1. Protein Identification by Sequence Database Search Nathan Edwards Department of Biochemistry and Mol. & Cell. Biology Georgetown University Medical Center

2. 2 Peptide Mass Fingerprint Cut out 2D-Gel Spot

3. 3 Peptide Mass Fingerprint Trypsin Digest

4. 4 Peptide Mass Fingerprint MS

5. 5 Peptide Mass Fingerprint

6. 6 Trypsin: digestion enzyme Highly specific Cuts after K & R except if followed by P Protein sequence from sequence database In silico digest Mass computation For each protein sequence in turn: Compare computer generated masses with observed spectrum

7. 7 Protein Sequence Myoglobin GLSDGEWQQV LNVWGKVEAD IAGHGQEVLI RLFTGHPETL EKFDKFKHLK TEAEMKASED LKKHGTVVLT ALGGILKKKG HHEAELKPLA QSHATKHKIP IKYLEFISDA IIHVLHSKHP GDFGADAQGA MTKALELFRN DIAAKYKELG FQG

8. 8 Protein Sequence Myoglobin GLSDGEWQQV LNVWGKVEAD IAGHGQEVLI RLFTGHPETL EKFDKFKHLK TEAEMKASED LKKHGTVVLT ALGGILKKKG HHEAELKPLA QSHATKHKIP IKYLEFISDA IIHVLHSKHP GDFGADAQGA MTKALELFRN DIAAKYKELG FQG

9. 9 Amino-Acid Masses Amino-AcidResidual MWAmino-AcidResidual MW AAlanine71.03712MMethionine131.04049 CCysteine103.00919NAsparagine114.04293 DAspartic acid115.02695PProline97.05277 EGlutamic acid129.04260QGlutamine128.05858 FPhenylalanine147.06842RArginine156.10112 GGlycine57.02147SSerine87.03203 HHistidine137.05891TThreonine101.04768 IIsoleucine113.08407VValine99.06842 KLysine128.09497WTryptophan186.07932 LLeucine113.08407YTyrosine163.06333

10. 10 Peptide Masses 1811.90 GLSDGEWQQVLNVWGK 1606.85 VEADIAGHGQEVLIR 1271.66 LFTGHPETLEK 1378.83 HGTVVLTALGGILK 1982.05 KGHHEAELKPLAQSHATK 1853.95 GHHEAELKPLAQSHATK 1884.01 YLEFISDAIIHVLHSK 1502.66 HPGDFGADAQGAMTK 748.43 ALELFR

11. 11 Peptide Mass Fingerprint GLSDGEWQQVLNVWGK VEADIAGHGQEVLIR LFTGHPETLEK HGTVVLTALGGILK KGHHEAELKPLAQSHATK GHHEAELKPLAQSHATK YLEFISDAIIHVLHSK HPGDFGADAQGAMTK ALELFR

12. 12 Sample Preparation for Tandem Mass Spectrometry Enzymatic Digest and Fractionation

13. 13 Single Stage MS MS

14. 14 Tandem Mass Spectrometry (MS/MS) MS/MS

15. 15 Peptide Fragmentation H…-HN-CH-CO-NH-CH-CO-NH-CH-CO-…OH R i-1 RiRi R i+1 AA residue i-1 AA residue i AA residue i+1 N-terminus C-terminus Peptides consist of amino-acids arranged in a linear backbone.

