U.S. patent application number 09/782004 was filed with the patent office on 2002-04-25 for protein design automation for protein libraries.
Invention is credited to Bentzien, Joerg, Dahiyat, Bassil I., Fiebig, Klaus M., Hayes, Robert J..
Application Number | 20020048772 09/782004 |
Document ID | / |
Family ID | 26877359 |
Filed Date | 2002-04-25 |
United States Patent
Application |
20020048772 |
Kind Code |
A1 |
Dahiyat, Bassil I. ; et
al. |
April 25, 2002 |
Protein design automation for protein libraries
Abstract
The invention relates to the use of protein design automation
(PDA) to generate computationally prescreened secondary libraries
of proteins, and to methods and compositions utilizing the
libraries.
Inventors: |
Dahiyat, Bassil I.; (Los
Angeles, CA) ; Hayes, Robert J.; (Altadena, CA)
; Bentzien, Joerg; (Pasadena, CA) ; Fiebig, Klaus
M.; (Frankfurt, DE) |
Correspondence
Address: |
FLEHR HOHBACH TEST
ALBRITTON & HERBERT LLP
Suite 3400
4 Embarcadero Center
San Francisco
CA
94111-4187
US
|
Family ID: |
26877359 |
Appl. No.: |
09/782004 |
Filed: |
February 12, 2001 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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60181630 |
Feb 10, 2000 |
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Current U.S.
Class: |
435/7.1 ;
702/19 |
Current CPC
Class: |
C07K 1/047 20130101;
C07K 1/063 20130101 |
Class at
Publication: |
435/7.1 ;
702/19 |
International
Class: |
G01N 033/53; G06F
019/00; G01N 033/48; G01N 033/50 |
Claims
We claim:
1. A method for generating a secondary library of scaffold protein
variants comprising: a) providing a primary library comprising a
rank-ordered list of scaffold protein primary variant sequences; b)
generating a list of primary variant positions in said primary
library; c) combining a plurality of said primary variant positions
to generate a secondary library of secondary sequences.
2. A method for generating a secondary library of scaffold protein
variants comprising: a) providing a primary library comprising a
rank-ordered list of scaffold protein primary variant sequences; b)
generating a probability distribution of amino acid residues in a
plurality of variant positions; c) combining a plurality of said
amino acid residues to generate a secondary library of secondary
sequences.
3. A method according to claim 1 further comprising synthesizing a
plurality of said secondary sequences.
4. A method according to claim 2 wherein said synthesizing is done
by multiple PCR with pooled oligonucleotides.
5. A method according to claim 4 wherein said pooled
oligonucleotides are added in equimolar amounts.
6. A method according to claim 4 wherein said pooled
oligonucleotides are added in amounts that correspond to the
frequency of the mutation.
7. A composition comprising a plurality of secondary variant
proteins comprising a subset of said secondary library.
8. A composition comprising a plurality of nucleic acids encoding a
plurality of secondary variant proteins comprising a subset of said
secondary library.
9. A method for generating a secondary library of scaffold protein
variants comprising: a) providing a first library rank-ordered list
of scaffold protein primary variants; b) generating a probability
distribution of amino acid residues in a plurality of variant
positions; c) synthesizing a plurality of scaffold protein
secondary variants comprising a plurality of said amino acid
residues to form a secondary library; wherein at least one of said
secondary variants is different from said primary variants.
Description
FIELD OF THE INVENTION
[0001] The invention relates to the use of a variety of computation
methods, including protein design automation (PDA), to generate
computationally prescreened secondary libraries of proteins, and to
methods of making and methods and compositions utilizing the
libraries.
BACKGROUND OF THE INVENTION
[0002] Directed molecular evolution can be used to create proteins
and enzymes with novel functions and properties. Starting with a
known natural protein, several rounds of mutagenesis, functional
screening, and propagation of successful sequences are performed.
The advantage of this process is that it can be used to rapidly
evolve any protein without knowledge of its structure. Several
different mutagenesis strategies exist, including point mutagenesis
by error-prone PCR, cassette mutagenesis, and DNA shuffling. These
techniques have had many successes; however, they are all
handicapped by their inability to produce more than a tiny fraction
of the potential changes. For example, there are 20.sup.500
possible amino acid changes for an average protein approximately
500 amino acids long. Clearly, the mutagenesis and functional
screening of so many mutants is impossible; directed evolution
provides a very sparse sampling of the possible sequences and hence
examines only a small portion of possible improved proteins,
typically point mutants or recombinations of existing sequences. By
sampling randomly from the vast number of possible sequences,
directed evolution is unbiased and broadly applicable, but
inherently inefficient because it ignores all structural and
biophysical knowledge of proteins.
[0003] In contrast, computational methods can be used to screen
enormous sequence libraries (up to 10.sup.80 in a single
calculation) overcoming the key limitation of experimental library
screening methods such as directed molecular evolution. There are a
wide variety of methods known for generating and evaluating
sequences. These include, but are not limited to, sequence
profiling (Bowie and Eisenberg, Science 253(5016): 164-70, (1991)),
rotamer library selections (Dahiyat and Mayo, Protein Sci 5(5):
895-903 (1996); Dahiyat and Mayo, Science 278(5335): 82-7 (1997);
Desjarlais and Handel, Protein Science 4: 2006-2018 (1995); Harbury
et al, PNAS USA 92(18): 8408-8412 (1995); Kono et al., Proteins:
Structure, Function and Genetics 19: 244-255 (1994); Hellinga and
Richards, PNAS USA 91: 5803-5807 (1994)); and residue pair
potentials (Jones, Protein Science 3: 567-574, (1994)).
[0004] In particular, U.S. Ser. Nos. 60/061,097, 60/043,464,
60/054,678, 09/127,926 and PCT US98/07254 describe a method termed
"Protein Design Automation", or PDA, that utilizes a number of
scoring functions to evaluate sequence stability.
[0005] It is an object of the present invention to provide
computational methods for prescreening sequence libraries to
generate and select secondary libraries, which can then be made and
evaluated experimentally.
SUMMARY OF THE INVENTION
[0006] In accordance with the objects outlined above, the present
invention provides methods for generating a secondary library of
scaffold protein variants comprising providing a primary library
comprising a rank-ordered list of scaffold protein primary variant
sequences. A list of primary variant positions in the primary
library is then generated, and a plurality of the primary variant
positions is then combined to generate a secondary library of
secondary sequences.
[0007] In an additional aspect, the invention provides methods for
generating a secondary library of scaffold protein variants
comprising providing a primary library comprising a rank-ordered
list of scaffold protein primary variant sequences, and generating
a probability distribution of amino acid residues in a plurality of
variant positions. The plurality of the amino acid residues is
combined to generate a secondary library of secondary sequences.
These sequences may then be optionally synthesized and tested, in a
variety of ways, including multiplexing PCR with pooled
oligonucleotides, error prone PCR, gene shuffling, etc.
[0008] In a further aspect, the invention provides compositions
comprising a plurality of secondary variant proteins or nucleic
acids encoding the proteins, wherein the plurality comprises all or
a subset of the secondary library. The invention further provides
cells comprising the library, particularly mammalian cells.
[0009] In an additional aspect, the invention provides methods for
generating a secondary library of scaffold protein variants
comprising providing a first library rank-ordered list of scaffold
protein primary variants;
[0010] generating a probability distribution of amino acid residues
in a plurality of variant positions; and synthesizing a plurality
of scaffold protein secondary variants comprising a plurality of
the amino acid residues to form a secondary library. At least one
of the secondary variants is different from the primary
variants.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] FIG. 1 depicts the synthesis of a full-length gene and all
possible mutations by PCR. Overlapping oligonucleotides
corresponding to the full-length gene (black bar, Step 1) are
synthesized, heated and annealed. Addition of Pfu DNA polymerase to
the annealed oligonucleotides results in the 5'.fwdarw.3' synthesis
of DNA (Step 2) to produce longer DNA fragments (Step 3). Repeated
cycles of heating, annealing (Step 4) results in the production of
longer DNA, including some full-length molecules. These can be
selected by a second round of PCR using primers (arrowed)
corresponding to the end of the full-length gene (Step 5).
[0012] FIG. 2 depicts the reduction of the dimensionality of
sequence space by PDA screening. From left to right, 1: without
PDA; 2: without PDA not counting Cysteine, Proline, Glycine; 3:
with PDA using the 1% criterion, modeling free enzyme; 4: with PDA
using the 1% criterion, modeling enzyme-substrate complex; 5: with
PDA using the 5% criterion modeling free enzyme; 6: with PDA using
the 5% criterion modeling enzyme-substrate complex.
[0013] FIG. 3 depicts the active site of B. circulans xylanase.
Those positions included in the PDA design are shown by their side
chain representation. In red are wild type residues (their
conformation was allowed to change, but not their amino acid
identity). In green are positions whose conformation and identity
were allowed to change (to any amino acid except proline, cysteine
and glycine).
[0014] FIG. 4 depicts cefotaxime resistance of E. coli expressing
wild type (WT) and PDA Screened .beta.-lactamase; results shown for
increasing concentrations of cefotaxime.
[0015] FIG. 5 depicts a preferred scheme for synthesizing a library
of the invention. The wild-type gene, or any starting gene, such as
the gene for the global minima gene, can be used. Oligonucleotides
comprising different amino acids at the different variant positions
can be used during PCR using standard primers. This generally
requires fewer oligonucleotides and can result in fewer errors.
[0016] FIG. 6 depicts and overlapping extension method. At the top
of FIG. 6 is the template DNA showing the locations of the regions
to be mutated (black boxes) and the binding sites of the relevant
primers (arrows). The primers R1 and R2 represent a pool of
primers, each containing a different mutation; as described herein,
this may be done using different ratios of primers if desired. The
variant position is flanked by regions of homology sufficient to
get hybridization. In this example, three separate PCR reactions
are done for step 1. The first reaction contains the template plus
oligos F1 and R1. The second reaction contains template plus F2 and
R2, and the third contains the template and F3 and R3. The reaction
products are shown. In Step 2, the products from Step 1 tube 1 and
Step 1 tube 2 are taken. After purification away from the primers,
these are added to a fresh PCR reaction together with F1 and R4.
During the Denaturation phase of the PCR, the overlapping regions
anneal and the second strand is synthesized. The product is then
amplified by the outside primers. In Step 3, the purified product
from Step 2 is used in a third PCR reaction, together with the
product of Step 1, tube 3 and the primers F1 and R3. The final
product corresponds to the full length gene and contains the
required mutations.
[0017] FIG. 7 depicts a ligation of PCR reaction products to
synthesize the libraries of the invention. In this technique, the
primers also contain an endonuclease restriction site (RE), either
blunt, 5' overhanging or 3' overhanging. We set up three separate
PCR reactions for Step 1. The first reaction contains the template
plus oligos F1 and R1. The second reaction contains template plus
F2 and R2, and the third contains the template and F3 and R3. The
reaction products are shown. In Step 2, the products of step 1 are
purified and then digested with the appropriate restriction
endonuclease. The digestion products from Step 2, tube 1 and Step
2, tube 2 and ligate them together with DNA ligase (step 3). The
products are then amplified in Step 4 using primer F1 and R4. The
whole process is then repeated by digesting the amplified products,
ligating them to the digested products of Step 2, tube 3, and then
amplifying the final product by primers F1 and R3. It would also be
possible to ligate all three PCR products from Step 1 together in
one reaction, providing the two restriction sites (RE1 and RE2)
were different.
[0018] FIG. 8 depicts blunt end ligation of PCR products. In this
technique, the primers such as F1 and R1 do not overlap, but they
abut. Again three separate PCR reactions are performed. The
products from tube 1 and tube 2 are ligated, and then amplified
with outside primers F1 and R4. This product is then ligated with
the product from Step 1, tube 3. The final products are then
amplified with primers F1 and R3.
[0019] FIG. 9 depicts M13 single stranded template production of
mutated PCR products. Primer1 and Primer2 (each representing a pool
of primers corresponding to desired mutations) are mixed with the
M13 template containing the wildtype gene or any starting gene. PCR
produces the desired product (11) containing the combinations of
the desired mutations incorporated in Primer1 and Primer2. This
scheme can be used to produce a gene with mutations, or fragments
of a gene with mutations that are then linked together via ligation
or PCR for example.
DETAILED DESCRIPTION OF THE INVENTION
[0020] The present invention is directed to methods of using
computational screening of protein sequence libraries (that can
comprise up to 10.sup.80 or more members) to select smaller
libraries of protein sequences (that can comprise up to 10.sup.13
members), that can then be used in a number of ways. For example,
the proteins can be actually synthesized and experimentally tested
in the desired assay, for improved function and properties.
Similarly, the library can be additionally computationally
manipulated to create a new library which then itself can be
experimentally tested.
[0021] The invention has two broad uses; first, the invention can
be used to prescreen libraries based on known scaffold proteins.
That is, computational screening for stability (or other
properties) may be done on either the entire protein or some subset
of residues, as desired and described below. By using computational
methods to generate a threshold or cutoff to eliminate disfavored
sequences, the percentage of useful variants in a given variant set
size can increase, and the required experimental outlay is
decreased.
[0022] In addition, the present invention finds use in the
screening of random peptide libraries. As is known, signaling
pathways in cells often begin with an effector stimulus that leads
to a phenotypically describable change in cellular physiology.
Despite the key role intracellular signaling pathways play in
disease pathogenesis, in most cases, little is understood about a
signaling pathway other than the initial stimulus and the ultimate
cellular response.
[0023] Historically, signal transduction has been analyzed by
biochemistry or genetics. The biochemical approach dissects a
pathway in a "stepping-stone" fashion: find a molecule that acts
at, or is involved in, one end of the pathway, isolate assayable
quantities and then try to determine the next molecule in the
pathway, either upstream or downstream of the isolated one. The
genetic approach is classically a "shot in the dark": induce or
derive mutants in a signaling pathway and map the locus by genetic
crosses or complement the mutation with a cDNA library. Limitations
of biochemical approaches include a reliance on a significant
amount of pre-existing knowledge about the constituents under study
and the need to carry such studies out in vitro, post-mortem.
Limitations of purely genetic approaches include the need to first
derive and then characterize the pathway before proceeding with
identifying and cloning the gene.
[0024] Screening molecular libraries of chemical compounds for
drugs that regulate signal systems has led to important discoveries
of great clinical significance. Cyclosporin A (CsA) and FK506, for
examples, were selected in standard pharmaceutical screens for
inhibition of T-cell activation. It is noteworthy that while these
two drugs bind completely different cellular proteins--cyclophilin
and FK506 binding protein (FKBP), respectively, the effect of
either drug is virtually the same--profound and specific
suppression of T-cell activation, phenotypically observable in T
cells as inhibition of mRNA production dependent on transcription
factors such as NF-AT and NF-KB. Libraries of small peptides have
also been successfully screened in vitro in assays for bioactivity.
The literature is replete with examples of small peptides capable
of modulating a wide variety of signaling pathways. For example, a
peptide derived from the HIV-1 envelope protein has been shown to
block the action of cellular calmodulin.
[0025] Accordingly, generation of random or semi-random sequence
libraries of proteins and peptides allows for the selection of
proteins (including peptides, oligopeptides and polypeptides) with
useful properties. The sequences in these experimental libraries
can be randomized at specific sites only, or throughout the
sequence. The number of sequences that can be searched in these
libraries grows expontentially with the number of positions that
are randomized. Generally, only up to 10.sup.12- 10.sup.15
sequences can be contained in a library because of the physical
constraints of laboratories (the size of the instruments, the cost
of producing large numbers of biopolymers, etc.). Other practical
considerations can often limit the size of the libraries to
10.sup.6 or fewer. These limits are reached for only 10 amino acid
positions. Therefore, only a sparse sampling of sequences is
possible in the search for improved proteins or peptides in
experimental sequence libraries, lowering the chance of success and
almost certainly missing desirable candidates. Because of the
randomness of the changes in these sequences, most of the
candidates in the library are not suitable, resulting in a waste of
most of the effort in producing the library.
[0026] However, using the automated protein design techniques
outlined below, virtual libraries of protein sequences can be
generated that are vastly larger than experimental libraries. Up to
10.sup.80 candidate sequences can be screened computationally and
those that meet design criteria which favor stable and functional
proteins can be readily selected. An experimental library
consisting of the favorable candidates found in the virtual library
screening can then be generated, resulting in a much more efficient
use of the experimental library and overcoming the limitations of
random protein libraries.
[0027] Two principle benefits come from the virtual library
screening: (1) the automated protein design generates a list of
sequence candidates that are favored to meet design criteria; it
also shows which positions in the sequence are readily changed and
which positions are unlikely to change without disrupting protein
stability and function. An experimental random library can be
generated that is only randomized at the readily changeable,
non-disruptive sequence positions. (2) The diversity of amino acids
at these positions can be limited to those that the automated
design shows are compatible with these positions. Thus, by limiting
the number of randomized positions and the number of possibilities
at these positions, the number of wasted sequences produced in the
experimental library is reduced, thereby increasing the probability
of success in finding sequences with useful properties.
[0028] In addition, by computationally screening very large
libraries of mutants, greater diversity of protein sequences can be
screened (i.e. a larger sampling of sequence space), leading to
greater improvements in protein function. Further, fewer mutants
need to be tested experimentally to screen a given library size,
reducing the cost and difficulty of protein engineering. By using
computational methods to pre-screen a protein library, the
computational features of speed and efficiency are combined with
the ability of experimental library screening to create new
activities in proteins for which appropriate computational models
and structure-function relationships are unclear.
[0029] Similarly, novel methods to create secondary libraries
derived from very large computational mutant libraries allow the
rapid testing of large numbers of computationally designed
sequences.
[0030] In addition, as is more fully outlined below, the libraries
may be biased in any number of ways, allowing the generation of
secondary libraries that vary in their focus; for example, domains,
subsets of residues, active or binding sites, surface residues,
etc., may all be varied or kept constant as desired.
[0031] In general, as more fully outlined below, the invention can
take on a wide variety of configurations. In general, primary
libraries, e.g. libraries of all or a subset of possible proteins
are generated computationally. This can be done in a wide variety
of ways, including sequence alignments of related proteins,
structural alignments, structural prediction models, databases, or
(preferably) protein design automation computational analysis.
Similarly, primary libraries can be generated via sequence
screening using a set of scaffold structures that are created by
perturbing the starting structure (using any number of techniques
such as molecular dynamics, Monte Carlo analysis) to make changes
to the protein (including backbone and sidechain torsion angle
changes). Optimal sequences can be selected for each starting
structures (or, some set of the top sequences) to make primary
libraries.
[0032] Some of these techniques result in the list of sequences in
the primary library being"scored", or "ranked" on the basis of some
particular criteria. In some embodiments, lists of sequences that
are generated without ranking can then be ranked using techniques
as outlined below.
[0033] In a preferred embodiment, some subset of the primary
library is then experimentally generated to form a secondary
library. Alternatively, some or all of the primary library members
are recombined to form a secondary library, e.g. with new members.
Again, this may be done either computationally or experimentally or
both.
[0034] Alternatively, once the primary library is generated, it can
be manipulated in a variety of ways. In one embodiment, a different
type of computational analysis can be done; for example, a new type
of ranking may be done. Alternatively, and the primary library can
be recombined, e.g. residues at different positions mixed to form a
new, secondary library. Again, this can be done either
computationally or experimentally, or both.
[0035] Accordingly, the present invention provides methods for
generating secondary libraries of scaffold protein variants. By
"protein" herein is meant at least two amino acids linked together
by a peptide bond. As used herein, protein includes proteins,
oligopeptides and peptides. The peptidyl group may comprise
naturally occurring amino acids and peptide bonds, or synthetic
peptidomimetic structures, i.e. "analogs", such as peptoids (see
Simon et al., PNAS USA 89(20):9367 (1992)). The amino acids may
either be naturally occurring or non-naturally occurring; as will
be appreciated by those in the art, any structure for which a set
of rotamers is known or can be generated can be used as an amino
acid. The side chains may be in either the (R) or the (S)
configuration. In a preferred embodiment, the amino acids are in
the (S) or L-configuration.
[0036] The scaffold protein may be any protein for which a three
dimensional structure is known or can be generated; that is, for
which there are three dimensional coordinates for each atom of the
protein. Generally this can be determined using X-ray
crystallographic techniques, NMR techniques, de novo modelling,
homology modelling, etc. In general, if X-ray structures are used,
structures at 2 .ANG. resolution or better are preferred, but not
required.
[0037] The scaffold proteins may be from any organism, including
prokaryotes and eukaryotes, with enzymes from bacteria, fungi,
extremeophiles such as the archebacteria, insects, fish, animals
(particularly mammals and particularly human) and birds all
possible.
[0038] Thus, by "scaffold protein" herein is meant a protein for
which a secondary library of variants is desired. As will be
appreciated by those in the art, any number of scaffold proteins
find use in the present invention. Specifically included within the
definition of "protein" are fragments and domains of known
proteins, including functional domains such as enzymatic domains,
binding domains, etc., and smaller fragments, such as turns, loops,
etc. That is, portions of proteins may be used as well. In
addition, "protein" as used herein includes proteins, oligopeptides
and peptides. In addition, protein variants, i.e. non-naturally
occuring protein analog structures, may be used.
[0039] Suitable proteins include, but are not limited to,
industrial and pharmaceutical proteins, including ligands, cell
surface receptors, antigens, antibodies, cytokines, hormones,
transcription factors, signaling modules, cytoskeletal proteins and
enzymes. Suitable classes of enzymes include, but are not limited
to, hydrolases such as proteases, carbohydrases, lipases;
isomerases such as racemases, epimerases, tautomerases, or mutases;
transferases, kinases, oxidoreductases, and phophatases. Suitable
enzymes are listed in the Swiss-Prot enzyme database. Suitable
protein backbones include, but are not limited to, all of those
found in the protein data base compiled and serviced by the
Research Collaboratory for Structural Bioinformatics (RCSB,
formerly the Brookhaven National Lab).
