U.S. patent application number 11/703941 was filed with the patent office on 2009-10-08 for data analysis to provide a revised data set for use in peptide sequencing determination.
Invention is credited to Andreas Huhmer, Rovshan Goumbatoglu Sadygov.
Application Number | 20090254285 11/703941 |
Document ID | / |
Family ID | 39678581 |
Filed Date | 2009-10-08 |
United States Patent
Application |
20090254285 |
Kind Code |
A1 |
Sadygov; Rovshan Goumbatoglu ;
et al. |
October 8, 2009 |
Data analysis to provide a revised data set for use in peptide
sequencing determination
Abstract
In one aspect of the present invention, the less "useful"
spectral data is disregarded from the spectral data resulting from
the fragmentation by ETD and candidate charge states for the
"useful" data assigned. Knowledge of the first order ion product
charge state reduces the subset of comparison data hence aiding in
the eventual identification of the precursor ion, and thus aiding
in peptide sequence database searching capabilities. Such
capabilities include, but are not limited to, computational
requirements for database search and data storage, CPU time, the
volume taken up on the hard disk to store results, visualization
and dissemination of data, and overall improvement in the
confidence in the precursor identification. Thus determination of
the peptide sequence can be resolved in less time, costing less
money, and requiring less computer power.
Inventors: |
Sadygov; Rovshan Goumbatoglu;
(San Jose, CA) ; Huhmer; Andreas; (Mountain View,
CA) |
Correspondence
Address: |
THERMO FINNIGAN LLC
355 RIVER OAKS PARKWAY
SAN JOSE
CA
95134
US
|
Family ID: |
39678581 |
Appl. No.: |
11/703941 |
Filed: |
February 7, 2007 |
Current U.S.
Class: |
702/22 |
Current CPC
Class: |
H01J 49/0036 20130101;
G16B 20/00 20190201; Y10T 436/24 20150115 |
Class at
Publication: |
702/22 |
International
Class: |
G06F 19/00 20060101
G06F019/00 |
Claims
1. A method of analyzing product ion data for use in peptide
sequence determination by searching a database for matches to mass
spectra, the method comprising: (a) subjecting a precursor ion of a
sample having a peak abundance to fragmentation by Electron
Transfer Dissociation (ETD) to generate ion product data over a
spectral range; (b) determining the ion product data quality and
utilizing only ion product data of at least a predetermined
quality, if any, for further processing in subsequent steps; (c)
identifying peaks of the ion product data of the at least
predetermined quality that represent first order ion products and
higher order ion products, wherein the first order ion products
comprise one or more members of the group consisting of charge
reduced precursors, electron transfer products, anion adducts, side
chain losses and hydrogen transfer products and wherein the higher
order ion products comprise at least one ion product resulting from
a dissociation reaction of a first order ion product; (d) utilizing
the ion product data of the at least predetermined quality and the
identified peaks of the first and higher order ion product data to
determine candidate charge states of the first order ion products;
and (e) submitting the identified peaks and the determined
candidate charge states to a nucleotide sequence database searching
program for performing the peptide sequence determination.
2. The method of claim 1, further comprising: (d1) assigning a
probability score to each of the candidate charge states prior to
the submitting step (e) such that the nucleotide sequence database
searching program performs searches of the database utilizing the
candidate charge states in order of their respective probability
scores.
3. The method of claim 2, further comprising: (d2) utilizing the
probability score to identify a probable precursor ion prior to the
submitting step (e).
4. (canceled)
5. The method of claim 1, wherein: the step (c) of identifying
peaks of the ion product data of the at least predetermined quality
that represent first order ion products and higher order ion
products comprises utilizing mass-to-charge ratio intervals between
spectral peaks of the ion product data.
6. The method of claim 1, wherein: the quality of all the ion
product data determined in step (b) is below a threshold value and
the ion product data is used to ascertain that the candidate charge
state corresponding to an observed peak of the ion product data is
+2.
7. The method of claim 1, wherein: the quality of all the ion
product data determined in step (b) is below a threshold value and
it is determined that the ion product data is not useful for
peptide sequencing purposes.
8. The method of claim 1, wherein: the step (b) of determining the
ion product data quality comprises comparing peak abundances of the
ion product data to a threshold value, said threshold value being
0.0001 percent of the precursor ion peak abundance.
9. The method of claim 1, wherein: the step (b) of determining the
ion product data quality comprises determining a number of spectral
peaks of the ion product data occurring over half the spectral scan
range.
10. The method of claim 1, wherein: the step (b) of determining the
ion product data quality comprises determining a number of spectral
peaks of the ion product data occurring over the whole spectral
scan range.
11-12. (canceled)
13. The method of claim 1, wherein: the higher order ion products
comprise one or more members of the group consisting of fragment
ions, products of fragment ion adducts and products of fragment ion
neutral losses.
14. The method of claim 1, wherein the step (d) of utilizing the
ion product data of the at least predetermined quality and the
identified peaks of the first and higher order ion product data to
determine candidate charge states of the first order ion products
comprises: determining at least one candidate state charge state by
identifying complementary second order ion products and applying a
Fast Fourier Transform to the complementary second order data.
15. (canceled)
16. The method of claim 1, further comprising: (c1) determining,
after step (c), a ratio of the first to higher order ion products;
and (c2) excluding the ion product data from farther processing in
subsequent steps if the ratio of the first to higher order ion
products less than a predetermined threshold.
17-19. (canceled)
20. The method of claim 1, wherein the step (d) of utilizing the
ion product data of the at least predetermined quality and the
identified peaks of the first and higher order ion product data to
determine candidate charge states of the first order ion products
comprises: identifying neutral loss ion peaks adjacent to peaks
representing the first order ion products to distinguish between
and test for presence of +1 and +2 first order ion products and
higher charge state first order ion products.
21. The method of claim 1, wherein the step (d) of utilizing the
ion product data of the at least predetermined quality and the
identified peaks of the first and higher order ion product data to
determine candidate charge states of the first order ion products
comprises: analyzing the densities of peaks corresponding to higher
order ion products between peaks corresponding to first order ion
products to distinguish between different candidate charge state
values.
22. The method of claim 1, wherein the step (d) of utilizing the
ion product data of the at least predetermined quality and the
identified peaks of the first and higher order ion product data to
determine candidate charge states of the first order ion products
comprises: utilizing intensity ratios of spectral peaks of the ion
product data to distinguish between a possible higher and a
possible lower candidate charge state.
