1. 1 Mass Spectrometry-based Proteomics Xuehua Shen (Adapted from slides with textbook)

2. 2 Outline Motivation of proteomics Mass spectrometry-based proteomics Instrumentation of mass spectrometry De novo sequencing algorithm Database search Algorithms of real software (e.g., sequence tags)

3. 3 Motivation Proteins are working units of the cells –The number of found genes is much less than the number of expressed proteins –Directly related with cell processes and diseases >1,000,000 distinct protein forms ~30,000 human genes DNA Alternative splicing mRNA Protein Post-translational Modification >100,000 RNA messages SNP

4. 4 Tools for Proteomics Edman degradation reaction NMR (Nuclear Magnetic Resonance) X-ray crystallography Protein array Mass Spectrometry

5. 5 Mass Spectrometry-based Proteomics Primary sequence (sequencing, identification) Post-translational modification (PTM) (characterization) Quantitative proteomics (quantification) Protein-protein interaction

6. 6

7. 7 Components of Mass Spectrometer Ion source (ESI and MALDI) Mass analyzer (ion traps, TOF, Quadrupole, FT, etc.) –Mass-to-charge ratio (m/z) Ion detector

8. 8 Peptide and Intact Protein Peptide: a fragment of protein Some enzymes, e.g. trypsin, break protein into peptides. Some technology put intact protein into the mass spectrometer

9. 9 Peptide Fragmentation Peptides tend to fragment along the backbone. Fragments can also loose neutral chemical groups like NH 3 and H 2 O. H...-HN-CH-CO... NH-CH-CO-NH-CH-CO-…OH R i-1 RiRi R i+1 H+H+ N-TerminusC-Terminus Collision Induced Dissociation

10. 10 Ideal Mass Spectrum

11. 11 Real Mass Spectrum

12. 12 N- and C-terminal Peptides N-terminal peptides C-terminal peptides

13. 13 Terminal peptides and ion types Peptide Mass (D) 57 + 97 + 147 + 114 = 415 Peptide Mass (D) 57 + 97 + 147 + 114 – 18 = 397 without

14. 14 N- and C-terminal Peptides N-terminal peptides C-terminal peptides 415 486 301 154 57 71 185 332 429

15. 15 N- and C-terminal Peptides N-terminal peptides C-terminal peptides 415 486 301 154 57 71 185 332 429

16. 16 N- and C-terminal Peptides 415 486 301 154 57 71 185 332 429

17. 17 N- and C-terminal Peptides 415 486 301 154 57 71 185 332 429 Problem: Reconstruct peptide from the set of masses of fragment

18. 18 Mass Spectra GVDLK mass 0 57 Da = ‘G’ 99 Da = ‘V’ L K DVG The peaks in the mass spectrum: –Prefix –Fragments with neutral losses (-H 2 O, -NH 3 ) –Noise and missing peaks. and Suffix Fragments. D H2OH2O

19. 19 Protein Identification with MS/MS GVDLK mass 0 Intensity mass 0 MS/MS Peptide Identification:

20. 20 Protein Identification by Tandem Mass Spectrometry Sequence MS/MS instrument De Novo interpretation Sherenga Database search Sequest

21. 21 De Novo vs. Database Search W R A C V G E K D W L P T L T W R A C V G E K D W L P T L T De Novo AVGELTK Database Search Database of known peptides MDERHILNM, KLQWVCSDL, PTYWASDL, ENQIKRSACVM, TLACHGGEM, NGALPQWRT, HLLERTKMNVV, GGPASSDA, GGLITGMQSD, MQPLMNWE, ALKIIMNVRT, AVGELTK, HEWAILF, GHNLWAMNAC, GVFGSVLRA, EKLNKAATYIN.. Mass, Score

22. 22 Current Status It is still a open problem of protein sequencing no matter whether using de novo sequencing or database search methods Following algorithms only deal with simplified (or ideal) spectrums Some algorithms combine de novo sequencing and database search

23. 23 Pros and Cons of de novo Sequencing Advantage: –Gets the sequences that are not necessarily in the database. –An additional similarity search step using these sequences may identify the related proteins in the database. Disadvantage: –Requires higher quality data. –Often contains errors.