[0040] Specifically, preferred scaffold proteins include, but are
not limited to, those with known structures (including variants)
including cytokines (IL-1ra (+receptor complex), IL-1 (receptor
alone), IL-1a, IL-1b (including variants and or receptor complex),
IL-2, IL-3, IL-4, IL-5, IL-6, IL-8, IL-10, IFN-.beta., INF-.gamma.,
INF-.alpha.-2a; IFN-.alpha.-2B, TNF-.alpha.; CD40 ligand (chk),
Human Obesity Protein Leptin, Granulocyte Colony-Stimulating
Factor, Bone Morphogenetic Protein-7, Ciliary Neurotrophic Factor,
Granulocyte-Macrophage Colony-Stimulating Factor, Monocyte
Chemoattractant Protein 1, Macrophage Migration Inhibitory Factor,
Human Glycosylation-Inhibiting Factor, Human Rantes, Human
Macrophage Inflammatory Protein 1 Beta, human growth hormone,
Leukemia Inhibitory Factor, Human Melanoma Growth Stimulatory
Activity, neutrophil activating peptide-2, Cc-Chemokine Mcp-3,
Platelet Factor M2, Neutrophil Activating Peptide 2, Eotaxin,
Stromal Cell-Derived Factor-1, Insulin, Insulin-like Growth Factor
I, Insulin-like Growth Factor II, Transforming Growth Factor B1,
Transforming Growth Factor B2, Transforming Growth Factor B3,
Transforming Growth Factor A, Vascular Endothelial growth factor
(VEGF), acidic Fibroblast growth factor, basic Fibroblast growth
factor, Endothelial growth factor, Nerve growth factor, Brain
Derived Neurotrophic Factor, Ciliary Neurotrophic Factor, Platelet
Derived Growth Factor, Human Hepatocyte Growth Factor, Glial
Cell-Derived Neurotrophic Factor, (as well as the 55 cytokines in
PDB 1/12/99)); Erythropoietin; other extracellular signalling
moeities, including, but not limited to, hedgehog Sonic, hedgehog
Desert, hedgehog Indian, hCG; coaguation factors including, but not
limited to, TPA and Factor VIIa; transcription factors, including
but not limited to, p53, p53 tetramerization domain, Zn fingers (of
which more than 12 have structures), homeodomains (of which 8 have
structures), leucine zippers (of which 4 have structures);
antibodies, including, but not limited to, cFv; viral proteins,
including, but not limited to, hemagglutinin trimerization domain
and hiv Gp41 ectodomain (fusion domain); intracellular signalling
modules, including, but not limited to, SH2 domains (of which 8
structures are known), SH3 domains (of which 11 have structures),
and Pleckstin Homology Domains; receptors, including, but not
limited to, the extracellular Region Of Human Tissue Factor
Cytokine-Binding Region Of Gp130, G-CSF receptor, erythropoietin
receptor, Fibroblast Growth Factor receptor, TNF receptor, IL-1
receptor, IL-1 receptor/IL1ra complex, IL-4 receptor, INF-.gamma.
receptor alpha chain, MHC Class I, MHC Class II, T Cell Receptor,
Insulin receptor, insulin receptor tyrosine kinase and human growth
hormone receptor.
[0041] Once a scaffold protein is chosen, a primary library is
generated using computational processing. Generally speaking, in
some embodiments, the goal of the computational processing is to
determine a set of optimized protein sequences. By "optimized
protein sequence" herein is meant a sequence that best fits the
mathematical equations of the computational process. As will be
appreciated by those in the art, a global optimized sequence is the
one sequence that best fits the equations (for example, when PDA is
used, the global optimized sequence is the sequence that best fits
Equation 1, below); i.e. the sequence that has the lowest energy of
any possible sequence. However, there are any number of sequences
that are not the global minimum but that have low energies.
[0042] Thus, a "primary library" as used herein is a collection of
optimized sequences, generally, but not always, in the form of a
rank-ordered list. In theory, all possible sequences of a protein
may be ranked; however, currently 10.sup.13 sequences is a
practical limit. Thus, in general, some subset of all possible
sequences is used as the primary library; generally, the top
10.sup.3 to 10.sup.13 sequences are chosen as the primary library.
The cutoff for inclusion in the rank ordered list of the primary
library can be done in a variety of ways. For example, the cutoff
may be just an arbitrary exclusion point: the top 10.sup.5
sequences may comprise the primary library. Alternatively, all
sequences scoring within a certain limit of the global optimum can
be used; for example, all sequences with 10 kcal/mol of the global
optimum could be used as the primary library. This method has the
advantage of using a direct measure of fidelity to a three
dimensional structure to determine inclusion. This approach can be
used to insure that library mutations are not limited to positions
that have the lowest energy gap between different mutations.
Alternatively, the cutoff may be enforced when a predetermined
number of mutations per position is reached. As a rank ordered
sequence list is lengthened and the library is enlarged, more
mutations per position are defined. Alternatively, the total number
of sequences defined by the recombination of all mutations can be
used as a cutoff criterion for the primary sequence library.
Preferred values for the total number of sequences range from 100
to 10.sup.20, particularly preferred values range from 1000 to
10.sup.13, especially preferred values range from 1000 to 10.sup.7
Alternatively, the first occurrence in the list of predefined
undesirable residues can be used as a cutoff criterion. For
example, the first hydrophilic residue occurring in a core position
would limit the list. It should also be noted that while these
methods are described in conjunction with limiting the size of the
primary library, these same techniques may be used to formulate the
cutoff for inclusion in the secondary library as well.
[0043] Thus, the present invention provides methods to generate a
primary library optionally comprising a rank ordered list of
sequences, generally in terms of theoretical quantitative
stability, as is more fully described below. Generating a primary
library to optimize the stability of a conformation can be used to
stabilize the active site transition state conformation of an
enzyme, which will improve its activity. Similarly, stabilizing a
ligand-receptor complex or enzyme-substrate complex will improve
the binding affinity.
[0044] The primary libraries can be generated in a variety of ways.
In essence, any methods that can result in either the relative
ranking of the possible sequences of a protein based on measurable
stability parameters, or a list of suitable sequences can be used.
As will be appreciated by those in the art, any of the methods
described herein or known in the art may be used alone, or in
combination with other methods.
[0045] Generally, there are a variety of computational methods that
can be used to generate a primary library. In a preferred
embodiment, sequence based methods are used. Alternatively,
structure based methods, such as PDA, described in detail below,
are used.
[0046] In a preferred embodiment, the scaffold protein is an enzyme
and highly accurate electrostatic models can be used for enzyme
active site residue scoring to improve enzyme active site libraries
(see Warshel, computer Modeling of Chemical Reactions in Enzymes
and Solutions, Wiley & Sons, New York, (1991), hereby expressly
incorporated by reference) These accurate models can assess the
relative energies of sequences with high precision, but are
computationally intensive.
[0047] Similarly, molecular dynamics calculations can be used to
computationally screen sequences by individually calculating mutant
sequence scores and compiling a rank ordered list.
[0048] In a preferred embodiment, residue pair potentials can be
used to score sequences (Miyazawa et al., Macromolecules
18(3):534-552 (1985), expressly incorporated by reference) during
computational screening.
[0049] In a preferred embodiment, sequence profile scores (Bowie et
al., Science 253(5016):164-70 (1991), incorporated by reference)
and/or potentials of mean force (Hendlich et al., J. Mol. Biol.
216(1):167-180 (1990), also incorporated by reference) can also be
calculated to score sequences. These methods assess the match
between a sequence and a 3D protein structure and hence can act to
screen for fidelity to the protein structure. By using different
scoring functions to rank sequences, different regions of sequence
space can be sampled in the computational screen.
[0050] Furthermore, scoring functions can be used to screen for
sequences that would create metal or co-factor binding sites in the
protein (Hellinga, Fold Des. 3(1):R1-8 (1998), hereby expressly
incorporated by reference). Similarly, scoring functions can be
used to screen for sequences that would create disulfide bonds in
the protein. These potentials attempt to specifically modify a
protein structure to introduce a new structural motif.
[0051] In a preferred embodiment, sequence and/or structural
alignment programs can be used to generate primary libraries. As is
known in the art, there are a number of sequence-based alignment
programs; including for example, Smith-Waterman searches,
Needleman-Wunsch, Double Affine Smith-Waterman, frame search,
Gribskov/GCG profile search, Gribskov/GCG profile scan, profile
frame search, Bucher generalized profiles, Hidden Markov models,
Hframe, Double Frame, Blast, Psi-Blast, Clustal, and GeneWise.
[0052] The source of the sequences can vary widely, and include
taking sequences from one or more of the known databases,
including, but not limited to, SCOP (Hubbard, et al., Nucleic Acids
Res 27(1):254-256. (1999)); PFAM (Bateman, et al., Nucleic Acids
Res 27(1):260-262. (1999)); VAST (Gibrat, et Curr Opin Struct Biol
6(3):377-385. (1996)); CATH (Orengo, et al., Structure
5(8):1093-1108. (1997)); PhD Predictor
(http://www.embl-heidelberg.de/predictprotein/predictprotein.html);
Prosite (Hofmann, et al., Nucleic Acids Res 27(1):215-219. (1999));
PIR (http://www.mips.biochem.mpg.de/proj/protseqdb/); GenBank
(http://www.ncbi.nlm.nih.gov/); PDB (www.rcsb.org) and BIND (Bader,
et al., Nucleic Acids Res 29(1):242-245. (2001)).
[0053] In addition, sequences from these databases can be subjected
to continguous analysis or gene prediction; see Wheeler, et al.,
Nucleic Acids Res 28(1):10-14. (2000) and Burge and Karlin, J. Mol
Biol 268(1):78-94. (1997).
[0054] As is known in the art, there are a number of sequence
alignment methodologies that can be used. For example, sequence
homology based alignment methods can be used to create sequence
alignments of proteins related to the target structure (Altschul et
al., J. Mol. Biol. 215(3):403 (1990), incorporated by reference).
These sequence alignments are then examined to determine the
observed sequence variations. These sequence variations are
tabulated to define a primary library. In addition, as is further
outlined below, these methods can also be used to generate
secondary libraries.
[0055] Sequence based alignments can be used in a variety of ways.
For example, a number of related proteins can be aligned, as is
known in the art, and the "variable" and "conserved" residues
defined; that is, the residues that vary or remain identical
between the family members can be defined. These results can be
used to generate a probability table, as outlined below. Similarly,
these sequence variations can be tabulated and a secondary library
defined from them as defined below. Alternatively, the allowed
sequence variations can be used to define the amino acids
considered at each position during the computational screening.
Another variation is to bias the score for amino acids that occur
in the sequence alignment, thereby increasing the likelihood that
they are found during computational screening but still allowing
consideration of other amino acids. This bias would result in a
focused primary library but would not eliminate from consideration
amino acids not found in the alignment. In addition, a number of
other types of bias may be introduced. For example, diversity may
be forced; that is, a "conserved" residue is chosen and altered to
force diversity on the protein and thus sample a greater portion of
the sequence space. Alternatively, the positions of high
variability between family members (i.e. low conservation) can be
randomized, either using all or a subset of amino acids. Similarly,
outlier residues, either positional outliers or side chain
outliers, may be eliminated.
[0056] Similarly, structural alignment of structurally related
proteins can be done to generate sequence alignments. There are a
wide variety of such structural alignment programs known. See for
example VAST from the NCBI
(http://www.ncbi.nim.nih.gov:80/Structure/VAST/vast.shtml); SSAP
(Orengo and Taylor, Methods Enzymol 266(617-635 (1996)) SARF2
(Alexandrov, Protein Eng 9(9):727-732. (1996)) CE (Shindyalov and
Bourne, Protein Eng 11(9):739-747. (1998)); (Orengo et al.,
Structure 5(8):1093-108 (1997); Dali (Holm et al., Nucleic Acid
Res. 26(1):316-9 (1998), all incorporated by reference). These
structurally-generated sequence alignments can then be examined to
determine the observed sequence variations.
[0057] Primary libraries can be generated by predicting secondary
structure from sequence, and then selecting sequences that are
compatible with the predicted secondary structure. There are a
number of secondary structure prediction methods, including, but
not limited to, threading (Bryant and Altschul, Curr Opin Struct
Biol 5(2):236-244. (1995)), Profile 3D (Bowie, et al., Methods
Enzymol 266(598-616 (1996); MONSSTER (Skolnick, et al., J Mol Biol
265(2):217-241. (1997); Rosetta (Simons, et al., Proteins
37(S3):171-176 (1999); PSI-BLAST (Altschul and Koonin, Trends
Biochem Sci 23(11):444-447. (1998)); Impala (Schaffer, et al.,
Bioinformatics 15(12):1000-1011. (1999)); HMMER (McClure, et al.,
Proc Int Conf Intell Syst Mol Biol 4(155-164 (1996)); Clustal W
(http://www.ebi.ac.uk/clustalw/); BLAST (Altschul, et al., J Mol
Biol 215(3):403-410. (1990)), helix-coil transition theory (Munoz
and Serrano, Biopolymers 41:495, 1997), neural networks, local
structure alignment and others (e.g., see in Selbig et al.,
Bioinformatics 15:1039, 1999).
[0058] Similarly, as outlined above, other computational methods
are known, including, but not limited to, sequence profiling (Bowie
and Eisenberg, Science 253(5016): 164-70, (1991)), rotamer library
selections (Dahiyat and Mayo, Protein Sci 5(5): 895-903 (1996);
Dahiyat and Mayo, Science 278(5335): 82-7 (1997); Desjarlais and
Handel, Protein Science 4: 2006-2018 (1995); Harbury et al, PNAS
USA 92(18): 8408-8412 (1995); Kono et al., Proteins: Structure,
Function and Genetics 19: 244-255 (1994); Hellinga and Richards,
PNAS USA 91: 5803-5807 (1994)); and residue pair potentials (Jones,
Protein Science 3: 567-574, (1994); PROSA (Heindlich et al., J.
Mol. Biol. 216:167-180 (1990); THREADER (Jones et al., Nature
358:86-89 (1992), and other inverse folding methods such as those
described by Simons et al. (Proteins, 34:535-543, 1999), Levitt and
Gerstein (PNAS USA , 95:5913-5920, 1998), Godzik et al., PNAS, V89,
PP 12098-102; Godzik and Skolnick (PNAS USA , 89:12098-102, 1992),
Godzik et al. (J. Mol. Biol. 227:227-38, 1992) and two profile
methods (Gribskov et al. PNAS 84:4355-4358 (1987) and Fischer and
Eisenberg, Protein Sci. 5:947-955 (1996), Rice and Eisenberg J.
Mol. Biol. 267:1026-1038(1997)), all of which are expressly
incorporated by reference. In addition, other computational methods
such as those described by Koehl and Levitt (J. Mol. Biol.
293:1161-1181 (1999); J. Mol. Biol. 293:1183-1193 (1999); expressly
incorporated by reference) can be used to create a protein sequence
library which can optionally then be used to generate a smaller
secondary library for use in experimental screening for improved
properties and function.
[0059] In addition, there are computational methods based on
forcefield calculations such as SCMF that can be used as well for
SCMF, see Delarue et la. Pac. Symp. Biocomput. 109-21 (1997), Koehl
et al., J. Mol. Biol. 239:249 (1994); Koehl et al., Nat. Struc.
Biol. 2:163 (1995); Koehl et al., Curr. Opin. Struct. Biol. 6:222
(1996); Koehl et al., J. Mol. Bio. 293:1183 (1999); Koehl et al.,
J. Mol. Biol. 293:1161 (1999); Lee J. Mol. Biol. 236:918 (1994);
and Vasquez Biopolymers 36:53-70 (1995); all of which are expressly
incorporated by reference. Other forcefield calculations that can
be used to optimize the conformation of a sequence within a
computational method, or to generate de novo optimized sequences as
outlined herein include, but are not limited to, OPLS-AA
(Jorgensen, et al., J. Am. Chem. Soc. (1996), v 118, pp
11225-11236; Jorgensen, W. L.; BOSS, Version 4.1; Yale University:
New Haven, Conn. (1999)); OPLS (Jorgensen, et al., J. Am. Chem.
Soc. (1988), v 110, pp 1657ff; Jorgensen, et al., J Am. Chem. Soc.
(1990), v 112, pp 4768ff); UNRES (United Residue Forcefield; Liwo,
et al., Protein Science (1993), v 2, pp1697-1714; Liwo, et al.,
Protein Science (1993), v 2, pp1715-1731; Liwo, et al., J. Comp.
Chem. (1997), v 18, pp849-873; Liwo, et al., J. Comp. Chem. (1997),
v 18, pp874-884; Liwo, et al., J. Comp. Chem. (1998), v 19,
pp259-276; Forcefield for Protein Structure Prediction (Liwo, et
al., Proc. Natl. Acad. Sci. USA (1999), v 96, pp5482-5485); ECEPP/3
(Liwo et al., J Protein Chem 1994 May;13(4):375-80); AMBER 1.1
force field (Weiner, et al., J. Am. Chem. Soc. v 106, pp765-784);
AMBER 3.0 force field (U. C. Singh et al., Proc. Natl. Acad. Sci.
USA. 82:755-759); CHARMM and CHARMM22 (Brooks, et al., J. Comp.
Chem. v4, pp 187-217); cvff3.0 (Dauber-Osguthorpe, et al.,(1988)
Proteins: Structure, Function and Genetics, v4,pp31-47); cff91
(Maple, et al., J. Comp. Chem. v15, 162-182); also, the DISCOVER
(cvff and cff91) and AMBER forcefields are used in the INSIGHT
molecular modeling package (Biosym/MSI, San Diego Calif.) and HARMM
is used in the QUANTA molecular modeling package (Biosym/MSI, San
Diego Calif.), all of which are expressly incorporated by
reference. In fact, as is outlined below, these forcefield methods
may be used to generate the secondary library directly; that is, no
primary library is generated; rather, these methods can be used to
generate a probability table from which the secondary library is
directly generated, for example by using these forcefields during
an SCMF calculation.
[0060] In a preferred embodiment, the computational method used to
generate the primary library is Protein Design Automation (PDA), as
is described in U.S. Ser. Nos. 60/061,097, 60/043,464, 60/054,678,
09/127,926 and PCT US98/07254, all of which are expressly
incorporated herein by reference. Briefly, PDA can be described as
follows. A known protein structure is used as the starting point.
The residues to be optimized are then identified, which may be the
entire sequence or subset(s) thereof. The side chains of any
positions to be varied are then removed. The resulting structure
consisting of the protein backbone and the remaining sidechains is
called the template. Each variable residue position is then
preferably classified as a core residue, a surface residue, or a
boundary residue; each classification defines a subset of possible
amino acid residues for the position (for example, core residues
generally will be selected from the set of hydrophobic residues,
surface residues generally will be selected from the hydrophilic
residues, and boundary residues may be either). Each amino acid can
be represented by a discrete set of all allowed conformers of each
side chain, called rotamers. Thus, to arrive at an optimal sequence
for a backbone, all possible sequences of rotamers must be
screened, where each backbone position can be occupied either by
each amino acid in all its possible rotameric states, or a subset
of amino acids, and thus a subset of rotamers.
[0061] Two sets of interactions are then calculated for each
rotamer at every position: the interaction of the rotamer side
chain with all or part of the backbone (the "singles" energy, also
called the rotamer/template or rotamer/backbone energy), and the
interaction of the rotamer side chain with all other possible
rotamers at every other position or a subset of the other positions
(the "doubles" energy, also called the rotamer/rotamer energy). The
energy of each of these interactions is calculated through the use
of a variety of scoring functions, which include the energy of van
der Waal's forces, the energy of hydrogen bonding, the energy of
secondary structure propensity, the energy of surface area
salvation and the electrostatics. Thus, the total energy of each
rotamer interaction, both with the backbone and other rotamers, is
calculated, and stored in a matrix form.
[0062] The discrete nature of rotamer sets allows a simple
calculation of the number of rotamer sequences to be tested. A
backbone of length n with m possible rotamers per position will
have m.sup.n possible rotamer sequences, a number which grows
exponentially with sequence length and renders the calculations
either unwieldy or impossible in real time. Accordingly, to solve
this combinatorial search problem, a "Dead End Elimination" (DEE)
calculation is performed. The DEE calculation is based on the fact
that if the worst total interaction of a first rotamer is still
better than the best total interaction of a second rotamer, then
the second rotamer cannot be part of the global optimum solution.
Since the energies of all rotamers have already been calculated,
the DEE approach only requires sums over the sequence length to
test and eliminate rotamers, which speeds up the calculations
considerably. DEE can be rerun comparing pairs of rotamers, or
combinations of rotamers, which will eventually result in the
determination of a single sequence which represents the global
optimum energy.
[0063] Once the global solution has been found, a Monte Carlo
search may be done to generate a rank-ordered list of sequences in
the neighborhood of the DEE solution. Starting at the DEE solution,
random positions are changed to other rotamers, and the new
sequence energy is calculated. If the new sequence meets the
criteria for acceptance, it is used as a starting point for another
jump. After a predetermined number of jumps, a rank-ordered list of
sequences is generated. Monte Carlo searching is a sampling
technique to explore sequence space around the global minimum or to
find new local minima distant in sequence space. As is more
additionally outlined below, there are other sampling techniques
that can be used, including Boltzman sampling, genetic algorithm
techniques and simulated annealing. In addition, for all the
sampling techniques, the kinds of jumps allowed can be altered
(e.g. random jumps to random residues, biased jumps (to or away
from wild-type, for example), jumps to biased residues (to or away
from similar residues, for example), etc.). Similarly, for all the
sampling techniques, the acceptance criteria of whether a sampling
jump is accepted can be altered.
[0064] As outlined in U.S. Ser. No. 09/127,926, the protein
backbone (comprising (for a naturally occuring protein) the
nitrogen, the carbonyl carbon, the .alpha.-carbon, and the carbonyl
oxygen, along with the direction of the vector from the
.alpha.-carbon to the .beta.-carbon) may be altered prior to the
computational analysis, by varying a set of parameters called
supersecondary structure parameters.
[0065] Once a protein structure backbone is generated (with
alterations, as outlined above) and input into the computer,
explicit hydrogens are added if not included within the structure
(for example, if the structure was generated by X-ray
crystallography, hydrogens must be added). After hydrogen addition,
energy minimization of the structure is run, to relax the hydrogens
as well as the other atoms, bond angles and bond lengths. In a
preferred embodiment, this is done by doing a number of steps of
conjugate gradient minimization (Mayo et al., J. Phys. Chem.
94:8897 (1990)) of atomic coordinate positions to minimize the
Dreiding force field with no electrostatics. Generally from about
10 to about 250 steps is preferred, with about 50 being most
preferred.