23. The method of claim 1, wherein the step (d) of utilizing the
ion product data of the at least predetermined quality and the
identified peaks of the first and higher order ion product data to
determine candidate charge states of the first order ion products
comprises: utilizing other corresponding ion product data in
addition to the ion product data to indicate possible candidate
charge states, wherein the other corresponding ion product data is
obtained over a same spectral range and from the same sample as the
ion product data and comprises a peak of another precursor ion
having a different charge state from the precursor ion.
24. The method of claim 1, wherein the step (d) of utilizing the
ion product data of the at least predetermined quality and the
identified peaks of the first and higher order ion product data to
determine candidate charge states of the first order ion products
comprises: ranking a plurality of sums of intensities of identified
peaks of first order ion products, each of said sums of the form i
= 1 n A i n ##EQU00001## wherein n is a possible candidate charge
state for a particular first order ion product and A.sub.i.sup.n is
the intensity of an identified peak of another first order ion
product that has possible candidate charge state i when the
particular first order ion product has charge state n, and
utilizing an appropriate filter to evaluate the ranking.
25. The method of claim 24, wherein: the appropriate filter is a
Chebyshev inequality.
26. The method of claim 1, wherein the step (d) of utilizing the
ion product data of the at least predetermined quality and the
identified peaks of the first and higher order ion product data to
determine candidate charge states of the first order ion products
comprises: summing intensities of the identified peaks of the ion
product data corresponding to possible candidate first order ion
products.
27. A method of analyzing product ion data for use in peptide
sequence determination by searching a database for matches to mass
spectra, the method comprising: (a) subjecting a precursor ion with
a peak abundance to fragmentation by Electron Capture Dissociation
(ECD) to generate product ion data over a spectral range; (b)
determining the ion product data quality and utilizing only ion
product data of at least a predetermined quality, if any, for
further processing, in subsequent steps; (c) identifying peaks of
the ion product data of the at least predetermined quality that
represent first order ion products and higher order ion products;
(d) utilizing the ion product data of the at least predetermined
quality and the identified peaks of the first and higher order ion
product data to determine, by at least two different charge state
analyses, tentative candidate charge states of the first order ion
products; (e) combining the results of the at least two different
charge state analyses to determine candidate charge states of the
first order ion products; and (f) submitting the identified peaks
and the determined candidate charge states to a nucleotide sequence
database searching program for performing the peptide sequence
determination.
28. The method of claim 27, wherein: wherein each of the at least
two charge state analyses is chosen from the group consisting of:
(I) determining at least one candidate state charge state by
identifying complementary second order ion products and applying a
Fast Fourier Transform to the complementary second order data, (II)
identifying neutral loss ion peaks adjacent to peaks representing
the first order ion products to distinguish between and test for
presence of +1 and +2 first order ion products and higher charge
state first order ion products, (III) analyzing the densities of
peaks corresponding to higher order ion products between peaks
corresponding to first order ion products to distinguish between
different candidate charge state values, (IV) utilizing intensity
ratios of peaks of the ion product data to distinguish between a
possible higher candidate charge state and a possible lower
candidate charge state, and (V) ranking the intensities of peaks of
the ion product data corresponding to each of a plurality of
possible candidate charge states for the first order ion products,
and utilizing an appropriate filter.
29. A storage medium encoded with machine-readable computer program
code for analyzing product ion data for use in peptide sequence
determination by searching a database for matches to mass spectra,
the storage medium including instructions for: (a) obtaining ion
product data over a spectral range, the ion product data having
been generated by Electron Transfer Dissociation (ETD); (b)
determining the ion product data quality and utilizing only ion
product data of at least a predetermined quality, if any, for
further processing; (c) identifying peaks of the ion product data
of the at least predetermined quality that represent first order
ion products and higher order ion products, wherein the first order
ion products comprise one or more members of the group consisting
of charge reduced precursors, electron transfer products, anion
adducts, side chain losses and hydrogen transfer products and
wherein the higher order ion products comprise at least one ion
product resulting from a dissociation reaction of a first order ion
product; (d) utilizing the ion product data of the at least
predetermined quality and the identified peaks of the first and
higher order ion product data to determine candidate charge states
of the first order ion products; and (e) submitting the identified
peaks and the determined candidate charge states to a nucleotide
sequence database searching program for performing the peptide
sequence determination.
30. The method of claim 1, wherein the step (d) of utilizing the
ion product data of the at least predetermined quality and the
identified peaks of the first and higher order ion product data to
determine candidate charge states of the first order ion products
comprises: (d1) performing at least two charge state analyses
simultaneously; and (d2) combining the results of the at least two
charge state analyses to determine candidate charge states of the
first order ion products, wherein the performing of each of the at
least two charge state analyses is chosen from the group consisting
of: (I) determining at least one candidate state charge state by
identifying complementary second order ion products and applying a
Fast Fourier Transform to the complementary second order data, (II)
identifying neutral loss ion peaks adjacent to peaks representing
the first order ion products to distinguish between and test for
presence of +1 and +2 first order ion products and higher charge
state first order ion products, (III) analyzing the densities of
peaks corresponding to higher order ion products between peaks
corresponding to first order ion products to distinguish between
different candidate charge state values, (IV) utilizing intensity
ratios of peaks of the ion product data to distinguish between a
possible higher candidate charge state and a possible lower
candidate charge state, and (V) ranking the intensities of peaks of
the ion product data corresponding to each of a plurality of
possible candidate charge states for the first order ion products,
and utilizing an appropriate filter.
31. The method of claim 1, wherein the step (d) of utilizing the
ion product data of the at least predetermined quality and the
identified peaks of the first and higher order ion product data to
determine candidate charge states of the first order ion products
comprises: (d1) performing a first charge state analysis; (d2)
determining, from the results of the first charge state analyses;
if another charge state analysis must be performed; (d3) performing
another charge state analysis, different from all prior charge
state analyses, if the determination of step (d2) indicates that
another analysis must be performed; (d4) determining, from the
results of all prior charge state analyses; if another charge state
analysis must be performed; and (d5) repeating steps (d3) and (d4)
until the determination made in the most recent execution of step
(d3) indicates that another charge state analysis need not be
performed or until the number of repetitions of steps (d3) and (d4)
has reached a predetermined limit, wherein the performing of each
charge state analysis is chosen from the group consisting of: (I)
determining at least one candidate state charge state by
identifying complementary second order ion products and applying a
Fast Fourier Transform to the complementary second order data, (II)
identifying neutral loss ion peaks adjacent to peaks representing
the first order ion products to distinguish between and test for
presence of +1 and +2 first order ion products and higher charge
state first order ion products, (III) analyzing the densities of
peaks corresponding to higher order ion products between peaks
corresponding to first order ion products to distinguish between
different candidate charge state values, (IV) utilizing intensity
ratios of peaks of the ion product data to distinguish between a
possible higher candidate charge state and a possible lower
candidate charge state, and (V) ranking the intensities of peaks of
the ion product data corresponding to each of a plurality of
possible candidate charge states for the first order ion products,
and utilizing an appropriate filter.
