other heterocycles. Modified nucleosides
or nucleotides can also include modifications on the sugar moiety,
e.g., wherein one or more of the hydroxyl groups are replaced with
halogen, aliphatic groups, or are functionalized as ethers, amines,
or the like. The term "nucleotidic unit" is intended to encompass
nucleosides and nucleotides.
[0317] Furthermore, modifications to nucleotidic units include
rearranging, appending, substituting for or otherwise altering
functional groups on the purine or pyrimidine base which form
hydrogen bonds to a respective complementary pyrimidine or purine.
The resultant modified nucleotidic unit optionally may form a base
pair with other such modified nucleotidic units but not with A, T,
C, G or U. Abasic sites may be incorporated which do not prevent
the function of the polynucleotide. Some or all of the residues in
the polynucleotide can optionally be modified in one or more
ways.
[0318] Standard A-T and G-C base pairs form under conditions which
allow the formation of hydrogen bonds between the N3-H and C4-oxy
of thymidine and the N1 and C6-NH2, respectively, of adenosine and
between the C2-oxy, N3 and C4-NH2, of cytidine and the C2-NH2,
N'--H and C6-oxy, respectively, of guanosine. Thus, for example,
guanosine (2-amino-6-oxy-9-.beta.-D-ribofuranosyl-purine) may be
modified to form isoguanosine
(2-oxy-6-amino-9-.beta.-D-ribofuranosyl-purine). Such modification
results in a nucleoside base which will no longer effectively form
a standard base pair with cytosine. However, modification of
cytosine (1-.beta.-D-ribofuranosyl-2-oxy-4-amino-pyrimidine) to
form isocytosine
(1-.beta.-D-ribofuranosyl-2-amino-4-oxy-pyrimidine-) results in a
modified nucleotide which will not effectively base pair with
guanosine but will form a base pair with isoguanosine (U.S. Pat.
No. 5,681,702 to Collins et al.). Isocytosine is available from
Sigma Chemical Co. (St. Louis, Mo.); isocytidine may be prepared by
the method described by Switzer et al. (1993) Biochemistry
32:10489-10496 and references cited therein;
2'-deoxy-5-methyl-isocytidine may be prepared by the method of Tor
et al. (1993) J. Am. Chem. Soc. 115:4461-4467 and references cited
therein; and isoguanine nucleotides may be prepared using the
method described by Switzer et al. (1993), supra, and Mantsch et
al. (1993) Biochem. 14:5593-5601, or by the method described in
U.S. Pat. No. 5,780,610 to Collins et al. Other normatural base
pairs may be synthesized by the method described in Piccirilli et
al. (1990) Nature 343:33-37 for the synthesis of
2,6-diaminopyrimidine and its complement
(1-methylpyrazolo-[4,3]pyrimidine-5,7-(4H,6H)-dione. Other such
modified nucleotidic units which form unique base pairs are known,
such as those described in Leach et al. (1992) J. Am. Chem. Soc.
114:3675-3683 and Switzer et al., supra.
[0319] The phrase "DNA sequence" refers to a contiguous nucleic
acid sequence. The sequence can be either single stranded or double
stranded, DNA or RNA, but double stranded DNA sequences are
preferable. The sequence can be an oligonucleotide of 6 to 20
nucleotides in length to a full length genomic sequence of
thousands of base pairs.
[0320] The term "protein" refers to contiguous "amino acids" or
amino acid "residues." Typically, proteins have a function.
However, for purposes of this invention, proteins also encompasses
polypeptides and smaller contiguous amino acid sequences that do
not have a functional activity. "Polypeptide" and "protein" are
used interchangeably herein and include a molecular chain of amino
acids linked through peptide bonds. The terms do not refer to a
specific length of the product. Thus, "peptides," "oligopeptides,"
and "proteins" are included within the definition of polypeptide.
The terms include polypeptides containing in co- and/or
post-translational modifications of the polypeptide made in vivo or
in vitro, for example, glycosylations, acetylations,
phosphorylations, PEGylations and sulphations. In addition, protein
fragments, analogs (including amino acids not encoded by the
genetic code, e.g. homocysteine, ornithine, p-acetylphenylalanine,
D-amino acids, and creatine), natural or artificial mutants or
variants or combinations thereof, fusion proteins, derivatized
residues (e.g. alkylation of amine groups, acetylations or
esterifications of carboxyl groups) and the like are included
within the meaning of polypeptide.
[0321] "Amino acids" or "amino acid residues" may be referred to
herein by either their commonly known three letter symbols or by
the one-letter symbols recommended by the IUPAC-IUB Biochemical
Nomenclature Commission. Nucleotides, likewise, may be referred to
by their commonly accepted single-letter codes.
[0322] "Sequence variants" refers to variants of discrete
antibodies (that is antibodies whose sequence can be uniquely
defined) including polynucleotide and polypeptide and variants.
Sequence variants are sequences that are related to one another or
to a common nucleic acid or amino acid "reference sequence" but
contain some differences in nucleotide or amino acid sequence from
each other. These changes can be transitions, transversions,
conservative substitutions, non-conservative substitutions,
deletions, insertions or substitutions with non-naturally occurring
nucleotides or amino acids (mimetics). The phrase "optimizing a
sequence" refers to the process of creating nucleic acid or protein
variants so that the desired functionality and or properties of the
protein or nucleic acid are improved. One of skill will realize
that optimizing an antibody could involve selecting a variant with
lower functionality than the parental protein if that is
desired.
[0323] The term "antibody" as used herein includes antibodies
obtained from both polyclonal and monoclonal preparations, as well
as: hybrid (chimeric) antibody molecules (see, for example, Winter
et al. (1991) Nature 349:293-299; and U.S. Pat. No. 4,816,567);
F(ab')2 and F(ab) fragments; Fv molecules (noncovalent
heterodimers, see, for example, Inbar et al. (1972) Proc Natl Acad
Sci USA 69:2659-2662; and Ehrlich et al. (1980) Biochem
19:4091-4096); single-chain Fv molecules (sFv) (see, for example,
Huston et al. (1988) Proc Natl Acad Sci USA 85:5879-5883); dimeric
and trimeric antibody fragment constructs; minibodies (see, e.g.,
Pack et al. (1992) Biochem 31:1579-1584; Cumber et al. (1992) J
Immunology 149B:120-126); humanized antibody molecules (see, for
example, Riechmann et al. (1988) Nature 332:323-327; Verhoeyan et
al. (1988) Science 239:1534-1536; and U.K. Patent Publication No.
GB 2,276,169, published Sep. 21, 1994); and, any functional
fragments obtained from such molecules, wherein such fragments
retain specific-binding properties of the parent antibody
molecule.
[0324] As used herein, the terms "antibody" and "antibodies"
further refer to monoclonal antibodies, multispecific antibodies,
human antibodies, humanized antibodies, camelised antibodies,
chimeric antibodies, single-chain Fvs (scFv), single chain
antibodies, single domain antibodies, Fab fragments, F(ab)
fragments, disulfide-linked Fvs (sdFv), anti-idiotypic (anti-Id)
antibodies, and epitope-binding fragments of any of the above. In
particular, antibodies include immunoglobulin molecules and
immunologically active fragments of immunoglobulin molecules, i.e.,
molecules that contain an antigen binding site. Immunoglobulin
molecules can be of any type (e.g., IgG, IgE, IgM, IgD, IgA and
IgY), class (e.g., IgG.sub.1, IgG.sub.2, IgG.sub.3, IgG.sub.4,
IgA.sub.1 and IgA.sub.2) or subclass.
[0325] A typical antibody contains two heavy chains paired with two
light chains. A full-length heavy chain is about 50 kD in size
(approximately 446 amino acids in length), and is encoded by a
heavy chain variable region gene (about 116 amino acids) and a
constant region gene. There are different constant region genes
encoding heavy chain constant region of different isotypes such as
alpha, gamma (IgG1, IgG2, IgG3, IgG4), delta, epsilon, and mu
sequences. A full-length light chain is about 25 Kd in size
(approximately 214 amino acids in length), and is encoded by a
light chain variable region gene (about 110 amino acids) and a
kappa or lambda constant region gene. The variable regions of the
light and/or heavy chain are responsible for binding to an antigen,
and the constant regions are responsible for the effector functions
typical of an antibody.
[0326] As used herein, the term "CDR" refers to the complement
determining region within antibody variable sequences. There are
three CDRs in each of the variable regions of the heavy chain and
the light chain, which are designated CDR1, CDR2 and CDR3, for each
of the variable regions. The exact boundaries of these CDRs have
been defined differently according to different systems. The system
described by Kabat (Kabat et al., Sequences of Proteins of
Immunological Interest, National Institutes of Health, Bethesda,
Md. (1987) and (1991)) not only provides an unambiguous residue
numbering system applicable to any variable region of an antibody,
but also provides precise residue boundaries defining the three
CDRs. These CDRs may be referred to as Kabat CDRs. Chothia and
coworkers (Chothia & Lesk, J. Mol. Biol. 196:901-917, 1987, and
Chothia et al., Nature 342:877-883 (1989)) found that certain
sub-portions within Kabat CDRs adopt nearly identical peptide
backbone conformations, despite having great diversity at the level
of amino acid sequence. These sub-portions were designated as L1,
L2 and L3 or H1, H2 and H3 where the "L" and the "H" designates the
light chain and the heavy chains regions, respectively. These
regions can be referred to as Chothia CDRs, which have boundaries
that overlap with Kabat CDRs. Other boundaries defining CDRs
overlapping with the Kabat CDRs have been described by Padlan
(FASEB J. 9:133-139 (1995)) and MacCallum (J Mol Biol 262(5):732-45
(1996)). Still other CDR boundary definitions may not strictly
follow one of the above systems, but will nonetheless overlap with
the Kabat CDRs, although they may be shortened or lengthened in
light of prediction or experimental findings that particular
residues or groups of residues or even entire CDRs do not
significantly impact antigen binding. The methods used herein can
utilize CDRs defined according to any of these systems, although
preferred embodiments use Kabat or Chothia defined CDRs.
[0327] As used herein, the term "epitopes" refers to fragments of a
polypeptide or protein having antigenic or immunogenic activity in
an animal, preferably in a mammal, and most preferably in a human.
An epitope having immunogenic activity is a fragment of a
polypeptide or protein that elicits an antibody response in an
animal. An epitope having antigenic activity is a fragment of a
polypeptide or protein to which an antibody immunospecifically
binds as determined by any method well-known to one of skill in the
art, for example by immunoassays. Antigenic epitopes need not
necessarily be immunogenic.
[0328] As used herein, the term "fragment" refers to a peptide or
polypeptide (including, but not limited to an antibody) comprising
an amino acid sequence of at least 5 contiguous amino acid
residues, at least 10 contiguous amino acid residues, at least 15
contiguous amino acid residues, at least 20 contiguous amino acid
residues, at least 25 contiguous amino acid residues, at least 40
contiguous amino acid residues, at least 50 contiguous amino acid
residues, at least 60 contiguous amino residues, at least 70
contiguous amino acid residues, at least contiguous 80 amino acid
residues, at least contiguous 90 amino acid residues, at least
contiguous 100 amino acid residues, at least contiguous 125 amino
acid residues, at least 150 contiguous amino acid residues, at
least contiguous 175 amino acid residues, at least contiguous 200
amino acid residues, or at least contiguous 250 amino acid residues
of the amino acid sequence of another polypeptide or protein. In a
specific embodiment, a fragment of a protein or polypeptide retains
at least one function of the protein or polypeptide.