[0066] The protein backbone structure contains at least one
variable residue position. As is known in the art, the residues, or
amino acids, of proteins are generally sequentially numbered
starting with the N-terminus of the protein. Thus a protein having
a methionine at it's N-terminus is said to have a methionine at
residue or amino acid position 1, with the next residues as 2, 3,
4, etc. At each position, the wild type (i.e. naturally occuring)
protein may have one of at least 20 amino acids, in any number of
rotamers. By "variable residue position" herein is meant an amino
acid position of the protein to be designed that is not fixed in
the design method as a specific residue or rotamer, generally the
wild-type residue or rotamer.
[0067] In a preferred embodiment, all of the residue positions of
the protein are variable. That is, every amino acid side chain may
be altered in the methods of the present invention. This is
particularly desirable for smaller proteins, although the present
methods allow the design of larger proteins as well. While there is
no theoretical limit to the length of the protein which may be
designed this way, there is a practical computational limit.
[0068] In an alternate preferred embodiment, only some of the
residue positions of the protein are variable, and the remainder
are "fixed", that is, they are identified in the three dimensional
structure as being in a set conformation. In some embodiments, a
fixed position is left in its original conformation (which may or
may not correlate to a specific rotamer of the rotamer library
being used). Alternatively, residues may be fixed as a non-wild
type residue; for example, when known site-directed mutagenesis
techniques have shown that a particular residue is desirable (for
example, to eliminate a proteolytic site or alter the substrate
specificity of an enzyme), the residue may be fixed as a particular
amino acid. Alternatively, the methods of the present invention may
be used to evaluate mutations de novo, as is discussed below. In an
alternate preferred embodiment, a fixed position may be "floated";
the amino acid at that position is fixed, but different rotamers of
that amino acid are tested. In this embodiment, the variable
residues may be at least one, or anywhere from 0.1% to 99.9% of the
total number of residues. Thus, for example, it may be possible to
change only a few (or one) residues, or most of the residues, with
all possibilities in between.
[0069] In a preferred embodiment, residues which can be fixed
include, but are not limited to, structurally or biologically
functional residues; alternatively, biologically functional
residues may specifically not be fixed. For example, residues which
are known to be important for biological activity, such as the
residues which form the active site of an enzyme, the substrate
binding site of an enzyme, the binding site for a binding partner
(ligand/receptor, antigen/antibody, etc.), phosphorylation or
glycosylation sites which are crucial to biological function, or
structurally important residues, such as disulfide bridges, metal
binding sites, critical hydrogen bonding residues, residues
critical for backbone conformation such as proline or glycine,
residues critical for packing interactions, etc. may all be fixed
in a conformation or as a single rotamer, or "floated".
[0070] Similarly, residues which may be chosen as variable residues
may be those that confer undesirable biological attributes, such as
susceptibility to proteolytic degradation, dimerization or
aggregation sites, glycosylation sites which may lead to immune
responses, unwanted binding activity, unwanted allostery,
undesirable enzyme activity but with a preservation of binding,
etc.
[0071] In a preferred embodiment, each variable position is
classified as either a core, surface or boundary residue position,
although in some cases, as explained below, the variable position
may be set to glycine to minimize backbone strain. In addition, as
outlined herein, residues need not be classified, they can be
chosen as variable and any set of amino acids may be used. Any
combination of core, surface and boundary positions can be
utilized: core, surface and boundary residues; core and surface
residues; core and boundary residues, and surface and boundary
residues, as well as core residues alone, surface residues alone,
or boundary residues alone.
[0072] The classification of residue positions as core, surface or
boundary may be done in several ways, as will be appreciated by
those in the art. In a preferred embodiment, the classification is
done via a visual scan of the original protein backbone structure,
including the side chains, and assigning a classification based on
a subjective evaluation of one skilled in the art of protein
modelling. Alternatively, a preferred embodiment utilizes an
assessment of the orientation of the C.alpha.-C.beta. vectors
relative to a solvent accessible surface computed using only the
template C.alpha. atoms, as outlined in U.S. Ser. Nos. 60/061,097,
60/043,464, 60/054,678, 09/127,926 and PCT US98/07254.
Alternatively, a surface area calculation can be done.
[0073] Once each variable position is classified as either core,
surface or boundary, a set of amino acid side chains, and thus a
set of rotamers, is assigned to each position. That is, the set of
possible amino acid side chains that the program will allow to be
considered at any particular position is chosen. Subsequently, once
the possible amino acid side chains are chosen, the set of rotamers
that will be evaluated at a particular position can be determined.
Thus, a core residue will generally be selected from the group of
hydrophobic residues consisting of alanine, valine, isoleucine,
leucine, phenylalanine, tyrosine, tryptophan, and methionine (in
some embodiments, when the .alpha. scaling factor of the van der
Waals scoring function, described below, is low, methionine is
removed from the set), and the rotamer set for each core position
potentially includes rotamers for these eight amino acid side
chains (all the rotamers if a backbone independent library is used,
and subsets if a rotamer dependent backbone is used). Similarly,
surface positions are generally selected from the group of
hydrophilic residues consisting of alanine, serine, threonine,
aspartic acid, asparagine, glutamine, glutamic acid, arginine,
lysine and histidine. The rotamer set for each surface position
thus includes rotamers for these ten residues. Finally, boundary
positions are generally chosen from alanine, serine, threonine,
aspartic acid, asparagine, glutamine, glutamic acid, arginine,
lysine histidine, valine, isoleucine, leucine, phenylalanine,
tyrosine, tryptophan, and methionine. The rotamer set for each
boundary position thus potentially includes every rotamer for these
seventeen residues (assuming cysteine, glycine and proline are not
used, although they can be). Additionally, in some preferred
embodiments, a set of 18 naturally occuring amino acids (all except
cysteine and proline, which are known to be particularly
disruptive) are used.
[0074] Thus, as will be appreciated by those in the art, there is a
computational benefit to classifying the residue positions, as it
decreases the number of calculations. It should also be noted that
there may be situations where the sets of core, boundary and
surface residues are altered from those described above; for
example, under some circumstances, one or more amino acids is
either added or subtracted from the set of allowed amino acids. For
example, some proteins which dimerize or multimerize, or have
ligand binding sites, may contain hydrophobic surface residues,
etc. In addition, residues that do not allow helix "capping" or the
favorable interaction with an .alpha.-helix dipole may be
subtracted from a set of allowed residues. This modification of
amino acid groups is done on a residue by residue basis.
[0075] In a preferred embodiment, proline, cysteine and glycine are
not included in the list of possible amino acid side chains, and
thus the rotamers for these side chains are not used. However, in a
preferred embodiment, when the variable residue position has a
.phi. angle (that is, the dihedral angle defined by 1) the carbonyl
carbon of the preceding amino acid; 2) the nitrogen atom of the
current residue; 3) the .alpha.-carbon of the current residue; and
4) the carbonyl carbon of the current residue) greater than
0.degree., the position is set to glycine to minimize backbone
strain.
[0076] Once the group of potential rotamers is assigned for each
variable residue position, processing proceeds as outlined in U.S.
Ser. No. 09/127,926 and PCT US98/07254. This processing step
entails analyzing interactions of the rotamers with each other and
with the protein backbone to generate optimized protein sequences.
Simplistically, the processing initially comprises the use of a
number of scoring functions to calculate energies of interactions
of the rotamers, either to the backbone itself or other rotamers.
Preferred PDA scoring functions include, but are not limited to, a
Van der Waals potential scoring function, a hydrogen bond potential
scoring function, an atomic salvation scoring function, a secondary
structure propensity scoring function and an electrostatic scoring
function. As is further described below, at least one scoring
function is used to score each position, although the scoring
functions may differ depending on the position classification or
other considerations, like favorable interaction with an
.alpha.-helix dipole. As outlined below, the total energy which is
used in the calculations is the sum of the energy of each scoring
function used at a particular position, as is generally shown in
Equation 1:
E.sub.total=nE.sub.vdw+nE.sub.as+nE.sub.h-bonding+nE.sub.ss+nE.sub.elec
Equation 1
[0077] In Equation 1, the total energy is the sum of the energy of
the van der Waals potential (E.sub.vdw), the energy of atomic
salvation (E.sub.as), the energy of hydrogen bonding
(E.sub.h-bonding), the energy of secondary structure (E.sub.ss) and
the energy of electrostatic interaction (E.sub.elec). The term n is
either 0 or 1, depending on whether the term is to be considered
for the particular residue position.
[0078] As outlined in U.S. Ser. Nos. 60/061,097, 60/043,464,
601054,678, 09/127,926 and PCT US98/07254, any combination of these
scoring functions, either alone or in combination, may be used.
Once the scoring functions to be used are identified for each
variable position, the preferred first step in the computational
analysis comprises the determination of the interaction of each
possible rotamer with all or part of the remainder of the protein.
That is, the energy of interaction, as measured by one or more of
the scoring functions, of each possible rotamer at each variable
residue position with either the backbone or other rotamers, is
calculated. In a preferred embodiment, the interaction of each
rotamer with the entire remainder of the protein, i.e. both the
entire template and all other rotamers, is done. However, as
outlined above, it is possible to only model a portion of a
protein, for example a domain of a larger protein, and thus in some
cases, not all of the protein need be considered. The term
"portion", as used herein, with regard to a protein refers to a
fragment of that protein. This fragment may range in size from 10
amino acid residues to the entire amino acid sequence minus one
amino acid. Accordingly, the term "portion", as used herein, with
regard to a nucleic refers to a fragment of that nucleic acid. This
fragment may range in size from 10 nucleotides to the entire
nucleic acid sequence minus one nucleotide.
[0079] In a preferred embodiment, the first step of the
computational processing is done by calculating two sets of
interactions for each rotamer at every position: the interaction of
the rotamer side chain with the template or backbone (the "singles"
energy), and the interaction of the rotamer side chain with all
other possible rotamers at every other position (the "doubles"
energy), whether that position is varied or floated. It should be
understood that the backbone in this case includes both the atoms
of the protein structure backbone, as well as the atoms of any
fixed residues, wherein the fixed residues are defined as a
particular conformation of an amino acid.
[0080] Thus, "singles" (rotamer/template) energies are calculated
for the interaction of every possible rotamer at every variable
residue position with the backbone, using some or all of the
scoring functions. Thus, for the hydrogen bonding scoring function,
every hydrogen bonding atom of the rotamer and every hydrogen
bonding atom of the backbone is evaluated, and the EHB is
calculated for each possible rotamer at every variable position.
Similarly, for the van der Waals scoring function, every atom of
the rotamer is compared to every atom of the template (generally
excluding the backbone atoms of its own residue), and the E.sub.Vdw
is calculated for each possible rotamer at every variable residue
position. In addition, generally no van der Waals energy is
calculated if the atoms are connected by three bonds or less. For
the atomic solvation scoring function, the surface of the rotamer
is measured against the surface of the template, and the E.sub.as
for each possible rotamer at every variable residue position is
calculated. The secondary structure propensity scoring function is
also considered as a singles energy, and thus the total singles
energy may contain an E.sub.ss term. As will be appreciated by
those in the art, many of these energy terms will be close to zero,
depending on the physical distance between the rotamer and the
template position; that is, the farther apart the two moieties, the
lower the energy.
[0081] For the calculation of "doubles" energy (rotamer/rotamer),
the interaction energy of each possible rotamer is compared with
every possible rotamer at all other variable residue positions.
Thus, "doubles" energies are calculated for the interaction of
every possible rotamer at every variable residue position with
every possible rotamer at every other variable residue position,
using some or all of the scoring functions. Thus, for the hydrogen
bonding scoring function, every hydrogen bonding atom of the first
rotamer and every hydrogen bonding atom of every possible second
rotamer is evaluated, and the E.sub.HB is calculated for each
possible rotamer pair for any two variable positions. Similarly,
for the van der Waals scoring function, every atom of the first
rotamer is compared to every atom of every possible second rotamer,
and the E.sub.vdW is calculated for each possible rotamer pair at
every two variable residue positions. For the atomic solvation
scoring function, the surface of the first rotamer is measured
against the surface of every possible second rotamer, and the
E.sub.as for each possible rotamer pair at every two variable
residue positions is calculated. The secondary structure propensity
scoring function need not be run as a "doubles" energy, as it is
considered as a component of the "singles" energy. As will be
appreciated by those in the art, many of these double energy terms
will be close to zero, depending on the physical distance between
the first rotamer and the second rotamer; that is, the farther
apart the two moieties, the lower the energy.
[0082] In addition, as will be appreciated by those in the art, a
variety of force fields that can be used in the PCA calculations
can be used, including, but not limited to, Dreiding I and Dreiding
II (Mayo et al, J. Phys. Chem. 948897 (1990)), AMBER (Weiner et
al., J. Amer. Chem. Soc. 106:765 (1984) and Weiner et al., J. Comp.
Chem. 106:230 (1986)), MM2 (Allinger J. Chem. Soc. 99:8127 (1977),
Liljefors et al., J. Com. Chem. 8:1051 (1987)); MMP2 (Sprague et
al., J. Comp. Chem. 8:581 (1987)); CHARMM (Brooks et al., J. Comp.
Chem. 106:187 (1983)); GROMOS; and MM3 (Allinger et al., J. Amer.
Chem. Soc. 111:8551 (1989)), OPLS-AA (Jorgensen, et al., J. Am.
Chem. Soc. (1996), v 118, pp 11225-11236; Jorgensen, W. L.; BOSS,
Version 4.1; Yale University: New Haven, Conn. (1999)); OPLS
(Jorgensen, et al., J. Am. Chem. Soc. (1988), v 110, pp 1657ff;
Jorgensen, et al., J Am. Chem. Soc. (1990), v 112, pp 4768ff);
UNRES (United Residue Forcefield; Liwo, et al., Protein Science
(1993), v 2, pp1697-1714; Liwo, et al., Protein Science (1993), v
2, pp1715-1731; Liwo, et al., J. Comp. Chem. (1997), v 18,
pp849-873; Liwo, et al., J. Comp. Chem. (1997), v 18, pp874-884;
Liwo, et al., J. Comp. Chem. (1998), v 19, pp259-276; Forcefield
for Protein Structure Prediction (Liwo, et al., Proc. Natl. Acad.
Sci. USA (1999), v 96, pp5482-5485); ECEPP/3 (Liwo et al., J
Protein Chem 1994 May;13(4):375-80); AMBER 1.1 force field (Weiner,
et al., J. Am. Chem. Soc. v106, pp765-784); AMBER 3.0 force field
(U. C. Singh et al., Proc. Natl. Acad. Sci. USA. 82:755-759);
CHARMM and CHARMM22 (Brooks, et al., J. Comp. Chem. v4, pp
187-217); cvff3.0 (Dauber-Osguthorpe, et al.,(1988) Proteins:
Structure, Function and Genetics, v4,pp31-47); cff91 (Maple, et
al., J. Comp. Chem. v15, 162-182); also, the DISCOVER (cvff and
cff91) and AMBER forcefields are used in the INSIGHT molecular
modeling package (Biosym/MSI, San Deigo Calif.) and HARMM is used
in the QUANTA molecular modeling package (Biosym/MSI, San Deigo
Calif.), all of which are expressly incorporated by reference.
[0083] Once the singles and doubles energies are calculated and
stored, the next step of the computational processing may occur. As
outlined in U.S. Ser. No. 09/127,926 and PCT US98/07254, preferred
embodiments utilize a Dead End Elimination (DEE) step, and
preferably a Monte Carlo step.
[0084] PDA, viewed broadly, has three components that may be varied
to alter the output (e.g. the primary library): the scoring
functions used in the process; the filtering technique, and the
sampling technique.
[0085] In a preferred embodiment, the scoring functions may be
altered. In a preferred embodiment, the scoring functions outlined
above may be biased or weighted in a variety of ways. For example,
a bias towards or away from a reference sequence or family of
sequences can be done; for example, a bias towards wild-type or
homolog residues may be used. Similarly, the entire protein or a
fragment of it may be biased; for example, the active site may be
biased towards wild-type residues, or domain residues towards a
particular desired physical property can be done. Furthermore, a
bias towards or against increased energy can be generated.
Additional scoring function biases include, but are not limited to
applying electrostatic potential gradients or hydrophobicity
gradients, adding a substrate or binding partner to the
calculation, or biasing towards a desired charge or
hydrophobicity.
[0086] In addition, in an alternative embodiment, there are a
variety of additional scoring functions that may be used.
Additional scoring functions include, but are not limited to
torsional potentials, or residue pair potentials, or residue
entropy potentials. Such additional scoring functions can be used
alone, or as functions for processing the library after it is
scored initially. For example, a variety of functions derived from
data on binding of peptides to MHC (Major Histocompatibility
Complex) can be used to rescore a library in order to eliminate
proteins containing sequences which can potentially bind to MHC,
i.e. potentially immunogenic sequences.
[0087] In a preferred embodiment, a variety of filtering techniques
can be done, including, but not limited to, DEE and its related
counterparts. Additional filtering techniques include, but are not
limited to branch-and-bound techniques for finding optimal
sequences (Gordon and Majo, Structure Fold. Des. 7:1089-98, 1999),
and exhaustive enumeration of sequences. It should be noted
however, that some techniques may also be done without any
filtering techniques; for example, sampling techniques can be used
to find good sequences, in the absence of filtering.
[0088] As will be appreciated by those in the art, once an
optimized sequence or set of sequences is generated, (or again,
these need not be optimized or ordered) a variety of sequence space
sampling methods can be done, either in addition to the preferred
Monte Carlo methods, or instead of a Monte Carlo search. That is,
once a sequence or set of sequences is generated, preferred methods
utilize sampling techniques to allow the generation of additional,
related sequences for testing.
[0089] These sampling methods can include the use of amino acid
substitutions, insertions or deletions, or recombinations of one or
more sequences. As outlined herein, a preferred embodiment utilizes
a Monte Carlo search, which is a series of biased, systematic, or
random jumps. However, there are other sampling techniques that can
be used, including Boltzman sampling, genetic algorithm techniques
and simulated annealing. In addition, for all the sampling
techniques, the kinds of jumps allowed can be altered (e.g. random
jumps to random residues, biased jumps (to or away from wild-type,
for example), jumps to biased residues (to or away from similar
residues, for example), etc.). Jumps where multiple residue
positions are coupled (two residues always change together, or
never change together), jumps where whole sets of residues change
to other sequences (e.g., recombination). Similarly, for all the
sampling techniques, the acceptance criteria of whether a sampling
jump is accepted can be altered, to allow broad searches at high
temperature and narrow searches close to local optima at low
temperatures. See Metropolis et al., J. Chem Phys v21, pp 1087,
1953, hereby expressly incorporated by reference.
[0090] In addition, it should be noted that the preferred methods
of the invention result in a rank ordered list of sequences; that
is, the sequences are ranked on the basis of some objective
criteria. However, as outlined herein, it is possible to create a
set of non-ordered sequences, for example by generating a
probability table directly (for example using SCMF analysis or
sequence alignment techniques) that lists sequences without ranking
them. The sampling techniques outlined herein can be used in either
situation.
[0091] In a preferred embodiment, Boltzman sampling is done. As
will be appreciated by those in the art, the temperature criteria
for Boltzman sampling can be altered to allow broad searches at
high temperature and narrow searches close to local optima at low
temperatures (see e.g., Metropolis et al., J. Chem. Phys. 21:1087,
1953).
[0092] In a preferred embodiment, the sampling technique utilizes
genetic algorithms, e.g., such as those described by Holland
(Adaptation in Natural and Artificial Systems, 1975, Ann Arbor, U.
Michigan Press). Genetic algorithm analysis generally takes
generated sequences and recombines them computationally, similar to
a nucleic acid recombination event, in a manner similar to "gene
shuffling". Thus the "jumps" of genetic algorithm analysis
generally are multiple position jumps. In addition, as outlined
below, correlated multiple jumps may also be done. Such jumps can
occur with different crossover positions and more than one
recombination at a time, and can involve recombination of two or
more sequences. Furthermore, deletions or insertions (random or
biased) can be done. In addition, as outlined below, genetic
algorithm analysis may also be used after the secondary library has
been generated.
[0093] In a preferred embodiment, the sampling technique utilizes
simulated annealing, e.g., such as described by Kirkpatrick et al.
(Science, 220:671-680, 1983). Simulated annealing alters the cutoff
for accepting good or bad jumps by altering the temperature. That
is, the stringency of the cutoff is altered by altering the
temperature. This allows broad searches at high temperature to new
areas of sequence space, altering with narrow searches at low
temperature to explore regions in detail.
[0094] In addition, as outlined below, these sampling methods can
be used to further process a secondary library to generate
additional secondary libraries (sometimes referred to herein as
tertiary libraries).
[0095] Thus, the primary library can be generated in a variety of
computational ways, including structure based methods such as PDA,
or sequence based methods, or combinations as outlined herein.
[0096] Accordingly, the computational processing results in a set
of sequences, that may be optimized protein sequences if some sort
of ranking or scoring functions are used. These optimized protein
sequences are generally, but not always, significantly different
from the wild-type sequence from which the backbone was taken. That
is, each optimized protein sequence preferably comprises at least
about 5-10% variant amino acids from the starting or wild-type
sequence, with at least about 15-20% changes being preferred and at
least about 30% changes being particularly preferred.
[0097] The cutoff for the primary library is then enforced,
resulting in a set of primary sequences forming the primary
library. As outlined above, this may be done in a variety of ways,
including an arbitrary cutoff, an energy limitation, or when a
certain number of residue positions have been varied. In general,
the size of the primary library will vary with the size of the
protein, the number of residues that are changing, the
computational methods used, the cutoff applied and the discretion
of the user. In general, it is preferable to have the primary
library be large enough to randomly sample a reasonable sequence
space to allow for robust secondary libraries. Thus, primary
libraries that range from about 50 to about 10.sup.13 are
preferred, with from about 1000 to about 10.sup.7 being
particularly preferred, and from about 1000 to about 100,000 being
especially preferred.
[0098] In a preferred embodiment when scoring is used, although
this is not required, the primary library comprises the globally
optimal sequence in its optimal conformation, i.e. the optimum
rotamer at each variable position. That is, computational
processing is run until the simulation program converges on a
single sequence which is the global optimum. In a preferred
embodiment, the primary library comprises at least two optimized
protein sequences. Thus for example, the computational processing
step may eliminate a number of disfavored combinations but be
stopped prior to convergence, providing a library of sequences of
which the global optimum is one. In addition, further computational
analysis, for example using a different method, may be run on the
library, to further eliminate sequences or rank them differently.
