Method for identification and sequencing of proteins

1. A method for interpreting peptide sequences via combination of spectra from different peptides comprising:

providing a sample comprising a plurality of proteins;

defining a plurality of peptide pairs wherein one peptide of the pair differs from the other peptide by a modification or mutation;

obtaining a spectrum for each peptide of each peptide pair to form a spectral pair;

aligning the spectra of each spectra in the spectral pair to identify signal peaks and noise peaks, wherein aligning the spectra further comprises:

generating a spectral product matrix by mapping the two spectra onto respective x and y axes of a scatter graph;

identifying matching peaks between the two spectra where vertical and horizontal lines extending from the peaks of each of the two spectra intersect;

connecting pairs of adjacent matching peaks with at least one jump to form an optimal path from an origin of the scatter graph, wherein the optimal path is a longest sequence of valid jumps on the spectral product matrix; and

identifying the matching peaks in the optimal path as signal peaks and the peaks outside the optimal path as noise peaks;

removing the noise peaks;

generating a de novo sequence reconstruction by combining the signal peaks for the spectral pairs;

conducting a sequence database search with a computer comprising a processor and a memory using the de novo sequence; and

generating an output comprising results of the sequence database search.

2. The method according to claim 1 , wherein the at least one jump is selected from a group consisting of: a horizontal or vertical jump, a diagonal jump, and an oblique jump.

3. The method according to claim 2 , wherein the horizontal or vertical jump occurs during a prefix or suffix spectral alignment, and wherein the diagonal jump occurs during a modified or unmodified spectral alignment, and wherein the oblique jump occurs during a partial-overlap spectral alignment.

4. The method according to claim 1 , further comprising, prior to generating a de novo sequence, clustering the spectra of each spectral pair into a spectral star to separate prefix and suffix ladders of the spectra.

5. The method according to claim 1 , wherein peptide sequences of the peptide pairs are not known.

6. The method according to claim 1 , further comprising, before defining a plurality of peptide pairs, introducing chemical damage to a portion of the sample to produce modified peptides.

7. The method according to claim 6 , wherein the modification is caused by a chemical reaction.

8. The method according to claim 6 , wherein the modification is caused by an enzyme.

9. The method according to claim 1 , wherein the peptide is a member of a mixture.

10. The method according to claim 1 , further comprising identifying modifications by:

combining the spectral pairs into a spectral network having a plurality of vertices wherein each vertex corresponds to one spectrum and each edge to one spectral pair;

analyzing each spectral pair of the network to identify modifications;

annotating unmodified peptides within the spectral pairs;

propagating annotated peptides by pairing annotated spectra with non-annotated spectra to identify modifications between the annotated/non-annotated spectral pair;

repeating the steps of annotating and propagating until all spectra are annotated; and

generating an output comprising annotations of the peptides.

11. A method for identifying proteins using mass spectrometry, comprising:

defining a plurality of peptide pairs wherein one peptide of the pair differs from the other peptide by a modification, mutation or extension;

obtaining a spectrum for each peptide of each peptide pair to form a spectral pair;

aligning the spectra of each spectra in the spectral pair to identify signal peaks and noise peaks, wherein aligning the spectra further comprises:

generating a spectral product matrix by mapping the two spectra onto respective x and y axes of a scatter graph;

identifying matching peaks between the two spectra where vertical and horizontal lines extending from the peaks of each of the two spectra intersect;

connecting pairs of adjacent matching peaks with at least one jump to form an optimal path from an origin of the scatter graph, wherein the optimal path is a longest sequence of valid jumps on the spectral product matrix; and

identifying the matching peaks in the optimal path as signal peaks and the peaks outside the optimal path as noise peaks;

removing the noise peaks;

generating a consensus sequence reconstruction by combining the separated signal peaks for the spectral pairs;

conducting a database search using the consensus sequence; and

generating an output comprising results of the database search.

12. The method according to claim 11 , further comprising:

identifying protein modifications by comparing the consensus sequence reconstruction and sequence variants detected by spectral pairs.

13. The method according to claim 11 , wherein the at least one jump is selected from a group consisting of: a horizontal or vertical jump, a diagonal jump, and an oblique jump.

14. The method according to claim 13 , wherein the horizontal or vertical jump occurs during a prefix or suffix spectral alignment, and wherein the diagonal jump occurs during a modified or unmodified spectral alignment, and wherein the oblique jump occurs during a partial-overlap spectral alignment.

15. The method according to claim 11 , further comprising, prior to generating a consensus sequence, clustering the spectra of each spectral pair into a spectral star to separate prefix and suffix ladders of the spectra.

16. The method according to claim 11 , wherein peptide sequences of the peptide pairs are not known.

17. The method according to claim 11 , further comprising, before defining a plurality of peptide pairs, introducing chemical damage to a portion of the sample to produce modified peptides.

18. The method according to claim 17 , wherein the modification is caused by a chemical reaction.

19. The method according to claim 17 , wherein the modification is caused by an enzyme.

20. The method according to claim 11 , wherein the peptide is a member of a mixture.

21. The method according to claim 11 , further comprising identifying modifications by:

combining the spectral pairs into a spectral network having a plurality of vertices wherein each vertex corresponds to one spectrum and each edge to one spectral pair;

analyzing each spectral pair of the network to identify modifications;

annotating unmodified peptides within the spectral pairs;

propagating annotated peptides by pairing annotated spectra with non-annotated spectra to identify modifications between the annotated/non-annotated spectral pair;

repeating the steps of annotating and propagating until all spectra are annotated; and

generating an output comprising annotations of the peptides.

22. A method of computationally increasing signal to noise ratio in tandem mass spectra, comprising:

obtaining a first set of mass spectra of a peptide;

causing a modification to a segment of said peptide;

obtaining second set of mass spectra of the modified peptide;

generating pairs of mass spectra from the first and second sets of mass spectra;

aligning the spectra of each spectra in the spectral pair to identify signal peaks and noise peaks, wherein aligning the spectra further comprises:

generating a spectral product matrix by mapping the two spectra onto respective x and y axes of a scatter graph;

identifying matching peaks between the two spectra where vertical and horizontal lines extending from the peaks of each of the two spectra intersect;

connecting pairs of adjacent matching peaks with at least one jump to form an optimal path from an origin of the scatter graph, wherein the optimal path is a longest sequence of valid jumps on the spectral product matrix; and

identifying the matching peaks in the optimal path as signal peaks and the peaks outside the optimal path as noise peaks;

removing the noise peaks;

combining the separated signal peaks to produce a consensus spectrum; and

generating an output comprising the consensus spectrum.