Description
FIELD OF THE INVENTION
[0001] The invention relates to a method for analyzing product ion
data to produce a revised data set that can be used in peptide
sequencing determination. More specifically, the invention relates
to determining the charge states of product ions generated from
precursor ions by a non-ergodic technique.
BACKGROUND OF THE INVENTION
[0002] Mass spectrometry has become the method of choice for fast
and efficient identification of proteins in biological samples.
Tandem mass spectrometry of peptides in a complex protein mixture
can be used to identify and quantify the proteins present in the
original mixture. Tandem mass spectrometers achieve this by
selecting single m/z values and subjecting the precursor ions to
fragmentation, providing product ions that can be used to sequence
and identify peptides. The information created by the product ions
of a peptide can be used to search peptide and nucleotide sequence
databases to identify the amino acid sequence represented by the
spectrum and thus identify the protein from which the peptide was
derived. To identify peptides, database searching programs
typically compare each MS/MS spectrum against amino acid sequences
in the database, and a probability score is assigned to rank the
most likely peptide match. The algorithms typically utilize
mass-to-charge ratio (m/z) information for identification purposes
of the various product ions.
[0003] Fragmentation can be provided by various methodologies and
mechanisms. Ion activation techniques that involve excitation of
protonated or multiply protonated peptides, include
collision-induced dissociation (CID), and infrared multiphoton
dissociation (IRMPD) for example, and have been used to identify
sequences. In these dissociation methods translational energy is
imparted to the peptide and is converted into vibrational energy
that is then distributed throughout the bonds of the peptide. When
the energy imparted to a particular bond exceeds that required to
break the bond, fragmentation occurs and product ions are formed.
The cleavage may not always however, occur along the backbone of
the peptide if, for example, the side-chain of the peptide has
elements that inhibit cleavage along the backbone, by providing a
lower energy pathway and cleavage site on a side-chain. This
preferential cleavage of the side-chain bonds rather than the
polypeptide bonds often results in the provision of information
primarily about the side-chain sequences and not the peptide
sequence.
[0004] Other mechanisms of fragmentation include for example, those
in which the capture of a thermal electron is exothermic and causes
the peptide backbone to fragment by a non-ergodic process, those
that do not involve intramolecular vibrational energy
redistribution. Such methodologies include Electron Capture
Dissociation (ECD) and Electron Transfer Dissociation (ETD). ECD
and ETD occur on a time scale that is short compared with the
internal energy distribution that occurs in the CID process, and
consequently, most sequence specific fragment forming bond
dissociations are typically randomly along the peptide backbone,
and not of the side-chains.
[0005] Though non-ergodic reactions such as ETD or ECD
fragmentation appear to offer the best solutions for peptide
determination, these techniques create their own problems. ECD can
not be performed with trap-type mass analyzers since the electrons
created by the reaction do not typically retain their thermal
energy long enough to be trapped, thus ECD is typically performed
on a FT-ICT mass spectrometer. These instruments are expensive. ETD
fragmentation particularly of large peptides and proteins, which
can be performed by an ion trap, often leads to spectra too
complicated for direct interpretation. Typically, these larger
peptides are highly charged, and their fragment ions are similarly
multiply charged, with charge states of +2, +3, +4, +5, +6 and even
+7 observed. The limited m/z resolution of currently available mass
analyzers makes interpretation of these highly charged product m/z
spectral data difficult. In addition, the charge state
determination is more complicated and important than for CID where
normally charge states up to only +4 are observed.
[0006] A precursor subjected to the ETD fragmentation process
fragments mainly along its backbone, generating predominantly
fragments of the precursor ion. However, in addition to the
fragment ions, peaks are generally seen for ions which have been
subjected to neutral loss, such as water (-18 Da) for example. Ions
from side chain cleavage are generally not observed. Despite the
absence of side chain cleavage, the spectral data obtained via the
ETD process is typically possesses spectral information that may
contain little or no "useful" information in terms of peptide
sequencing or identification.
[0007] For large peptides and proteins, and the large number of
possible charge states, the number of possible matches in a
database is also larger. For example, if the precursor ion has a
charge state of +3, each fragment of the precursor found in the
MS/MS or MSN spectral data can have a possible charge state of +3,
+2 or +1. Since it is not possible to directly determine the charge
state of each of the fragments in a MS/MS spectrum (the spectrum
only provides mass to charge ratio information), if the precursor
ion is not known, several searches must be performed. In this case,
separate searches considering possible +3, +2 and +1 precursor ion
charge states may need to be performed. This is consuming in terms
of time and space, in terms, for example, of computer storage
space, the number of searches performed, computer execution time,
and the valuable time of the scientist in reviewing the data.
SUMMARY
[0008] In one aspect of the present invention, the less "useful"
spectral data is disregarded from the spectral data resulting from
the fragmentation by a non-ergodic reaction such as ETD, and
candidate charges for the "useful" data are assigned. To facilitate
this, first order ion products and second order ion products are
identified. Knowledge of the product ion charge reduces the subset
of comparison data hence aiding in the eventual identification of
the precursor ion, and thus aiding in peptide sequence database
searching capabilities. Such capabilities include, but are not
limited to, computational requirements for search requirements and
data storage such as the CPU time taken in searching, the volume
taken up on the hard disk to store large quantities of search
results for redundant charge states, visualization and
dissemination of data, and overall improvement in the confidence in
the precursor identification. Thus allowing the determination of
the peptide to be determined in less time, costing less money, and
requiring less computer power.
[0009] Less "useful" spectral data may comprise data considered to
be below a certain threshold, that threshold being that of the
noise level, or not sufficient data above a minimum threshold in
terms of peaks above the threshold level. Less "useful" data may
also comprise data that is defined as a second order ion product
rather than a first order ion product.
[0010] Analysis of the "useful" data may comprise utilizing not
only the first order ion product data, but also the second order
ion product data as part of the analysis process.
[0011] In another aspect of the present invention, a storage medium
encoded with machine-readable computer program code is provided,
the storage medium including instructions for identifying the first
and second ion products from the spectral data resulting from the
fragmentation by a non-ergodic reaction such as ETD. The
instruction enabling less "useful" spectral data to be disregarded
and candidate charges for the "useful" data to be assigned.