Alternatively, as is more fully described in U.S. Ser. Nos.
60/061,097, 60/043,464, 60/054,678, 09/127,926 and PCT US98/07254,
the global optimum may be reached, and then further computational
processing may occur, which generates additional optimized
sequences in the neighborhood of the global optimum.
[0099] In addition, in some embodiments, primary library sequences
that did not make the cutoff are included in the primary library.
This may be desirable in some situations to evaluate the primary
library generation method, to serve as controls or comparisons, or
to sample additional sequence space. For example, in a preferred
embodiment, the wild-type sequence is included.
[0100] It should also be noted that different ranking systems can
be used. For example, a list of naturally occurring sequences can
be used to calculate all possible recombinations of these
sequences, with an optional rank ordering step. Alternatively, once
a primary library is generated, one could rank order only those
recombinations that occur at cross-over points with at least a
threshold of identity over a given window. For example, 100%
identity over a window of 6 amino acids, or 80% identity over a
window of 10 amino acids. Alternatively, as for all the systems
outlined herein, the homology could be considered at the DNA level,
by computationally considering the translation for the amino acids
to their respective DNA codons. Different codon usages could be
considered. A preferred embodiment considers only recombinations
with crossover points that have DNA sequence identity sufficient
for DNA hybridization of the differing sequences.
[0101] As is further outlined below, It should also be noted that
combining different primary libraries may be done. For example,
positions in a protein that show a great deal of mutational
diversity in computational screening can be fixed as outlined below
and a different primary library regenerated. A rank ordered list of
the same length as the first would now show diversity in previously
rarely changing positions. The variants from the first primary
library can be combined with the variants from the second primary
library to provide a combined library at lower computational cost
than creating a very long rank ordered list. This approach can be
particularly useful to sample sequence diversity in both low energy
gap, readily changing surface positions and high energy gap, rarely
changing core positions. In addition, primary libraries can be
generated by combining one or more of the different calculations to
form one big primary library.
[0102] Thus, the present invention provides primary libraries
comprising a list of computationally derived sequences. In a
preferred embodiment, these sequences are in the form of a rank
ordered list. From this primary library, a secondary library is
generated. As outlined herein, there are a number of different ways
to generate a secondary library.
[0103] In a preferred embodiment, the primary library of the
scaffold protein is used to generate a secondary library. As will
be appreciated by those in the art, the secondary library can be
either a subset of the primary library, or contain new library
members, i.e. sequences that are not found in the primary library.
That is, in general, the variant positions and/or amino acid
residues in the variant positions can be recombined in any number
of ways to form a new library that exploits the sequence variations
found in the primary library. That is, having identified "hot
spots" or important variant positions and/or residues, these
positions can be recombined in novel ways to generate novel
sequences to form a secondary library. Thus, in a preferred
embodiment, the secondary library comprises at least one member
sequence that is not found in the primary library, and preferably a
plurality of such sequences.
[0104] In one embodiment, all or a portion of the primary library
serves as the secondary library. That is, a cutoff is applied to
the primary sequences and these sequences serve as the secondary
library, without further manipulation or recombination. The library
members can be made as outlined below, e.g. by direct synthesis or
by constructing the nucleic acids encoding the library members,
expressing them in a suitable host, optionally followed by
screening.
[0105] In a preferred embodiment, the secondary library is
generated by tabulating the amino acid positions that vary from a
reference sequence. The reference sequence can be arbitrarily
selected, or preferably is chosen either as the wild-type sequence
or the global optimum sequence, with the latter being preferred.
That is, each amino acid position that varies in the primary
library is tabulated. Of course, if the original computational
analysis fixed some positions, the variable positions of the
secondary library will comprise either just these original variable
positions or some subset of these original variable positions. That
is, assuming a protein of 100 amino acids, the original
computational screen can allow all 100 positions to be varied.
However, due to the cutoff in the primary library, only positions
may vary. Alternatively, assuming the same 100 amino acid protein,
the original computational screen could have varied only 25
positions, keeping the other 75 fixed; this could result in only 12
of the 25 being varied in the cutoff primary library. These primary
library positions can then be recombined to form a secondary
library, wherein all possible combinations of these variable
positions form the secondary library. It should be noted that the
non-variable positions are set to the reference sequence
positions.
[0106] The formation of the secondary library using this method may
be done in two general ways; either all variable positions are
allowed to be any amino acid, or subsets of amino acids are allowed
for each position.
[0107] In a preferred embodiment, all amino acid residues are
allowed at each variable position identified in the primary
library. That is, once the variable positions are identified, a
secondary library comprising every combination of every amino acid
at each variable position is made.
[0108] In a preferred embodiment, subsets of amino acids are
chosen. The subset at any position may be either chosen by the
user, or may be a collection of the amino acid residues generated
in the primary screen. That is, assuming core residue 25 is
variable and the primary screen gives 5 different possible amino
acids for this position, the user may chose the set of good core
residues outlined above (e.g. hydrophobic residues), or the user
may build the set by choosing the 5 different amino acids generated
in the primary screen. Alternatively, combinations of these
techniques may be used, wherein the set of identified residues is
manually expanded. For example, in some embodiments, fewer than the
number of amino acid residues is chosen; for example, only three of
the five may be chosen. Alternatively, the set is manually
expanded; for example, if the computation picks two different
hydrophobic residues, additional choices may be added. Similarly,
the set may be biased, for example either towards or away from the
wild-type sequence, or towards or away from known domains, etc.
[0109] In addition, this may be done by analyzing the primary
library to determine which amino acid positions in the scaffold
protein have a high mutational frequency, and which positions have
a low mutation frequency. The secondary library can be generated by
randomizing the amino acids at the positions that have high numbers
of mutations, while keeping constant the positions that do not have
mutations above a certain frequency. For example, if the position
has less than 20% and more preferably 10% mutations, it may be kept
constant as the reference sequence position.
[0110] In a preferred embodiment, the secondary library is
generated from a probability distribution table. As outlined
herein, there are a variety of methods of generating a probability
distribution table, including using PDA, sequence alignments,
forcefield calculations such as SCMF calculations, etc. In
addition, the probability distribution can be used to generate
information entropy scores for each position, as a measure of the
mutational frequency observed in the library.
[0111] In this embodiment, the frequency of each amino acid residue
at each variable position in the list is identified. Frequencies
can be thresholded, wherein any variant frequency lower than a
cutoff is set to zero. This cutoff is preferably 1%, 2%, 5%, 10% or
20%, with 10% being particularly preferred. These frequencies are
then built into the secondary library. That is, as above, these
variable positions are collected and all possible combinations are
generated, but the amino acid residues that "fill" the secondary
library are utilized on a frequency basis. Thus, in a non-frequency
based secondary library, a variable position that has 5 possible
residues will have 20% of the proteins comprising that variable
position with the first possible residue, 20% with the second, etc.
However, in a frequency based secondary library, a variable
position that has 5 possible residues with frequencies of 10%, 15%,
25%, 30% and 20%, respectively, will have 10% of the proteins
comprising that variable position with the first possible residue,
15% of the proteins with the second residue, 25% with the third,
etc. As will be appreciated by those in the art, the actual
frequency may depend on the method used to actually generate the
proteins; for example, exact frequencies may be possible when the
proteins are synthesized. However, when the frequency-based primer
system outlined below is used, the actual frequencies at each
position will vary, as outlined below.
[0112] As will be appreciated by those in the art and outlined
herein, probability distribution tables can be generated in a
variety of ways. In addition to the methods outlined herein,
self-consistent mean field (SCMF) methods can be used in the direct
generation of probability tables. SCMF is a deterministic
computational method that uses a mean field description of rotamer
interactions to calculate energies. A probability table generated
in this way can be used to create secondary libraries as described
herein. SCMF can be used in three ways: the frequencies of amino
acids and rotamers for each amino acid are listed at each position;
the probabilities are determined directly from SCMF (see Delarue et
la. Pac. Symp. Biocomput. 109-21 (1997), expressly incorporated by
reference). In addition, highly variable positions and non-variable
positions can be identified. Alternatively, another method is used
to determine what sequence is jumped to during a search of sequence
space; SCMF is used to obtain an accurate energy for that sequence;
this energy is then used to rank it and create a rank-ordered list
of sequences (similar to a Monte Carlo sequence list). A
probability table showing the frequencies of amino acids at each
position can then be calculated from this list (Koehl et al., J.
Mol. Biol. 239:249 (1994); Koehl et al., Nat. Struc. Biol. 2:163
(1995); Koehl et al., Curr. Opin. Struct. Biol. 6:222 (1996); Koehl
et al., J. Mol. Bio. 293:1183 (1999); Koehl et al., J. Mol. Biol.
293:1161 (1999); Lee J. Mol. Biol. 236:918 (1994); and Vasquez
Biopolymers 36:53-70 (1995); all of which are expressly
incorporated by reference. Similar methods include, but are not
limited to, OPLS-AA (Jorgensen, et al., J. Am. Chem. Soc. (1996), v
118, pp 11225-11236; Jorgensen, W. L.; BOSS, Version 4.1; Yale
University: New Haven, Conn. (1999)); OPLS (Jorgensen, et al., J.
Am. Chem. Soc. (1988), v 110, pp 1657ff; Jorgensen, et al., J Am.
Chem. Soc. (1990), v 112, pp 4768ff); UNRES (United Residue
Forcefield; Liwo, et al., Protein Science (1993), v 2, pp1697-1714;
Liwo, et al., Protein Science (1993), v 2, pp1715-1731; Liwo, et
al., J. Comp. Chem. (1997), v 18, pp849-873; Liwo, et al, J. Comp.
Chem. (1997), v 18, pp874-884; Liwo, et al., J. Comp. Chem. (1998),
v 19, pp259-276; Forcefield for Protein Structure Prediction (Liwo,
et al., Proc. Natl. Acad. Sci. USA (1999), v 96, pp5482-5485);
ECEPP/3 (Liwo et al., J Protein Chem 1994 May;13(4):375-80); AMBER
1.1 force field (Weiner, et al., J. Am. Chem. Soc. v 106,
pp765-784); AMBER 3.0 force field (U. C. Singh et al., Proc. Natl.
Acad. Sci. USA. 82:755-759); CHARMM and CHARMM22 (Brooks, et al.,
J. Comp. Chem. v4, pp 187-217); cvff3.0 (Dauber-Osguthorpe, et
al.,(1988) Proteins: Structure, Function and Genetics, v4,pp31-47);
cff91 (Maple, et al., J. Comp. Chem. v15, 162-182); also, the
DISCOVER (cvff and cff91) and AMBER forcefields are used in the
INSIGHT molecular modeling package (Biosym/MSI, San Deigo Calif.)
and HARMM is used in the QUANTA molecular modeling package
(Biosym/MSI, San Deigo Calif.).
[0113] In addition, as outlined herein, a preferred method of
generating a probability distribution table is through the use of
sequence alignment programs. In addition, the probability table can
be obtained by a combination of sequence alignments and
computational approaches. For example, one can add amino acids
found in the alignment of homologous sequences to the result of the
computation. Preferable one can add the wild type amino acid
identity to the probability table if it is not found in the
computation.
[0114] As will be appreciated, a secondary library created by
recombining variable positions and/or residues at the variable
position may not be in a rank-ordered list. In some embodiments,
the entire list may just be made and tested. Alternatively, in a
preferred embodiment, the secondary library is also in the form of
a rank ordered list. This may be done for several reasons,
including the size of the secondary library is still too big to
generate experimentally, or for predictive purposes. This may be
done in several ways. In one embodiment, the secondary library is
ranked using the scoring functions of PDA to rank the library
members. Alternatively, statistical methods could be used. For
example, the secondary library may be ranked by frequency score;
that is, proteins containing the most of high frequency residues
could be ranked higher, etc. This may be done by adding or
multiplying the frequency at each variable position to generate a
numerical score. Similarly, the secondary library different
positions could be weighted and then the proteins scored; for
example, those containing certain residues could be arbitrarily
ranked.
[0115] As outlined herein, secondary libraries can be generated in
two general ways. The first is computationally, as above, wherein
the primary library is further computationally manipulated, for
example by recombining the possible variant positions and/or amino
acid residues at each variant position or by recombining portions
of the sequences containing one or more variant position. It may be
ranked, as outlined above. This computationally-derived secondary
library can then be experimentally generated by synthesizing the
library members or nucleic acids encoding them, as is more fully
outlined below. Alternatively, the secondary library is made
experimentally; that is, nucleic acid recombination techniques are
used to experimentally generate the combinations. This can be done
in a variety of ways, as outlined below.
[0116] In a preferred embodiment, the different protein members of
the secondary library may be chemically synthesized. This is
particularly useful when the designed proteins are short,
preferably less than 150 amino acids in length, with less than 100
amino acids being preferred, and less than 50 amino acids being
particularly preferred, although as is known in the art, longer
proteins can be made chemically or enzymatically. See for example
Wilken et al, Curr. Opin. Biotechnol. 9:412-26 (1998), hereby
expressly incorporated by reference.
[0117] In a preferred embodiment, particularly for longer proteins
or proteins for which large samples are desired, the secondary
library sequences are used to create nucleic acids such as DNA
which encode the member sequences and which can then be cloned into
host cells, expressed and assayed, if desired. Thus, nucleic acids,
and particularly DNA, can be made which encodes each member protein
sequence. This is done using well known procedures. The choice of
codons, suitable expression vectors and suitable host cells will
vary depending on a number of factors, and can be easily optimized
as needed.
[0118] In a preferred embodiment, multiple PCR reactions with
pooled oligonucleotides is done, as is generally depicted in FIG.
1. In this embodiment, overlapping oligonucleotides are synthesized
which correspond to the full length gene. Again, these
oligonucleotides may represent all of the different amino acids at
each variant position or subsets.
[0119] In a preferred embodiment, these oligonucleotides are pooled
in equal proportions and multiple PCR reactions are performed to
create full length sequences containing the combinations of
mutations defined by the secondary library. In addition, this may
be done using error-prone PCR methods.
[0120] In a preferred embodiment, the different oligonucleotides
are added in relative amounts corresponding to the probability
distribution table. The multiple PCR reactions thus result in full
length sequences with the desired combinations of mutaions in the
desired proportions.
[0121] The total number of oligonucleotides needed is a function of
the number of positions being mutated and the number of mutations
being considered at these positions:
[0122] (number of oligos for constant positions) +M1+M2+M3+ . . .
Mn=(total number of oligos required), where Mn is the number of
mutations considered at position n in the sequence.
[0123] In a preferred embodiment, each overlapping oligonucleotide
comprises only one position to be varied; in alternate embodiments,
the variant positions are too close together to allow this and
multiple variants per oligonucleotide are used to allow complete
recombination of all the possibilities. That is, each oligo can
contain the codon for a single position being mutated, or for more
than one position being mutated. The multiple positions being
mutated must be close in sequence to prevent the oligo length from
being impractical. For multiple mutating positions on an
oligonucleotide, particular combinations of mutations can be
included or excluded in the library by including or excluding the
oligonucleotide encoding that combination. For example, as
discussed herein, there may be correlations between variable
regions; that is, when position X is a certain residue, position Y
must (or must not) be a particular residue. These sets of variable
positions are sometimes referred to herein as a "cluster". When the
clusters are comprised of residues close together, and thus can
reside on one oligonuclotide primer, the clusters can be set to the
"good" correlations, and eliminate the bad combinations that may
decrease the effectiveness of the library. However, if the residues
of the cluster are far apart in sequence, and thus will reside on
different oligonuclotides for synthesis, it may be desirable to
either set the residues to the "god" correlation, or eliminate them
as variable residues entirely. In an alternative embodiment,the
library may be generated in several steps, so that the cluster
mutations only appear together. This procedure, i.e., the procedure
of identifying mutation clusters and either placing them on the
same oligonucleotides or eliminating them from the library or
library generation in several steps preserving clusters, can
considerably enrich the experimental library with properly folded
protein. Identification of clusters can be carried out by a number
of wasy, e.g. by using known pattern recognition methods,
comparisons of frequencies of occurrence of mutations or by using
energy analysis of the sequences to be experimentally generated
(for example, if the energy of interaction is high, the positions
are correlated). these correlations may be positional correlations
(e.g. variable positions 1 and 2 always change together or never
change together) or sequence correlations (e.g. if there is a
residue A at position 1, there is always residue B at position 2).
See: Pattern discovery in Biomolecular Data: Tools, Techniques, and
Applications; edited by Jason T. L. Wang, Bruce A. Shapiro, Dennis
Shasha. New York: Oxford University, 1999; Andrews, Harry C.
Introduction to mathematical techniques in patter recognition; New
York, Wiley-Interscience [1972]; Applications of Pattern
Recognition; Editor, K. S. Fu. Boca Raton, Fla. CRC Press, 1982;
Genetic Algorithms for Pattern Recognition; edited by Sankar K.
Pal, Paul P. Wang. Boca Raton : CRC Press, c1996; Pandya, Abhijit
S., Pattern recognition with Neural networks in C++/Abhijit S.
Pandya, Robert B. Macy. Boca Raton, Fla.: CRC Press, 1996; Handbook
of pattern recognition and computer vision/edited by C. H. Chen, L.
F. Pau, P. S. P. Wang. 2.sup.nd ed. Signapore ; River Edge, N.J.:
World Scientific, c1999; Friedman, Introduction to Pattern
Recognition : Statistical, Structural, Neural, and Fuzzy Logic
Approaches; River Edge, N.J.: World Scientific, c1999, Series
title: Serien a machine perception and artificial intelligence;
vol. 32; all of which are expressly incorporated by reference. In
addition programs used to search for consensus motifs can be used
as well.
[0124] In addition, correlations and shuffling can be fixed or
optimized by altering the design of the oligonucleotides; that is,
by deciding where the oligonucleotides (primers) start and stop
(e.g. where the sequences are "cut"). The start and stop sites of
oligos can be set to maximize the number of clusters that appear in
single oligonucleotides, thereby enriching the library with higher
scoring sequences. Different oligonucleotides start and stop site
options can be computationally modeled and ranked according to
number of clusters that are represented on single oligos, or the
percentage of the resulting sequences consistent with the predicted
libarary of sequences.
[0125] The total number of oligonucleotides required increases when
multiple mutable positions are encoded by a single oligonucleotide.
The annealed regions are the ones that remain constant, i.e. have
the sequence of the reference sequence.
[0126] Oligonucleotides with insertions or deletions of codons can
be used to create a library expressing different length proteins.
In particular computational sequence screening for insertions or
deletions can result in secondary libraries defining different
length proteins, which can be expressed by a library of pooled
oligonucleotide of different lengths.
[0127] In a preferred embodiment, the secondary library is done by
shuffling the famil7 (e.g. a set of variants); that is, some set of
the top sequences (if a rank-ordered list is used) can be shuffled,
either with or without error-prone PCR. "Shuffling" in this context
means a recombination of related sequences, generally in a random
way. It can include "shuffling" as defined and exemplified in U.S.
Pat. Nos. 5,830,721; 5,811,238; 5,605,793; 5,837,458 and PCT US/1
9256, all of which are expressly incorporated by reference in their
entirety. This set of sequences can also be an artificial set; for
example, from a probability table (for example generated using
SCMF) or a Monte Carlo set. Similarly, the "family" can be the top
10 and the bottom 10 sequences, the top 100 sequence, etc. This may
also be done using error-prone PCR.
[0128] Thus, in a preferred embodiment, in silico shuffling is done
using the computational methods described therein. That is,
starting with either two libraries or two sequences, random
recombinations of the sequences can be generated and evaluated.
[0129] In a preferred embodiment, error-prone PCR is done to
generate the secondary library. See U.S. Pat. Nos. 5,605,793,
5,811,238, and 5,830,721, all of which are hereby incorporated by
reference. This can be done on the optimal sequence or on top
members of the library, or some other artificial set or family. In
this embodiment, the gene for the optimal sequence found in the
computational screen of the primary library can be synthesized.
Error prone PCR is then performed on the optimal sequence gene in
the presence of oligonucleotides that code for the mutations at the
variant positions of the secondary library (bias oligonucleotides).
The addition of the oligonucleotides will create a bias favoring
the incorporation of the mutations in the secondary library.
Alternatively, only oligonucleotides for certain mutations may be
used to bias the library.
[0130] In a preferred embodiment, gene shuffling with error prone
PCR can be performed on the gene for the optimal sequence, in the
presence of bias oligonucleotides, to create a DNA sequence library
that reflects the proportion of the mutations found in the
secondary library. The choice of the bias oligonucleotides can be
done in a variety of ways; they can chosen on the basis of their
frequency, i.e. oligonucleotides encoding high mutational frequency
positions can be used; alternatively, oligonucleotides containing
the most variable positions can be used, such that the diversity is
increased; if the secondary library is ranked, some number of top
scoring positions can be used to generate bias oligonucleotides;
random positions may be chosen; a few top scoring and a few low
scoring ones may be chosen; etc. What is important is to generate
new sequences based on preferred variable positions and
sequences.
[0131] In a preferred embodiment, PCR using a wild type gene or
other gene can be used, as is schematically depicted in FIG. 5. In
this embodiment, a starting gene is used; generally, although this
is not required, the gene is the wild type gene. In some cases it
may be the gene encoding the global optimized sequence, or any
other sequence of the list. In this embodiment, oligonucleotides
are used that correspond to the variant positions and contain the
different amino acids of the secondary library. PCR is done using
PCR primers at the termini, as is known in the art. This provides
two benefits; the first is that this generally requires fewer
oligonucleotides and can result in fewer errors. In addition, it
has experimental advantages in that if the wild type gene is used,
it need not be synthesized.
[0132] In a preferred embodiment, a variety of additional steps may
be done to one or more secondary libraries; for example, further
computational processing can occur, secondary libraries can be
recombined, or cutoffs from different secondary libraries can be
combined. In a preferred embodiment, a secondary library may be
computationally remanipulated to form an additional secondary
library (sometimes referred to herein as "tertiary libraries"). For
example, any of the secondary library sequences may be chosen for a
second round of PDA, by freezing or fixing some or all of the
changed positions in the first secondary library. Alternatively,
only changes seen in the last probability distribution table are
allowed. Alternatively, the stringency of the probability table may
be altered, either by increasing or decreasing the cutoff for
inclusion. Similarly, the secondary library may be recombined
experimentally after the first round; for example, the best
gene/genes from the first screen may be taken and gene assembly
redone (using techniques outlined below, multiple PCR, error prone
PCR, shuffling, etc.). Alternatively, the fragments from one or
more good gene(s) to change probabilities at some positions. This
biases the search to an area of sequence space found in the first
round of computational and experimental screening.