24. 24 Outline Motivation of proteomics Mass spectrometry-based proteomics Instrumentation of mass spectrometry De novo sequencing Database search Algorithms of real software (e.g., sequence tags)

25. 25 De novo Peptide Sequencing Sequence

26. 26 Peptide Sequencing Problem Goal: Find a peptide with maximal match between an experimental and theoretical spectrum. Input: –S: experimental spectrum –Δ : set of possible ion types –m: parent mass Output: –P: peptide with mass m, whose theoretical spectrum matches the experimental S spectrum the best

27. 27 Procedure of De Novo Sequencing Build spectrum graph –How to create vertices (from masses) –How to create edges (from mass differences) Find best path or rank paths of spectrum graph –How to find candidate paths –How to score paths

28. 28 S E Q U E N C E b Mass/Charge (M/Z) From Sequence to Spectrum

29. 29 a Mass/Charge (M/Z) S E Q U E N C E From Sequence to Spectrum (cont.)

30. 30 S E Q U E N C E Mass/Charge (M/Z) a is an ion type shift in b From Sequence to Spectrum (cont.)

31. 31 y Mass/Charge (M/Z) E C N E U Q E S From Sequence to Spectrum (cont.)

32. 32 Mass/Charge (M/Z) Intensity From Sequence to Spectrum (cont.)

33. 33 Mass/Charge (M/Z) Intensity From Sequence to Spectrum (cont.)

34. 34 noise Mass/Charge (M/Z) From Sequence to Spectrum (cont.)

35. 35 MS/MS Spectrum Mass/Charge (M/z) Intensity

36. 36 Some Mass Differences between Peaks Correspond to Amino Acids s s s e e e e e e e e q q q u u u n n n e c c c

37. 37 Now decoding from spectrum to sequence…? Build spectrum graph

38. 38 Peptide Fragmentation HO NH 3 + | | R 1 O R 2 O R 3 O R 4 | || | || | || | H -- N --- C --- C --- N --- C --- C --- N --- C --- C --- N --- C -- COOH | | | | | | | H H H H H H H y3y3 b2b2 y2y2 y1y1 b3b3 a2a2 a3a3 b2-H2Ob2-H2O y 3 -H 2 O b 3 - NH 3 y 2 - NH 3 Different ion types (b, y, b-NH 3, b-H 2 O) Fragment at one site (internal ions)

39. 39 Example of Ion Type Δ={δ 1, δ 2,…, δ k } Ion types {b, b-NH 3, b-H 2 O} correspond to Δ={0, 17, 18} *Note: In reality the δ value of ion type b is -1 but we will “hide” it for the sake of simplicity

40. 40 Why Peptide Sequencing hard Two ladders of overlapping masses, could not tell whether it is b ion or y ion Incomplete fragmentation Chemical noise Mass accuracy of the instrument is not good enough (Q=K, G+V=156.090, R=156.101) Q: Is sequencing shorter or longer peptide harder?

41. 41 Vertices of Spectrum Graph Vertices are generated by reverse shifts corresponding to ion types Δ={δ 1, δ 2,…, δ k } Every mass s in an MS/MS spectrum generates k vertices V(s) = {s+δ 1, s+δ 2, …, s+δ k } corresponding to potential N-terminal peptides Vertices of the spectrum graph: {initial vertex}  V(s 1 )  V(s 2 ) ...  V(s m )  {terminal vertex}

42. 42 Reverse Shifts Shift in H 2 O+NH 3 Shift in H 2 O

43. 43 Edges of Spectrum Graph Two vertices with mass difference corresponding to an amino acid A: –Connect with an edge labeled by A (Directed Graph) Gap edges for di- and tri-peptides – Potential sequence tag method (covered later)

44. 44 Best Path of Spectrum Graph How to find candidate paths There are many paths, how to find the correct one? We need scoring to evaluate paths

45. 45 Find Candidate Paths Heuristics: find a path with maximum number of edges Longest path problem in DAG DFS (Depth First Search)

46. 46 Path Score p(P,S) = probability that peptide P produces spectrum S= {s 1,s 2,…s q } Scoring = computing probabilities

47. 47 Finding Optimal Paths in the Spectrum Graph For a given MS/MS spectrum S, find a peptide P’ maximizing p(P,S) over all possible peptides P: Peptides = paths in the spectrum graph P’ = the optimal path in the spectrum graph Some software rank paths

48. 48 Ratio Test Scoring for Partial Peptides Incorporates premiums for observed ions and penalties for missing ions. Example: for k=4, assume that for a partial peptide P’ we only see ions δ 1,δ 2,δ 4. The score is calculated as:

49. 49 Why Not Sequence De Novo? De novo sequencing is still not very accurate! Less than 30% of the peptides sequenced were completely correct! Algorithm Amino Acid Accuracy Whole Peptide Accuracy Lutefisk (Taylor and Johnson, 1997). 0.5660.189 SHERENGA (Dancik et. al., 1999). 0.6900.289 Peaks (Ma et al., 2003). 0.6730.246 PepNovo (Frank and Pevzner, 2005). 0.7270.296

50. 50 De Novo vs. Database Search W R A C V G E K D W L P T L T W R A C V G E K D W L P T L T De Novo AVGELTK Database Search Database of known peptides MDERHILNM, KLQWVCSDL, PTYWASDL, ENQIKRSACVM, TLACHGGEM, NGALPQWRT, HLLERTKMNVV, GGPASSDA, GGLITGMQSD, MQPLMNWE, ALKIIMNVRT, AVGELTK, HEWAILF, GHNLWAMNAC, GVFGSVLRA, EKLNKAATYIN..

51. 51 Outline Motivation of proteomics Mass spectrometry-based proteomics Instrumentation: Mass Spectrometry De novo sequencing algorithm Database search Algorithms of real software (e.g., sequence tags)

52. 52 Peptide Identification Problem Goal: Find a peptide from the database with maximal match between an experimental and theoretical spectrum. Input: –S: experimental spectrum –database of peptides –Δ : set of possible ion types –m: parent mass Output: –A peptide of mass m from the database whose theoretical spectrum matches the experimental S spectrum the best

53. 53 Match between Spectra and the Shared Peak Count The match between two spectra is the number of masses (peaks) they share (Shared Peak Count or SPC) In practice mass-spectrometrists use the weighted SPC that reflects intensities of the peaks Match between experimental and theoretical spectra is defined similarly

54. 54 MS/MS Database Search Database search in mass-spectrometry has been successful in identification of already known proteins. Experimental spectrum can be compared with theoretical spectra of database peptides to find the best fit. SEQUEST (Yates et al., 1995) But reliable algorithms for identification of peptides is a much more difficult problem. Q: Why can a peptide be not identical to a sequence in the database

55. 55 Deficiency of the Shared Peaks Count Shared peaks count (SPC): intuitive measure of spectral similarity. Problem: SPC diminishes very quickly as the number of mutations increases. Only a small portion of correlations between the spectra of mutated peptides is captured by SPC.

56. 56 SPC Diminishes Quickly S(PRTEIN) = {98, 133, 246, 254, 355, 375, 476, 484, 597, 632} S(PRTEYN) = {98, 133, 254, 296, 355, 425, 484, 526, 647, 682} S(PGTEYN) = {98, 133, 155, 256, 296, 385, 425, 526, 548, 583} no mutations SPC=10 1 mutation SPC=5 2 mutations SPC=2

57. 57 Post-Translational Modifications Proteins are involved in cellular signaling and metabolic regulation. They are subject to a large number of biological modifications. Almost all protein sequences are post- translationally modified and 200 types of modifications of amino acid residues are known.

58. 58 Examples of Post-Translational Modification Post-translational modifications increase the number of “letters” in amino acid alphabet and lead to a combinatorial explosion in both database search and de novo approaches.

59. 59 Search for Modified Peptides: Virtual Database Approach Yates et al.,1995: an exhaustive search in a virtual database of all modified peptides. Exhaustive search leads to a large combinatorial problem, even for a small set of modifications types. Problem (Yates et al.,1995). Extend the virtual database approach to a large set of modifications.

60. 60 Modified Peptide Identification Problem Goal: Find a modified peptide from the database with maximal match between an experimental and theoretical spectrum. Input: –S: experimental spectrum –database of peptides –Δ : set of possible ion types –m: parent mass –Parameter k (# of mutations/modifications) Output: –A peptide of mass m that is at most k mutations/modifications apart from a database peptide and whose theoretical spectrum matches the experimental S spectrum the best

61. 61 Spectrum Alignment See 8.14 and 8.15 in the text book for one algorithm Complicated for real spectrums

62. 62 Outline Motivation of proteomics Mass spectrometry-based proteomics Instrumentation: Mass Spectrometry De novo sequencing algorithm Database search Algorithms of real software (e.g., sequence tags)

63. 63 Combining de novo and Database Search in Mass-Spectrometry So far de novo and database search were presented as two separate techniques Database search is rather slow: many labs generate more than 100,000 spectra per day. SEQUEST takes approximately 1 minute to compare a single spectrum against SWISS-PROT (54Mb) on a desktop. It will take SEQUEST more than 2 months to analyze the MS/MS data produced in a single day. Q: Can slow database search be combined with fast de novo analysis?

