Protein Identification Using Tandem Mass Spectrometry

1. Protein Identification Using Tandem Mass Spectrometry Nathan Edwards Informatics Research Applied Biosystems

2. Outline • Proteomics context • Tandem mass spectrometry • Peptide fragmentation • Peptide identification • De novo • Sequence database search • Mascot screen shots • Traps and pitfalls • Summary

3. Proteomics Context High-throughput proteomics focus • (Differential) Quantitation • How much of each protein is there? • Identification • What proteins are present? Two established workflows • 2-D Gels • LC-MS, LC-MALDI

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

5. Single Stage MS MS

6. Tandem Mass Spectrometry(MS/MS) • Acquire mass spectrum of sample • Select interesting ion by m/z value • Fragment the selected “parent” ion • Acquire mass spectrum of parent ion’s fragments

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

8. Peptide Fragmentation Peptides consist of amino-acids arranged in a linear backbone. N-terminus H…-HN-CH-CO-NH-CH-CO-NH-CH-CO-…OH Ri-1 Ri Ri+1 C-terminus AA residuei-1 AA residuei AA residuei+1

9. Peptide Fragmentation Peptides consist of amino-acids arranged in a linear backbone. N-terminus H+ H…-HN-CH-CO-NH-CH-CO-NH-CH-CO-…OH Ri-1 Ri Ri+1 C-terminus AA residuei-1 AA residuei AA residuei+1 Ionized peptide (addition of a proton)

10. Peptide Fragmentation Peptides consist of amino-acids arranged in a linear backbone. N-terminus H+ H…-HN-CH-CO NH-CH-CO-NH-CH-CO-…OH Ri Ri+1 Ri-1 AA residuei AA residuei+1 C-terminus AA residuei-1 Fragmented peptide C-terminus fragment observed

11. Peptide Fragmentation yn-i yn-i-1 -HN-CH-CO-NH-CH-CO-NH- CH-R’ Ri i+1 R” bi i+1 bi+1

12. Peptide Fragmentation Peptide: S-G-F-L-E-E-D-E-L-K

13. Peptide Fragmentation 88 145 292 405 534 663 778 907 1020 1166 b ions S G F L E E D E L K 1166 1080 1022 875 762 633 504 389 260 147 y ions 100 % Intensity 0 m/z 250 500 750 1000

14. Peptide Fragmentation 88 145 292 405 534 663 778 907 1020 1166 b ions S G F L E E D E L K 1166 1080 1022 875 762 633 504 389 260 147 y ions y6 100 y7 % Intensity y5 y2 y3 y8 y4 y9 0 m/z 250 500 750 1000

15. Peptide Fragmentation 88 145 292 405 534 663 778 907 1020 1166 b ions S G F L E E D E L K 1166 1080 1022 875 762 633 504 389 260 147 y ions y6 100 y7 % Intensity y5 b3 b4 y2 y3 b5 y8 y4 b8 y9 b6 b7 b9 0 m/z 250 500 750 1000

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

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

18. De Novo Interpretation 100 % Intensity 0 m/z 250 500 750 1000

19. L De Novo Interpretation 100 % Intensity E 0 m/z 250 500 750 1000

20. SGF L L L F De Novo Interpretation 100 % Intensity G E E E D E KL E E D 0 m/z 250 500 750 1000

21. 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

22. De Novo Interpretation

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

24. Sequence Database Search 88 145 292 405 534 663 778 907 1020 1166 b ions S G F L E E D E L K 1166 1080 1022 875 762 633 504 389 260 147 y ions 100 % Intensity 0 m/z 250 500 750 1000

25. Sequence Database Search 88 145 292 405 534 663 778 907 1020 1166 b ions S G F L E E D E L K 1166 1080 1022 875 762 633 504 389 260 147 y ions y6 100 y7 % Intensity y5 b3 b4 y2 y3 b5 y8 y4 b8 y9 b6 b7 b9 0 m/z 250 500 750 1000

26. 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!

27. 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

28. Peptide Candidate Filtering >ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK… No missed cleavage sites MKWVTFISLLFLFSSAYSRGVFR R DAHK SEVAHR FK DLGEENFK ALVLIAFAQYLQQCPFEDHVK LVNEVTEFAK …

29. Peptide Candidate Filtering >ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK… One missed cleavage site MKWVTFISLLFLFSSAYSRGVFRR RDAHK DAHKSEVAHR SEVAHRFK FKDLGEENFK DLGEENFKALVLIAFAQYLQQCPFEDHVK ALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK …

30. 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

31. Peptide Molecular Weight i=0 Same peptide,i = # of C13 isotope i=1 i=2 i=3 i=4

32. Peptide Molecular Weight i=0 Same peptide,i = # of C13 isotope i=1 i=2 i=3 i=4

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

34. 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

35. Mascot Search Engine

36. Mascot MS/MS Ions Search

37. Mascot MS/MS Search Results

38. Mascot MS/MS Search Results

39. Mascot MS/MS Search Results

40. Mascot MS/MS Search Results

41. Mascot MS/MS Search Results

42. Mascot MS/MS Search Results

43. Mascot MS/MS Search Results

44. Mascot MS/MS Search Results

45. Mascot MS/MS Search Results

46. Mascot MS/MS Search Results

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

48. Sequence Database SearchTraps 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

49. Sequence Database SearchTraps 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

50. Sequence Database SearchTraps 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