[0329] As used herein, the term "immunospecifically binds to an
antigen" and analogous terms refer to peptides, polypeptides,
proteins (including, but not limited to fusion proteins and
antibodies) or fragments thereof that specifically bind to an
antigen or a fragment and do not specifically bind to other
antigens. A peptide, polypeptide, or protein that
immunospecifically binds to an antigen may bind to other antigens
with lower affinity as determined by, e.g., immunoassays, BIAcore,
or other assays known in the art. Antibodies or fragments that
immunospecifically bind to an antigen may be cross-reactive with
related antigens. Preferably, antibodies or fragments that
immunospecifically bind to an antigen do not cross-react with other
antigens.
[0330] As used herein, the term "in combination" refers to the use
of more than one therapies (e.g., more than one prophylactic agent
and/or therapeutic agent). The use of the term "in combination"
does not restrict the order in which therapies (e.g., prophylactic
and/or therapeutic agents) are administered to a subject. A first
therapy (e.g., a first prophylactic or therapeutic agent) can be
administered prior to (e.g., 5 minutes, 15 minutes, 30 minutes, 45
minutes, 1 hour, 2 hours, 4 hours, 6 hours, 12 hours, 24 hours, 48
hours, 72 hours, 96 hours, 1 week, 2 weeks, 3 weeks, 4 weeks, 5
weeks, 6 weeks, 8 weeks, or 12 weeks before), concomitantly with,
or subsequent to (e.g., 5 minutes, 15 minutes, 30 minutes, 45
minutes, 1 hour, 2 hours, 4 hours, 6 hours, 12 hours, 24 hours, 48
hours, 72 hours, 96 hours, 1 week, 2 weeks, 3 weeks, 4 weeks, 5
weeks, 6 weeks, 8 weeks, or 12 weeks after) the administration of a
second therapy (e.g., a second prophylactic or therapeutic agent)
to a subject.
[0331] As used herein, the term "pharmaceutically acceptable"
refers approved by a regulatory agency of the federal or a state
government, or listed in the U.S. Pharmacopeia, European
Pharmacopeia, or other generally recognized pharmacopeia for use in
animals, and more particularly, in humans.
[0332] As used herein, the terms "prevent," "preventing," and
"prevention" refer to the inhibition of the development or onset of
a disorder or the prevention of the recurrence, onset, or
development of one or more symptoms of a disorder in a subject
resulting from the administration of a therapy (e.g., a
prophylactic or therapeutic agent), or the administration of a
combination of therapies (e.g., a combination of prophylactic or
therapeutic agents).
[0333] As used herein, the terms "prophylactic agent" and
"prophylactic agents" refer to any agent(s) which can be used in
the prevention of a disorder or one or more of the symptoms
thereof. In certain embodiments, the term "prophylactic agent"
refers to an antibody of the invention. In certain other
embodiments, the term "prophylactic agent" refers to an agent other
than an antibody of the invention. Preferably, a prophylactic agent
is an agent which is known to be useful to or has been or is
currently being used to the prevent or impede the onset,
development, progression and/or severity of a disorder or one or
more symptoms thereof.
[0334] As used herein, the term "prophylactically effective amount"
refers to the amount of a therapy (e.g., prophylactic agent) which
is sufficient to result in the prevention of the development,
recurrence, or onset of a disorder or one or more symptoms thereof,
or to enhance or improve the prophylactic effect(s) of another
therapy (e.g., a prophylactic agent).
[0335] As used herein, the terms "subject" and "patient" are used
interchangeably. As used herein, the terms "subject" and "subjects"
refer to an animal, preferably a mammal including a non-primate
(e.g., a cow, pig, horse, cat, dog, rat, and mouse) and a primate
(e.g., a monkey, such as a cynomolgous monkey, a chimpanzee, and a
human), and most preferably a human. In one embodiment, the subject
is a non-human animal such as a bird (e.g., a quail, chicken, or
turkey), a farm animal (e.g., a cow, horse, pig, or sheep), a pet
(e.g., a cat, dog, or guinea pig), or laboratory animal (e.g., an
animal model for a disorder). In a preferred embodiment, the
subject is a human (e.g., an infant, child, adult, or senior
citizen).
[0336] As used herein, the terms "therapeutic agent" and
"therapeutic agents" refer to any agent(s) which can be used in the
prevention, treatment, management, or amelioration of a disorder or
one or more symptoms thereof. In certain embodiments, the term
"therapeutic agent" refers to an antibody of the invention. In
certain other embodiments, the term "therapeutic agent" refers an
agent other than an antibody of the invention. Preferably, a
therapeutic agent is an agent which is known to be useful for, or
has been or is currently being used for the prevention, treatment,
management, or amelioration of a disorder or one or more symptoms
thereof.
[0337] As used herein, the term "therapeutically effective amount"
refers to the amount of a therapy (e.g., an antibody of the
invention), which is sufficient to reduce the severity of a
disorder, reduce the duration of a disorder, ameliorate one or more
symptoms of a disorder, prevent the advancement of a disorder,
cause regression of a disorder, or enhance or improve the
therapeutic effect(s) of another therapy.
[0338] As used herein, the terms "therapies" and "therapy" can
refer to any protocol(s), method(s), and/or agent(s) that can be
used in the prevention, treatment, management, and/or amelioration
of a disorder or one or more symptoms thereof. In certain
embodiments, the terms "therapy" and "therapy" refer to anti-viral
therapy, anti-bacterial therapy, anti-fungal therapy, anti-cancer
agent, biological therapy, supportive therapy, and/or other
therapies useful in treatment, management, prevention, or
amelioration of a disorder or one or more symptoms thereof known to
one skilled in the art, for example, a medical professional such as
a physician.
[0339] As used herein, the terms "treat," "treatment," and
"treating" refer to the reduction or amelioration of the
progression, severity, and/or duration of a disorder or
amelioration of one or more symptoms thereof resulting from the
administration of one or more therapies (including, but not limited
to, the administration of one or more prophylactic or therapeutic
agents).
[0340] The term "sequence alignment" refers to the result when at
least two antibody sequences are compared for maximum
correspondence, as measured using a sequence comparison algorithms.
Optimal alignment of sequences for comparison can be conducted by
any technique known or developed in the art, and the invention is
not intended to be limited in the alignment technique used.
Exemplary alignment methods include the local homology algorithm of
Smith & Waterman, Adv. Appl. Math. 2:482 (1981), the homology
alignment algorithm of Needleman & Wunsch, J. Mol. Biol. 48:443
(1970), the search for similarity method of Pearson & Lipman,
Proc. Nat'l. Acad. Sci. USA 85:2444 (1988), by computerized
implementations of these algorithms (e.g., GAP, BESTFIT, FASTA, and
TFASTA in the Wisconsin Genetics Software Package, Genetics
Computer Group, 575 Science Dr., Madison, Wis.), and by
inspection.
[0341] The "three dimensional structure" of a protein is also
termed the "tertiary structure" or the structure of the protein in
three dimensional space. Typically the three dimensional structure
of a protein is determined through X-ray crystallography and the
coordinates of the atoms of the amino acids determined. The
coordinates are then converted through an algorithm into a visual
representation of the protein in three dimensional space. From this
model, the local "environment" of each residue can be determined
and the "solvent accessibility" or exposure of a residue to the
extraprotein space can be determined. In addition, the "proximity
of a residue to a site of functionality" or active site and more
specifically, the "distance of the .alpha. or .beta. carbons of the
residue to the site of functionality" can be determined. For
glycine residues, which lack a .beta. carbon, the .alpha. carbon
can be substituted. Also from the three dimensional structure of a
protein, the residues that "contact with residues of interest" can
be determined. These would be residues that are close in three
dimensional space and would be expected to form bonds or
interactions with the residues of interest. And because of the
electron interactions across bonds, residues that contact residues
in contact with residues of interest can be investigated for
possible mutability. Additionally, nuclear magnetic resonance
spectroscopy can be used to determine the structure. Additionally,
molecular modeling can be used to determine the structure, and can
be based on an homologous structure or ab initio. Energy
minimization techniques can also be employed.
[0342] Although not dependent on three dimensional space, the
"residue chemistry" of each amino acid is influenced by its
position in a protein. "Residue chemistry" refers to
characteristics that a residue possesses in the context of a
protein or by itself. These characteristics include, but are not
limited to, polarity, hydrophobicity, net charge, molecular weight,
propensity to form a particular secondary structure, and space
filling size.
[0343] As used herein, the term "carrier" refers to a diluent,
adjuvant, excipient, or vehicle. Carriers can be liquids, such as
water and oils, including those of petroleum, animal, vegetable or
synthetic origin, such as peanut oil, soybean oil, mineral oil,
sesame oil and the like. The vehicles (e.g., pharmaceutical
vehicles) can be saline, gum acacia, gelatin, starch paste, talc,
keratin, colloidal silica, urea, and the like. In addition,
auxiliary, stabilizing, thickening, lubricating and coloring agents
can be used. When administered to a patient, the carriers are
preferably sterile. Water can be the carrier when composition is
administered intravenously. Saline solutions and aqueous dextrose
and glycerol solutions can also be employed as liquid vehicles,
particularly for injectable solutions. Suitable vehicles also
include excipients such as starch, glucose, lactose, sucrose,
gelatin, malt, rice, flour, chalk, silica gel, sodium stearate,
glycerol monostearate, talc, sodium chloride, dried skim milk,
glycerol, propyleneglycol, water, ethanol and the like.
Compositions, if desired, can also contain minor amounts of wetting
or emulsifying agents, or pH buffering agents.
[0344] As used herein the term "functional domain" means a segment
of a protein that has one or more of the following properties (i) a
structurally independent section of a protein, (ii) a section of a
protein that is homologous to a section of another protein, (iii) a
segment of protein involved in one or more specific functions, (iv)
an independently evolving unit in a protein, (v) a segment of
protein containing a particular sequence motif, (vi) a section of
the protein containing an active site, a binding site or a
regulatory site. See, for example, Suhail A Islam, Jingchu Luo and
Michael J E Sternberg, 1995, "Identification and analysis of
domains in proteins," Protein Engineering 8, 513-525; Orengo et
al., 1997, "CATH--A Hierarchic Classification of Protein Domain
Structures," Structure 5, 1093-1108; and Pearl et al., 2000,
"Assigning genomic sequences to CATH," Nucleic Acids Research 28,
277-282, which are each hereby incorporated by reference in their
entirety.
[0345] An expert system 100 is computer program that represents and
reasons with the knowledge of some specialist subject (antibodies)
with a view to solving problems or giving advice (via rank ordering
of substitutions with reasoning)
[0346] Knowledge acquisition is the transfer and transformation of
potential problem-solving expertise (e.g. knowledge of analysing
nucleotide or protein structure, nucleotide or protein phylogeny)
from the knowledge source to a program.
[0347] Knowledge base 108 is the encoded knowledge for an expert
system 100. In a rule-based expert system 100, a knowledge base 108
typically incorporates definitions of attributes and rules along
with control information.
[0348] An inference engine 106 is software that provides the
reasoning mechanism in expert system 100. In a rule based expert
system 100, it typically implements forward chaining and backward
chaining strategies.
[0349] A substitution in an antibody is the replacement of one
monomer with a different monomer.
[0350] A virtual surrogate screen is a measure of the activity of
an antibody in dimensions that are mathematically constructed from
physical measurements of antibody properties in two or more
assays.
[0351] The terms screen, assay, test and measurement are used
interchangeably to mean a method of determining one or more
property of an antibody.
[0352] A high throughput screen, assay, test or measurement is used
to describe any method for determining one or more property of a
plurality of antibodies either sequentially of simultaneously. The
actual number of antibody variants whose properties can be
determined by a test that is considered a high throughput screen
varies from as few as 84 samples per day (Decker et al. (2003) Appl
Biochem Biotechnol 105: 689-703) to many millions. For the purposes
of this invention we define a high throughput screen as an assay
that can measure one or more antibody property for 400 antibody
variants in 1 week, preferably a test that can measure one or more
antibody property for 1,000 antibody variants in 1 week, more
preferably a test that can measure one or more antibody properties
for 10,000 antibody variants in one week.
