Method, system, and computer program product for scoring theoretical peptides
View Patent ↗The present teachings provide for identification of peptides using small sequence tags to focus computational resources on searching regions of a protein database that are the most likely to yield correct identifications. They allow for the incorporation of modifications and in doing so focuses the search to peptides with a precursor mass match. Additionally, probability or relevance factors can be used to determine peptide hypotheses. Various embodiments are presented that search for peptides when a single precursor is selected or when multiple precursors are simultaneously fragmented.
1. A system for scoring theoretical peptides used to identify peptides in a sample, comprising:
a database that stores a plurality of known peptide sequences;
a mass spectrometer that analyzes the sample by selecting a precursor ion, fragmenting the precursor ion and producing fragment ion data; and
a processor in communication with the mass spectrometer and the database that
a) receives the fragment ion data from the mass spectrometer,
b) identifies a plurality of sequence tags from the fragment ion data,
c) compares the plurality of sequence tags to one or more sequence segments of each peptide sequence of the plurality of known peptide sequences in the database, and scores each segment of the one or more sequence segments based on tag scores of sequence tags from the plurality of sequence tags included by each segment producing a plurality of scored segments,
d) generates a plurality of peptide features that affect theoretical peptide generation and associates a probability with each feature that indicates a sample implied frequency of each peptide feature, wherein the plurality of peptide features include one or more of modifications, digestions, species of origin, or mutations,
e) generates, for each scored segment of the plurality of scored segments, one or more theoretical peptides by,
(i) calculating a feature probability threshold based on the score of the segment so that the higher the score of segment the lower the feature probability threshold,
(ii) generating one or more theoretical peptides for the segment by applying each peptide feature of the plurality of peptide features that has a probability above the feature probability threshold to the segment, and
(iii) selecting each theoretical peptide of the one or more generated theoretical peptides that has a mass within a mass tolerance of the mass of the precursor ion, and
f) scores the one or more theoretical peptides for each scored segment by performing a comparison of each theoretical peptide with the fragment ion data.
2. The system of claim 1 , wherein the processor adjusts a score of a scored segment of the plurality of scored segments by adding to the score a component related to a weighted score of one or more segments adjacent to the scored segment.
3. The system of claim 1 , wherein the processor reduces the size of the plurality of scored segments before the processor generates one or more theoretical peptides for each scored segment of the plurality of scored segments by setting a maximum number of scored segments to be examined, maintaining the maximum number of scored segments that have the highest scores in the plurality of scored segments, and removing a remainder of scored segments from the plurality of scored segments.
4. The system of claim 1 , wherein the processor generates one or more theoretical peptides for each scored segment of the plurality of scored segments by considering post-translational modifications.
5. The system of claim 1 , wherein the processor generates one or more theoretical peptides for each scored segment of the plurality of scored segments by considering digestion features.
6. The system of claim 1 , wherein the processor generates one or more theoretical peptides for each scored segment of the plurality of scored segments by considering single nucleotide polymorphisms.
7. The system of claim 1 , wherein the processor generates one or more theoretical peptides for each scored segment of the plurality of scored segments by considering phosphorylation.
8. The system of claim 1 , wherein the processor assigns a probability to each theoretical peptide generated for a scored segment.
9. A method for scoring theoretical peptides used to identify peptides in a sample, comprising:
a) analyzing the sample by selecting a precursor ion, fragmenting the precursor ion and producing fragment ion data using a mass spectrometer;
b) receiving fragment ion data from the mass spectrometer using a processor;
c) identifying a plurality of sequence tags from the fragment ion data using the processor;
d) comparing the plurality of sequence tags to one or more sequence segments of each peptide sequence of a plurality of known peptide sequences stored in a database and scoring each segment of the one or more sequence segments based on tag scores of sequence tags from the plurality of sequence tags included by each segment producing a plurality of scored segments using the processor;
e) generating a plurality of peptide features that affect theoretical peptide generation and associating a probability with each feature that indicates a sample implied frequency of each peptide feature, wherein the plurality of peptide features include one or more of modifications, digestions, species of origin, or mutations;
f) generating, for each scored segment of the plurality of scored segments, one or more theoretical peptides by,
(i) calculating a feature probability threshold based on the score of the segment so that the higher the score of segment the lower the feature probability threshold,
(ii) generating one or more theoretical peptides for the segment by applying each peptide feature of the plurality of peptide features that has a probability above the feature probability threshold to the segment, and
(iii) selecting each theoretical peptide of the one or more generated theoretical peptides that has a mass within a mass tolerance of the mass of the precursor ion; and
g) scoring the one or more theoretical peptides for each scored segment by performing a comparison of each theoretical peptide with the fragment ion data using the processor.