[0133] In a preferred embodiment, a tertiary libarary can be
generated from combining secondary libraries. For example, a
probability distribution table from a secondary library can be
generated and recombined, wither computationally or experimentally,
as outlined herein. A PDA secondary library may be combined with a
sequence alignment secondary library, and either recombined (again,
computationally or experimentally) or just the cutoffs from each
joined to make a new tertiary library. The top sequences from
several libraries can be recombined. Primary and secondary
libraries can similarly be combined. Sequences from the top of a
library can be combined with sequences from the bottom of the
library to more broadly sample sequence space, or only sequences
distant from the top of the library can be combined. Primary and/or
secondary libraries that analyzed different parts of a protein can
be combined to a tertiary library that treats the combined parts of
the protein. These combinations can be done to analyze large
proteins, especially large multidomain proteins or complete
protesomes.
[0134] In a preferred embodiment, a tertiary library can be
generated using correlations in the secondary library. That is, a
residue at a first variable position may be correlated to a residue
at second variable position (or correlated to residues at
additional positions as well). For example, two variable positions
may sterically or electrostatically interact, such that if the
first residue is X, the second residue must be Y. This may be
either a positive or negative correlation. This correlation, or
"cluster" of residues, may be both detected and used in a variety
of ways. (For the generation of correlations, see the earlier cited
art).
[0135] In addition, primary and secondary libraries can be combined
to form new libaries; these can be random combinations or the
libraries, combining the "top" sequences, or weighting the
combinations (positions or residues from the first library are
scored higher than those of the second library).
[0136] As outlined herein, any number of protein attributes may be
altered in these methods, including, but not limited to, enzyme
activity, stability, solubility, aggregation, binding affinity,
binding specificity, substrate specificity, structural integrity,
immunogenicity, toxicity, generate peptide and peptidomimmetic
libraries, create new antibody CDR's, generate new DNA, RNA
bindings, etc.
[0137] It should be noted that therapeutic proteins utilized in
these methods will preferentially have residues in the hydrophobic
cores screened, to prevent changes in the molecular surface of the
protein that might induce immunogenic responses. Therapeutic
proteins can also be designed in the region surrounding their
binding sites to their receptors. Such a region can be defined, for
example, by including in the design all residues within a certain
distance, for example 4.5 .ANG. of the binding site residues. This
range can vary from 4 to 6-10 .ANG.. This design will serve to
improve activity and specificity.
[0138] In addition, a step method can be done; see Zhao et al.,
Nature Biotech. 16:258 (1998), hereby incorporated by
reference.
[0139] In a preferred embodiment, the methods of the invention are
used not on known scaffold proteins, but on random peptides, to
search a virtual library for those sequences likely to adapt a
stable conformation. As discussed above, there is a current benefit
and focus on screening random peptide libraries to find novel
binding/modulators. However, the sequences in these experimental
libraries can be randomized at specific sites only, or throughout
the sequence. The number of sequences that can be searched in these
libraries grows expontentially with the number of positions that
are randomized. Generally, only up to 10.sup.12- 10.sup.15
sequences can be contained in a library because of the physical
constraints of laboratories (the size of the instruments, the cost
of producing large numbers of biopolymers, etc.). Other practical
considerations can often limit the size of the libraries to
10.sup.6 or fewer. These limits are reached for only 10 amino acid
positions. Therefore, only a sparse sampling of sequences is
possible in the search for improved proteins or peptides in
experimental sequence libraries, lowering the chance of success and
almost certainly missing desirable candidates. Because of the
randomness of the changes in these sequences, most of the
candidates in the library are not suitable, resulting in a waste of
most of the effort in producing the library.
[0140] However, using the automated protein design techniques
outlined herein, virtual libraries of protein sequences can be
generated that are vastly larger than experimental libraries. Up to
10.sup.75 candidate sequences (or more) can be screened
computationally and those that meet design criteria which favor
stable and functional proteins can be readily selected. An
experimental library consisting of the favorable candidates found
in the virtual library screening can then be generated, resulting
in a much more efficient use of the experimental library and
overcoming the limitations of random protein libraries. Thus, the
methods of the invention allow the virtual screening of a set of
random peptides for peptides likely to take on a particular
structure, and thus eliminating the large number of unpreferred or
unallowed conformations without having to make and test the
peptides.
[0141] In addition, it is possible to randomize regions or domains
of protein as well.
[0142] Thus, in a preferred embodiment, the invention provides
libraries of completely defined set of variant scaffold proteins,
wherein at least 85% of the possible members are in the library,
with at least about 90% and 95% being particularly preferred.
However, it is also possible that errors are introduced into the
libraries experimentally, and thus the libraries contain preferably
less than 25% non-defined (e.g. error) sequences; with less than
10%, less than 5% and less than 1% particularly preferred. Thus
libraries that have all members as well as some error members, or
some members as well as error members are included herein.
[0143] As mentioned above, two principle benefits come from the
virtual library screening: (1) the automated protein design
generates a list of sequence candidates that are favored to meet
design criteria; it also shows which positions in the sequence are
readily changed and which positions are unlikely to change without
disrupting protein stability and function. An experimental random
library can be generated that is only randomized at the readily
changeable, non-disruptive sequence positions. (2) The diversity of
amino acids at these positions can be limited to those that the
automated design shows are compatible with these positions. Thus,
by limiting the number of randomized positions and the number of
possibilities at these positions, the number of wasted sequences
produced in the experimental library is reduced, thereby increasing
the probability of success in finding sequences with useful
properties.
[0144] For example, the table below lists the 10 favored sequences
candidates from the virtual screening of 12 positions in a protein.
It shows that positions 9, 10 and 12 are most likely to have
changes that do not disrupt the function of the protein, suggesting
that a random experimental library that randomizes positions 9, 10
and 12 will have a higher fraction of desirable sequences. Also,
the virtual library suggests that position 10 is most compatible
with lie or Phe residues, further limiting the size of the library
and allowing a more complete screening of good sequences.
1 1 2 3 4 5 6 7 8 9 10 11 12 1 LEU LEU ILE ILE ALA LEU LEU LEU LEU
PHE ALA LEU 2 LEU LEU ILE ILE ALA LEU LEU LEU LEU ILE ALA LEU 3 LEU
LEU ILE ILE ALA LEU LEU LEU LEU ILE ALA LEU 4 LEU LEU ILE ILE ALA
LEU LEU LEU LEU PHE ALA ILE 5 LEU LEU ILE ILE ALA LEU LEU LEU LEU
PHE ALA ILE 6 LEU LEU ILE ILE ALA LEU LEU LEU LEU ILE ALA ILE 7 LEU
LEU ILE ILE ALA LEU LEU LEU ILE PHE ALA LEU 8 LEU LEU ILE ILE ALA
LEU LEU LEU LEU ILE ALA ILE 9 LEU LEU ILE ILE ALA LEU LEU LEU ILE
PHE ALA LEU 10 LEU LEU ILE ILE ALA LEU LEU LEU LEU LEU ALA LEU
[0145] The automated design method uses physical chemical criteria
to screen sequences, resulting in sequences that are likely to be
stable, structured, and that preserve function, if needed.
Different design criteria can be used to produce candidate sets
that are biased for properties such as charged, solubility, or
active site characteristics (polarity, size), or are biased to have
certain amino acids at certain positions. That is, The candidate
bioactive agents and candidate nucleic acids are randomized, either
fully randomized or they are biased in their randomization, e.g. in
nucleotide/residue frequency generally or per position. By
"randomized" or grammatical equivalents herein is meant that each
nucleic acid and peptide consists of essentially random nucleotides
and amino acids, respectively. Thus, any amino acid residue may be
incorporated at any position. The synthetic process can be designed
to generate randomized peptides and/or nucleic acids, to allow the
formation of all or most of the possible combinations over the
length of the nucleic acid, thus forming a library of randomized
candidate nucleic acids.
[0146] In one embodiment, the library is fully randomized, with no
sequence preferences or constants at any position. In a preferred
embodiment, the library is biased. That is, some positions within
the sequence are either held constant, or are selected from a
limited number of possibilities. For example, in a preferred
embodiment, the nucleotides or amino acid residues are randomized
within a defined class, for example, of hydrophobic amino acids,
hydrophilic residues, sterically biased (either small or large)
residues, towards the creation of cysteines, for cross-linking,
prolines for SH-3 domains, serines, threonines, tyrosines or
histidines for phosphorylation sites, etc., or to purines, etc.
[0147] In a preferred embodiment, the bias is towards peptides or
nucleic acids that interact with known classes of molecules. For
example, it is known that much of intracellular signaling is
carried out via short regions of polypeptides interacting with
other polypeptides through small peptide domains. For instance, a
short region from the HIV-1 envelope cytoplasmic domain has been
previously shown to block the action of cellular calmodulin.
Regions of the Fas cytoplasmic domain, which shows homology to the
mastoparan toxin from Wasps, can be limited to a short peptide
region with death-inducing apoptotic or G protein inducing
functions. Magainin, a natural peptide derived from Xenopus, can
have potent anti-tumour and anti-microbial activity. Short peptide
fragments of a protein kinase C isozyme (.beta.PKC), have been
shown to block nuclear translocation of .beta.PKC in Xenopus
oocytes following stimulation. And, short SH-3 target peptides have
been used as psuedosubstrates for specific binding to SH-3
proteins. This is of course a short list of available peptides with
biological activity, as the literature is dense in this area. Thus,
there is much precedent for the potential of small peptides to have
activity on intracellular signaling cascades. In addition, agonists
and antagonists of any number of molecules may be used as the basis
of biased randomization of candidate bioactive agents as well.
[0148] In general, the generation of a prescreened random peptide
libraries may be described as follows. Any structure, whether a
known structure, for example a portion of a known protein, a known
peptide, etc., or a synthetic structure, can be used as the
backbone for PDA. For example, structures from X-ray
crystallographic techniques, NMR techniques, de novo modelling,
homology modelling, etc. may all be used to pick a backbone for
which sequences are desired. Similarly, a number of molecules or
protein domains are suitable as starting points for the generation
of biased randomized candidate bioactive agents. A large number of
small molecule domains are known, that confer a common function,
structure or affinity. In addition, as is appreciated in the art,
areas of weak amino acid homology may have strong structural
homology. A number of these molecules, domains, and/or
corresponding consensus sequences, are known, including, but are
not limited to, SH-2 domains, SH-3 domains, Pleckstrin, death
domains, protease cleavage/recognition sites, enzyme inhibitors,
enzyme substrates, Traf, etc. Similarly, there are a number of
known nucleic acid binding proteins containing domains suitable for
use in the invention. For example, leucine zipper consensus
sequences are known.
[0149] Thus, in general, known peptide ligands can be used as the
starting backbone for the generation of the primary library.
[0150] In addition, structures known to take on certain
conformations may be used to create a backbone, and then sequences
screened for those that are likely to take on that conformation.
For example, there are a wide variety of "ministructures" known,
sometimes referred to as "presentation structures", that can confer
conformational stability or give a random sequence a
conformationally restricted form. Proteins interact with each other
largely through conformationally constrained domains. Although
small peptides with freely rotating amino and carboxyl termini can
have potent functions as is known in the art, the conversion of
such peptide structures into pharmacologic agents is difficult due
to the inability to predict side-chain positions for peptidomimetic
synthesis. Therefore the presentation of peptides in
conformationally constrained structures will benefit both the later
generation of pharmaceuticals and will also likely lead to higher
affinity interactions of the peptide with the target protein. This
fact has been recognized in the combinatorial library generation
systems using biologically generated short peptides in bacterial
phage systems. A number of workers have constructed small domain
molecules in which one might present randomized peptide
structures.
[0151] Thus, synthetic presentation structures, i.e. artificial
polypeptides, are capable of presenting a randomized peptide as a
conformationally-restricted domain. Preferred presentation
structures maximize accessibility to the peptide by presenting it
on an exterior loop. Accordingly, suitable presentation structures
include, but are not limited to, minibody structures, loops on
beta-sheet turns and coiled-coil stem structures in which residues
not critical to structure are randomized, zinc-finger domains,
cysteine-linked (disulfide) structures, transglutaminase linked
structures, cyclic peptides, B-loop structures, helical barrels or
bundles, leucine zipper motifs, etc.
[0152] In a preferred embodiment, the presentation structure is a
coiled-coil structure, allowing the presentation of the randomized
peptide on an exterior loop. See, for example, Myszka et al.,
Biochem. 33:2362-2373 (1994), hereby incorporated by reference, and
FIG. 3). Using this system investigators have isolated peptides
capable of high affinity interaction with the appropriate target.
In general, coiled-coil structures allow for between 6 to 20
randomized positions; (see Martin et al., EMBO J. 13(22):5303-5309
(1994), incorporated by reference).
[0153] In a preferred embodiment, the presentation structure is a
minibody structure. A "minibody" is essentially composed of a
minimal antibody complementarity region. The minibody presentation
structure generally provides two randomizing regions that in the
folded protein are presented along a single face of the tertiary
structure. See for example Bianchi et al., J. Mol. Biol.
236(2):649-59 (1994), and references cited therein, all of which
are incorporated by reference). Investigators have shown this
minimal domain is stable in solution and have used phage selection
systems in combinatorial libraries to select minibodies with
peptide regions exhibiting high affinity, Kd=10.sup.-7, for the
pro-inflammatory cytokine IL-6.
[0154] Once the backbone is chosen and the primary library of the
random peptides generated as outlined above, the secondary library
generation and creation proceeds as for the known scaffold protein,
including recombination of variant positions and/or amino acid
residues, either computationally or experimentally. Again,
libraries of DNA expressing the protein sequences defined by the
automated protein design methods can be produced. Codons can be
randomized at only the nucleotide sequence triplets that define the
residue positions specified by the automated design method. Also,
mixtures of base triplets that code for particular amino acids
could be introduced into the DNA synthesis reaction to attach a
full triplet defining an amino acid in one reaction step. Also, a
library of random DNA oligomers could be designed that biases the
desired positions toward certain amino acids, or that restricts
those positions to certain amino acids. The amino acids biased for
would be those specified in the virtual screening, or a subset of
those.
[0155] Multiple DNA libraries are synthesized that code for
different subsets of amino acids at certain positions, allowing
generation of the amino acid diversity desired without having to
fully randomize the codon and thereby waste sequences in the
library on stop codons, frameshifts, undesired amino acids, etc.
This can be done by creating a library that at each position to be
randomized is only randomized at one or two of the positions of the
triplet, where the position(s) left constant are those that the
amino acids to be considered at this position have in common.
Multiple DNA libraries would be created to insure that all amino
acids desired at each position exist in the aggregate library.
Alternatively, "shuffling", as is generally known in the art, can
be done with multiple libraries. In addition, in silico shuffling
can also be done.
[0156] Alternatively, the random peptide libraries may be done
using the frequency tabulation and experimental generation methods
including multiplexed PCR, shuffling, etc. There are a wide variety
of experimental techniques that can be used to experimentally
generate the libraries of the invention, including, but not limited
to, Rachitt-Enchira (http://www.enchira.com/gene_shuffling.htm);
error-prone PCR, for example using modified nucleotides; known
mutagenesis techniques including the use of multi-cassettes; DNA
shuffling (Crameri, et al., Nature 391(6664):288-291. (1998));
heterogeneous DNA samples (U.S. Pat No. 5,939,250); ITCHY
(Ostermeier, et al., Nat Biotechnol 17(12):1205-1209. (1999)); StEP
(Zhao, et al., Nat Biotechnol 16(3):258-261. (1998)), GSSM (U.S.
Pat. No. 6,171,820,U.S. Pat. No. 5,965,408); in vivo homologous
recombination, ligase assisted gene assembly, end-complementary
PCR, profusion (Roberts and Szostak, Proc Natl Acad Sci USA
94(23):12297-12302. (1997)); yeast/bacteria surface display (Lu, et
al., Biotechnology (NY) 13(4):366-372. (1995);Seed and Aruffo, Proc
Natl Acad Sci USA 84(10):3365-3369. (1987);Boder and Wittrup, Nat
Biotechnol 15(6):553-557. (1997)).
[0157] Using the nucleic acids of the present invention which
encode library members, a variety of expression vectors are made.
The expression vectors may be either self-replicating
extrachromosomal vectors or vectors which integrate into a host
genome. Generally, these expression vectors include transcriptional
and translational regulatory nucleic acid operably linked to the
nucleic acid encoding the library protein. The term "control
sequences" refers to DNA sequences necessary for the expression of
an operably linked coding sequence in a particular host organism.
The control sequences that are suitable for prokaryotes, for
example, include a promoter, optionally an operator sequence, and a
ribosome binding site. Eukaryotic cells are known to utilize
promoters, polyadenylation signals, and enhancers.
[0158] Nucleic acid is "operably linked" when it is placed into a
functional relationship with another nucleic acid sequence. For
example, DNA for a presequence or secretory leader is operably
linked to DNA for a polypeptide if it is expressed as a preprotein
that participates in the secretion of the polypeptide; a promoter
or enhancer is operably linked to a coding sequence if it affects
the transcription of the sequence; or a ribosome binding site is
operably linked to a coding sequence if it is positioned so as to
facilitate translation. Generally, "operably linked" means that the
DNA sequences being linked are contiguous, and, in the case of a
secretory leader, contiguous and in reading phase. However,
enhancers do not have to be contiguous. Linking is accomplished by
ligation at convenient restriction sites. If such sites do not
exist, the synthetic oligonucleotide adaptors or linkers are used
in accordance with conventional practice. The transcriptional and
translational regulatory nucleic acid will generally be appropriate
to the host cell used to express the library protein, as will be
appreciated by those in the art; for example, transcriptional and
translational regulatory nucleic acid sequences from Bacillus are
preferably used to express the library protein in Bacillus.
Numerous types of appropriate expression vectors, and suitable
regulatory sequences are known in the art for a variety of host
cells.
[0159] In general, the transcriptional and translational regulatory
sequences may include, but are not limited to, promoter sequences,
ribosomal binding sites, transcriptional start and stop sequences,
translational start and stop sequences, and enhancer or activator
sequences. In a preferred embodiment, the regulatory sequences
include a promoter and transcriptional start and stop
sequences.
[0160] Promoter sequences include constitutive and inducible
promoter sequences. The promoters may be either naturally occurring
promoters, hybrid or synthetic promoters. Hybrid promoters, which
combine elements of more than one promoter, are also known in the
art, and are useful in the present invention.
[0161] In addition, the expression vector may comprise additional
elements. For example, the expression vector may have two
replication systems, thus allowing it to be maintained in two
organisms, for example in mammalian or insect cells for expression
and in a prokaryotic host for cloning and amplification.
Furthermore, for integrating expression vectors, the expression
vector contains at least one sequence homologous to the host cell
genome, and preferably two homologous sequences which flank the
expression construct. The integrating vector may be directed to a
specific locus in the host cell by selecting the appropriate
homologous sequence for inclusion in the vector. Constructs for
integrating vectors and appropriate selection and screening
protocols are well known in the art and are described in e.g.,
Mansour et al., Cell, 51:503 (1988) and Murray, Gene Transfer and
Expression Protocols, Methods in Molecular Biology, Vol. 7
(Clifton: Humana Press, 1991).
[0162] In addition, in a preferred embodiment, the expression
vector contains a selection gene to allow the selection of
transformed host cells containing the expression vector, and
particularly in the case of mammalian cells, ensures the stability
of the vector, since cells which do not contain the vector will
generally die. Selection genes are well known in the art and will
vary with the host cell used. By "selection gene" herein is meant
any gene which encodes a gene product that confers resistance to a
selection agent. Suitable selection agents include, but are not
limited to, neomycin (or its analog G418), blasticidin S,
histinidol D, bleomycin, puromycin, hygromycin B, and other
drugs.
[0163] In a preferred embodiment, the expression vector contains a
RNA splicing sequence upstream or downstream of the gene to be
expressed in order to increase the level of gene expression. See
Barret et al., Nucleic Acids Res. 1991; Groos et al., Mol. Cell.
Biol. 1987; and Budiman et al., Mol. Cell. Biol. 1988.
[0164] A preferred expression vector system is a retroviral vector
system such as is generally described in Mann et al., Cell,
33:153-9 (1993); Pear et al., Proc. Natl. Acad. Sci. U.S.A.,
90(18):8392-6 (1993); Kitamura et al., Proc. Natl. Acad. Sci.
U.S.A., 92:9146-50 (1995); Kinsella et al., Human Gene Therapy,
7:1405-13; Hofmann et al.,Proc. Natl. Acad. Sci. U.S.A.,
93:5185-90; Choate et al., Human Gene Therapy, 7:2247 (1996);
PCT/US97101019 and PCT/U.S. Pat. No. 97/01048, and references cited
therein, all of which are hereby expressly incorporated by
reference.
[0165] The library proteins of the present invention are produced
by culturing a host cell transformed with nucleic acid, preferably
an expression vector, containing nucleic acid encoding an library
protein, under the appropriate conditions to induce or cause
expression of the library protein. The conditions appropriate for
library protein expression will vary with the choice of the
expression vector and the host cell, and will be easily ascertained
by one skilled in the art through routine experimentation. For
example, the use of constitutive promoters in the expression vector
will require optimizing the growth and proliferation of the host
cell, while the use of an inducible promoter requires the
appropriate growth conditions for induction. In addition, in some
embodiments, the timing of the harvest is important. For example,
the baculoviral systems used in insect cell expression are lytic
viruses, and thus harvest time selection can be crucial for product
yield.
[0166] As will be appreciated by those in the art, the type of
cells used in the present invention can vary widely. Basically, a
wide variety of appropriate host cells can be used, including
yeast, bacteria, archaebacteria, fungi, and insect and animal
cells, including mammalian cells. Of particular interest are
Drosophila melanogaster cells, Saccharomyces cerevisiae and other
yeasts, E. coli, Bacillus subtilis, SF9 cells, C129 cells, 293
cells, Neurospora, BHK, CHO, COS, and HeLa cells, fibroblasts,
Schwanoma cell lines, immortalized mammalian myeloid and lymphoid
cell lines, Jurkat cells, mast cells and other endocrine and
exocrine cells, and neuronal cells. See the ATCC cell line catalog,
hereby expressly incorporated by reference. In addition, the
expression of the secondary libraries in phage display systems,
such as are well known in the art, are particularly preferred,
especially when the secondary library comprises random peptides. In
one embodiment, the cells may be genetically engineered, that is,
contain exogeneous nucleic acid, for example, to contain target
molecules.