5.8 Synthesis of Antibody Sequence Variants
[0353] Antibody variants can be synthesized by methods for
constructing or obtaining specific nucleic acid or polypeptide
sequences described in the art. Antibody variants are designed, for
example, in step 03 of FIG. 2, as described in Section 5.2,
above.
[0354] Oligonucleotides and polyucleotides can be synthesized using
a variety of chemistries including phosphoramidite chemistry;
optionally this synthesis may be performed using a commercially
available DNA synthesizer. Oligonucleotides and polynucleotides may
also be purchased from a commercial supplier of synthetic DNA.
[0355] Chemically synthesized oligonucleotides can be incorporated
into larger polynucleotides to create one or more of the designed
sequence variants using site-directed mutagenesis. Suitable
site-directed techniques include those in which a template strand
is used to prime the synthesis of a complementary strand lacking a
modification in the parent strand, such as methylation or
incorporation of uracil residues; introduction of the resulting
hybrid molecules into a suitable host strain results in degradation
of the template strand and replication of the desired mutated
strand. See (Kunkel (1985) Proc Natl Acad Sci USA 82: 488-92);
QuikChange.TM. kits available from Stratagene, Inc., La Jolla,
Calif. PCR methods for introducing site-directed changes can also
be employed. Site-directed mutagenesis using a single stranded DNA
template and mutagenic oligos is well known in the art (Ling &
Robinson 1997, Anal Biochem 254:157 1997). It has also been shown
that several oligos can be incorporated at the same time using
these methods (Zoller 1992, Curr Opin Biotechnol 3: 348). Single
stranded DNA templates are synthesized by degrading double stranded
DNA (Strandase.TM. by Novagen). The resulting product after strain
digestion can be heated and then directly used for sequencing.
Alternatively, the template can be constructed as a phagemid or M13
vector. Other techniques of incorporating mutations into DNA are
known and can be found in, e.g., Deng et al. 1992, Anal Biochem
200:81.
[0356] Multiple chemically synthesized oligonucleotides can
together be assembled into larger polynucleotides to create one or
more of the designed sequence variants. Oligonucleotides can be
assembled into larger single- or double-stranded polynucleotides in
vivo or in vitro by a variety of methods including but not limited
to annealing, restriction enzyme digestion and ligation,
particularly using restriction enzymes whose cleavage site is
distinct from their recognition sites (see for example Pierce 1994,
Biotechniques 16:708-15; Mandecki & Bolling 1988, Gene
68:101-7), ligation (see for example Edge at al 1981, Nature
292:756-62; Jayaraman & Puccini 1992 Biotechniques 12:392-8),
ligation followed by polymerase chain reaction amplification (see
for example Jayaraman et al 1991, Proc Natl Acad Sci USA.
88:4084-8), overlap extension using thermostable nucleotide
polymerases and/or ligases (see for example Ye et al. 1992, Biochem
Biophys Res Commun. 186:143-9; Horton et al 1989 Gene. 77:61-8;
Stemmer et al 1995 Gene. 164:49-53), dual asymmetric PCR (see for
example Sandhu et al 1992, Biotechniques 12:14-6) stepwise
elongation of sequences (see for example Majumder 1992, Gene.
110:89-94), the ligase chain reaction (see for example Au et al
1998, Biochem Biophys Res Commun. 248:200-3; Chalmers & Curnow
2001, Biotechniques 30:249-52), insertional mutagenesis (see for
example Ciccarelli et al 1990 Nucleic Acids Res. 18:1243-8), the
exchangeable template reaction (see Khudyakov et al 1993, Nucleic
Acids Res. 21:2747-54), sequential ligation of one or more
oligonucleotides to an anchored oligonucleotide (for example a
biotinylated oligonucleotide immobilized on streptavidin resin),
cotransformation into an appropriate host cell such as mammalian,
yeast or bacterial cells capable of joining polynucleotides (see
for example Raymond et al 1999, BioTechniques 26: 134-141), or any
combination of steps involving the activity of one or more of a
polymerase, a ligase, a restriction enzyme, and a recombinase.
Oligonucleotides can optionally be designed to improve their
assembly into larger polynucleotides and subsequent processing, for
example by optimizing annealing properties and eliminating
restriction sites (see for example Hoover & Lubkowski 2002,
Nucleic Acids Res. 30:e43).
[0357] Synthesis of polynucleotide sequence variants can also be
multiplexed. Individual variants can subsequently be identified,
for example by picking and sequencing single clones. Other methods
of deconvolution include testing for an easily measured phenotype
(examples include but are not limited to colorigenic, fluorigenic
or turbidity-altering reactions that can be visualized on agar
plates), then grouping clones according to activity and selecting
one or more clone from each group. Optionally the one or more clone
from each group may be sequenced.
[0358] One example of multiplexed variant synthesis is to
incorporate one or more oligonucleotides containing one or more
alternative nucleotide substitutions into one or more
polynucleotide reference sequences simultaneously. Oligonucleotides
synthesized from mixtures of nucleotides can be used. The synthesis
of oligonucleotide libraries is well known in the art. In one
alternative, degenerate oligos from trinucleotides can be used
(Gaytan, et al., 1998, Chem Biol 5:519; Lyttle, et al 1995,
Biotechniques 19:274; Virnekas, et al 1994, Nucl. Acids Res
22:5600; Sondek & Shortie 1992, Proc. Natl. Acad. Sci. USA
89:3581). In another alternative, degenerate oligos can be
synthesized by resin splitting (Lahr, et al 1999, Proc. Natl. Acad.
Sci. USA 96:14860; Chatellier, et al., 1995, Anal. Biochem.
229:282; and Haaparanta & Huse 1995, Mol Divers 1:39). Mixtures
of individual primers for the substitutions to be introduced by
site directed mutagenesis can be simultaneously employed in a
single reaction to produce the desired combinations of mutations.
Simultaneous mutation of adjacent residues can be accomplished by
preparing a plurality of oligonucleotides representing the desired
combinations. In an alternative embodiment, sequences are assembled
using PCR to link synthetic oligos (Horton, et al 1989, Gene 77:61;
Shi, et al 1993, PCR Methods Appl. 3:46; and Cao 1990, Technique
2:109). PCR with a mixture of mutagenic oligos can be used to
create a multiplexed set of sequence variants that can subsequently
be deconvoluted.
[0359] Cassette mutagenesis can also be used in creating multiple
polynucleotide sequence variants. Using this technique, a set of
sequences can be generated by ligating fragments obtained by
oligonucleotide synthesis, PCR or combinations thereof. Segments
for ligation can, for example, be generated by PCR and subsequent
digestion with type II restriction enzymes. This enables
introduction of mutations via the PCR primers. Furthermore, type II
restriction enzymes generate non-palindromic cohesive ends which
significantly reduce the likelihood of ligating fragments in the
wrong order. Techniques for ligating many fragments can be found in
Berger, et al., Anal Biochem 214:571 (1993).
[0360] Antibody variants can be synthesized as nucleic acid
sequence variants according to any of the processes described here,
followed by expression either in vivo or in an in vitro cell-free
system. They may also be made directly using commercial peptide
synthesizers. Antibody variants may additionally be synthesized by
chemically ligating one or more synthetic peptides to one or more
polypeptide segments created by expression of a polynucleotide (see
for example Pal et al 2003 Protein Expr Purif. 29:185-92).
[0361] Antibody variants may optionally include non-natural amino
acids, incorporated at specific positions in the protein sequence
by a variety of methods (see for example Hyun Bae et al 2003, J
Mol. Biol. 328:1071-81; Hohsaka & Sisido 2002, Curr Opin Chem.
Biol. 6:809-15; L1 and Roberts 2003, Chem. Biol 10:233-9).
[0362] The particular chemical and/or molecular biological methods
used to construct the antibody sequence variants are not critical;
any method(s) that provide the desired sequence variants can be
used.
5.9 Representative Tests for Antibody Function
[0363] Section 5.2 described how a designed set of antibody
variants was designed. This set of antibodies is then synthesized
using, for example, the techniques described in Section 5.8. Then
the antibodies are tested for relevant biological activity and/or
antibody properties. Determination of what constitutes a relevant
antibody property is a case specific exercise. Non-limiting
examples of antibody properties that can be relevant in some
embodiments of the present invention include, but are not limited,
to antigenicity, immunogenicity, immunomodulatory activity,
expression of the antibody in a homologous host, expression of the
antibody in a heterologous host, expression of the antibody in a
plant cell, susceptibility of the antibody to in vitro
post-translational modifications and susceptibility of the antibody
to in vivo post-translational modifications.
[0364] Of particular relevance for this invention are antibody
properties whose measurements are intensive in their use of such
resources as time, space, equipment and experimental animals. Such
characterizations can be rate limiting for empirical-based protein
engineering approaches such as those methods applying directed
evolution or screening libraries produced by other methods. A
common solution to this limitation is to develop a high-throughput
screen. See, for example, Olsen et al. (2000) Curr Opin Biotechnol
11:331-7.
[0365] High throughput screens typically do not measure the complex
combination of functions that are desired in the final engineered
antibody. High throughput screens can be used to measure some
properties of the antibody, and the method of this invention allows
the properties measured in two or more of these high throughput
screens to be combined and used to create a virtual surrogate
screen for the properties of interest. High throughput screens that
may be used to measure potentially relevant antibody properties
include but are not limited to: flow cytometry (Daugherty et al.
(2000) J Immunol Methods 243: 211-27; Georgiou (2000) Adv Protein
Chem 55: 293-315; Olsen et al. (2000) Curr Opin Biotechnol 11:
331-7) solid phase digital imaging (Joo et al. (1999) Chem Biol 6:
699-706; Joern et al. (2001) J Biomol Screen 6: 219-23),
computational and cellular immunogenicity assays (Tangri et al.
(2002) Curr Med Chem 9: 2191-9), fluorescence anisotropy (Turconi
et al. (2001) J Biomol Screen 6: 275-90), flow cytometry,
scintillation proximity (Jenh et al. (1998) Anal Biochem 256:
47-55; Skorey et al. (2001) Anal Biochem 291: 269-78) or magnetic
bead capture (Yeung et al. (2002) Biotechnol Prog 18: 212-20) for
measurement of surface density or binding affinity or avidity, cell
surface display (Kim et al. (2000) Appl Environ Microbiol 66:
788-93) [Little], fluorescence polarization assays for measuring
protein phosphorylation or other cellular components (Parker et al.
(2000) Biomol Screen 5: 77-88; Allen et al. (2002) J Biomol Screen
7: 35-44; Kristjansdottir et al. (2003) Anal Biochem 316: 41-9),
assays that link cellular survival or growth to protein activity
(Luthi et al. (2003) Biochim Biophys Acta 1620: 167-78), assays
that couple a reaction to a colorimetric or fluorimetric assay
including two-hybrid or three-hybrid systems (Young et al. (1998)
Nat Biotechnol 16: 946-50; Baker et al. (2002) Proc Natl Acad Sci
USA 99: 16537-42), a, electrospray and matrix adsorption laser
desorption mass spectrometry (LC-MS and MALDI) for detection of
small molecules and antibodies (Jankowski et al. (2001) Anal
Biochem 290: 324-9; Raillard et al. (2001) Chem Biol 8: 891-8),
high performance liquid chromatography (HPLC), enzyme-linked
immunosorbent assays (Fahey et al. (2001) Anal Biochem 290: 272-6;
Mallon et al. (2001) Anal Biochem 294: 48-54), detection of markers
for cellular differentiation (Sottile et al. (2001) Anal Biochem
293: 124-8), induction of a reporter gene in vivo or in vitro
(Thompson et al. (2000) Toxicol Sci 57: 43-53), small molecule or
protein binding competition assays (Warrior et al. (1999) J Biomol
Screen 4: 129-135; McMahon et al. (2000) J Biomol Screen 5: 169-76)
and time resolved fluorescence (Zhang et al. (2000) Anal Biochem
281: 182-6).