16. 16 Peptide Fragmentation

17. 17 Peptide Fragmentation -HN-CH-CO-NH-CH-CO-NH- RiRi R i+1 bibi y n-i y n-i-1 b i+1

18. 18 Peptide Fragmentation -HN-CH-CO-NH-CH-CO-NH- RiRi CH-R’ bibi y n-i y n-i-1 b i+1 R” i+1 aiai x n-i cici z n-i

19. 19 Peptide Fragmentation Peptide: S-G-F-L-E-E-D-E-L-K MWion MW 88b1b1 S GFLEEDELKy9y9 1080 145b2b2 SG FLEEDELKy8y8 1022 292b3b3 SGF LEEDELKy7y7 875 405b4b4 SGFL EEDELKy6y6 762 534b5b5 SGFLE EDELKy5y5 633 663b6b6 SGFLEE DELKy4y4 504 778b7b7 SGFLEED ELKy3y3 389 907b8b8 SGFLEEDE LKy2y2 260 1020b9b9 SGFLEEDEL Ky1y1 147

20. 20 Peptide Fragmentation

21. 21 Peptide Fragmentation

22. 22 Peptide Fragmentation

23. 23 Peptide Identification Given: The mass of the precursor ion, and The MS/MS spectrum Output: The amino-acid sequence of the peptide

24. 24 Peptide Identification Two paradigms: De novo interpretation Sequence database search

25. 25 De Novo Interpretation

26. 26 De Novo Interpretation

27. 27 De Novo Interpretation

28. 28 De Novo Interpretation Amino-AcidResidual MWAmino-AcidResidual MW AAlanine71.03712MMethionine131.04049 CCysteine103.00919NAsparagine114.04293 DAspartic acid115.02695PProline97.05277 EGlutamic acid129.04260QGlutamine128.05858 FPhenylalanine147.06842RArginine156.10112 GGlycine57.02147SSerine87.03203 HHistidine137.05891TThreonine101.04768 IIsoleucine113.08407VValine99.06842 KLysine128.09497WTryptophan186.07932 LLeucine113.08407YTyrosine163.06333

29. 29 De Novo Interpretation …from Lu and Chen (2003), JCB 10:1

30. 30 De Novo Interpretation

31. 31 De Novo Interpretation …from Lu and Chen (2003), JCB 10:1

32. 32 De Novo Interpretation Find good paths in spectrum graph Can’t use same peak twice Forbidden pairs: NP-hard “Nested” forbidden pairs: Dynamic Prog. Simple peptide fragmentation model Usually many apparently good solutions Needs better fragmentation model Needs better path scoring

33. 33 De Novo Interpretation Amino-acids have duplicate masses! Incomplete ladders create ambiguity. Noise peaks and unmodeled fragments create ambiguity “Best” de novo interpretation may have no biological relevance Current algorithms cannot model many aspects of peptide fragmentation Identifies relatively few peptides in high- throughput workflows

34. 34 Sequence Database Search Compares peptides from a protein sequence database with spectra Filter peptide candidates by Precursor mass Digest motif Score each peptide against spectrum Generate all possible peptide fragments Match putative fragments with peaks Score and rank

35. 35 Sequence Database Search

36. 36 Sequence Database Search

37. 37 Sequence Database Search

38. 38 Sequence Database Search No need for complete ladders Possible to model all known peptide fragments Sequence permutations eliminated All candidates have some biological relevance Practical for high-throughput peptide identification Correct peptide might be missing from database!

39. 39 Peptide Candidate Filtering Digestion Enzyme: Trypsin Cuts just after K or R unless followed by a P. Basic residues (K & R) at C-terminal attract ionizing charge, leading to strong y-ions “Average” peptide length about 10-15 amino-acids Must allow for “missed” cleavage sites

40. 40 Peptide Candidate Filtering >ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKAL VLIAFAQYLQQCPFEDHVKLVNEVTEFAK… No missed cleavage sites MK WVTFISLLFLFSSAYSR GVFR R DAHK SEVAHR FK DLGEENFK ALVLIAFAQYLQQCPFEDHVK LVNEVTEFAK …

41. 41 Peptide Candidate Filtering >ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKAL VLIAFAQYLQQCPFEDHVKLVNEVTEFAK… One missed cleavage site MKWVTFISLLFLFSSAYSR WVTFISLLFLFSSAYSRGVFR GVFRR RDAHK DAHKSEVAHR SEVAHRFK FKDLGEENFK DLGEENFKALVLIAFAQYLQQCPFEDHVK ALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK …

42. 42 Peptide Candidate Filtering Peptide molecular weight Only have m/z value Need to determine charge state Ion selection tolerance Mass for each amino-acid symbol? Monoisotopic vs. Average “Default” residual mass Depends on sample preparation protocol Cysteine almost always modified

43. 43 Peptide Molecular Weight Same peptide, i = # of C 13 isotope i=0 i=1 i=2 i=3 i=4

44. 44 Peptide Molecular Weight Same peptide, i = # of C 13 isotope i=0 i=1 i=2 i=3 i=4

45. 45 Peptide Molecular Weight …from “Isotopes” – An IonSource.Com Tutorial

46. 46 Peptide Molecular Weight Peptide sequence WVTFISLLFLFSSAYSR Potential phosphorylation? S,T,Y + 80 Da WVTFISLLFLFSSAYSR2018.06 WVTFISLLFLFSSAYSR2098.06 WVTFISLLFLFSSAYSR2098.06 WVTFISLLFLFSSAYSR2098.06 WVTFISLLFLFSSAYSR2098.06 WVTFISLLFLFSSAYSR2098.06 WVTFISLLFLFSSAYSR2098.06 WVTFISLLFLFSSAYSR2178.06 WVTFISLLFLFSSAYSR2178.06 …… WVTFISLLFLFSSAYSR2418.06 - 7 Molecular Weights - 64 “Peptides”

47. 47 Peptide Scoring Peptide fragments vary based on The instrument The peptide’s amino-acid sequence The peptide’s charge state Etc… Search engines model peptide fragmentation to various degrees. Speed vs. sensitivity tradeoff y-ions & b-ions occur most frequently

48. 48 Mascot Search Engine

49. 49 Mascot Peptide Mass Fingerprint

50. 50 Mascot MS/MS Ions Search

51. 51 Mascot Sequence Query

52. 52 Mascot MS/MS Search Results

53. 53 Mascot MS/MS Search Results

54. 54 Mascot MS/MS Search Results

55. 55 Mascot MS/MS Search Results

56. 56 Mascot MS/MS Search Results

57. 57 Mascot MS/MS Search Results

58. 58 Mascot MS/MS Search Results

59. 59 Sequence Database Search Traps and Pitfalls Search options may eliminate the correct peptide Precursor mass tolerance too small Fragment m/z tolerance too small Incorrect precursor ion charge state Non-tryptic or semi-tryptic peptide Incorrect or unexpected modification Sequence database too conservative Unreliable taxonomy annotation

60. 60 Sequence Database Search Traps and Pitfalls Search options can cause infinite search times Variable modifications increase search times exponentially Non-tryptic search increases search time by two orders of magnitude Large sequence databases contain many irrelevant peptide candidates

61. 61 Sequence Database Search Traps and Pitfalls Best available peptide isn’t necessarily correct! Score statistics (e-values) are essential! What is the chance a peptide could score this well by chance alone? The wrong peptide can look correct if the right peptide is missing! Need scores (or e-values) that are invariant to spectrum quality and peptide properties

62. 62 Sequence Database Search Traps and Pitfalls Search engines often make incorrect assumptions about sample prep Proteins with lots of identified peptides are not more likely to be present Peptide identifications do not represent independent observations All proteins are not equally interesting to report

63. 63 Sequence Database Search Traps and Pitfalls Good spectral processing can make a big difference Poorly calibrated spectra require large m/z tolerances Poorly baselined spectra make small peaks hard to believe Poorly de-isotoped spectra have extra peaks and misleading charge state assignments

64. 64 Summary Protein identification from tandem mass spectra is a key proteomics technology. Protein identifications should be treated with healthy skepticism. Look at all the evidence! Spectra remain unidentified for a variety of reasons. Lots of open algorithmic problems!