64. 64 What Can be Done with De Novo? Given an MS/MS spectrum: –Can de novo predict the entire peptide sequence? –Can de novo predict a set of partial sequences, that with high probability, contains at least one correct tag? A Covering Set of Tags - No! (accuracy is less than 30%). - Yes!

65. 65 Peptide Sequence Tags A Peptide Sequence Tag is short substring of a peptide. Example: G V D L K G V DG V D V D L D L KD L K Tags:

66. 66 Filtration with Peptide Sequence Tags Peptide sequence tags can be used as filters in database searches. The Filtration: Consider only database peptides that contain the tag (in its correct relative mass location). First suggested by Mann and Wilm (1994). Similar concepts also used by: –GutenTag - Tabb et. al. 2003. –MultiTag - Sunayev et. al. 2003. –OpenSea - Searle et. al. 2004.

67. 67 Why Filter Database Candidates? Effective filtration can greatly speed-up the process, enabling expensive searches involving post-translational modifications. Goal: generate a small set of covering tags and use them to filter the database peptides.

68. 68 Summary Protein sequencing Mass spectrum De novo search and database search Difficulty of protein sequencing

69. 69 The End

70. 70 Quality Measure of Mass Spectrometer Sensitivity Mass accuracy Resolution Dynamic range

71. 71 Exhaustive Search for Modified Peptides YFDSTDYNMAK 2 5 =32 possibilities, with 2 types of modifications! Phosphorylation? Oxidation? For each peptide, generate all modifications. Score each modification.

72. 72 Peptide Identification Problem: Challenge Very similar peptides may have very different spectra! Goal: Define a notion of spectral similarity that correlates well with the sequence similarity. If peptides are a few mutations/modifications apart, the spectral similarity between their spectra should be high.

73. 73 Why Filtration ? Scoring Protein Query Sequence Alignment – Smith Waterman Algorithm Sequence matches Protein Sequences Filtration Filtered Sequences Sequence Alignment – BLAST Database actgcgctagctacggatagctgatcc agatcgatgccataggtagctgatcc atgctagcttagacataaagcttgaat cgatcgggtaacccatagctagctcg atcgacttagacttcgattcgatcgaat tcgatctgatctgaatatattaggtccg atgctagctgtggtagtgatgtaaga BLAST filters out very few correct matches and is almost as accurate as Smith – Waterman algorithm. Database actgcgctagctacggatagctgatcc agatcgatgccataggtagctgatcc atgctagcttagacataaagcttgaat cgatcgggtaacccatagctagctcg atcgacttagacttcgattcgatcgaat tcgatctgatctgaatatattaggtccg atgctagctgtggtagtgatgtaaga

74. 74 Filtration and MS/MS Scoring MS/MS spectrum Peptide Sequencing – SEQUEST / Mascot Sequence matches Peptide Sequences Filtration Peptide Sequences Database MDERHILNMKLQWVCSDLPT YWASDLENQIKRSACVMTLA CHGGEMNGALPQWRTHLLE RTYKMNVVGGPASSDALITG MQSDPILLVCATRGHEWAILF GHNLWACVNMLETAIKLEGVF GSVLRAEKLNKAAPETYIN..

75. 75 Filtration in MS/MS Sequencing Filtration in MS/MS is more difficult than in BLAST. Early approaches using Peptide Sequence Tags were not able to substitute the complete database search. Current filtration approaches are mostly used to generate additional identifications rather than replace the database search. Can we design a filtration based search that can replace the database search, and is orders of magnitude faster?

76. 76 Asking the Old Question Again: Why Not Sequence De Novo? De novo sequencing is still not very accurate! Algorithm Amino Acid Accuracy Whole Peptide Accuracy Lutefisk (Taylor and Johnson, 1997). 0.5660.189 SHERENGA (Dancik et. al., 1999). 0.6900.289 Peaks (Ma et al., 2003). 0.6730.246 PepNovo (Frank and Pevzner, 2005). 0.7270.296