[0366] Measurements of cell lines and primary cell cultures for
cell-surface receptor surface density, measurements of cell surface
receptor internalization rates, cell surface receptor
post-translational modifications including phosphorylation, binding
of antigens including but not limited to cellular growth factor
receptors, receptors or mediators of tumor-driven angiogenesis, B
cell surface antigens and proteins synthesized by or in response to
pathogens, antigens produced by the induction of antibody-mediated
cell killing, antigens produced by antibody-dependent macrophage
activity, histamine, and antigens produced by induction of or
cross-reaction with anti-idiotype antibodies.
[0367] Examples of antibody properties or activities whose
measurement may be resource, time or cost-limited and that
therefore cannot be accurately measured in high throughput are
tests for the immunogenicity of an antibody, in vivo or
cell-culture based viral titer measurements, any experiment in
which an experimental animal or human being is used as a part of
the measurement of one or more properties of the antibody, the
level of expression of the antibody in a host, any experiment in
which the antibody is produced within a plant particularly when the
plant must be transformed with a polynucleotide encoding the
antibody and the antibody be expressed within the plant,
susceptibility of the antibody to be modified inside a living cell,
susceptibility of the antibody to be modified not inside a living
cell, measurement of the composition of a complex mixture of
compounds whose composition has been altered by the action of the
antibody (for example metabolomics or metabonomics, alteration of
the properties of a cell for example alteration of the growth,
replication or differentiation patterns of a cell or population of
cells, therapeutic efficacy of an antibody and modulation of a
signaling pathway.
[0368] Antibodies of the present invention or fragments thereof can
be assayed in a variety of ways well-known to one of skill in the
art. In particular, antibodies of the invention or fragments
thereof can be assayed for the ability to immunospecifically bind
to an antigen. Such an assay can be performed in solution (e.g.,
Houghten, 1992, Bio/Techniques 13:412 421), on beads (Lam, 1991,
Nature 354:82 84), on chips (Fodor, 1993, Nature 364:555 556), in
bacteria (U.S. Pat. No. 5,223,409), in spores (U.S. Pat. Nos.
5,571,698; 5,403,484; and 5,223,409), in plasmids (Cull et al.,
1992, Proc. Natl. Acad. Sci. USA 89:1865 1869) or in phage (Scott
and Smith, 1990, Science 249:386 390; Cwirla et al., 1990, Proc.
Natl. Acad. Sci. USA 87:6378 6382; and Felici, 1991, J. Mol. Biol.
222:301 310) (each of these references is incorporated herein in
its entirety by reference).
[0369] The antibodies of the invention or fragments thereof can be
assayed for immunospecific binding to a specific antigen and
cross-reactivity with other antigens by any method known in the
art. Immunoassays that can be used to analyze immunospecific
binding and cross-reactivity include, but are not limited to,
competitive and non-competitive assay systems using techniques such
as Western blots, radioimmunoassays, ELISA (enzyme linked
immunosorbent assay), "sandwich" immunoassays, immunoprecipitation
assays, precipitin reactions, gel diffusion precipitin reactions,
immunodiffusion assays, agglutination assays, complement-fixation
assays, immunoradiometric assays, fluorescent immunoassays, protein
A immunoassays, to name but a few. Such assays are routine and
well-known in the art (see, e.g., Ausubel et al., eds., 1994,
Current Protocols in Molecular Biology, Vol. 1, John Wiley &
Sons, Inc., New York, which is incorporated by reference herein in
its entirety).
[0370] Antibodies of the invention or fragments thereof can also be
assayed for their ability to inhibit the binding of an antigen to
its host cell receptor using techniques known to those of skill in
the art. For example, cells expressing a receptor can be contacted
with a ligand for that receptor in the presence or absence of an
antibody or fragment thereof that is an antagonist of the ligand
and the ability of the antibody or fragment thereof to inhibit the
ligand's binding can measured by, for example, flow cytometry or a
scintillation assay. The ligand or the antibody or antibody
fragment can be labeled with a detectable compound such as a
radioactive label (e.g., .sup.32P, .sup.35S, and .sup.125I) or a
fluorescent label (e.g., fluorescein isothiocyanate, rhodamine,
phycoerythrin, phycocyanin, allophycocyanin, o-phthaldehyde and
fluorescamine) to enable detection of an interaction between the
ligand and its receptor. Alternatively, the ability of antibodies
or fragments thereof to inhibit a ligand from binding to its
receptor can be determined in cell-free assays. For example, a
ligand can be contacted with an antibody or fragment thereof that
is an antagonist of the ligand and the ability of the antibody or
antibody fragment to inhibit the ligand from binding to its
receptor can be determined. Preferably, the antibody or the
antibody fragment that is an antagonist of the ligand is
immobilized on a solid support and the ligand is labeled with a
detectable compound. Alternatively, the ligand is immobilized on a
solid support and the antibody or fragment thereof is labeled with
a detectable compound. A ligand can be partially or completely
purified (e.g., partially or completely free of other polypeptides)
or part of a cell lysate. Alternatively, a ligand can be
biotinylated using techniques well known to those of skill in the
art (e.g., biotinylation kit, Pierce Chemicals; Rockford,
Ill.).
[0371] An antibody or a fragment thereof constructed and/or
identified in accordance with the present invention can be tested
in vitro and/or in vivo for its ability to modulate the biological
activity of cells. Such ability can be assessed by, e.g., detecting
the expression of antigens and genes; detecting the proliferation
of cells; detecting the activation of signaling molecules (e.g.,
signal transduction factors and kinases); detecting the effector
function of cells; or detecting the differentiation of cells.
Techniques known to those of skill in the art can be used for
measuring these activities. For example, cellular proliferation can
be assayed by .sup.3H-thymidine incorporation assays and trypan
blue cell counts. Antigen expression can be assayed, for example,
by immunoassays including, but are not limited to, competitive and
non-competitive assay systems using techniques such as western
blots, immunohistochemistry radioimmunoassays, ELISA (enzyme linked
immunosorbent assay), "sandwich" immunoassays, immunoprecipitation
assays, precipitin reactions, gel diffusion precipitin reactions,
immunodiffusion assays, agglutination assays, complement-fixation
assays, immunoradiometric assays, fluorescent immunoassays, protein
A immunoassays, and FACS analysis. The activation of signaling
molecules can be assayed, for example, by kinase assays and
electrophoretic shift assays (EMSAs).
[0372] The antibodies, fragments thereof, or compositions of the
invention are preferably tested in vitro and then in vivo for the
desired therapeutic or prophylactic activity prior to use in
humans. For example, assays which can be used to determine whether
administration of a specific pharmaceutical composition is
indicated include cell culture assays in which a patient tissue
sample is grown in culture and exposed to, or otherwise contacted
with, a pharmaceutical composition, and the effect of such
composition upon the tissue sample is observed. The tissue sample
can be obtained by biopsy from the patient. This test allows the
identification of the therapeutically most effective therapy (e.g.,
prophylactic or therapeutic agent) for each individual patient. In
various specific embodiments, in vitro assays can be carried out
with representative cells of cell types involved a particular
disorder to determine if a pharmaceutical composition of the
invention has a desired effect upon such cell types. For example,
in vitro assay can be carried out with cell lines.
[0373] In yet other forms of antibody assays, the effect of an
antibody, a fragment thereof, or a composition of the invention on
peripheral blood lymphocyte counts can be monitored/assessed using
standard techniques known to one of skill in the art. Peripheral
blood lymphocytes counts in a subject can be determined by, e.g.,
obtaining a sample of peripheral blood from said subject,
separating the lymphocytes from other components of peripheral
blood such as plasma using, e.g., Ficoll-Hypaque (Pharmacia)
gradient centrifugation, and counting the lymphocytes using trypan
blue. Peripheral blood T-cell counts in subject can be determined
by, e.g., separating the lymphocytes from other components of
peripheral blood such as plasma using, e.g., a use of
Ficoll-Hypaque (Pharmacia) gradient centrifugation, labeling the
T-cells with an antibody directed to a T-cell antigen which is
conjugated to FITC or phycoerythrin, and measuring the number of
T-cells by FACS.
[0374] The antibodies, fragments, or compositions of the invention
used to treat, manage, prevent, or ameliorate a viral infection or
one or more symptoms thereof can be tested for their ability to
inhibit viral replication or reduce viral load in in vitro assays.
For example, viral replication can be assayed by a plaque assay
such as described, e.g., by Johnson et al., 1997, Journal of
Infectious Diseases 176:1215-1224 176:1215-1224. The antibodies or
fragments thereof administered according to the methods of the
invention can also be assayed for their ability to inhibit or
downregulate the expression of viral polypeptides. Techniques known
to those of skill in the art, including, but not limited to,
western blot analysis, northern blot analysis, and RT-PCR can be
used to measure the expression of viral polypeptides.
[0375] Antibodies, fragments, or compositions of the invention can
be tested in additional in vitro assays that are well-known in the
art. Such additional In vitro assays known in the art can also be
used to test the existence or development of resistance of bacteria
to a therapy. Such in vitro assays are described in Gales et al.,
2002, Diag. Nicrobiol. Infect. Dis. 44(3):301-311; Hicks et al.,
2002, Clin. Microbiol. Infect. 8(11): 753-757; and Nicholson et
al., 2002, Diagn. Microbiol. Infect. Dis. 44(1): 101-107.
[0376] The antibodies, fragments, or compositions of the invention
can be assayed for the ability to treat, manage, prevent, or
ameliorate a fungal infection or one or more symptoms thereof. Any
of the standard anti-fungal assays well-known in the art can be
used to assess such activity. For instance, tests recommended by
the National Committee for Clinical Laboratories (NCCLS) (See
National Committee for Clinical Laboratories Standards. 1995,
Proposed Standard M27T. Villanova, Pa., all of which is
incorporated herein by reference in its entirety) and other methods
known to those skilled in the art (Pfaller et al., 1993, Infectious
Dis. Clin. N. Am. 7: 435-444) can be used. Such antifungal
properties can also be determined from a fungal lysis assay, as
well as by other methods, including, inter alia, growth inhibition
assays, fluorescence-based fungal viability assays, flow cytometry
analyses, and other standard assays known to those skilled in the
art.
[0377] Further, any in vitro assays known to those skilled in the
art can be used to evaluate the prophylactic and/or therapeutic
utility of an antibody disclosed herein for a particular disorder
or one or more symptoms thereof.
[0378] The antibodies, compositions, or combination therapies of
the invention can be tested in suitable animal model systems prior
to use in humans. Such animal model systems include, but are not
limited to, rats, mice, chicken, cows, monkeys, pigs, dogs,
rabbits, etc. Any animal system well-known in the art may be used.
Several aspects of the procedure may vary; such aspects include,
but are not limited to, the temporal regime of administering the
therapies (e.g., prophylactic and/or therapeutic agents) whether
such therapies are administered separately or as an admixture, and
the frequency of administration of the therapies.
[0379] Animal models can be used to assess the efficacy of the
antibodies, fragments thereof, or compositions of the invention for
treating, managing, preventing, or ameliorating a particular
disorder or one or more symptom thereof.