[0167] In a preferred embodiment, the library proteins are
expressed in mammalian cells. Any mammalian cells may be used, with
mouse, rat, primate and human cells being particularly preferred,
although as will be appreciated by those in the art, modifications
of the system by pseudotyping allows all eukaryotic cells to be
used, preferably higher eukaryotes. As is more fully described
below, a screen will be set up such that the cells exhibit a
selectable phenotype in the presence of a random library member. As
is more fully described below, cell types implicated in a wide
variety of disease conditions are particularly useful, so long as a
suitable screen may be designed to allow the selection of cells
that exhibit an altered phenotype as a consequence of the presence
of a library member within the cell.
[0168] Accordingly, suitable mammalian cell types include, but are
not limited to, tumor cells of all types (particularly melanoma,
myeloid leukemia, carcinomas of the lung, breast, ovaries, colon,
kidney, prostate, pancreas and testes), cardiomyocytes, endothelial
cells, epithelial cells, lymphocytes (T-cell and B cell) , mast
cells, eosinophils, vascular intimal cells, hepatocytes, leukocytes
including mononuclear leukocytes, stem cells such as haemopoetic,
neural, skin, lung, kidney, liver and myocyte stem cells (for use
in screening for differentiation and de-differentiation factors),
osteoclasts, chondrocytes and other connective tissue cells,
keratinocytes, melanocytes, liver cells, kidney cells, and
adipocytes. Suitable cells also include known research cells,
including, but not limited to, Jurkat T cells, NIH3T3 cells, CHO,
Cos, etc. See the ATCC cell line catalog, hereby expressly
incorporated by reference.
[0169] Mammalian expression systems are also known in the art, and
include retroviral systems. A mammalian promoter is any DNA
sequence capable of binding mammalian RNA polymerase and initiating
the downstream (3') transcription of a coding sequence for library
protein into mRNA. A promoter will have a transcription initiating
region, which is usually placed proximal to the 5' end of the
coding sequence, and a TATA box, using a located 25-30 base pairs
upstream of the transcription initiation site. The TATA box is
thought to direct RNA polymerase 11 to begin RNA synthesis at the
correct site. A mammalian promoter will also contain an upstream
promoter element (enhancer element), typically located within 100
to 200 base pairs upstream of the TATA box. An upstream promoter
element determines the rate at which transcription is initiated and
can act in either orientation. Of particular use as mammalian
promoters are the promoters from mammalian viral genes, since the
viral genes are often highly expressed and have a broad host range.
Examples include the SV40 early promoter, mouse mammary tumor virus
LTR promoter, adenovirus major late promoter, herpes simplex virus
promoter, and the CMV promoter.
[0170] Typically, transcription termination and polyadenylation
sequences recognized by mammalian cells are regulatory regions
located 3' to the translation stop codon and thus, together with
the promoter elements, flank the coding sequence. The 3' terminus
of the mature mRNA is formed by site-specific post-translational
cleavage and polyadenylation. Examples of transcription terminator
and polyadenlytion signals include those derived form SV40.
[0171] The methods of introducing exogenous nucleic acid into
mammalian hosts, as well as other hosts, is well known in the art,
and will vary with the host cell used. Techniques include
dextran-mediated transfection, calcium phosphate precipitation,
polybrene mediated transfection, protoplast fusion,
electroporation, viral infection, encapsulation of the
polynucleotide(s) in liposomes, and direct microinjection of the
DNA into nuclei.
[0172] In a preferred embodiment, library proteins are expressed in
bacterial systems. Bacterial expression systems are well known in
the art.
[0173] A suitable bacterial promoter is any nucleic acid sequence
capable of binding bacterial RNA polymerase and initiating the
downstream (3') transcription of the coding sequence of library
protein into mRNA. A bacterial promoter has a transcription
initiation region which is usually placed proximal to the 5' end of
the coding sequence. This transcription initiation region typically
includes an RNA polymerase binding site and a transcription
initiation site. Sequences encoding metabolic pathway enzymes
provide particularly useful promoter sequences. Examples include
promoter sequences derived from sugar metabolizing enzymes, such as
galactose, lactose and maltose, and sequences derived from
biosynthetic enzymes such as tryptophan. Promoters from
bacteriophage may also be used and are known in the art. In
addition, synthetic promoters and hybrid promoters are also useful;
for example, the tac promoter is a hybrid of the trp and lac
promoter sequences. Furthermore, a bacterial promoter can include
naturally occurring promoters of non-bacterial origin that have the
ability to bind bacterial RNA polymerase and initiate
transcription.
[0174] In addition to a functioning promoter sequence, an efficient
ribosome binding site is desirable. In E. coli, the ribosome
binding site is called the Shine-Delgarno (SD) sequence and
includes an initiation codon and a sequence 3-9 nucleotides in
length located 3- 11 nucleotides upstream of the initiation
codon.
[0175] The expression vector may also include a signal peptide
sequence that provides for secretion of the library protein in
bacteria. The signal sequence typically encodes a signal peptide
comprised of hydrophobic amino acids which direct the secretion of
the protein from the cell, as is well known in the art. The protein
is either secreted into the growth media (gram-positive bacteria)
or into the periplasmic space, located between the inner and outer
membrane of the cell (gram-negative bacteria).
[0176] The bacterial expression vector may also include a
selectable marker gene to allow for the selection of bacterial
strains that have been transformed. Suitable selection genes
include genes which render the bacteria resistant to drugs such as
ampicillin, chloramphenicol, erythromycin, kanamycin, neomycin and
tetracycline. Selectable markers also include biosynthetic genes,
such as those in the histidine, tryptophan and leucine biosynthetic
pathways.
[0177] These components are assembled into expression vectors.
Expression vectors for bacteria are well known in the art, and
include vectors for Bacillus subtilis, E. coli, Streptococcus
cremoris, and Streptococcus lividans, among others.
[0178] The bacterial expression vectors are transformed into
bacterial host cells using techniques well known in the art, such
as calcium chloride treatment, electroporation, and others.
[0179] In one embodiment, library proteins are produced in insect
cells. Expression vectors for the transformation of insect cells,
and in particular, baculovirus-based expression vectors, are well
known in the art and are described e.g., in O'Reilly et al.,
Baculovirus Expression Vectors: A Laboratory Manual (New York:
Oxford University Press, 1994).
[0180] In a preferred embodiment, library protein is produced in
yeast cells. Yeast expression systems are well known in the art,
and include expression vectors for Saccharomyces cerevisiae,
Candida albicans and C. maltosa, Hansenula polymorpha,
Kluyveromyces fragilis and K. lactis, Pichia guillerimondii and P.
pastoris, Schizosaccharomyces pombe, and Yarrowia lipolytica.
Preferred promoter sequences for expression in yeast include the
inducible GAL1, 10 promoter, the promoters from alcohol
dehydrogenase, enolase, glucokinase, glucose-6-phosphate isomerase,
glyceraldehyde-3-phosphate-dehydrogenase, hexokinase,
phosphofructokinase, 3-phosphoglycerate mutase, pyruvate kinase,
and the acid phosphatase gene. Yeast selectable markers include
ADE2, HIS4, LEU2, TRP1, and ALG7, which confers resistance to
tunicamycin; the neomycin phosphotransferase gene, which confers
resistance to G418; and the CUP1 gene, which allows yeast to grow
in the presence of copper ions.
[0181] The library protein may also be made as a fusion protein,
using techniques well known in the art. Thus, for example, for the
creation of monoclonal antibodies, if the desired epitope is small,
the library protein may be fused to a carrier protein to form an
immunogen. Alternatively, the library protein may be made as a
fusion protein to increase expression, or for other reasons. For
example, when the library protein is an library peptide, the
nucleic acid encoding the peptide may be linked to other nucleic
acid for expression purposes. Similarly, other fusion partners may
be used, such as targeting sequences which allow the localization
of the library members into a subcellular or extracellular
compartment of the cell, rescue sequences or purification tags
which allow the purification or isolation of either the library
protein or the nucleic acids encoding them; stability sequences,
which confer stability or protection from degradation to the
library protein or the nucleic acid encoding it, for example
resistance to proteolytic degradation, or combinations of these, as
well as linker sequences as needed.
[0182] Thus, suitable targeting sequences include, but are not
limited to, binding sequences capable of causing binding of the
expression product to a predetermined molecule or class of
molecules while retaining bioactivity of the expression product,
(for example by using enzyme inhibitor or substrate sequences to
target a class of relevant enzymes); sequences signalling selective
degradation, of itself or co-bound proteins; and signal sequences
capable of constitutively localizing the candidate expression
products to a predetermined cellular locale, including a)
subcellular locations such as the Golgi, endoplasmic reticulum,
nucleus, nucleoli, nuclear membrane, mitochondria, chloroplast,
secretory vesicles, lysosome, and cellular membrane; and b)
extracellular locations via a secretory signal. Particularly
preferred is localization to either subcellular locations or to the
outside of the cell via secretion.
[0183] In a preferred embodiment, the library member comprises a
rescue sequence. A rescue sequence is a sequence which may be used
to purify or isolate either the candidate agent or the nucleic acid
encoding it. Thus, for example, peptide rescue sequences include
purification sequences such as the His.sub.6 tag for use with Ni
affinity columns and epitope tags for detection,
immunoprecipitation or FACS (fluoroscence-activated cell sorting).
Suitable epitope tags include myc (for use with the commercially
available 9E10 antibody), the BSP biotinylation target sequence of
the bacterial enzyme BirA, flu tags, lacZ, and GST.
[0184] Alternatively, the rescue sequence may be a unique
oligonucleotide sequence which serves as a probe target site to
allow the quick and easy isolation of the retroviral construct, via
PCR, related techniques, or hybridization.
[0185] In a preferred embodiment, the fusion partner is a stability
sequence to confer stability to the library member or the nucleic
acid encoding it. Thus, for example, peptides may be stabilized by
the incorporation of glycines after the initiation methionine (MG
or MGG0), for protection of the peptide to ubiquitination as per
Varshavsky's N-End Rule, thus conferring long half-life in the
cytoplasm. Similarly, two prolines at the C-terminus impart
peptides that are largely resistant to carboxypeptidase action. The
presence of two glycines prior to the prolines impart both
flexibility and prevent structure initiating events in the
di-proline to be propagated into the candidate peptide structure.
Thus, preferred stability sequences are as follows:
MG(X).sub.nGGPP, where X is any amino acid and n is an integer of
at least four.
[0186] In one embodiment, the library nucleic acids, proteins and
antibodies of the invention are labeled. By "labeled" herein is
meant that nucleic acids, proteins and antibodies of the invention
have at least one element, isotope or chemical compound attached to
enable the detection of nucleic acids, proteins and antibodies of
the invention. In general, labels fall into three classes: a)
isotopic labels, which may be radioactive or heavy isotopes; b)
immune labels, which may be antibodies or antigens; and c) colored
or fluorescent dyes. The labels may be incorporated into the
compound at any position.
[0187] In a preferred embodiment, the library protein is purified
or isolated after expression. Library proteins may be isolated or
purified in a variety of ways known to those skilled in the art
depending on what other components are present in the sample.
Standard purification methods include electrophoretic, molecular,
immunological and chromatographic techniques, including ion
exchange, hydrophobic, affinity, and reverse-phase HPLC
chromatography, and chromatofocusing. For example, the library
protein may be purified using a standard anti-library antibody
column. Ultrafiltration and diafiltration techniques, in
conjunction with protein concentration, are also useful. For
general guidance in suitable purification techniques, see Scopes,
R., Protein Purification, Springer-Verlag, NY (1982). The degree of
purification necessary will vary depending on the use of the
library protein. In some instances no purification will be
necessary.
[0188] Once expressed and purified if necessary, the library
proteins and nucleic acids are useful in a number of
applications.
[0189] In general, the secondary libraries are screened for
biological activity. These screens will be based on the scaffold
protein chosen, as is known in the art. Thus, any number of protein
activities or attributes may be tested, including its binding to
its known binding members (for example, its substrates, if it is an
enzyme), activity profiles, stability profiles (pH, thermal, buffer
conditions), substrate specificity, immunogenicity, toxicity,
etc.
[0190] When random peptides are made, these may be used in a
variety of ways to screen for activity. In a preferred embodiment,
a first plurality of cells is screened. That is, the cells into
which the library member nucleic acids are introduced are screened
for an altered phenotype. Thus, in this embodiment, the effect of
the library member is seen in the same cells in which it is made;
i.e. an autocrine effect.
[0191] By a "plurality of cells" herein is meant roughly from about
10.sup.3 cells to 10.sup.8 or 10.sup.9, with from 10.sup.6 to
10.sup.8 being preferred. This plurality of cells comprises a
cellular library, wherein generally each cell within the library
contains a member of the secondary library, i.e. a different
library member, although as will be appreciated by those in the
art, some cells within the library may not contain one and and some
may contain more than one. When methods other than retroviral
infection are used to introduce the library members into a
plurality of cells, the distribution of library members within the
individual cell members of the cellular library may vary widely, as
it is generally difficult to control the number of nucleic acids
which enter a cell during electroporation, etc.
[0192] In a preferred embodiment, the library nucleic acids are
introduced into a first plurality of cells, and the effect of the
library members is screened in a second or third plurality of
cells, different from the first plurality of cells, i.e. generally
a different cell type. That is, the effect of the library member is
due to an extracellular effect on a second cell; i.e. an endocrine
or paracrine effect. This is done using standard techniques. The
first plurality of cells may be grown in or on one media, and the
media is allowed to touch a second plurality of cells, and the
effect measured. Alternatively, there may be direct contact between
the cells. Thus, "contacting" is functional contact, and includes
both direct and indirect. In this embodiment, the first plurality
of cells may or may not be screened.
[0193] If necessary, the cells are treated to conditions suitable
for the expression of the library members (for example, when
inducible promoters are used), to produce the library proteins.
[0194] Thus, in one embodiment, the methods of the present
invention comprise introducing a molecular library of library
members into a plurality of cells, a cellular library. The
plurality of cells is then screened, as is more fully outlined
below, for a cell exhibiting an altered phenotype. The altered
phenotype is due to the presence of a library member.
[0195] By "altered phenotype" or "changed physiology" or other
grammatical equivalents herein is meant that the phenotype of the
cell is altered in some way, preferably in some detectable and/or
measurable way. As will be appreciated in the art, a strength of
the present invention is the wide variety of cell types and
potential phenotypic changes which may be tested using the present
methods. Accordingly, any phenotypic change which may be observed,
detected, or measured may be the basis of the screening methods
herein. Suitable phenotypic changes include, but are not limited
to: gross physical changes such as changes in cell morphology, cell
growth, cell viability, adhesion to substrates or other cells, and
cellular density; changes in the expression of one or more RNAs,
proteins, lipids, hormones, cytokines, or other molecules; changes
in the equilibrium state (i.e. half-life) or one or more RNAs,
proteins, lipids, hormones, cytokines, or other molecules; changes
in the localization of one or more RNAs, proteins, lipids,
hormones, cytokines, or other molecules; changes in the bioactivity
or specific activity of one or more RNAs, proteins, lipids,
hormones, cytokines, receptors, or other molecules; changes in
phosphorylation; changes in the secretion of ions, cytokines,
hormones, growth factors, or other molecules; alterations in
cellular membrane potentials, polarization, integrity or transport;
changes in infectivity, susceptability, latency, adhesion, and
uptake of viruses and bacterial pathogens; etc. By "capable of
altering the phenotype" herein is meant that the library member can
change the phenotype of the cell in some detectable and/or
measurable way.
[0196] The altered phenotype may be detected in a wide variety of
ways, and will generally depend and correspond to the phenotype
that is being changed. Generally, the changed phenotype is detected
using, for example: microscopic analysis of cell morphology;
standard cell viability assays, including both increased cell death
and increased cell viability, for example, cells that are now
resistant to cell death via virus, bacteria, or bacterial or
synthetic toxins; standard labeling assays such as fluorometric
indicator assays for the presence or level of a particular cell or
molecule, including FACS or other dye staining techniques;
biochemical detection of the expression of target compounds after
killing the cells; etc. In some cases, as is more fully described
herein, the altered phenotype is detected in the cell in which the
randomized nucleic acid was introduced; in other embodiments, the
altered phenotype is detected in a second cell which is responding
to some molecular signal from the first cell.
[0197] In a preferred embodiment, the library member is isolated
from the positive cell. This may be done in a number of ways. In a
preferred embodiment, primers complementary to DNA regions common
to the constructs, or to specific components of the library such as
a rescue sequence, defined above, are used to "rescue" the unique
random sequence. Alternatively, the member is isolated using a
rescue sequence. Thus, for example, rescue sequences comprising
epitope tags or purification sequences may be used to pull out the
library member, using immunoprecipitation or affinity columns. In
some instances, this may also pull out things to which the library
member binds (for example the primary target molecule) if there is
a sufficiently strong binding interaction between the library
member and the target molecule. Alternatively, the peptide may be
detected using mass spectroscopy.
[0198] Once rescued, the sequence of the librarymember is
determined. This information can then be used in a number of
ways.
[0199] In a preferred embodiment, the member is resynthesized and
reintroduced into the target cells, to verify the effect. This may
be done using retroviruses, or alternatively using fusions to the
HIV-1 Tat protein, and analogs and related proteins, which allows
very high uptake into target cells. See for example, Fawell et
al;., PNAS USA 91:664 (1994); Frankel et al., Cell 55:1189 (1988);
Savion et al., J. Biol. Chem. 256:1149 (1981); Derossi et al., J.
Biol. Chem. 269:10444 (1994); and Baldin et al., EMBO J. 9:1511
(1990), all of which are incorporated by reference.
[0200] In a preferred embodiment, the sequence of the member is
used to generate more libraries, as outlined herein.
[0201] In a preferred embodiment, the library member is used to
identify target molecules, i.e. the molecules with which the member
interacts. As will be appreciated by those in the art, there may be
primary target molecules, to which the library member binds or acts
upon directly, and there may be secondary target molecules, which
are part of the signalling pathway affected by the library member;
these might be termed "validated targets".
[0202] The screening methods of the present invention may be useful
to screen a large number of cell types under a wide variety of
conditions. Generally, the host cells are cells that are involved
in disease states, and they are tested or screened under conditions
that normally result in undesirable consequences on the cells. When
a suitable library member is found, the undesirable effect may be
reduced or eliminated. Alternatively, normally desirable
consequences may be reduced or eliminated, with an eye towards
elucidating the cellular mechanisms associated with the disease
state or signalling pathway.
[0203] In a preferred embodiment, the library may be put onto a
chip or substrate as an array to make a "protein chip" or "biochip"
to be used in high-throughput screening (HTS) techniques. Thus, the
invention provides substrates with arrays comprising libraries
(generally secondary or tertiary libraries" of proteins.
[0204] By "substrate" or "solid support" or other grammatical
equivalents herein is meant any material that can be modified to
contain discrete individual sites appropriate for the attachment or
association of beads and is amenable to at least one detection
method. As will be appreciated by those in the art, the number of
possible substrates is very large. Possible substrates include, but
are not limited to, glass and modified or functionalized glass,
plastics (including acrylics, polystyrene and copolymers of styrene
and other materials, polypropylene, polyethylene, polybutylene,
polyurethanes, Teflon.RTM., etc.), polysaccharides, nylon or
nitrocellulose, resins, silica or silica-based materials including
silicon and modified silicon, carbon, metals, inorganic glasses,
plastics, optical fiber bundles, and a variety of other polymers.
In general, the substrates allow optical detection and do not
themselves appreciably fluorescese.
[0205] Generally the substrate is flat (planar), although as will
be appreciated by those in the art, other configurations of
substrates may be used as well; for example, three dimensional
configurations can be used. Similarly, the arrays may be placed on
the inside surface of a tube, for flow-through sample analysis to
minimize sample volume.
[0206] By "array" herein is meant a plurality of library members in
an array format; the size of the array will depend on the
composition and end use of the array. Arrays containing from about
2 different library members to many thousands can be made.
Generally, the array will comprise from 10.sup.2 to 10.sup.8
different proteins (all numbers are per square centimeter), with
from about 10.sup.3 to about 10.sup.6 being preferred and from
about 10.sup.3 to 10.sup.5 being particularly preferred. In
addition, in some arrays, multiple substrates may be used, either
of different or identical compositions. Thus for example, large
arrays may comprise a plurality of smaller substrates.
[0207] As will be appreciated by those in the art, the library
members may either be synthesized directly on the substrate, or
they may be made and then attached after synthesis. In a preferred
embodiment, linkers are used to attach the proteins to the
substrate, to allow both good attachment, sufficient flexibility to
allow good interaction with the target molecule, and to avoid
undesirable binding reactions.
[0208] In a preferred embodiment, the library members are
synthesized first, and tehn covalently or otherwise immobilized to
the substrate. This may be done in a variety of ways, including
known spotting techniques, ink jet techniques, etc.
[0209] In a preferred embodiment, the library may be put onto a
chip or substrate as an array to make a "protein chip" or "biochip"
to be used in high-throughput screening (HTS) techniques. Thus, the
invention provides substrates with arrays comprising libraries
(generally secondary or tertiary libraries" of proteins.
[0210] By "substrate" or "solid support" or other grammatical
equivalents herein is meant any material that can be modified to
contain discrete individual sites appropriate for the attachment or
association of beads and is amenable to at least one detection
method. As will be appreciated by those in the art, the number of
possible substrates is very large. Possible substrates include, but
are not limited to, glass and modified or functionalized glass,
plastics (including acrylics, polystyrene and copolymers of styrene
and other materials, polypropylene, polyethylene, polybutylene,
polyurethanes, Teflon.RTM., etc.), polysaccharides, nylon or
nitrocellulose, resins, silica or silica-based materials including
silicon and modified silicon, carbon, metals, inorganic glasses,
plastics, optical fiber bundles, and a variety of other polymers.
In general, the substrates allow optical detection and do not
themselves appreciably fluorescese.
