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Protein Identification Using Tandem Mass Spectrometry

Protein Identification Using Tandem Mass Spectrometry. Nathan Edwards Informatics Research Applied Biosystems. Outline. Proteomics context Tandem mass spectrometry Peptide fragmentation Peptide identification De novo Sequence database search Mascot screen shots Traps and pitfalls

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Protein Identification Using Tandem Mass Spectrometry

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  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

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