[0380] The antibodies, fragments thereof of compositions of the
present invention can be assayed for their ability to decrease the
time course of a particular disorder by at least 25%, preferably at
least 50%, at least 60%, at least 75%, at least 85%, at least 95%,
or at least 99%. The antibodies, compositions, or combination
therapies of the invention can also be assayed for their ability to
increase the survival period of organisms (e.g., humans) suffering
from a particular disorder by at least 25%, preferably at least
50%, at least 60%, at least 75%, at least 85%, at least 95%, or at
least 99%. Further, antibodies, fragments thereof, compositions, or
combination therapies of the invention can be assayed their ability
reduce the hospitalization period of humans suffering from viral
respiratory infection by at least 60%, preferably at least 75%, at
least 85%, at least 95%, or at least 99%.
[0381] The toxicity and/or efficacy of the antibodies, fragments
thereof, or compositions of the present invention can be assayed by
standard pharmaceutical procedures in cell cultures or experimental
animals, e.g., for determining the LD50 (the dose lethal to 50% of
the population) and the ED50 (the dose therapeutically effective in
50% of the population). The dose ratio between toxic and
therapeutic effects is the therapeutic index and it can be
expressed as the ratio LD50/ED50. Antibodies that exhibit large
therapeutic indices are preferred. While antibodies that exhibit
toxic side effects can be used, care should be taken to design a
delivery system that targets such agents to the site of affected
tissue in order to minimize potential damage to uninfected cells
and, thereby, reduce side effects.
[0382] Technological advances in the future may make it possible to
measure in higher throughput properties that can currently be
measured only in low throughput. One skilled in the art will
readily see that the methods of this invention may be used to
correlate any antibody properties that are not easily measured with
a high-throughput assay with other properties that are readily
measured in high throughput.
5.10 Kits
[0383] The invention provides kits comprising a set of variant or a
single variant in a set of variants that have been refined by the
apparatus and methods describe herein.
[0384] The invention also provides a pharmaceutical pack or kit
comprising one or more containers filled with a variant of set of
variants of the present invention. The pharmaceutical pack or kit
may further comprise one or more other prophylactic or therapeutic
agents useful for the treatment of a particular disease. The
invention also provides a pharmaceutical pack or kit comprising one
or more containers filled with one or more of the ingredients of
the pharmaceutical compositions of the invention. Optionally
associated with such container(s) can be a notice in the form
prescribed by a governmental agency regulating the manufacture, use
or sale of pharmaceuticals or biological products, which notice
reflects approval by the agency of manufacture, use or sale for
human administration.
5.11 Articles of Manufacture
[0385] The present invention also encompasses a finished packaged
and labeled pharmaceutical product. This article of manufacture
includes the appropriate unit dosage form in an appropriate vessel
or container such as a glass vial or other container that is
hermetically sealed. In the case of dosage forms suitable for
parenteral administration the active ingredient is sterile and
suitable for administration as a particulate free solution. In
other words, the invention encompasses both parenteral solutions
and lyophilized powders, each being sterile, and the latter being
suitable for reconstitution prior to injection. Alternatively, the
unit dosage form may be a solid suitable for oral, transdermal,
topical or mucosal delivery.
[0386] In a preferred embodiment, the unit dosage form is suitable
for intravenous, intramuscular or subcutaneous delivery. Thus, the
invention encompasses solutions, preferably sterile, suitable for
each delivery route.
[0387] As with any pharmaceutical product, the packaging material
and container are designed to protect the stability of the product
during storage and shipment. Further, the products of the invention
include instructions for use or other informational material that
advise the physician, technician or patient on how to appropriately
prevent or treat the disease or disorder in question. In other
words, the article of manufacture includes instruction means
indicating or suggesting a dosing regimen including, but not
limited to, actual doses, monitoring procedures (such as methods
for monitoring mean absolute lymphocyte counts, tumor cell counts,
and tumor size) and other monitoring information.
[0388] More specifically, the invention provides an article of
manufacture comprising packaging material, such as a box, bottle,
tube, vial, container, sprayer, insufflator, intravenous (i.v.)
bag, envelope and the like; and at least one unit dosage form of a
pharmaceutical agent contained within said packaging material. The
invention further provides an article of manufacture comprising
packaging material, such as a box, bottle, tube, vial, container,
sprayer, insufflator, intravenous (i.v.) bag, envelope and the
like; and at least one unit dosage form of each pharmaceutical
agent contained within said packaging material.
[0389] In a specific embodiment, an article of manufacture
comprises packaging material and a pharmaceutical agent and
instructions contained within said packaging material, wherein said
pharmaceutical agent is a humanized antibody and a pharmaceutically
acceptable carrier, and said instructions indicate a dosing regimen
for preventing, treating or managing a subject with a particular
disease. In another embodiment, an article of manufacture comprises
packaging material and a pharmaceutical agent and instructions
contained within said packaging material, wherein said
pharmaceutical agent is a humanized antibody, a prophylactic or
therapeutic agent other than the humanized antibody and a
pharmaceutically acceptable carrier, and said instructions indicate
a dosing regimen for preventing, treating or managing a subject
with a particular disease. In another embodiment, an article of
manufacture comprises packaging material and two pharmaceutical
agents and instructions contained within said packaging material,
wherein said first pharmaceutical agent is a humanized antibody and
a pharmaceutically acceptable carrier and said second
pharmaceutical agent is a prophylactic or therapeutic agent other
than the humanized antibody, and said instructions indicate a
dosing regimen for preventing, treating or managing a subject with
a particular disease.
[0390] The present invention provides that the adverse effects that
may be reduced or avoided by the methods of the invention are
indicated in informational material enclosed in an article of
manufacture for use in preventing, treating or ameliorating one or
more symptoms associated with a disease. Adverse effects that may
be reduced or avoided by the methods of the invention include but
are not limited to vital sign abnormalities (e.g., fever,
tachycardia, bardycardia, hypertension, hypotension), hematological
events (e.g., anemia, lymphopenia, leukopenia, thrombocytopenia),
headache, chills, dizziness, nausea, asthenia, back pain, chest
pain (e.g., chest pressure), diarrhea, myalgia, pain, pruritus,
psoriasis, rhinitis, sweating, injection site reaction, and
vasodilatation. Since some of the therapies may be
immunosuppressive, prolonged immunosuppression may increase the
risk of infection, including opportunistic infections. Prolonged
and sustained immunosuppression may also result in an increased
risk of developing certain types of cancer.
[0391] Further, the information material enclosed in an article of
manufacture can indicate that foreign proteins may also result in
allergic reactions, including anaphylaxis, or cytosine release
syndrome. The information material should indicate that allergic
reactions may exhibit only as mild pruritic rashes or they may be
severe such as erythroderma, Stevens Johnson syndrome, vasculitis,
or anaphylaxis. The information material should also indicate that
anaphylactic reactions (anaphylaxis) are serious and occasionally
fatal hypersensitivity reactions. Allergic reactions including
anaphylaxis may occur when any foreign protein is injected into the
body. They may range from mild manifestations such as urticaria or
rash to lethal systemic reactions. Anaphylactic reactions occur
soon after exposure, usually within 10 minutes. Patients may
experience paresthesia, hypotension, laryngeal edema, mental status
changes, facial or pharyngeal angioedema, airway obstruction,
bronchospasm, urticaria and pruritus, serum sickness, arthritis,
allergic nephritis, glomerulonephritis, temporal arthritis, or
eosinophilia.
6. EXAMPLES
6.1 Engineering a Protein (Proteinase K) Using Expert Substitution
Selection Methods and Sequence-Activity Relationships
[0392] The design, synthesis and analysis of sequence variants of
proteinase K is described here as an example of the use of
sequence-activity relationships to engineer desired properties into
a protein. Also described is the analysis of these variants using
six different functional tests, and methods for determining
components of a virtual screen.
[0393] FIG. 6 shows the amino acid sequence of proteinase K that
occurs naturally in the fungus Tritirachium album Limber (Gunkel et
al. (1989) Eur J Biochem 179: 185-194) (SEQ ID NO.: 2) together
with an E. coli leader peptide (SEQ ID NO.: 1). FIGS. 7A and 7B
shows a nucleotide sequence designed to encode proteinase K (SEQ ID
NO.: 3). The sequence has been modified from the original
Tritirachium album sequence by removing an intron, adding an E.
coli leader peptide and altering the codons used to resemble the
distribution found in the highly expressed genes of E coli. The
gene was synthesized for the natural proteinase K from
oligonucleotides.
[0394] Several different criteria were used to identify positions
and substitutions to make in the proteinase K sequence as detailed
below
6.1.1 Principal Component Analysis to Identify Substitutions that
May Contribute to Thermostability
[0395] The proteinase K gene was used as probe against GenBank
using BLAST based algorithms. A BLAST score was chosen as a cut-off
that identified more than ten but less than one hundred related
sequences. This search identified the 49 sequences identified in
FIG. 8.
[0396] The sequences (49 rows.times.728 variables) were represented
in a Free-Wilson method of qualitative binary description of
monomers (Kubinyi, 3D QSAR in drug design theory methods and
applications. Pergamon Press, Oxford, 1990, pp 589-638), and
distributed in a maximally compressed space using principal
component analysis so that the first principal component (PC)
captured 10.8 percent of all variance information (eigenvalue of
79), the second principal component (PC) captured 7.8 percent of
all variance information (eigenvalue of 57), the third principal
component (PC) captured 6.9 percent of all variance information
(eigenvalue of 50), the fourth principal component (PC) captured
6.2 percent of all variance information (eigenvalue of 45), the
fifth principal component (PC) captured 5.4 percent of all variance
information (eigenvalue of 39) and so on until 728.sup.th principal
component (PC) captured 0 percent of all variance information
(Eigenvalue 0).
[0397] All sequences were plotted in the first six principal
components, which captured a total of 42 percent of all variance
information present in the 728 dimensions. Sequences 46, 47, 48, 49
are all derived from thermophilic organisms and are all well
separated from the proteinase K homologs 1-45 in both of the first
two principal components, as shown in FIG. 9.
[0398] A corresponding plot of all loads describes the influence of
each variable on the sample distribution in the various PC's. The
correlation between loads (influence of variables--in this case
amino acid residues) and score (distribution of samples--here
proteinase K homologs) illustrates graphically which residues are
unique in determining the phylogenetic separation of genes 46-49
from genes 1-45. This is shown in FIG. 10.
[0399] Subsequently, the lower left corner of the bottom left
quadrant of the loads plot was magnified and the variables labeled
(FIG. 11). By adding the PC1 and PC2 value for each variable one
can rank order the influence of each residue for their reciprocal
effect on sample distribution. This distribution of residue effects
can be due to common ancestral history or can be due to functional
constraints among this group of samples.
[0400] As can be seen in FIG. 11, residues that are completely
co-evolving (due to sampling effects, phylogenetic ancestry or
other) will have the exact same load and consequently collapse the
variable space in as many dimensions as there are absolute
coevolving residues. This is illustrated in the graph where
residues 15D, 18D, 19Q, 22L, 23P, 65Y, 66D, 110R, 137P, 164D, 189C,
198R all are completely co-evolving and all have profound effect on
the distribution of samples 46-49 in PC1 and PC2. After removing
residues that are unique for only one of the extreme samples,
residues that are common to the thermophiles but unique to one
individual were retained and further explored. Variables here can
be amino acids as depicted in this example, or any type of feature.
Features include, but are not limited to, physico-chemical
properties of one or more amino acid residues. The residues can be
a block or modulated within the gene, or it can be a combination of
residues not genetically linked such as in the example above of
residues 15D, 18D, 19Q, 22L, 23P, 65Y, 66D, 110R, 137P, 164D, 189C,
198R.
[0401] The loads for the amino acids most responsible for the
clustering of thermophilic proteinase K homologs are shown in FIG.
12. This information was then incorporated into knowledge base 108.
This is an example of pre-processing information.