[0012] These and other aspects of the invention will become
apparent from the following description. In the description,
reference is made to the accompanying drawings that form a part
hereof, and in which there is shown a preferred embodiment of the
invention. Such embodiment does not necessarily represent the full
scope of the invention and reference is made therefore, to the
claims herein for interpreting the scope of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] FIG. 1 depicts a nomenclature typically adopted for the
fragment of peptides and proteins.
[0014] FIG. 2 is a flowchart illustrating the steps that are
performed in order to assign a probability score to the candidate
charge states of the first order ion products, in accordance with
an aspect of the present invention.
[0015] FIG. 3 is a flowchart illustrating the steps that may be
performed in order to determine the candidate charge states of the
first order ion products, in accordance with one aspect of the
present invention.
[0016] FIG. 4 is a flowchart illustrating the steps that may be
performed in order to determine the candidate charge states of the
first order ion products, in accordance with another aspect of the
present invention.
[0017] FIG. 5 is a flowchart illustrating the steps that may be
performed in order to determine the candidate charge states of the
first order ion products, in accordance with yet a further aspect
of the present invention.
[0018] FIG. 6 illustrates experimental product ion spectral data,
and shows ion product data that is below a threshold value,
according to an aspect of the present invention.
[0019] FIG. 7 illustrates experimental product ion spectral data,
and shows what a typical +2 spectrum may look like.
[0020] FIG. 8 illustrates experimental product ion spectral data,
and shows that charge states above +3 can be excluded since there
are no peaks above 1142 amu, according to an aspect of the present
invention.
[0021] FIG. 9 illustrates experimental product ion spectral data,
and shows that the candidate charge states are +3 and +6, but that
the +3 charge state should be assigned, according to an aspect of
the present invention.
[0022] FIG. 10 illustrates experimental product ion spectral data,
and shows that the charge state of +7 should be assigned, according
to an aspect of the present invention.
[0023] FIG. 11 illustrates experimental product ion spectral data,
and shows that the charge state of +6 should be assigned, according
to an aspect of the present invention.
[0024] Like reference numerals refer to corresponding parts
throughout the several views of the drawings.
DETAILED DESCRIPTION OF EMBODIMENTS
[0025] The present invention addresses some of the shortcomings of
the known art. A method for determining the charge states of
product ions generated from precursor ions by a non-ergodic
technique such as ETD or ECD is provided in one aspect of the
invention. In another, an improved method for determining the
charge state of a precursor ion for use in peptide sequencing
determination is also provided.
[0026] Before describing the invention in detail, a few terms that
are used throughout the description are explained. As used in this
specification, a peptide or polypeptide is a polymeric molecule
containing two or more amino acids joined by peptide (amide) bonds.
As used in this specification, a peptide typically represents a
subunit of a parent polypeptide, such as a fragment produced by
cleavage or fragmentation of the parent polypeptide using known
techniques. Peptides and polypeptides can be naturally occurring
(e.g., proteins or fragments thereof) or of synthetic nature.
Polypeptides can also consist of a combination of naturally
occurring amino acids and artificial amino acids. Peptides and
polypeptides can be derived from any source, such as mammals (e.g.,
humans), plants, fungi, bacteria, and/or viruses, and can be
obtained from cell samples, tissue samples, bodily fluids, or
environmental samples, such as soil, water, and air samples.
[0027] A nomenclature typically adopted (and used herein) for the
fragments of peptides and proteins has been suggested in the
literature and is depicted in FIG. 1. The three possible cleavage
points of the peptide backbone are called a, b and c when the
charge is retained at the N-terminal fragment of the peptide and x,
y and z when the charge is retained by the C-terminal fragment. The
numbering indicates, which bond is cleaved counting from the N- and
the C-terminus respectively, and thus also the number of amino acid
residues in the fragment ion. The number of hydrogen atoms
transferred to or lost from the fragment is indicated with
apostrophes to the right and the left of the letter
respectively.
[0028] Electron transfer dissociation (ETD) is a non-ergodic
process, a unimolecular dissociation that yields product ions that
represent cleavages between most of a peptide's or protein's amino
acids. ETD produces mainly c and z* fragment ions (ion products)
and to a much smaller extent a*, y ions and z' and c* ions. The ETD
process generally results in almost complete sequence coverage for
small peptide ions, with the exception of dissociation of
N-terminal residues of proline, which unlike the case for all other
amino acids, requires dissociation of two bonds.
[0029] For a productive ETD experiment multiply-charged peptide
cations are reacted with an electron transfer reagent to initiate
the dissociation of the cation yielding sequence specific ion
products according to equation (1).
[M+nH].sup.n++A-*.fwdarw.[C+(n-m)H].sup.(n-m-1)++[Z+mH].sup.m++A
(1)
where A-* is the electron transfer reagent, the [M+nH].sup.n+ is
the cation and the [C+(n-m)H].sup.(n-m-1)+ and [Z+mH].sup.m+ are
the c and z* type fragment ions, respectively.
[0030] The reaction of the electron transfer anion proceeds through
both electron transfer (with and without dissociation) and proton
transfer (without dissociation). Electron transfer reactions that
proceed with dissociation give rise to cleavage along the peptide
backbone, loss of neutral molecules and cleavage of the Cysteine
bond (if present), these are first order ion products.
[0031] ETD, therefore, is a process of three competing reactions,
one of which yields the desired product ion representing sequence
specific information (second order ion products), while the other
reaction pathways yield product ions that provide no specific
information about the amino acid sequence of proteins or
peptides.
[0032] A competing side reaction pathway for the creation of
fragment ions in reaction pathway (1) is the proton transfer
reaction according to equation:
[M+nH].sup.n++A-.fwdarw.[M+(n-1)H].sup.(n-1)++AH (2)
where A- is the transfer reagent.
[0033] Another competing side reaction pathway without first order
ion product formation is the anion attachment according to equation
(3):
[M+nH].sup.n++A.sup.-.fwdarw.[M+nH+A].sup.(n-1)+ (3)
where [M+nH+A].sup.(n-1)+ anion adduct.
[0034] Conversion of the precursor cation into desired first order
product ions is highly dependent on the ion-ion reaction conditions
chosen in the experiment and are variable with the choice of anion
reagent, reaction temperature, nature of carrier gas, gas pressure
etc.