[0211] Generally the substrate is flat (planar), although as will
be appreciated by those in the art, other configurations of
substrates may be used as well; for example, three dimensional
configurations can be used. Similarly, the arrays may be placed on
the inside surface of a tube, for flow-through sample analysis to
minimize sample volume.
[0212] By "array" herein is meant a plurality of library members in
an array format; the size of the array will depend on the
composition and end use of the array. Arrays containing from about
2 different library members to many thousands can be made.
Generally, the array will comprise from 10.sup.2 to 10.sup.3
different proteins (all numbers are per square centimeter), with
from about 10.sup.3 to about 10.sup.6 being preferred and from
about 10.sup.3 to 10.sup.5 being particularly preferred. In
addition, in some arrays, multiple substrates may be used, either
of different or identical compositions. Thus for example, large
arrays may comprise a plurality of smaller substrates.
[0213] As will be appreciated by those in the art, the library
members may either be synthesized directly on the substrate, or
they may be made and then attached after synthesis. In a preferred
embodiment, linkers are used to attach the proteins to the
substrate, to allow both good attachment, sufficient flexibility to
allow good interaction with the target molecule, and to avoid
undesirable binding reactions.
[0214] In a preferred embodiment, the library members are
synthesized first, and tehn covalently or otherwise immobilized to
the substrate. This may be done in a variety of ways, including
known spotting techniques, ink jet techniques, etc.
[0215] By "nucleic acid" or "oligonucleotide" or grammatical
equivalents herein means at least two nucleotides covalently linked
together. A nucleic acid of the present invention will generally
contain phosphodiester bonds, although in some cases, as outlined
below, nucleic acid analogs are included that may have alternate
backbones, comprising, for example, phosphoramide (Beaucage et al.,
Tetrahedron 49(10):1925 (1993) and references therein; Letsinger,
J. Org. Chem. 35:3800 (1970); Sprinzl et al., Eur. J. Biochem.
81:579 (1977); Letsinger et al., Nucl. Acids Res. 14:3487 (1986);
Sawai et al, Chem. Lett. 805 (1984), Letsinger et al., J. Am. Chem.
Soc. 110:4470 (1988); and Pauwels et al., Chemica Scripta 26:141
91986)), phosphorothioate (Mag et al., Nucleic Acids Res. 19:1437
(1991); and U.S. Pat. No. 5,644,048), phosphorodithioate (Briu et
al., J. Am. Chem. Soc. 111:2321 (1989), O-methylphophoroamidite
linkages (see Eckstein, Oligonucleotides and Analogues: A Practical
Approach, Oxford University Press), and peptide nucleic acid
backbones and linkages (see Egholm, J. Am. Chem. Soc. 114:1895
(1992); Meier et al., Chem. Int. Ed. Engl. 31:1008 (1992); Nielsen,
Nature, 365:566 (1993); Carlsson et al., Nature 380:207 (1996), all
of which are incorporated by reference). Other analog nucleic acids
include those with positive backbones (Denpcy et al., Proc. Natl.
Acad. Sci. USA 92:6097 (1995); non-ionic backbones (U.S. Pat. Nos.
5,386,023, 5,637,684, 5,602,240, 5,216,141 and 4,469,863;
Kiedrowshi et al., Angew. Chem. Intl. Ed. English 30:423 (1991);
Letsinger et al., J. Am. Chem. Soc. 110:4470 (1988); Letsinger et
al., Nucleoside & Nucleotide 13:1597 (1994); Chapters 2 and 3,
ASC Symposium Series 580, "Carbohydrate Modifications in Antisense
Research", Ed. Y.S. Sanghui and P. Dan Cook; Mesmaeker et al.,
Bioorganic & Medicinal Chem. Lett. 4:395 (1994); Jeffs et al.,
J. Biomolecular NMR 34:17 (1994); Tetrahedron Lett. 37:743 (1996))
and non-ribose backbones, including those described in U.S. Pat.
Nos. 5,235,033 and 5,034,506, and Chapters 6 and 7, ASC Symposium
Series 580, "Carbohydrate Modifications in Antisense Research", Ed.
Y. S. Sanghui and P. Dan Cook. Nucleic acids containing one or more
carbocyclic sugars are also included within the definition of
nucleic acids (see Jenkins et al., Chem. Soc. Rev. (1995)
pp169-176). Several nucleic acid analogs are described in Rawls, C
& E News Jun. 2, 1997 page 35. All of these references are
hereby expressly incorporated by reference. These modifications of
the ribose-phosphate backbone may be done to facilitate the
addition of ETMs, or to increase the stability and half-life of
such molecules in physiological environments.
[0216] As will be appreciated by those in the art, all of these
nucleic acid analogs may find use in the present invention. In
addition, mixtures of naturally occurring nucleic acids and analogs
can be made. Alternatively, mixtures of different nucleic acid
analogs, and mixtures of naturally occuring nucleic acids and
analogs may be made.
[0217] The nucleic acids may be single stranded or double stranded,
as specified, or contain portions of both double stranded or single
stranded sequence. The nucleic acid may be DNA, both genomic and
cDNA, RNA or a hybrid, where the nucleic acid contains any
combination of deoxyribo- and ribo-nucleotides, and any combination
of bases, including uracil, adenine, thymine, cytosine, guanine,
inosine, xathanine hypoxathanine, isocytosine, isoguanine, etc. A
preferred embodiment utilizes isocytosine and isoguanine in nucleic
acids designed to be complementary to other probes, rather than
target sequences, as this reduces non-specific hybridization, as is
generally described in U.S. Pat. No. 5,681,702. As used herein, the
term "nucleoside" includes nucleotides as well as nucleoside and
nucleotide analogs, and modified nucleosides such as amino modified
nucleosides. In addition, "nucleoside" includes non-naturally
occurring analog structures. Thus for example the individual units
of a peptide nucleic acid, each containing a base, are referred to
herein as a nucleoside.
[0218] As will be appreciated by those in the art, the
proteinaceous library members may be attached to the substrate in a
wide variety of ways. The functionalization of solid support
surfaces such as certain polymers with chemically reactive groups
such as thiols, amines, carboxyls, etc. is generally known in the
art. Accordingly, substrates may be used that have surface
chemistries that facilitate the attachment of the desired
functionality by the user. Some examples of these surface
chemistries include, but are not limited to, amino groups including
aliphatic and aromatic amines, carboxylic acids, aldehydes, amides,
chloromethyl groups, hydrazide, hydroxyl groups, sulfonates and
sulfates.
[0219] These functional groups can be used to add any number of
different libraries to the substrates, generally using known
chemistries. For example, libraries containing carbohydrates may be
attached to an amino-functionalized support; the aldehyde of the
carbohydrate is made using standard techniques, and then the
aldehyde is reacted with an-amino group on the surface. In an
alternative embodiment, a sulfhydryl linker may be used. There are
a number of sulfhydryl reactive linkers known n the art such as
SPDP, maleimides, a-haloacetyls, and pyridyl disulfides (see for
example the 1994 Pierce Chemical Company catalog, technical section
on cross-linkers, pages 155-200, incorporated herein by reference)
which can be used to attach cysteine containing members to the
support. Alternatively, an amino group on the library member may be
used for attachment to an amino group on the surface. For example,
a large number of stable bifunctional groups are well known in the
art, including homobifunctional and heterobifunctional linkers (see
Pierce Catalog and Handbook, pages 155-200). In an additional
embodiment, carboxyl groups (either from the surface or from the
protein) nay be derivatized using well known linkers (see the
Pierce catalog). For example, carbodiimides activate carboxyl
groups for attack by good nucleophiles such as amines (see
Torchilin et al., Critical (Rev. Therapeutic Drug Carrier Systems,
7(4):275-308 (1991), expressly incorporated herein). In addition,
library proteins may also be attached using other techniques known
in the art, for example for the attachment of antibodies to
polymers; see Slinkin et al., Bioconj. Chem. 2:342-348 (1991);
Torchilin et al., supra; Trubetskoy et al., Bioconj. Chem.
3:323-327 (1992); King et al., Cancer Res. 54:6176-6185 (1994); and
Wilbur et al., Bioconjugate Chem. 5:220-235 (1994), all of which
are hereby expressly incorporated by reference). Similarly, when
the library members are made recombinantly, the use of epitope tags
(FLAG, etc.) or His6 tags allow the attachment of the members to
the surface i.e. with antibody coated surfaces, metal (Ni)
surfaces, etc.). In addition, labeling the library members with
biotin or other binding partner pairs allows the use of avidin
coated surfaces, etc. It should be understood that the proteins may
be attached in a variety of ways, including those listed above.
What is important is that manner of attachment does not
significantly alter the functionality of the protein; that is, the
protein should be attached in such a flexible manner as to allow
its interaction with a target.
[0220] Once the biochips are made, they may be used in any number
of formats for a wide variety of purposes, as will be appreciated
by those in the art. For example, the scaffold protein serving as
the library starting point may be an enzyme; by putting libraries
of variants on a chip, the variants can be screened for increased
activity by adding substrates, or for inhibitors. Similarly,
variant libraries of ligand scaffolds can be screened for increased
or decreased binding affinity to the binding partner, for example a
cell surface receptor. Thus, in this embodiment, for example, the
extracellular portion of the receptor can be added to the array and
binding affinity tested under any number of conditions; for
example, binding and/or activity may be tested under different pH
conditions, different buffer, salt or reagent concentrations,
different temperatures, in the presence of competitive binders,
etc.
[0221] Thus, in a preferred embodiment, the methods comprise
differential screening to identity bioactive gents that are capable
of either binding to the variant proteins and/or modulating the
activity of the variant proteins. "Modulation" in this context
includes both an increase in activity (e.g. enzymatic activity or
binding affinity) and a decrease.
[0222] Another preferred embodiment utilizes differential screening
to identify drug candidates that bind to the native protein, but
cannot bind to modified proteins.
[0223] Positive controls and negative controls may be used in the
assays. Preferably all control and test samples are performed in at
least triplicate to obtain statistically significant results.
Incubation of all samples is for a time sufficient for the binding
of the agent to the protein. Following incubation, all samples are
washed free of non-specifically bound material and the amount of
bound, generally labeled agent determined.
[0224] A variety of other reagents may be included in the screening
assays. These include reagents like salts, neutral proteins, e.g.
albumin, detergents, etc which may be used to facilitate optimal
protein-protein binding and/or reduce non-specific or background
interactions. Also reagents that otherwise improve the efficiency
of the assay, such as protease inhibitors, nuclease inhibitors,
anti-microbial agents, etc., may be used. The mixture of components
may be added in any order that provides for the requisite
binding.
[0225] In a preferred embodiment, the activity of the variant
protein is increased; in another preferred embodiment, the activity
of the variant protein is decreased. Thus, bioactive agents that
are antagonists are preferred in some embodiments, and bioactive
agents that are agonists may be preferred in other embodiments.
[0226] Thus, in a preferred embodiment, the biochips comprising the
secondary or tertiary libraries are used to screen candidate agents
for binding to library members. By "candidate bioactive agent" or
"candidate drugs" or grammatical equivalents herein is meant any
molecule, e.g. proteins (which herein includes proteins,
polypeptides, and peptides), small organic or inorganic molecules,
polysaccharides, polynucleotides, etc. which are to be tested
against a particular target. Candidate agents encompass numerous
chemical classes. In a preferred embodiment, the candidate agents
are organic molecules, particularly small organic molecules,
comprising functional groups necessary for structural interaction
with proteins, particularly hydrogen bonding, and typically include
at least an amine, carbonyl, hydroxyl or carboxyl group, preferably
at least two of the functional chemical groups. The candidate
agents often comprise cyclical carbon or heterocyclic structures
and/or aromatic or polyaromatic structures substituted with one or
more chemical functional groups.
[0227] Candidate agents are obtained from a wide variety of
sources, as will be appreciated by those in the art, including
libraries of synthetic or natural compounds. As will be appreciated
by those in the art, the present invention provides a rapid and
easy method for screening any library of candidate agents,
including the wide variety of known combinatorial chemistry-type
libraries.
[0228] In a preferred embodiment, candidate agents are synthetic
compounds. Any number of techniques are available for the random
and directed synthesis of a wide variety of organic compounds and
biomolecules, including expression of randomized oligonucleotides.
See for example WO 94/24314, hereby expressly incorporated by
reference, which discusses methods for generating new compounds,
including random chemistry methods as well as enzymatic methods. As
described in WO 94/24314, one of the advantages of the present
method is that it is not necessary to characterize the candidate
bioactive agents prior to the assay; only candidate agents that
bind to the target need be identified. In addition, as is known in
the art, coding tags using split synthesis reactions may be done,
to essentially identify the chemical moieties on the beads.
[0229] Alternatively, a preferred embodiment utilizes libraries of
natural compounds in the form of bacterial, fungal, plant and
animal extracts that are available or readily produced, and can be
attached to beads as is generally known in the art.
[0230] Additionally, natural or synthetically produced libraries
and compounds are readily modified through conventional chemical,
physical and biochemical means. Known pharmacological agents may be
subjected to directed or random chemical modifications, including
enzymatic modifications, to produce structural analogs.
[0231] In a preferred embodiment, candidate bioactive agents
include proteins, nucleic acids, and chemical moieties.
[0232] In a preferred embodiment, the candidate bioactive agents
are proteins. In a preferred embodiment, the candidate bioactive
agents are naturally occurring proteins or fragments of naturally
occurring proteins. Thus, for example, cellular extracts containing
proteins, or random or directed digests of proteinaceous cellular
extracts, may be attached to beads as is more fully described
below. In this way libraries of procaryotic and eucaryotic proteins
may be made for screening against any number of targets.
Particularly preferred in this embodiment are libraries of
bacterial, fungal, viral, and mammalian proteins, with the latter
being preferred, and human proteins being especially preferred.
[0233] In a preferred embodiment, the candidate bioactive agents
are peptides of from about 2 to about 50 amino acids, with from
about 5 to about 30 amino acids being preferred, and from about 8
to about 20 being particularly preferred. The peptides may be
digests of naturally occurring proteins as is outlined above,
random peptides, or "biased" random peptides. By"randomized" or
grammatical equivalents herein is meant that each nucleic acid and
peptide consists of essentially random nucleotides and amino acids,
respectively. Since generally these random peptides (or nucleic
acids, discussed below) are chemically synthesized, they may
incorporate any nucleotide or amino acid at any position. The
synthetic process can be designed to generate randomized proteins
or nucleic acids, to allow the formation of all or most of the
possible combinations over the length of the sequence, thus forming
a library of randomized candidate bioactive proteinaceous agents.
In addition, the candidate agents may themselves be the product of
the invention; that is, a library of proteinaceous candidate agents
may be made using the methods of the invention.
[0234] The library should provide a sufficiently structurally
diverse population of randomized agents to effect a
probabilistically sufficient range of diversity to allow binding to
a particular target. Accordingly, an interaction library must be
large enough so that at least one of its members will have a
structure that gives it affinity for the target. Although it is
difficult to gauge the required absolute size of an interaction
library, nature provides a hint with the immune response: a
diversity of 10.sup.7-10.sup.8 different antibodies provides at
least one combination with sufficient affinity to interact with
most potential antigens faced by an organism. Published in vitro
selection techniques have also shown that a library size of
10.sup.7-10.sup.8 is sufficient to find structures with affinity
for the target. A library of all combinations of a peptide 7 to 20
amino acids in length, such as generally proposed herein, has the
potential to code for 20.sup.7 (10.sup.9) to 20.sup.20. Thus, with
libraries of 10.sup.7-10.sup.8 different molecules the present
methods allow a "working" subset of a theoretically complete
interaction library for 7 amino acids, and a subset of shapes for
the 20.sup.20 library. Thus, in a preferred embodiment, at least
10.sup.6, preferably at least 10.sup.7, more preferably at least
10.sup.8 and most preferably at least 10.sup.9 different sequences
are simultaneously analyzed in the subject methods. Preferred
methods maximize library size and diversity.
[0235] Thus, in a preferred embodiment, the invention provides
biochips comprising libraries of variant proteins, with the library
comprising at least about 100 different variants, with at least
about 500 different variants being preferred, about 1000 different
variants being particularly preferred and about 5000-10,000 being
especially preferred.
[0236] In one embodiment, the candidate library is fully
randomized, with no sequence preferences or constants at any
position In a preferred embodiment, the candidate library is
biased. That is, some positions within the sequence are either held
constant, or are selected from a limited number of possibilities.
For example, in a preferred embodiment, the nucleotides or amino
acid residues are randomized within a defined class, for example,
of hydrophobic amino acids, hydrophilic residues, sterically biased
(either small or large) residues, towards the creation of
cysteines, for cross-linking, prolines for SH-3 domains, serines,
threonines, tyrosines or histidines for phosphorylation sites,
etc., or to purines, etc.
[0237] In a preferred embodiment, the bias is towards peptides or
nucleic acids that interact with known classes of molecules. For
example, when the candidate bioactive agent is a peptide, it is
known that much of intracellular signaling is carried out via short
regions of polypeptides interacting with other polypeptides through
small peptide domains. For instance, a short region from the HIV-1
envelope cytoplasmic domain has been previously shown to block the
action of cellular calmodulin. Regions of the Fas cytoplasmic
domain, which shows homology to the mastoparan toxin from Wasps,
can be limited to a short peptide region with death-inducing
apoptotic or G protein inducing functions. Magainin, a natural
peptide derived from Xenopus, can have potent anti-tumour and
anti-microbial activity. Short peptide fragments of a protein
kinase C isozyme (.beta.PKC), have been shown to block nuclear
translocation of .beta.PKC in Xenopus oocytes following
stimulation. And, short SH-3 target peptides have been used as
psuedosubstrates for specific binding to SH-3 proteins. This is of
course a short list of available peptides with biological activity,
as the literature is dense in this area. Thus, there is much
precedent for the potential of small peptides to have activity on
intracellular signaling cascades. In addition, agonists and
antagonists of any number of molecules may be used as the basis of
biased randomization of candidate bioactive agents as well.
[0238] Thus, a number of molecules or protein domains are suitable
as starting points for the generation of biased randomized
candidate bioactive agents. A large number of small molecule
domains are known, that confer a common function, structure or
affinity. In addition, as is appreciated in the art, areas of weak
amino acid homology may have strong structural homology. A number
of these molecules, domains, and/or corresponding consensus
sequences, are known, including, but are not limited to, SH-2
domains, SH-3 domains, Pleckstrin, death domains, protease
cleavage/recognition sites, enzyme inhibitors, enzyme substrates,
Traf, etc. Similarly, there are a number of known nucleic acid
binding proteins containing domains suitable for use in the
invention. For example, leucine zipper consensus sequences are
known.
[0239] In a preferred embodiment, the candidate bioactive agents
are nucleic acids. By "nucleic acid" or "oligonucleotide" or
grammatical equivalents herein means at least two nucleotides
covalently linked together. A nucleic acid of the present invention
will generally contain phosphodiester bonds, although some cases,
as outlined below, nucleic acid analogs are included that may have
alternate backbones, comprising, for example, phosphoramide
(Beaucage et al., Tetrahedron 49(10):1925 1993) and references
therein; Letsinger, J. Org. Chem. 35:3800 (1970); Sprinzl et al.,
Eur. J. Biochem. 81:579 (1977); Letsinger et al., Nucl. Acids Res.
14:3487 (1986); Sawai et al, Chem. Lett. 805 (1984), Letsinger et
al., J. Am. Chem. Soc. 110:4470 (1988); and Pauwels et al., Chemica
Scripta 26:141 (1986)), phosphorothioate (Mag et al., Nucleic Acids
Res. 19:1437 (1991); and U.S. Pat. No. 5,644,048),
phosphorodithioate (Briu et al., J. Am. Chem. Soc. 111:2321 (1989),
O-methylphophoroamidite linkages (see Eckstein, Oligonucleotides
and Analogues: A Practical approach, Oxford University Press), and
peptide nucleic acid backbones and linkages (see Egholm, J. Am.
Chem. Soc. 114:1895 (1992); Meier et al., Chem. Int. Ed. Engl.
31:1008 (1992); Nielsen, Nature, 365:566 (1993); Carlsson et al.,
Nature 380:207 (1996), all of which are incorporated by reference).
Other analog nucleic acids include those with positive backbones
(Denpcy et al., Proc. Natl. Acad. Sci. USA 92:6097 (1995);
non-ionic backbones (U.S. Pat. Nos. 5,386,023, 5,637,684,
5,602,240, 5,216,141 and 4,469,863; Kiedrowshi et al., Angew. Chem.
Intl. Ed. English 30:423 (1991); Letsinger et al., J. Am. Chem.
Soc. 110:4470 (1988); Letsinger et al., Nucleoside & Nucleotide
13:1597 (1994); Chapters 2 and 3, ASC Symposium Series 580,
"Carbohydrate Modifications in Antisense Research", Ed. Y. S.
Sanghui and P. Dan Cook; Mesmaeker et al., Bioorganic &
Medicinal Chem. Lett. 4:395 (1994); Jeffs et al., J. Biomolecular
NMR 34:17 (1994); Tetrahedron Lett. 37:743 (1996)) and non-ribose
backbones, including those described in U.S. Pat. Nos. 5,235,033
and 5,034,506, and Chapters 6 and 7, ASC Symposium Series 580,
"Carbohydrate Modifications in Antisense Research", Ed. Y. S.
Sanghui and P. Dan Cook. Nucleic acids containing one or more
carbocyclic sugars are also included within the definition of
nucleic acids (see Jenkins et al., Chem. Soc. Rev. (1995) pp
169-176). Several nucleic acid analogs are described in Rawls, C
& E News Jun. 2, 1997 page 35. All of these references are
hereby expressly incorporated by reference. These modifications of
the ribose-phosphate backbone may be done to facilitate the
addition of additional moieties such as labels, or to increase the
stability and half-life of such molecules in physiological
environments.
[0240] As will be appreciated by those in the art, all of these
nucleic acid analogs may find use in the present invention. In
addition, mixtures of naturally occurring nucleic acids and analogs
can be made. Alternatively, mixtures of different nucleic acid
analogs, and mixtures of naturally occuring nucleic acids and
analogs may be made.