6.1.2 Structural Information for Homologous Enzymes
[0402] The BLAST search of Genbank for proteinase K homologs also
revealed that proteinase K is homologous to subtilisin and other
serine proteases. Subtilisin in particular has been extensively
studied. The structures of naturally occurring and variant
subtilsins have been obtained, and there is a large body of data
regarding the functional effects of a substantial number of
mutations. See, for example, Bryan, 2000, Biochim Biophys Acta
1543:203-222. Sequence and structural alignments of proteinase K
with subtilisin allowed for the identification of homologous
positions in proteinase K having changes known to improve activity
or thermostabilize subtilisin. This information was incorporated
into the knowledge base 108. This is an example of pre-processing
information.
6.1.3 Sequence Information from Thermostable Close Homologs
[0403] Amongst the closest ten homologs of proteinase K identified
by BLAST search of Genbank, are enzymes known to be thermostable.
These enzymes were aligned positions that were conserved between
the thermostable homologs but not found in non-thermostable
homologs were identified. This information was then incorporated
into the knowledge base 108. This is an example of pre-processing
information.
6.1.4 Sequence Information from Close Homologs
[0404] One of the homologs identified in the BLAST search was
highly related to proteinase K (>95% sequence identity) and also
thermostable. The sequence of this protein was aligned with
proteinase K and all amino acid changes between the two enzymes
were identified. This information was then incorporated into the
knowledge base 108. This is an example of pre-processing
information.
6.1.5 Information Processing
[0405] Using the information described above that was placed in
knowledge base 108, the following rules 120 were defined.
[0406] (a) Changes that are already present in proteinase K were
eliminated.
[0407] (b) Changes that occur in the pro-region of the protein were
eliminated
[0408] (c) A score proportional to the load from the PCA analysis
was added.
[0409] (d) A score for conservative changes was added.
[0410] (e) A score for changes found in a close homolog (>95%
identical) was added.
[0411] (f) A score for change found in a close homolog that is
thermostable but not in close homologs that are not thermostable
was added.
[0412] An initial sequence space of 24 residues was defined using
rules (a) through (f). Changes with the top 24 scores were picked.
These residues are shown in FIG. 13.
[0413] These variations and all combinations of these variations
encompass a sequence space of over a million different sequences.
To reduce the number of variants to test in the first set of
variants a design based on prior knowledge and single site
statistics considerations was used (FIG. 2, step 03).
[0414] Based on information about the plasticity of serine
proteases and subtilisin genes, variants with six changes per clone
were designed. In this example all of the 24 top-scoring changes
were equally represented. In other embodiments, a set of variants
that represent each change with a frequency reflecting its actual
score could have been designed. In this case, 24 clones were
designed that cover the sequence space uniformly. One way to
measure the uniformity of the space covered is by counting the
number of instances a particular substitution (e.g., N95C) is seen
in the 24 clones. This number was set at six for all the variations
identified. This means, that in the set of variations synthesized,
each of the identified mutations occurs six times. For example, the
mutation N95C is found in six of the variants, the mutation P97S is
found in six of the variants, and so forth.
[0415] The variants defined by this process are listed in FIG. 14,
where FIG. 13 serves as the key for FIG. 14. For example, "95" in
FIG. 14 means "N95C", "355" in FIG. 14, means "P335S".
[0416] The polynucleotides encoding each proteinase K variant
defined in FIG. 14 were synthesized by PCR-based assembly of
synthetic oligonucleotides. The sequence of each variant was
confirmed using an ABI sequencer. The ability of each of these
variant proteins to hydrolyze casein was then measured simply to
determine whether the proteinase K variants had any protease
activity. This is the first step in exploring the sequence space.
(FIG. 2, step 04).
[0417] This data, as well as data measuring the activity of
proteinase K towards the hydrolysis of polylactide, can be used to
analyze the data using sequence-activity correlating methods to
evaluate the substitutions (steps 05 and 06 of FIG. 2). In turn,
this information can be used to update knowledge base 108 and to
perform additional iterations of the method to thus further explore
the sequence space for improvements in desired properties.
[0418] Preliminary data indicated that changes at residues 95, 97,
138, 208, 236, 237, 265 and 299 were found only in poorly
performing variants. Changes at residues 123, 145, 167, 273, 293,
310, 332, 337 and 355 were found in medium performing variants.
Changes at residues 107, 151, 180, 194, 199 and 267 were found in
high performing variants. Using this information the next round of
sequence sets was designed and is shown in FIG. 15.
[0419] Additionally, from the results of the experiments, expert
system 100, in conjunction with the sequence-activity correlating
methods inferred that the proline to serine change (seen at
positions 97 and 265) for flexibility and structural perturbation
twice resulted in disadvantageous changes. This information was
coded into the knowledge base 108 for future experiments. This is
one illustration of updating knowledge base 108.
[0420] The sequence of each constructed variant is shown in FIG.
16. The activity of the variants towards casein, which is a large
polymeric substrate like polylactide, was measured. Variant
activity towards a modified tetrapeptide,
N-succinyl-Ala-Ala-Pro-Leu-p-nitroanilide (AAPL-p-NA) which
undergoes a colorimetric change upon protease-mediated hydrolysis
(Sroga et al. (2002) Biotechnol Bioeng 78: 761-9), was also
measured. Using this substrate, the activity of the variants at
three different pH values (7, 5.5 and 4.5) was measured. The
activity of variants following a five minute heat treatment at
65.degree. C. was also measured. The activities observed for each
property measured are shown in FIG. 17.
[0421] For each of the proteinase K activities tested, a partial
least squares regression (PLSR) was used to model the relationship
between amino acid substitution and proteinase activity (the
sequence-activity relationship) for variants 10-49. The application
of these methods to nucleic acids, peptides and proteins has been
described previously. See, for example, Geladi et al., 1986,
Analytica Chimica Acta 186: 1-17; Hellberg et al., 1987, J Med Chem
30: 1126-35; Eriksson et al., 1990, Acta Chem Scand 44: 50-55;
Jonsson et al., 1993, Nucleic Acids Res. 21: 733-739; Norinder et
al., 1997, J Pept Res 49: 155-62; Bucht et al., 1999, Biochim
Biophys Acta 1431: 471-82.
[0422] The PLSR-based sequence activity model was used to assign a
regression coefficient to each varied amino acid. The predicted
activity for a proteinase K variant was calculated by summing the
regression coefficients for amino acid substitutions that are
present in that variant. In this case, terms to account for
interactions between the varied amino acids were not included,
although this can also be done. See, for example, Aita et al.,
2002, Biopolymers 64: 95-105. FIG. 18 shows a correlation between
the predictions of the sequence-activity model and the measured
ability of heat-treated proteinase K variants to hydrolyze
AAPL-p-NA.
[0423] The utility of the sequence-activity model was tested for
its ability to predict the activity of variants that have not been
measured, or to identify amino acid substitutions that contribute
positively to a specific protein property and that can then be
experimentally combined. To test the sequence activity model for
heat-tolerant hydrolyzers of AAPL-p-NA, the regression coefficients
from the model were tested, as shown in FIG. 19.
[0424] Four of the amino acid changes had been incorporated into
the variants were predicted to have a positive effect on the
activity of proteinase K after heating. These were K208H, V267I,
G293A and K332R. Among the variants synthesized in the initial set
of 48, one (N540) contained three of these changes (V267I, G293A
and K332R) and one (N519) contained the other (K208H). To test the
predictive power of our model, a variant (N556) containing all four
of these changes was synthesized and its activity was compared with
that of NS19 and N540.
[0425] As shown in FIG. 20, combining the four changes identified
by the PLSR model produced a variant with greater post-heat
treatment activity towards AAPL-p-NA than the single or triple
changes. By synthesizing and measuring the activities of only 48
variants a new variant that was further improved for measured
activity was designed. This demonstrates that the combination of
low-throughput screening and mathematical analysis is useful for
protein engineering.
[0426] The current paradigm for empirical protein engineering is to
employ high throughput screens to test libraries of thousands of
variants. See, for example, Lin et al., 2002, Angew Chem Int Ed
Engl 41: 4402-25. In general, high throughput screens do not
measure all of the properties that are important for the final
application. One common way of overcoming this discrepancy is the
use of tiered screens, in which high throughput screens that
measure only one or two of the properties of interest are followed
by lower throughput screens that more accurately reflect the
desired protein characteristics. See, for example, Ness et al.,
2000, Adv Protein Chem 55: 261-292. This technique relies on the
assumption that the high throughput primary screen will identify
the amino acid substitutions that are important for the final
function but will also select some false positives. False positives
do not actually contribute to the final function and are eliminated
by subsequent screens. The alternative possibility, that amino
acids that would be beneficial for the final application may be
missed by the initial high throughput screen (false negatives), is
seldom considered. By prematurely discarding substitutions that
would be beneficial for the desired function, the protein
engineering process may be unnecessarily prolonged or even
fail.
[0427] Having measured several properties of the proteinase K
variants described above and validated the predictive power of the
sequence-activity modeling of the present invention, the validity
of the high throughput screening approach was explored in more
depth. Although no high throughput screening was explored in this
example, all of the assays described above could easily be adapted
for use as high throughput primary screens. Hydrolysis of casein
incorporated into media plates has been used as a primary screen
for protease libraries. See, for example, Ness et al., 1999, Nat
Biotechnol 17: 893-896; Ness et al., 2002, Nat Biotechnol 20:
1251-5. Hydrolysis of AAPL-p-NA has also been described. See, for
example, Sroga et al., 2002, Biotechnol Bioeng 78: 761-9. Testing
AAPL-p-NA hydrolysis at lowered pH (5.5 or 4.5) might be considered
an appropriate surrogate for the low pH tolerance that will be
required by an enzyme that is producing lactic acid from
polylactide. Similarly testing AAPL-p-NA hydrolysis following heat
treatment may measure the stability that will be required for an
enzyme that must resist the thermal stresses of incorporation into
a plastic. Thermostability was expressed in three ways: (i) as the
absolute level of activity remaining following heat treatment, (ii)
as the activity remaining relative to the activity prior to heat
treatment, and (iii) as the product of these two values. Having
obtained values for each of these proteinase properties, the
correlation between the properties was examine, and the amino acid
substitutions that would be selected by each screen were
compared.
[0428] Three representative activities were selected for further
analysis: (i) activity towards AAPL-p-NA at pH 7.0, (ii) absolute
activity towards AAPL-p-NA following five minutes at 65.degree. C.,
and (iii) activity towards casein. For each of these activities
PLSR models similar to that shown in FIG. 18 were constructed, and
the regression coefficients for each amino acid substitution were
calculated as shown for thermal tolerance in FIG. 19. The changes
calculated to contribute positively to each property are shown in
FIG. 21.
[0429] The difference between beneficial amino acids selected by
the three different representative assays is striking Use of any of
these measurements as the primary assay would select some amino
acid changes that are not important for the others. These would be
false positives, for example, use of casein hydrolysis as a primary
screen would identify six changes (S107D, S123A, V1671, Y194S,
A199S and S273T) that have a negative effect on activity towards
AAPL-p-NA, with or without heating. Perhaps even more importantly,
the casein primary screen would have falsely attributed a negative
value to three of the four changes important for thermal tolerance
(K208H, V267I and G293A).
[0430] This failure of a tiered screening strategy is not simply a
result of selecting an inappropriate surrogate substrate. Similar
results would have been seen had activity towards AAPL-p-NA been
used as a primary screen followed by a test for thermal tolerance.
In this case half of the beneficial changes would still have been
discarded as false negatives (K208H and V267I). This analysis shows
that measuring properties that are different from those of the
final application can result both in incorporation of sequence
changes that do not contribute to the desired phenotype, as well as
omission of those that do.
[0431] A method for engineering proteins based on design, synthesis
and testing of small numbers of individual variants followed by
mathematical modeling to determine a sequence-activity relationship
has been described. Sequence-activity models that can be used
predictively to design improved variants have also been
described.