[0035] The ETD technique produces low energy electrons that are
captured by multiply-protonated species that transforms the
precursor ion from an even-electron closed-shell system to an
odd-electron hypervalent system that deposits the energy associated
with the electron capture in to the precursor ion. The desired
reaction pathway is the electron transfer reaction, which
transforms the precursor ion into an energetically excited, first
order ion according to equation (4):
[M+nH].sup.n++A.sup.-*.fwdarw.[M+nH].sup.(n-1)+*+A (4)
that then proceeds to dissociate into sequence specific product
ions. The desired product ions are the result of a unimolecular
dissociation of the excited first order ion product intermediate
according to equation (5):
[M+nH].sup.(n-1)+*.fwdarw.[C+(n-m)H].sup.(n-m-1)++[Z+mH].sup.m+*
(5)
[0036] However, first order ion products can undergo sequential
reactions that lead to higher-order charge reduced ions of the
precursor cation and, in extreme cases, to the neutralization of
the precursor. In these cases the ion-ion reaction leads to the
reduction of charge without any dissociation into first order ion
products according to equation (6):
[M+nH].sup.(n-1)+*+A.sup.-*.fwdarw.[M+nH].sup.(n-2)+**+A (6)
[0037] Similarly, the successive transfer of a proton from the
excited intermediate to the anion reagent can lead to the formation
of charge reduced species without dissociation into second order
fragment ions according to equation (7):
[M+nH].sup.(n-1)+*+A.sup.-*.fwdarw.[M+(n-1)H].sup.(n-2)++AH (7)
[0038] The successive reaction of the first order product ion with
electron transfer reagent can lead to a number of ion-ion reaction
products that can be comprised of a mixture of species formed
exclusively by proton transfer or electron transfer reactions or a
mixture of both electron and proton transfer reactions. It is to be
noted that the exact charge state and compositional nature of these
ion products are usually difficult to determine without use of a
high resolution mass spectrometer. Unit resolution mass
spectrometers can not distinguish between the different isobaric
species of the first order ion-ion products resulting from the
successive reaction of the first order ion product with electron
transfer reagent.
[0039] It has been shown previously that the precursor cation
charge state plays a major role in determining the extent of
electron transfer and the dissociation products observed resulting
from reactions with anions. That is, the ion-ion reaction of the
more highly charged cations is inherently more exothermic than the
reaction of the same peptide at lower charge state. It can be
expected that the difference in reaction exothermicity not only
influences the reaction rate and quantity of the first order ion
products, but also the nature and products of the successive
dissociative and non-dissociative products. Furthermore, the
kinetic stability of the first order ion products differ as the ion
products experience greater electrostatic repulsion with increase
in precursor ion charge state. To the extent that the electrostatic
repulsion reduces the dissociation barriers for the second order
ion products, it can be expected that ion product dissociation
rates will greatly increase with charge. Conversely, reaction rates
associated with the formation of non-dissociative first order ion
products will decrease accordingly. It is the charge state and the
compositional nature of the precursor cation that ultimately
determines the preferences of the diverse reaction channels leading
to first order dissociative and non-dissociative ion products and
their quantity.
[0040] The transfer of an electron to the precursor ion is a highly
exothermic reaction that produces localized excitation that yields
dissociation of the precursor into product ions or the loss of
neutral side chains. Usually, more than 80% of all cleavages
observed in ETD are of the c and z* type; however, other
fragmentation channels include losses of small molecules and
radicals from the first order reduced species. Those losses
constitute approximately less than 10% of all ion products and do
not make any sequence specific information available, but provide
information about the charge state (electronic state) and nature of
their precursors. Several different neutral loss species can be
identified in ETD spectra such as, for example a loss of 17 amu
(NH.sub.3), a loss of 44 amu (from either CO.sub.2 or
CH.sub.4N.sub.2, the latter being a portion of an Arginine side
chain), losses of 42 amu (NH--C--NH) and 59 amu
((H.sub.2N).sub.2C.dbd.NH) from portions of Arginine side chains, a
loss of 45 amu (CH.sub.3NO) from portions of Asparagine and
Glutamine side chains, losses of 72 amu
(--(CH.sub.2).sub.4--NH.sub.2) and 73 amu (C.sub.4H.sub.11N) from
Lysine side chain losses, and losses of 74 amu (C.sub.3H.sub.6S),
82 amu (C.sub.4H.sub.6N.sub.2) and 101.095 amu
(C.sub.4H.sub.11N.sub.3) originating, respectively, from losses of
Methione, Histidine and Arginine side chains. The observed neutral
losses are predominantly associated with neutral losses from first
order ion products.
[0041] Similarly, adducts can form from intermediate excited states
that give clues about the electron state and nature of its
precursor. In particular z* ions have the tendency to form adducts,
such as molecular oxygen adducts (z+32 amu) as well as hydroxyl
adducts (z+17 amu).
[0042] Having explained the meaning of a few terms that have been
used in describing the invention, the broad concepts of the
invention will now be explained with the aid of FIGS. 2-5. FIGS.
6-11 illustrate how the invention can be utilized to determine the
candidate charge states of the first order ion products, and hence
enable and improve the peptide searching capabilities.
[0043] FIG. 2 is a flowchart 200 depicting the steps for analyzing
product ion data to produce a revised data set that can be used in
peptide sequencing determination. As shown in FIG. 2, step 210
relies on the fact that ion product fragments have already been
generated by a non-ergodic fragmentation process. Non-ergodic
fragmentation processes include, but not limited to electron
capture dissociation (ECD) or electron transfer dissociation (EDT),
processes which as discussed briefly in the Background Section,
above, and known to those in the art, do not involve intramolecular
vibrational energy redistribution.
[0044] Once generated, the fragments of a precursor ion may include
products such as, but not limited to, charge reduced precursors,
electron transfer products, anion adducts, side chain losses,
hydrogen transfer products, fragment ions, products of fragment ion
adducts and products of fragment ion neutral losses. Therefore, the
spectral data representative of the fragments contains not only
first order ion products which have come directly from the
fragmentation of the intact and charged precursor, but second order
ion products which are the results of fragmentation of the first
order ion products. Furthermore, higher order ion products can also
be present, adding further to the difficulty in peptide sequence
identification.
[0045] Having generated the fragments of the precursor ion, ion
product data is generated in Step 210, via some analysis mechanism
such as an ion trap mass analyzer for example, a three-dimensional
ion trap, a two-dimensional ion trap, or an orbitrap mass analyzer.
In some instances, fragmentation and ion product data generation
may occur in one instrument such as a mass analyzer, in other
instances this may be a two step process, generating the fragments
in one instrument, and then transferring them to another to obtain
the mass spectral data, the ion product data. The fragmentation of
precursor ions and the generation of ion product data from the
fragments produced are known to those skilled in the art, and are
not discussed in detail herein. Typically, ion product data
comprises spectra of intensity/abundance vs. mass-to-charge ratio,
though other forms of spectra fall within the scope of the
invention.