[0241] The nucleic acids may be single stranded or double stranded,
as specified, or contain portions of both double stranded or single
stranded sequence. The nucleic acid may be DNA, both genomic and
cDNA, RNA or a hybrid, where the nucleic acid contains any
combination of deoxyribo- and ribonucleotides, and any combination
of bases, including uracil, adenine, thymine, cytosine, guanine,
inosine, xathanine hypoxathanine, isocytosine, isoguanine, etc. As
used herein, the term "nucleoside" includes nucleotides and
nucleoside and nucleotide analogs, and modified nucleosides such as
amino modified nucleosides. In addition, "nucleoside" includes
non-naturally occuring analog structures. Thus for example the
individual units of a peptide nucleic acid, each containing a base,
are referred to herein as a nucleoside.
[0242] As described above generally for proteins, nucleic acid
candidate bioactive agents may be naturally occuring nucleic acids,
random nucleic acids, or "biased" random nucleic acids. For
example, digests of procaryotic or eucaryotic genomes may be used
as is outlined above for proteins. Where the ultimate expression
product is a nucleic acid, at least 10, preferably at least 12,
more preferably at least 15, most preferably at least 21 nucleotide
positions need to be randomized, with more preferable if the
randomization is less than perfect. Similarly, at least 5,
preferably at least 6, more preferably at least 7 amino acid
positions need to be randomized; again, more are preferable if the
randomization is less than perfect.
[0243] In a preferred embodiment, the candidate bioactive agents
are organic moieties. In this embodiment, as is generily described
in WO 94/24314, candidate agents are synthesized from a series of
substrates that can be chemically modified. "Chemically modified"
herein includes traditional chemical reactions as well as enzymatic
reactions. These substrates generally include, but are not limited
to, alkyl groups (including alkanes, alkenes, alkynes and
heteroalkyl), aryl groups (including arenes and heteroaryl),
alcohols, ethers, amines, aldehydes, ketones, acids, esters,
amides, cyclic compounds, aeterocyclic compounds (including
purines, pyrimidines, benzodiazepins, beta-lactams, tetracylines,
ephalosporins, and carbohydrates), steroids (including estrogens,
androgens, cortisone, ecodysone, atc.), alkaloids (including
ergots, vinca, curare, pyrollizdine, and mitomycines),
organometallic compounds, hetero-atom bearing compounds, amino
acids, and nucleosides. Chemical (including enzymatic) reactions
may be done on the moieties to form new substrates or candidate
agents which can then be tested using the present invention.
[0244] As will be appreciated by those in the art, it is possible
to screen more than one type of candidate agent at a time. Thus,
the library of candidate agents used in any particular assay may
include only one type of agent (i.e. peptides), or multiple types
(peptides and organic agents).
[0245] Thus, in a preferred embodiment, the invention provides
biochips comprising variant libraries of at east one scaffold
protein, and methods of screening utilizing the biochips. Thus, for
example, the invention provides completely defined libraries of
variant scaffold proteins having a defined set number, wherein at
least 85-90-95% of the possible members are present in the
library.
[0246] I addition, as will also be appreciated by those in the art,
the biochips of the invention may be part of HTS system utilizing
any number of components. Fully robotic or microfluidic systems
include automated liquid-, particle-, cell- and organism-handling
including high throughput pipetting to perform all steps of gene
targeting and recombination applications. This includes liquid,
particle, cell, and organism manipulations such as aspiration,
dispensing, mixing, diluting, washing, accurate volumetric
ransfers; retrieving, and discarding of pipes tips; and repetitive
pipetting of identical volumes for nultiple deliveries from a
single sample aspiration. These manipulations are
cross-contamination-free liquid, particle, cell, and organism
transfers. This instrument performs automated replication of
microplate samples to filters, membranes, and/or daughter plates,
high-density transfers, full-plate serial dilutions, and high
capacity operation.
[0247] The system used can include a computer workstation
comprising a microprocessor programmed to manipulate a device
selected from the group consisting of a thermocycler, a
multichannel pipettor, a sample handler, a plate handler, a gel
loading system, an automated transformation system, a gene
sequencer, a colony picker, a bead picker, a cell sorter, an
incubator, a light microscope, a fluorescence microscope, a
spectrofluorimeter, a spectrophotometer, a luminometer, a CCD
camera nd combinations thereof.
[0248] In a preferred embodiment, the methods of the invention are
used to generate variant libraries to facilitate and correlate
single nucleotide polymorphism (SNP) analysis. That is, by drawing
on known SNP data and determining the effect of the SNP on the
protein, information concerning SNP analysis can be determined.
Thus, for example, making a "sequence alignment" of sorts using
known SNPs can result in a probability distribution table that can
be used to design all possible SNP variants, which can then be put
on a biochip and tested for activity and effect.
[0249] The following examples serve to more fully describe the
manner of using the above-described invention, as well as to set
forth the best modes contemplated for carrying out various aspects
of the invention. It is understood that these examples in no way
serve to limit the true scope of this invention, but rather are
presented for illustrative purposes. All references cited herein
are incorporated by reference.
EXAMPLES
Example 1
Computational Prescreening on .beta.-lactamase TEM-1
[0250] Preliminary experiments were performed on the
.beta.-lactamase gene TEM-1. Brookhaven Protein Data Bank entry
1BTL was used as the starting structure. All water molecules and
the SO.sub.4.sup.2- group were removed and explicit hydrogens were
generated on the structure. The structure was then minimized for 50
steps without electrostatics using the conjugate gradient method
and the Dreiding II force field. These steps were performed using
the BIOGRAF program (Molecular Simulations, Inc., San Diego,
Calif.). This minimized structure served as the template for all
the protein design calculations.
Computational Pre-screening
[0251] Computational pre-screening of sequences was performed using
PDA. A 4 .ANG. sphere was drawn around the heavy side chain atoms
of the four catalytic residues (S70, K73, S130, and E166) and all
amino acids having heavy side chain atoms within this distance
cutoff were selected. This yielded the following 7 positions: F72,
Y105, N132, N136, L169, N170, and K234. Two of these residues, N132
and K234, are highly conserved across several different
.beta.-lactamases and were therefore not included in the design,
leaving five variable positions (F72, Y105, N136, L169, N170).
These designed positions were allowed to change their identity to
any of the 20 naturally occurring amino acids except proline,
cysteine, and glycine (a total of 17 amino acids). Proline is
usually not allowed since it is difficult to define appropriate
rotamers for proline, cysteine is excluded to prevent formation of
disulfide bonds, and glycine is excluded because of conformational
flexibility.
[0252] Additionally, a second set of residues within 5 .ANG. of the
residues selected for PDA design were floated (their amino acid
identity was retained as wild type, but their conformation was
allowed to change). The heavy side chain atoms were again used to
determine which residues were within the cutoff. This yielded the
following 28 positions: M68, M69, S70, T71, K73, V74, L76, V103,
E104, S106, P107, I127, M129, S130, A135, L139, L148, L162, R164,
W165, E166, P167, D179, M211, D214, V216, S235, I247. A248 was
included as a floated position instead of I247. The two prolines,
P107 and P167, were excluded from the floated residues, as were
positions M69, R164, and W165, since their crystal structures
exhibit highly strained rotamers, leaving 23 floated residues from
the second set. The conserved residues N132 and K234 from the first
sphere (4 .ANG.) were also floated, resulting in a total of floated
residues.
[0253] The potential functions and parameters used in the PDA
calculations were as follows. The van der Waals scale factor was
set to 0.9, and the electrostatic potential was calculated using a
distance attenuation and a dielectric constant of 40. The well
depth for the hydrogen bond potential was set to 8 kcal/mol with a
local and remote backbone scale factor of 0.25 and 1.0
respectively. The solvation potential was only calculated for
designed positions classified as core (F72, L169, M68, T71, V74,
L76, I127, A135, L139, L148, L162, M211 and A248). Type 2 solvation
was used (Street and Mayo, 1998). The non-polar exposure
multiplication factor was set to 1.6, the non-polar burial energy
was set to 0.048 kcal/mol/A.sup.2, and the polar hydrogen burial
energy was set to 2.0 kcal/mol.
[0254] Dead End Elimination (DEE) optimization method (see
reference) was used to find the lowest energy, ground state
sequence. DEE cutoffs of 50 and 100 kcal/mol were used for singles
and doubles energy calculations, respectively.
[0255] Stating from the DEE ground state sequence, a Monte Carlo
(MC) calculation was performed that generated a list of the 1000
lowest energy sequences. The MC parameters were 100 annealing
cycles with 1,000,000 steps per cycle. The non-productive cycle
limit was set to 50. In the annealing schedule, the high and low
temperatures were set to 5000 and 100 K respectively.
[0256] The following probability distribution was then calculated
from the top 1000 sequences in the MC list (see Table 3 below). It
shows the number of occurrences of each of the amino acids selected
for each position (the 5 variable positions and the 25 floated
positions).
2TABLE 3 Monte Carlo analysis (amino acids and their number of
occurrences (for the top 1000 sequences) Posi- tion Amino acid
occurrences 68 M: 1000 70 S: 1000 71 T: 1000 72 Y: 591 F: 365 V: 35
E: 8 L: 1 73 K: 1000 74 V: 1000 76 L: 1000 103 V: 1000 104 E: 1000
105 M: 183 Q: 142 I: 132 N: 129 E: 126 S: 115 D: 97 A: 76 106 S:
1000 127 I: 1000 129 M: 1000 130 S: 1000 132 N: 1000 135 A: 1000
136 O: 530 M: 135 N: 97 V: 68 E: 66 S: 38 T: 33 A: 27 Q: 6 139 L:
1000 148 L: 1000 162 L: 1000 166 E: 1000 169 L: 689 E: 156 M: 64 S:
37 D: 23 A: 21 Q: 10 170 M: 249 L: 118 E: 113 D: 112 T: 90 Q: 87 S:
66 R: 44 A: 35 N: 24 F: 21 K: 15 Y: 9 H: 9 V: 8 179 D: 1000 211 M:
1000 214 D: 1000 216 V: 1000 234 K: 1000 235 S: 1000 248 A:
1000
[0257] This probability distribution was then transformed into a
rounded probability distribution (see Table 4). A 10% cutoff value
was used to round at the designed positions and the wild type amino
acids were forced to occur with a probability of at least 10%. An E
was found at position 169 15.6% of the time. However, since this
position is adjacent to another designed position, 170, its
closeness would have required a more complicated oligonucleotide
library design; E was therefore not included for this position when
generating the sequence library (only L was used).
3TABLE 4 PDA probability distribution for the designed positions of
.beta.-lactamase (rounded to the nearest 10%). 72 105 136 169 170 Y
50% M 20% D 70% L 100% M 30% F 50% Q 20% M 20% L 20% I 20% N 10% E
20% N 10% O 20% E 10% N 10% S 10% Y 10%
[0258] As seen from Table 4, the computational pre-screening
resulted in an enormous reduction in the size of the problem.
Originally, 17 different amino acids were allowed at each of the 5
designed positions, giving 17.sup.5=1,419,857 possible sequences.
This was pared down to just 2*7*3*1*5=210 possible sequences--a
reduction of nearly four orders of magnitude.
Generation of Sequence Library
[0259] Overlapping oligonucleotides corresponding to the full
length TEM-1 gene for .beta.-lacatamase and all desired mutations
were synthesized and used in a PCR reaction as described previously
(FIG. 1), resulting in a sequence library containing the 210
sequences described above.
Synthesis of Mutant TEM-1 Genes
[0260] To allow the mutation of the TEM-1 gene, pCR2.1 (Invitrogen)
was digested with Xbal and EcoRI, blunt ended with T4 DNA
polymerase, and religated. This removes the HindIII and XhoI sites
within the polylinker. A new XhoI site was then introduced into the
TEM-1 gene at position 2269 (numbering as of the original pCR2.1)
using a Quickchange Site-Directed Mutagenesis Kit as described by
the manufacturer (Stratagene). Similarly, a new HindIII site was
introduced at position 2674 to give pCR-Xen1.
[0261] To construct the mutated TEM-1 genes, overlapping 40 mer
oligonucleotides were synthesized corresponding to the sequence
between the newly introduced Xho1 and HindIII sites, designed to
allow a 20 nucleotide overlap with adjacent oligonucleotides. At
each of the designed positions (72,105, 136 and 170) multiple
oligonucleotides were synthesized, each containing a different
mutation so that all the possible combinations of mutant sequences
(210) could be made in the desired proportions as shown in Table 4.
For example, at position 72, two sets of oligonucleotides were
synthesized, one containing an F at position 72, the other
containing a Y. Each oligonucleotide was resuspended at a
concentration of 1 .mu.g/.mu.l, and equal molar concentrations of
the oligonucleotides were pooled.
[0262] At the redundant positions, each oligonucleotide was added
at a concentration that reflected the probabilities in Table 4. For
example, at position 72 equal amounts of the two oligonucleotides
were added to the pool, while at position 136, twice as much
M-containing oligonucleotide was added compared to the N-containing
oligonucleotide, and seven times as much D-containing
oligonucleotide was added compared to the N-containing
oligonucleotide.
DNA Library Assembly
[0263] For the first round of PCR, 2 .mu.l of pooled
oligonucleotides at the desired probabilities (Table 4) were added
to a 100 .mu.l reaction that contained 2 .mu.l 10 mM dNTPs, 10
.mu.l 10x Taq buffer (Qiagen), 1 .mu.l of Taq DNA polymerase (5
units/.mu.l: Qiagen) and 2 .mu.l Pfu DNA polymerase (2.5
units/.mu.l: Promega). The reaction mixture was assembled on ice
and subjected to 94.degree. C. for 5 minutes, 15 cycles of
94.degree. C. for 30 second, 52.degree. C. for 30 seconds and
72.degree. C. for 30 seconds, and a final extension step of
72.degree. C. for 10 minutes.
Isolation of Full Length Oligonucleotides
[0264] For the second round of PCR, 2.5 .mu.l of the first round
reaction was added to a 100 .mu.l reaction containing 2 .mu.l 10 mM
dNTPs, 10 .mu.l of 10.times.Pfu DNA polymerase buffer (Promega), 2
.mu.l Pfu DNA polymerase (2.5 2:5 units/.mu.l: Promega), and 1
.mu.g of oligonucleotides corresponding to the 5' and 3' ends of
the synthesized gene. The reaction mixture was assembled on ice and
subjected to 94.degree. C. for 5 minutes, 20 cycles of 94.degree.
C. for 30 seconds, 52.degree. C. for 30 seconds and 72.degree. C.
for 30 seconds, and a final extension step of 72.degree. C. for 10
minutes to isolate the full length oligonucleotides.
Purification of DNA Library
[0265] The PCR products were purified using a QlAquick PCR
Purification Kit (Qiagen), digested with Xho1 and HindIII,
electrophoresed through a 1.2% agarose gel and re-purified using a
QlAquick Gel Extraction Kit (Qiagen).
Verification of Sequence Library Identity
[0266] The PCR products containing the library of mutant TEM-1
.beta.-lactamase genes were then cloned between a promoter and
terminator in a kanamycin resistant plasmid and transformed into E.
coli. An equal number of bacteria were then spread onto media
containing either kanamycin or ampicillin. All transformed colonies
will be resistant to kanamycin, but only those with active mutated
.beta.-lactamase genes will grow on ampicillin.
[0267] After overnight incubation, several colonies were observed
on both plates, indicating that at least one of the above sequences
encodes an active .beta.-lactamase. The number of colonies on the
kanamycin plate far outnumbered those on the ampicillin plate
(roughly a 5:1 ratio) suggesting that either some of the sequences
destroy activity, or that the PCR introduces errors that yield an
inactive or truncated enzyme.
[0268] To distinguish between these possibilities, 60 colonies were
picked from the kanamycin plate and their plasmid DNA was
sequenced. This gave the distribution shown in Table 5.
4TABLE 5 Percentages predicted by PDA vs. those observed from
experiment for the designed positions. Wild Type PDA Residues
(Predicted Percentage/Observed Percentage) 72F Y 50/50 F 50/50 105Y
M 20/27 Q 20/18 I 20/21 N 10/7 E 10/7 S 10/10 Y 10/10 136N D 70/72
M 20/17 N 10/11 170N M 30/34 L 20/21 E 20/21 D 20/17 N 10/7
[0269] Note that the observed percentages of each amino acid at all
four positions closely match the predicted percentages. Sequencing
also revealed that only one of the 60 colonies contained a PCR
error, a G to C transition.
[0270] This small test demonstrates that multiple PCR with pooled
oligonucleotides can be used to construct a sequence library that
reflects the desired proportions of amino acid changes.
Experimental Screening of Sequence Library
[0271] The purified PCR product containing the library of mutated
sequences was then ligated into pCR-Xen1 that had previously been
digested with Xho1 and HindIII and purified. The ligation reaction
was transformed into competent TOP10 E. coli cells (Invitrogen).
After allowing the cells to recover for 1 hour at 37.degree. C.,
the cells were spread onto LB plates containing the antibiotic
cefotaxime at concentrations ranging from 0.1 .mu.g/ml to 50
.mu.g/ml and selected for increasing resistance.
[0272] A triple mutant was found that improved enzyme function by35
fold in only a single round of screening (see FIG. 4). This mutant
(Y105Q, N136D, N170L) survived at 50 .mu.g/ml cefotaxime.
Example 2
Secondary Library Generation of a Xylanase
PDA Pre-screening Leads to Enormous Reduction in Number of Possible
Sequences
[0273] To demonstrate that computational pre-screening is feasible
and will lead to a significant reduction in the number of sequences
that have to be experimentally screened, initial calculations for
the B. circulans xylanase with and without the substrate were
performed. The PDB structure 1XNB of B. circulans xylanase and 1BCX
for the enzyme substrate complex were used. 27 residues inside the
binding site were visually identified as belonging to the active
site. 8 of these residues were regarded as absolutely essential for
the enzymatic activity. These positions were treated as wild type
residues, which means that their conformation was allowed to change
but not their amino acid identity (see FIG. 2).
[0274] Three of the 20 naturally occurring amino acids were not
considered (cysteine, proline, and glycine). Therefore, 17
different amino acids were still possible at the remaining 19
positions; the problem yields 17.sup.19=2.4.times.10.sup.23
different amino acid sequences. This number is 10 orders of
magnitude larger than what can be handled by state of the art
directed evolution methods. Clearly these approaches cannot be used
to screen the complete dimensionality of the problem and consider
all sequences with multiple substitutions.
[0275] Therefore PDA calculations were performed to reduce the
search space. A list of the 10,000 lowest energy sequences was
created and the probability for each amino acid at each position
was determined (see Table 1).
5TABLE 1 Probability of amino acids at the designed positions
resulting from the PDA calculation of the wild type (WT) enzyme
structure. Only amino acids with a probability greater than 1 % are
shown. WT PDA Probability Distribution 5 Y W 37.2% F 25.8% Y 22.9%
H 14.0% 7 Q E 69.1% L 30.2% 11 D I 41.2% D 10.7% V 10.1% M 7.9% L
6.4% E 5.3% T 4.2% Q 3.8% Y 2.6% F 2.1% N 1.9% S 1.9% A 1.1% 37 V D
29.9% M 29.4% V 21.4% S 12.8% I 4.1% E 1.0% 39 G A 99.8% 63 N W
91.2% Q 6.7% A 1.4% 65 Y E 91.7% L 4.9% M 3.4% 67 T E 81.0% D 12.3%
L 3.9% A 1.7% 71 W V 37.8% F 25.5% W 8.5% M 6.0% D 5.8% E 4.3% I
1.0% 80 Y M 32.4% L 31.5% F 19.0% I 5.9% Y 5.7% E 3.7% 82 V V 88.6%
D 11.0% 88 Y N 91.1% K 6.6% W 1.3% 110 T D 99.9% 115 A A 35.6% Y
27.8% T 14.4% D 10.2% S 9.2% F 2.6% 118 E E 92.2% D 2.6% I 2.0% A
1.7% 125 F F 79.4% Y 11.8% M 7.3% L 1.5% 129 W E 91.3% S 8.6% 168 V
D 98.1% A 1.0% 170 A A 78.7% S 17.6% D 3.7%
[0276] If we consider all the amino acids obtained from the PDA
calculation, including those with probabilities less than 1%, we
obtain 4.1.times.10.sup.15 different amino acid sequences. This is
a reduction by 7 orders of magnitude. If one only considers those
amino acids that have at least a probability of more than 1% as
shown in Table 1 (1% criterion), the problem is decreased to
3.3.times.10.sup.9 sequences. If one neglects all amino acids with
a probability of less than 5% (5% criterion) there are only
4.0.times.10.sup.6 sequences left. This is a number that can be
easily handled by screening and gene shuffling techniques.
Increasing the list of low energy sequences to 100,000 does not
change these numbers significantly and the effect on the amino
acids obtained at each position is negligible. Changes occur only
among the amino acids with a probability of less than 1%.
[0277] Including the substrate in the PDA calculation further
reduced the number of amino acids found at each position. If we
consider those amino acids with a probability higher than 5%, we
obtain 2.4.times.10.sup.6 sequences (see Table 2).
6TABLE 2 Probability of amino acids at the designed positions
resulting from the PDA calculation of the enzyme substrate complex.
Only those amino acids with a probability greater than 1% are
shown. WT PDA Probability Distribution 5 Y Y 69.2% W 17.0% H 7.3% F
6.0% 7 Q Q 78.1% E 18.0% L 3.9% 11 D D 97.1% 37 V V 50.9% D 33.9% S
5.4% A 1.2% L 1.0% 39 G S 80.6% A 19.4% 63 N W 92.2% D 3.9% Q 2.9%
65 Y E 91.1% L 8.7% 67 T E 92.8% L 5.2% 71 W W 62.6% E 13.3% M
11.0% S 6.9% D 4.0% 80 Y M 66.4% F 13.6% E 10.7% I 6.0% L 1.3% 82 V
V 86.0% D 12.8% 88 Y W 55.1% Y 15.9% N 11.4% F 9.5% K 1.9% Q 1.4% D
1.4% M 1.4% 110 T D 99.9% 115 A D 46.1% S 27.8% T 17.1% A 7.9% 118
E I 47.6% D 43.0% E 3.6% V 2.5% A 1.4% 125 F Y 51.1% F 43.3% L 3.4%
M 2.0% 129 W L 63.2% M 28.1% E 7.5% 168 V D 98.2% 170 A T 92.3% A
5.9%
[0278] These preliminary calculations show that PDA can
significantly reduce the dimensionality of the problem and can
bring it into the scope of gene shuffling and screening techniques
(see FIG. 3).
* * * * *
References