[0432] By incorporating the principles of experimental design,
individual design and synthesis of sequence variants allows a more
efficient search of sequence space than a library approach
(Hellberg et al. (1991) Int J Pept Protein Res 37: 414-424).
Another advantage of the modeling approach is that it facilitates
empirical protein engineering but requires only very low numbers of
variants to be tested. This means that the need for high throughput
screens is obviated. This analysis indicates that high throughput
and tiered screening can be fundamentally flawed strategies for
protein engineering. Both conserved reaction conditions and use of
the same substrate appear susceptible to selection of false
positives and rejection of false negatives. The performance of high
throughput screens will be further compromised when the primary
screen is selected on the basis of throughput rather than faithful
replication of the final application.
6.2 Identifying a Set of Substitutions and Defining a Set of
Variants Representing that Sequence Space for Antibodies with
Improved Neutralization of Respiratory Syncitial Virus
[0433] In this example, the optimization procedures of the present
invention are illustrated for an antibody that binds and
neutralizes Respiratory Syncytial Virus (RSV). The sequence of one
such antibody is publicly available (Genbank accession # AAF21612).
A significant benefit of the computational antibody design system
using the methods described in this invention is that only
relatively small numbers of variants need to be synthesized and
tested. This allows the use of functional tests that are more
comprehensive than binding assays. Viral neutralization for
example, is an important antibody function but the sequence and
structural determinants are poorly understood.
[0434] Methods used to identify substitutions in the framework and
CDR regions of the heavy chain of the AAF21612 antibody sequence
are as follows. The sequence of the heavy chain of the AAF21612
antibody was aligned using the kabat numbering system with germline
human ig heavy chain sequences retrieved from the VBase database. A
total of 49 sequences were aligned. This alignment may not limited
to germline human sequences. Alternatively, all sequences that are
in the same structural class as AAF21612 as defined by Chothia and
Lesk (Chothia and Lesk, 1986, EMBO Journal 5, 823-826) can be
used.
[0435] These 49 sequences were processed and substitutions scored
according to a modified version of the scheme shown in FIG. 3. The
modified process is shown in FIG. 22.
[0436] Rule 1a.
[0437] Align the sequences using kabat numbering and select all
substitutions found in any of the germline sequences. Classify the
substitutions into two categories: (i) substitutions found in the
framework region and (ii) substitutions found in the CDR.
[0438] Rule 1b.
[0439] Reconstruct a phylogenetic tree using the Clustal W software
based on the amino acid alignment in the framework region. For each
substitution, calculate the evolutionary proximity of the closest
germline in which that substitution occurs. The evolutionary
proximity EP is calculated as follows:
p=n.sub.d/n
[0440] where, [0441] p is the p-distance, [0442] n.sub.d is the
number of amino acid differences between two sequences; and [0443]
n is the total number of amino acids in the protein.
[0444] Further,
d=-ln(1-p)
[0445] where, [0446] d is the Poisson-corrected p-distance between
two sequences; and [0447] ln(1-p) is the natural logarithm of the
p-distance. [0448] And,
[0448] EP=1/d [0449] where, [0450] EP is the evolutionary
proximity.
[0451] Rule 1c. For each substitution in the framework group and in
the CDR, calculate the favorability of that substitution using a
PAM100 matrix.
SM=PAM(A.sub.o,A.sub.s)/10
[0452] where, [0453] A.sub.o is the original amino acid at a
position, [0454] A.sub.s is the substitution amino acid, and [0455]
PAM(A.sub.o, A.sub.s) is a measure of the average probability that
A.sub.o is substituted with A.sub.s in a large set of protein
homolog families.
[0456] Rule 2b.
[0457] For each position, calculate the site heterogeneity, that is
a measure of the number of different amino acids present at that
position. The site heterogeneity is calculated as the number of
different amino acids seen at a position in the set of homologs
(SH).
[0458] Rule 3b.
[0459] For each position calculate the site entropy as follows:
SE=-.SIGMA.{(P.sub.Ai/N).times.ln(P.sub.Ai/N)}
[0460] where, [0461] N is the number of homologous sequences,
[0462] P.sub.Ai is the number of times amino acid i occurs at
position P, [0463] ln(P.sub.Ai/N) is the natural log of P.sub.Ai/N,
and [0464] E is the sum for all amino acids for position P.
[0465] Rule 4b.
[0466] For each substitution count the number of times it occurs in
the set of homologs (SN)
[0467] The total score is then calculated for framework and CDR
region substitutions as follows:
Score.sub.FW=f(EP).times.f(SH).times.f(SE).times.f(SN).times.f(SM),
where f( ) is a mathematical function. In this case the function
was the parameter in the parentheses multiplied by 1, but the use
of functions allows different weights to be applied in subsequent
cycles.
[0468] Score.sub.CDR=f'(SE).times.f'(SN).times.f'(SM), where f'( )
is a mathematical function. In this case the function f'( ) was the
parameter in the parentheses multiplied by 1, but the use of
functions f'( ) allows different weights to be applied in
subsequent cycles.
[0469] Based in the above scores, twenty substitutions in both the
CDR and framework were identified. The results from use of this
substitution-scoring scheme is shown in Table 1:
TABLE-US-00008 TABLE 1 Framework substitutions CDR substitutions
K78R 0.465651 V30M 35.63365 H73Q 0.398614 D65N 35.55048 S79A
0.389751 G51S 32.24937 L08V 0.352089 N31S 30.06633 S24T 0.345752
L52aY 30.05984 L01V 0.338391 L52aN 9.380159 S20A 0.337918 N31H
25.66902 G26S 0.337206 E56K 25.53363 D27S 0.333916 D65T 22.22917
V45I 0.321903 F33V 21.71887 L42V 0.311439 A53D 21.88011 C19A
0.280519 A53P 19.17291 S68N 0.279479 A53Q 12.47777 M74L 0.258614
F59V 16.86972 N75S 0.254877 V55L 16.06146 I69T 0.243678 G51N
13.5927 T21S 0.238389 E56Q 11.17192 R13S 0.227712 V55F 10.78488
V86R 0.221026 F33I 9.900269 G85A 0.21849 S62T 8.950517
[0470] A set of forty variants were then designed with the
following criteria:
[0471] 1. Include five substitutions in each variant
[0472] 2. Maximize the number of different pairs of substitutions
that occur. If each variant contains five substitutions, it
contains ten sets of pairs. There is thus a maximum of 400 pairs
represented in forty variants. The variant set below was optimally
design to maximize the number of pairs observed.
[0473] In addition, the relative number of framework versus CDR
substitution can be modulated. A maximum number of framework and/or
CDR substitutions in a variant is set.
[0474] This set was calculated by in silico evolution. An initial
set of variants each containing five substitutions was randomly
chosen. Substitutions were then altered randomly. If a change
increased the number of substitution pairs in the variant set it
was accepted. Otherwise it was rejected. The process continued for
10,000 iterations. The final set of variants is shown in Table
2.
TABLE-US-00009 TABLE 2 Variant-1 L01V S20A G26S T21S F59V Variant-2
S20A L42V C19A S68N G85A Variant-3 S79A L08V L01V R13S G85A
Variant-4 S79A N75S V30M F59V V55F Variant-5 N31S C19A E56K A53D
V86R Variant-6 H73Q S68N R13S V30M A53P Variant-7 K78R M74L V86R
G85A G51N Variant-8 L08V S20A L52aN G51N F33I Variant-9 V45I C19A
D65N L52aN N31S Variant-10 G26S M74L N75S R13S A53Q Variant-11 L01V
V86R L52aN A53D V55L Variant-12 L42V N31S L52aY G51S F59V
Variant-13 L08V D65T A53D L52aY F33V Variant-14 S68N G51S F59V V55L
F33I Variant-15 K78R H73Q S79A L52aY F33I Variant-16 G26S D27S V45I
G51N E56Q Variant-17 K78R C19A N75S I69T N31H Variant-18 V45I T21S
G85A V30M V55L Variant-19 K78R S20A D65N G51S E56Q Variant-20 K78R
D27S D65T F33V S62T Variant-21 S79A L42V A53Q V55L G51N Variant-22
M74L I69T D65T E56Q F33I Variant-23 S24T L01V I69T G51S A53P
Variant-24 V45I L42V M74L N31H A53P Variant-25 L42V I69T T21S V86R
E56K Variant-26 S20A I69T V30M N31S F33V Variant-27 G26S S68N L52aY
E56K D65T Variant-28 C19A V86R F33V A53Q F59V Variant-29 H73Q L08V
N31H V55L S62T Variant-30 K78R L08V G26S N31S V55F Variant-31 S20A
D27S E56K A53Q V55F Variant-32 S79A S24T S68N T21S A53D Variant-33
L42V R13S D65N V55F F33I Variant-34 D27S G85A G51S L52aN N31H
Variant-35 N75S T21S F33V A53P S62T Variant-36 R13S L52aY F33V V55L
E56Q Variant-37 L01V V45I S68N V55F S62T Variant-38 L08V S24T C19A
V30M E56Q Variant-39 S79A D27S C19A M74L N31S Variant-40 H73Q S24T
D27S V86R D65N
6.3 Identifying a Set of Substitutions and Defining a Set of
Variants Representing that Sequence Space for Humanizing and
Optimizing Murine Antibodies for Neutralization of Respiratory
Syncitial Virus
[0475] In this example, a humanization procedure for a murine
antibody RSV19 that binds and neutralize RSV (Respiratory Syncytial
Virus) is illustrated. A significant benefit of the computational
antibody design system using the methods described in this
invention is that only small numbers of variants will be
synthesized and tested. This allows the use of functional tests
that are more complicated than selection for binding. Antibody
humanization is an important antibody function but the sequence and
structural determinants are poorly understood.
[0476] The methods used to identify substitutions in the framework
and CDR regions of the heavy chain of the RSV-19 antibody sequence
are as follows. The sequence of the heavy chain of the RSV-19
antibody was aligned using the kabat numbering system with germline
human ig heavy chain sequences retrieved from VBase database This
alignment may not limited to germline human sequences.
Alternatively, all human antibody sequences that are in the same
structural class as AAF21612 as defined by Chothia and Lesk
(Chothia and Lesk, 1986, EMBO Journal 5, 823-826) can be used. A
total of 45 sequences were aligned.
[0477] The sequences were processed and substitutions scored
according to a modified version of the scheme shown in FIG. 3. The
modified process is shown in FIG. 23.
[0478] Rule 1a.
[0479] Align sequences using kabat numbering and select all
substitutions found in any of the germline sequences. Classify the
substitutions into two categories: (i) substitutions found in the
framework region and (ii) substitutions found in the CDR. Select
only these substitutions and consider them separately.
[0480] Rule 1b.
[0481] Reconstruct a phylogenetic tree using the Clustal W software
based on the amino acid alignment in the framework region. For each
substitution, calculate the evolutionary proximity of the closest
germline in which that substitution occurs. The evolutionary
proximity (EP) is calculated, where EP is as defined in Section
6.2.
[0482] Rule 1c.
[0483] For each substitution in the framework group and in the CDR,
calculate the favorability of that substitution using a PAM100
matrix. SM is as defined in Section 6.2.
[0484] Rule 2b.
[0485] For each position calculate the site heterogeneity, that is
a measure of the number of different amino acids present at that
position. The site heterogeneity is calculated as the number of
different amino acids seen at a position in the set of homologs
(SH).
[0486] Rule 3b.
[0487] For each position calculate the site entropy SE using the
algorithm describe in Section 6.2.
[0488] Rule 4b.
[0489] For each substitution, count the number of times it occurs
in the set of homologs (SN).