[0046] Having generated ion product data, the ion product data is
subjected to various type of data analysis. The analysis may be
performed on data from a single spectrum, or data from a combined
number of spectra. Using data from a number of spectra may enable
any errors that may exist to be reduced, and/or may enable the user
to identify fragments in one scan that may not have been present or
not present in sufficient abundance in another scan.
[0047] In one aspect of the invention, the aim is to analyze the
ion product data such that charge states can be assigned to the
useful peaks. Useful peaks are typically associated with
charge-reduced precursors, electron transfer products, anion
adducts, side chain losses and hydrogen transfer products. Once
charge states have been assigned to the useful peaks, a reduced set
of data can thus be generated prior to searching a database for
matches to the spectra to obtain the molecular weight of the
original precursor. The revised data set may be further reduced by
utilizing a probabilistic method to assign a probability score to
the each of the useful peaks, and subsequently utilizing the
highest probability scoring useful peak to aid in the search for
possible matches in a database. Hence providing for an improved
peptide sequence database capability in identifying a probable
precursor. The improved capability being not only in terms of time
and cost savings, but in improved confidence in the results
obtained, for example.
[0048] The data analysis may be carried out by means of a storage
medium encoded with machine-readable computer program code. For
example the data analysis may be carried out by a computer system
comprising for example a central processing unit (CPU), memory,
display and various additional input/output devices. Such a data
analysis system may form part of the overall mass analyzer or be a
separate stand alone unit, connected to the mass analyzer through
input/output interfaces known in the art. Those in the art will
also appreciate that the series of computer instructions that
embody the functionality described hereinbefore can be written in a
number of programming languages for use with many computer
architectures and numerous operating systems.
[0049] The first step of analysis, step 220, is to determine the
quality of the ion product data, ensuring the data is of a
predetermined quality before further processing. This step, in its
lowest form of analysis, disregards the intensity/abundance values
below some threshold value, typically the "noise" threshold value.
For example, the quality of ion product data may be considered to
fall below a threshold if the spectral peaks are not of an
intensity/abundance value of 0.0001% of the precursor abundance
value. In this instance of the present invention, it may be deduced
that since the minimum quota of data above the "noise" threshold is
not met, there is not sufficient data to enable one to utilize for
peptide sequencing purposes. In this instance, the process can be
stopped, ensuring that valuable user and CPU time is not
wasted.
[0050] In other forms, the quality determination is based on a
requirement for a minimum quota of data above a threshold value.
For example, in another aspect of the invention, data may be
considered to be below the threshold if there are fewer than ten,
twenty, thirty, forty or fifty spectral peaks over half a spectral
range, the spectral range being the range over which the product
data was originally generated. In a further aspect of the
invention, data may be considered to be below the threshold if
there are fewer than twenty, thirty, forty, fifty or sixty peaks
after the precursor ion mass-to-charge ratio value over the whole
spectral range. In yet a further aspect of the invention, even
though there may be sufficient peaks either over half a spectral
range or after the precursor, the peaks may not be above the
"noise" level, and hence still be considered to fall below the
desired threshold. Those skilled in the art should appreciate that
although numbers such as ten, twenty, thirty, forty, fifty and
sixty have been utilized, these are representative of any number,
and will depend on the size of the precursor ion, type of precursor
ion, fragmentation method, apparatus used, contamination, internal,
external and various other conditions and influences, the number
effectively dictated by the user typically combined with
experimentation and/or experience/teachings.
[0051] Typically, when it is determined that the predetermined
quality of the ion product data is below a threshold value, it may
be concluded that the ion products generated are not useful for
peptide sequencing purposes. Alternatively, it may be possible that
there is sufficient information to ascertain that the only possible
candidate charge state of any observed intensity peak is +2. When
it is determined that the predetermined quality of the ion product
data is above a threshold value, it may be concluded that the ion
product generated is useful for peptide sequencing purposes, and it
may be assigned a charge state of greater or equal to +2, such as
+2, +3, +4, +5, +6 or +7 for example.
[0052] The second step of analysis, step 230, is to identify
portions of the predetermined quality ion product data (above the
threshold) that represent first order and second ion products.
These portions of the ion product data may comprise the presence or
the absence of at least one spectral peak. The fragments generated
by the ETD process typically include charge reduced precursors,
electron transfer products, anion adducts, side chain loses,
hydrogen transfer products, fragment ions, products of fragment ion
adducts and products of fragment ion neutral loses. As explained
earlier, first order ion products are the reduced charge state ion
products, the electron transfer ion products, hydrogen transfer ion
products, or adduct ion products. Second order ion products are any
product that is the result of a true dissociation reaction forming
sequence specific fragments. In this step, precise identification
of the spectral peak that is associated to a charge reduced
precursors, electron transfer product, anion adduct, side chain
loss, hydrogen transfer product, fragment ion, product of a
fragment ion adduct or product of a fragment ion neutral loss is
not required, though may be useful. At this stage of the process,
there is a need to differentiate between first order ion products
and second order ion products; to differentiate between the first
order ion products which may include fragments including charge
reduced precursors, electron transfer products, anion adducts, side
chain loses and hydrogen transfer products, and second order ion
products which may include fragments including fragment ions,
products of fragment ion adducts and products of fragment ion
neutral loses. Once differentiated, the first order ion products
are the ones that generally provide the most useful information in
terms of precursor ion identification, and the eventual peptide
sequence determination. By differentiating between the first and
second ion products, one may therefore be able to revise, and
typically reduce the data set prior to further processing. In
addition, the ratio of the first and higher order ion products is
indicative of the efficiency of the ETD fragmentation process, a
lower ratio indicating that the ion product data generated is not
useful for peptide sequencing purposes.
[0053] Having now revised, and typically reduced this data set, in
step 240, candidate charge states are determined for each of the
first order ion products. This determination is typically carried
out by analysis of the data, the analysis utilized comprising
techniques that utilize at least one of peak abundance, peak
position, peak density, peak spacing, peak presence or peak
absence. This step can be simple or extremely complex depending
upon the initial precursor ion, its size and type, the
fragmentation method employed, the apparatus used, contamination,
internal, external and various other conditions and influences. In
one aspect of the present invention (depicted in a later figure as
step 305), the fragments comprising the second order ion products
are utilized to determine the candidate charge state of the first
order ion products. This may be achieved by, for example, firstly
identifying the complementary second order ion products
(complementary to the first order ion products), and then applying
a Fast Fourier Transform to the complementary second order ion
products. If it fits, the candidate charge state of the first order
ion product can be determined. Alternatively, the degree of fit may
be taken into account in a probabilistic method employed to assign
a probability score to the candidate charge state. Other possible
methods that can be used for candidate charge state determination
of the first ion product shall be discussed in greater detail in
connection with FIGS. 2-4 later.