10. The method of claim 9 , further comprising adjusting a score of a scored segment of the plurality of scored segments by adding to the score a component related to a weighted score of one or more segments adjacent to the scored segment.
11. The method of claim 9 , further comprising reducing the size of the plurality of scored segments before generating one or more theoretical peptides for each scored segment of the plurality of scored segments by setting a maximum number of scored segments to be examined, maintaining the maximum number of scored segments that have the highest scores in the plurality of scored segments, and removing a remainder of scored segments from the plurality of scored segments using the processor.
12. The method of claim 9 , wherein generating one or more theoretical peptides for each scored segment of the plurality of scored segments using the processor comprises considering post-translational modifications.
13. The method of claim 9 , wherein generating one or more theoretical peptides for each scored segment of the plurality of scored segments using the processor comprises considering digestion features.
14. The method of claim 9 , wherein generating one or more theoretical peptides for each scored segment of the plurality of scored segments using the processor comprises considering single nucleotide polymorphisms.
15. The method of claim 9 , wherein generating one or more theoretical peptides for each scored segment of the plurality of scored segments using the processor comprises considering phosphorylation.
16. The method of claim 9 , further comprising assigning a probability to each theoretical peptide generated for a scored segment using the processor.
17. The method of claim 16 , wherein the probability assigned to each theoretical peptide using the processor is associated with a probability of one or more sequence events that can affect the scored segment.
18. The method of claim 17 , wherein the one or more sequence events comprise a modification, a substitution, an insertion, a deletion, an edit, trans-peptidation, or crosslinking.
19. The method of claim 9 , further comprising generating one or more theoretical peptides for each scored segment of the plurality of scored segments using the processor so that a probability of a theoretical peptide of the number of theoretical peptides is indirectly proportional to the score of each scored segment.
20. A computer program product, comprising a computer-readable storage medium whose contents include a program with instructions being executed on a processor so as to perform a method for scoring theoretical peptides used to identify peptides in a sample, the method comprising:
a) providing a system, wherein the system comprises distinct software modules, and wherein the distinct software modules comprise a receiving data module, an identifying sequence tags module, a mapping sequence tags module, a generating theoretical peptides module, and a scoring theoretical peptides module;
b) receiving fragment ion data produced by a mass spectrometer that analyzed the sample by selecting a precursor ion and fragmenting the precursor ion using the receiving data module;
c) identifying a plurality of sequence tags from the fragment ion data using the identifying sequence tags module;
d) comparing the plurality of sequence tags to one or more sequence segments of each peptide sequence of a plurality of known peptide sequences stored in a database and scoring each segment of the one or more sequence segments based on tag scores of sequence tags from the plurality of sequence tags included by each segment producing a plurality of scored segments using the mapping sequence tags module;
e) generating a plurality of peptide features that affect theoretical peptide generation and associating a probability with each feature that indicates a sample implied frequency of each peptide feature, wherein the plurality of peptide features include one or more of modifications, digestions, species of origin, or mutations;
f) generating, for each scored segment of the plurality of scored segments, one or more theoretical peptides by,
(i) calculating a feature probability threshold based on the score of the segment so that the higher the score of segment the lower the feature probability threshold,
(ii) generating one or more theoretical peptides for the segment by applying each peptide feature of the plurality of peptide features that has a probability above the feature probability threshold to the segment, and
(iii) selecting each theoretical peptide of the one or more generated theoretical peptides that has a mass within a mass tolerance of the mass of the precursor ion; and
g) scoring the one or more theoretical peptides for each scored segment by performing a comparison of each theoretical peptide with the fragment ion data using the scoring theoretical peptides module.