[0490] The total score is then calculated for framework and CDR
region substitutions as follows:
Score.sub.FW=f(EP).times.f(SH).times.f(SE).times.f(SN).times.f(SM),
[0491] where f( ) is a mathematical function. In this case the
function was the parameter in the parentheses multiplied by 1, but
the use of functions allows different weights to be applied in
subsequent cycles.
Score.sub.CDR=f(SE).times.f(SN).times.f(SM),
[0492] where f'( ) is a mathematical function. In this case the
function was the parameter in the parentheses multiplied by 1, but
the use of functions allows different weights to be applied in
subsequent cycles.
[0493] Based on the above scores, twenty substitutions in both the
CDR and the framework were identified. The results of using this
substitution-scoring scheme are shown in Table 3:
TABLE-US-00010 TABLE 3 Framework substitutions CDR substitutions
I46V 0.389716 D31S 30.52868 K19R 0.364451 V53T 28.91288 D69N
0.330972 D52cS 28.58028 R13K 0.314539 N52aS 26.10288 T82aA 0.304669
M35bV 25.5501 I29F 0.275096 K30S 24.93946 N73T 0.270393 Q54Y
23.36634 S71K 0.268009 Q60K 23.19751 T70S 0.264867 D52bG 22.92382
A16G 0.262967 H35S 21.86205 A85V 0.261951 E52D 20.6664 S72N
0.258769 D50S 20.49003 T66S 0.253018 M65I 20.19161 T23A 0.2495
N52aG 20.19023 N90R 0.249449 A63V 19.17104 A67R 0.24173 K58S
18.77169 Q41K 0.22512 E52S 18.50896 D69T 0.218449 F59V 18.24618
N28T 0.217729 D52cN 17.85268 R38A 0.215293 P57D 17.60608
[0494] A set of forty variants were then designed with the
following criteria:
[0495] 1. Include four to six substitutions in each variant
[0496] 2. Maximize the number of different pairs of substitutions
that occur. If each variant contains five substitutions, it
contains ten sets of pairs. There is thus a maximum of 400 pairs
represented in forty variants. The variant set below was optimally
designed using the evolutionary algorithm to maximize the number of
pairs observed.
[0497] In addition, the relative number of framework versus CDR
substitution can be modulated. A maximum number of framework and/or
CDR substitutions in a variant can be set. For humanization,
substitutions of human residues in framework regions are preferred.
Substitutions in the CDR are designed to retain the activity while
changing the amino acid in framework region more biased towards
human sequences.
[0498] This set was calculated by in silico evolution. An initial
set of variants each containing five substitutions was chosen
randomly. Substitutions were then altered randomly. If a change
increased the number of substitution pairs in the variant set it
was accepted. Otherwise it was rejected. The process continued for
10000 iterations. The final set of variants is shown in Table
4.
TABLE-US-00011 TABLE 4 Variant-1 I29F N73T S71K Q41K N52aG
Variant-2 N73T A85V S72N T66S R38A Variant-3 D69N R13K I29F D69T
R38A Variant-4 D69N N90R D31S N52aG F59V Variant-5 N52aS Q54Y Q60K
H35S E52S Variant-6 K19R T66S D69T D31S D50S Variant-7 I46V T23A
N28T R38A K58S Variant-8 R13K N73T K30S K58S D52cN Variant-9 A16G
S72N V53T K30S D52bG Variant-10 S71K T23A N90R D69T M65I Variant-11
I29F N28T K30S H35S A63V Variant-12 A85V N52aS M35bV K30S D31S
Variant-13 R13K D52bG H35S D50S M65I Variant-14 T66S D52cS N52aG
A63V D50S Variant-15 I46V K19R D69N M35bV D52cN Variant-16 S71K
T70S A16G K58S E52S Variant-17 I46V S72N N90R A67R Q54Y Variant-18
A16G Q41K R38A D31S A63V Variant-19 I46V N73T V53T D52cS E52S
Variant-20 I46V T70S D52bG E52D P57D Variant-21 D69N A85V M65I A63V
K58S Variant-22 T23A A67R D52bG E52S D52cN Variant-23 T82aA I29F
A67R D52cS D50S Variant-24 A16G A85V T23A Q54Y D50S Variant-25 A85V
A67R Q41K N28T Q60K Variant-26 N73T A67R D31S N52aS E52D Variant-27
S71K T66S M35bV Q60K D52bG Variant-28 S72N N28T E52D M65I N52aG
Variant-29 K19R R13K Q54Y A63V P57D Variant-30 I46V R13K S71K N52aS
F59V Variant-31 N73T T70S Q60K M65I F59V Variant-32 D69N T82aA T66S
Q41K H35S Variant-33 A85V D69T V53T F59V D52cN Variant-34 T70S R38A
D52cS K30S Q54Y Variant-35 N90R Q41K E52D D50S D52cN Variant-36
D69T M35bV E52D A63V P57D Variant-37 I29F A16G T66S F59V P57D
Variant-38 R13K T82aA S72N D31S E52S Variant-39 D69N T70S S72N T23A
N52aS Variant-40 K19R T82aA T70S N28T V53T
7. REFERENCES CITED
[0499] All references cited herein are incorporated herein by
reference in their entirety and for all purposes to the same extent
as if each individual publication or patent or patent application
was specifically and individually indicated to be incorporated by
reference in its entirety for all purposes.
[0500] Aspects of the present invention can be implemented as a
computer program product that comprises a computer program
mechanism embedded in a computer readable storage medium. For
instance, the computer program product could contain the program
modules and/or data structures shown in FIG. 1. These program
modules may be stored on a CD-ROM, magnetic disk storage product,
digital video disk (DVD) or any other computer readable data or
program storage product. The software modules in the computer
program product may also be distributed electronically, via the
Internet or otherwise, by transmission of a computer data signal
(in which the software modules are embedded) on a carrier wave.
[0501] Many modifications and variations of this invention can be
made without departing from its spirit and scope, as will be
apparent to those skilled in the art. The specific embodiments
described herein are offered by way of example only, and the
invention is to be limited only by the terms of the appended
claims, along with the full scope of equivalents to which such
claims are entitled.
Sequence CWU 1
1
3120PRTE. ColiE Coli Leader Peptide 1Met Lys Lys Leu Leu Phe Ala
Ile Pro Leu Val Val Pro Phe Tyr Ser1 5 10 15His Ser Thr Met
202377PRTTritirachium album LimberTritirachium album Limber
proteinase K 2Ala Pro Ala Val Glu Gln Arg Ser Glu Ala Ala Pro Leu
Ile Glu Ala1 5 10 15Arg Gly Glu Met Val Ala Asn Lys Tyr Ile Val Lys
Phe Lys Glu Gly 20 25 30Ser Ala Leu Ser Ala Leu Asp Ala Ala Met Glu
Lys Ile Ser Gly Lys 35 40 45Pro Asp His Val Tyr Lys Asn Val Phe Ser
Gly Phe Ala Ala Thr Leu 50 55 60 Asp Glu Asn Met Val Arg Val Leu
Arg Ala His Pro Asp Val Glu Tyr65 70 75 80Ile Glu Gln Asp Ala Val
Val Thr Ile Asn Ala Ala Gln Thr Asn Ala 85 90 95Pro Trp Gly Leu Ala
Arg Ile Ser Ser Thr Ser Pro Gly Thr Ser Thr 100 105 110Tyr Tyr Tyr
Asp Glu Ser Ala Gly Gln Gly Ser Cys Val Tyr Val Ile 115 120 125Asp
Thr Gly Ile Glu Ala Ser His Pro Glu Phe Glu Gly Arg Ala Gln 130 135
140 Met Val Lys Thr Tyr Tyr Tyr Ser Ser Arg Asp Gly Asn Gly His
Gly145 150 155 160Thr His Cys Ala Gly Thr Val Gly Ser Arg Thr Tyr
Gly Val Ala Lys 165 170 175Lys Thr Gln Leu Phe Gly Val Lys Val Leu
Asp Asp Asn Gly Ser Gly 180 185 190Gln Tyr Ser Thr Ile Ile Ala Gly
Met Asp Phe Val Ala Ser Asp Lys 195 200 205Asn Asn Arg Asn Cys Pro
Lys Gly Val Val Ala Ser Leu Ser Leu Gly 210 215 220 Gly Gly Tyr Ser
Ser Ser Val Asn Ser Ala Ala Ala Arg Leu Gln Ser225 230 235 240Ser
Gly Val Met Val Ala Val Ala Ala Gly Asn Asn Asn Ala Asp Ala 245 250
255Arg Asn Tyr Ser Pro Ala Ser Glu Pro Ser Val Cys Thr Val Gly Ala
260 265 270Ser Asp Arg Tyr Asp Arg Arg Ser Ser Phe Ser Asn Tyr Gly
Ser Val 275 280 285Leu Asp Ile Phe Gly Pro Gly Thr Ser Ile Leu Ser
Thr Trp Ile Gly 290 295 300Gly Ser Thr Arg Ser Ile Ser Gly Thr Ser
Met Ala Thr Pro His Val305 310 315 320Ala Gly Leu Ala Ala Tyr Leu
Met Thr Leu Gly Lys Thr Thr Ala Ala 325 330 335Ser Ala Cys Arg Tyr
Ile Ala Asp Thr Ala Asn Lys Gly Asp Leu Ser 340 345 350Asn Ile Pro
Phe Gly Thr Val Asn Leu Leu Ala Tyr Asn Asn Tyr Gln 355 360 365Ala
Val Asp His His His His His His 370 37531193DNAArtificial
SequenceArtificial nucleic acid sequence encoding Tritirachium
album Limber proteinase K 3atgaaaaaac tgctgttcgc gattccgctg
gtggtgccgt tctatagcca tagcaccatg 60gcaccggccg ttgaacagcg ttctgaagca
gctcctctga ttgaggcacg tggtgaaatg 120gtagcaaaca agtacatcgt
gaagttcaag gagggttctg ctctgtctgc tctggatgct 180gctatggaaa
agatctctgg caagcctgat cacgtctata agaacgtgtt cagcggtttc
240gcagcaactc tggacgagaa catggtccgt gtactgcgtg ctcatccaga
cgttgaatac 300atcgaacagg acgctgtggt tactatcaac gcggcacaga
ctaacgcacc ttggggtctg 360gcacgtattt cttctacttc cccgggtacg
tctacttact actacgacga gtctgccggt 420caaggttctt gcgtttacgt
gatcgatacg ggcatcgagg cttctcatcc tgagtttgaa 480ggccgtgcac
aaatggtgaa gacctactac tactcttccc gtgacggtaa tggtcacggt
540actcattgcg caggtactgt tggtagccgt acctacggtg ttgctaagaa
aacgcaactg 600ttcggcgtta aagtgctgga cgacaacggt tctggtcagt
actccaccat tatcgcgggt 660atggatttcg tagcgagcga taaaaacaac
cgcaactgcc cgaaaggtgt tgtggcttct 720ctgtctctgg gtggtggtta
ctcctcttct gttaacagcg cagctgcacg tctgcaatct 780tccggtgtca
tggtcgcagt agcagctggt aacaataacg ctgatgcacg caactactct
840cctgctagcg agccttctgt ttgcaccgtg ggtgcatctg atcgttatga
tcgtcgtagc 900tccttcagca actatggttc cgtcctggat atcttcggcc
ctggtacttc tatcctgtct 960acctggattg gcggtagcac tcgttccatt
tccggtacga gcatggctac tccacatgtt 1020gctggtctgg cagcatacct
gatgaccctg ggtaagacca ctgctgcatc cgcttgtcgt 1080tacatcgcgg
atactgcgaa caaaggcgat ctgtctaaca tcccgttcgg caccgttaat
1140ctgctggcat acaacaacta tcaggctgtc gaccatcatc atcatcatca tag
1193
* * * * *
References