[0054] At this point, the candidate charge states have been
determined and a revised data set has been generated, one in which
not only data that falls below a desired threshold is disregarded,
but one in which first and second order ion products have also been
identified. This revised data set is typically a reduced data set,
data that is reduced in size compared to the fragmentation data
originally generated by the ETD process. Consequently further
processing of this revised data set can only improve peptide
sequence database searching, reducing for example the CPU time
required, computer storage space needed, the number of searches
that need to be performed, computer execution time, and the
valuable time of the scientist in reviewing the data.
[0055] Although an aspect of the present invention can be
illustrated by steps 210, 220, 230 and 240 of FIG. 2, in another
aspect of the present invention, additional value can be attained
by step 250 which employs a probabilistic method to assign a
probability score to the candidate charge states of the first order
ion products. Assignment of such a score enables the most likely
candidates to be compared to the database data first, and if a
match is found, processing of the less or least likely candidates
may not be required. Once again, step 250 provides for a revised
data set to be generated. However in this step the revision may not
necessarily involve data being disregarded, but being re-ordered,
with the most probable occurring in a position within the data set
that enables it to be further processed first or at least before
the less likely or least likely alternatives. Alternatively, it may
be found that certain candidate first ion products are not at all
likely, or below a certain threshold of probability, and in this
instance the revised data set may also be a reduced data set.
[0056] By implementing the method described hereinbefore, one can
not only improve peptide sequence database searching by reducing
for example the CPU time required, computer storage space needed,
the number of searches that need to be performed, and the valuable
time of the scientist in reviewing the data, but by also gaining a
higher confidence in the results. Having disregarded data that
falls below a threshold value, and optionally assigned a
probability score to the candidate charge states of the first order
ion products, one has reduced the probability of matching fragments
that should have been disregarded from fragment spectra in the
peptide sequence database. Therefore one has reduced the
probability of incorrectly determining the precursor ion and/or the
peptide sequence, and increased the confidence of correct
assignment. The database searching capabilities have therefore been
further improved.
[0057] As mentioned earlier, step 240 dictates that candidate
charge states are determined for each of the first order ion
products. FIGS. 3-5 illustrate various methods of achieving such a
determination. It should be recognized that these methods are
presented as examples of how candidate charge determination can be
achieved and should not be construed as limiting the invention to a
particular mode of operation.
[0058] Referring initially to FIG. 3, it can be seen that step 240
has been broken into five distinct steps, identified as steps 305,
310, 320, 330 and 340. As illustrated, these five steps occur
simultaneously, and the results of each analysis have to be
acquired and combined before the candidate charge state(s) of the
product ions can be determined. Although FIG. 3 illustrates that
the determination of the candidate charge states of the first order
ion products can be achieved in five steps, this number of steps is
not intended to limit the scope of the current invention to this
number, more steps may be added, or fewer may be employed. The
reference numerals have been retained to represent the similar step
taken with reference to FIGS. 4 and 5, though it will be apparent
later that the methods described do have their differences.
[0059] Step 305 was discussed previously, in which the
complementary second order ion products were utilized to determine
the candidate charge state of the corresponding first order ion
product. In step 310, the candidate charge state of the first order
ion product is determined by identifying neutral loss ion peaks,
utilizing a known mass-to-charge ratio interval between the neutral
loss peak and the first order ion product peak. Neutral loss peaks
are peaks from radicals or molecules that are lost from an ion to
produce an ion of lower mass, for example 17-18 amu representing
the loss of H.sub.2O and NH.sub.3. Neutral losses represent species
that have no charge. The presence of a neutral loss peak adjacent
to the first order ion product can be used to distinguish charge
states +1 and +2 first order products from higher charged first
order products.
[0060] In step 320, the candidate charge state of the first order
ion product is determined by checking for the presence of peak
densities of second order ion products between the first order ion
products. This analysis determines if the candidate charge state of
a higher value than another charge state should be selected. The
presence of a density of second order ion products is useful
particularly when consecutive ion states are determined as possible
charge states.
[0061] In step 330, the determination of the candidate charge state
of the first order ion product is determined by utilizing the
intensity ratios to distinguish between a higher and a lower
possibility of candidate charge state. For example, +2 charge state
ion are likely to be in greater abundance than other multiply
charged ions, except possibly for the original precursor.
[0062] In step 340, the determination of candidate charge state of
the first order ion product is determined by summing the
intensities of all first order ion products, as the most likely
charge state for first order ion product will be the one that
yields the highest ion intensity. For example, consider that the
candidate charge state of a first order ion product is +4, and the
intensity of peaks (in arbitrary units) corresponding to this
interpretation are A.sub.4 for the peak designated +4, A.sub.3 for
the peak designated +3, A.sub.2 for the peak designated +2, and
A.sub.1 for the peak designated +1. In this instance, the sum of
the intensities of all the first order products for the candidate
charge state of +4 is
.SIGMA.A.sub.i+4=A.sub.4+A.sub.3+A.sub.2+A.sub.1. Similarly, for
the candidate charge state of a first order ion product of +3, if
the intensity of peaks (in arbitrary units) corresponding to this
interpretation B.sub.3 for the peak designated +3, B.sub.2 for the
peak designated +2, and B.sub.1, for the peak designated +1. Thus
the sum of the intensities of all the first order products for the
candidate charge state of +3 is
.SIGMA.B.sub.i+3=B.sub.3+B.sub.2+B.sub.1. Likewise, for the
candidate charge state of a first order ion product of +2, if the
intensity of peaks (in arbitrary units) corresponding to this
interpretation C.sub.2 for the peak designated +2, and C.sub.1 for
the peak designated +1. In this instance, the sum of the
intensities of all the first order products for the candidate
charge state of +2 is .SIGMA.C.sub.i+2=C.sub.2+C.sub.1. Having
acquired this information, if
.SIGMA.B.sub.i+3>>.SIGMA.C.sub.i+2, and
.SIGMA.B.sub.i+3>>.SIGMA.A.sub.i+4, if a Chebychev inequality
is applied, it will be apparent that .SIGMA.B.sub.i+3 is the most
likely charge state for the first order product.
[0063] Other steps may include for example, analysis comprising
utilizing corresponding first order ion products in product data
over the same spectral range, from a different charge state of the
precursor ion generated from a different scan to indicate possible
candidate charge states. This step is discussed in greater detail
with respect to FIG. 11 below.
[0064] Steps 305, 310, 320, 330, and 340 have only briefly been
addressed above, but implementation of these analysis techniques
should be known to those skilled in the art, and will become
clearer when FIGS. 6-11 are discussed below.
[0065] It will be apparent that FIG. 4 is similar to FIG. 3, in
that the same steps are illustrated for the determination of the
candidate first order ion products, but in this instance the
analysis steps are carried out sequentially, and after the result
of each analysis step has been acquired, the candidate charge
state(s) of the first order ion product can be determined.
Similarly, FIG. 5 is similar to FIGS. 3 and 4, in that the same
steps are illustrated for the determination of the candidate first
order ion products, but in this instance, although each step is
carried out sequentially, it may be possible to determine the
candidate charge state(s) of the first order ion product after the
first analysis step 305 alone, in which case the remaining analysis
steps 310, 320, 330 and 340 need not be run. Alternatively it may
only be necessary to run two, three or four of the analysis steps
before the user is able to determine the candidate charge states of
the first order ion products. In the alternative, it may be
necessary to run all analysis steps.
[0066] FIGS. 6-11 illustrate how the invention can be utilized to
determine the candidate charge states of the first order ion
products, and hence enable and improve the peptide searching
capabilities.
[0067] FIG. 6 shows the mass-to-charge ratio spectral data obtained
after fragmentation of a 444.95 (m/z) precursor ion by the ETD
process. This is an example of a low quality spectrum, a spectrum
in which only one distinct and significant peak can be observed at
444.8 (m/z). The other peaks that have (m/z) identifications on the
spectral data plot are below the threshold value, and considered to
be "noise" and to contain no "useful" information with respect to
the first order ion products and hence the precursor ion.
[0068] FIG. 7 shows the mass-to-charge ratio spectral data obtained
after fragmentation of a 675.60 (m/z) precursor ion by the ETD
process. In this spectrum, two distinct and significant peaks can
be observed at 674.46 and 1347.52 (m/z). There are other peaks that
have (m/z) identifications on the spectral data plot, some of which
are below the threshold value, and considered to be "noise" and to
contain no "useful" information with respect to the first order ion
products and hence the precursor ion, but others such as 992.24 and
1303.16 (m/z) that may be considered useful. However, in this
example, the one peak at 674.46 (m/z) would be assigned a +2 charge
and the peak at 1347.52 (m/z) would be assigned a +1 charge. All
other candidate charge states except +2 for the first order ion
product would be excluded in the spectrum, as there are no
significant peaks larger than the proposed mass for the +2 charge.
This mass-to-charge ratio spectral representation is considered a
typical spectrum that results after ETD fragmentation for a +2
charged first order product ion. It would not be necessary to carry
out any further analysis to determined possible candidate charge
states of the first order ion products in this example, it would be
apparent from the data attained.
[0069] FIG. 8 shows the mass-to-charge ratio spectral data obtained
after fragmentation of a 382.31 (m/z) precursor ion by the ETD
process. In this spectrum, several distinct and significant peaks
can be observed, including those at 382.23 and 76.43 (m/z). In this
example, charge states higher than +3 are excluded as candidates
since there are no significant peaks greater than 1142 (m/z). The
candidate charge states for the first order in products could be +2
or +3 based on the significant peaks alone. However, the step 205
is utilized to further analyze the data, it can be observed that
there is a peak adjacent the 382.23 (m/z) peak, which may represent
a 8-9 amu loss from a +2 charged first order ion product. There is
also a peak adjacent the 763.43 (m/z) peak, which may represent a
16-18 amu loss from a +1 charged first order ion product. From this
information, the candidate charge state for the first order ion
product would be +2.
[0070] FIG. 9 shows the mass-to-charge ratio spectral data obtained
after fragmentation of a 583.55 (m/z) precursor ion by the ETD
process. In this spectrum a first possible interpretation of the
data would be that the 583.67 (m/z) peak represent the +3 charge
state, the 874.58 (m/z) peak represent the +2 charge state and the
1749.35 (m/z) peak represent the +1 charge state. This would be
consistent with the expectation held by those skilled in the art
that for the +3 charge state the +1, +2 and +3 first order ion
products will normally have the tallest peaks in the spectrum.
However, a second possible interpretation dictates that the +6
charge state is in principle possible since the peak at 1164.84
(m/z) could be the +3 charge state. However, if this were the case,
then the peak at 874.58 (m/z) would be the +4 charge state. To
those skilled in the art, it will be apparent that this
interpretation is unlikely since for the +6 charge state, the +3
and the +4 charge states do not normally demonstrate high
intensities, in particular the +4 peak. In this example it can be
seen that the 874.58 (m/z) peak is in fact the maximum peak size
across the entire spectral range and therefore the first
interpretation is the one that would be assigned, +3 for the 583.67
(m/z) peak.
[0071] FIGS. 10 and 11 show the mass-to-spectral data obtained
after fragmentation of 596.44 amu and 695.81 (m/z) precursor ions
respectively, by the ETD process. Taking FIG. 10 first, if the
charge state of +7 is considered as a candidate charge state for
the first order ion product identified as the peak appearing at
595.53 (m/z), then it can be deduced that the peak appearing at
1389.99 (m/z) is the +3 peak. There is a peak that appears at
around 833 (m/z) that could be the +5 peak, but the +4 and the +6
peaks do not appear to exist in this particular scan. In
particular, the peak that should represent the +6 charge state at
694 (m/z) is missing. However, by comparing complementary
information from another spectrum, for example that shown in FIG.
10, the charge states for the two different scans can be
ascertained with a certain degree of certainty. Looking at FIG. 11,
it will be apparent that both spectra share several common peaks,
for example peaks at 1389 (m/z) and 985 (m/z). In addition, it can
be seen that in FIG. 10, there is a peak at 694 (m/z). From the
complementary information from these two spectra, one is able
determine the candidate charge states of the first order ion
products illustrated. Considering the information above, one is
able to determine that the candidate charge state for the 595 (m/z)
peak in FIG. 10 is +7, and the candidate charge state for the 694
(m/z) peak in FIG. 11 is +6.
[0072] Although various exemplary aspects of the invention have
been disclosed, it should be apparent to those skilled in the that
various changes and modifications can be made without departing
from the scope of the present invention, and incorporating some, if
not all the advantages discussed above. These and other
modifications are intended to be within the scope of the present
invention.
* * * * *