Protein Identification Using Tandem Mass Spectrometry Nathan Edwards Center for Bioinformatics and Computational Biology University of Maryland, College Park

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 Sample Preparation for Tandem Mass Spectrometry Enzymatic Digest and Fractionation

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 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.

9 Peptide Fragmentation

10 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. H+H+ Ionized peptide (addition of a proton)

11 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. H+H+ Fragmented peptide C-terminus fragment observed

12 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

13 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

14 Peptide Fragmentation Peptide: S-G-F-L-E-E-D-E-L-K MWion MW 88b1b1 S GFLEEDELKy9y b2b2 SG FLEEDELKy8y b3b3 SGF LEEDELKy7y b4b4 SGFL EEDELKy6y b5b5 SGFLE EDELKy5y b6b6 SGFLEE DELKy4y b7b7 SGFLEED ELKy3y b8b8 SGFLEEDE LKy2y b9b9 SGFLEEDEL Ky1y1 147

15 Peptide Fragmentation m/z % Intensity K 1166 L 1020 E 907 D 778 E 663 E 534 L 405 F 292 G 145 S 88b ions y ions

16 Peptide Fragmentation K 1166 L 1020 E 907 D 778 E 663 E 534 L 405 F 292 G 145 S 88b ions m/z % Intensity y ions y6y6 y7y7 y2y2 y3y3 y4y4 y5y5 y8y8 y9y9

17 Peptide Fragmentation K 1166 L 1020 E 907 D 778 E 663 E 534 L 405 F 292 G 145 S 88b ions m/z % Intensity y ions y6y6 y7y7 y2y2 y3y3 y4y4 y5y5 y8y8 y9y9 b3b3 b5b5 b6b6 b7b7 b8b8 b9b9 b4b4

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

19 Peptide Identification Two paradigms: De novo interpretation Sequence database search

20 De Novo Interpretation m/z % Intensity

21 De Novo Interpretation m/z % Intensity EL

22 De Novo Interpretation m/z % Intensity ELF KL SGF G E D E L E E D E L

23 De Novo Interpretation Amino-AcidResidual MWAmino-AcidResidual MW AAlanine MMethionine CCysteine NAsparagine DAspartic acid PProline EGlutamic acid QGlutamine FPhenylalanine RArginine GGlycine SSerine HHistidine TThreonine IIsoleucine VValine KLysine WTryptophan LLeucine YTyrosine

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

25 De Novo Interpretation

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

27 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

28 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

29 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

30 Sequence Database Search m/z % Intensity KLEDEELFG S

31 Sequence Database Search m/z % Intensity K 1166 L 1020 E 907 D 778 E 663 E 534 L 405 F 292 G 145 S 88b ions y ions

32 Sequence Database Search K 1166 L 1020 E 907 D 778 E 663 E 534 L 405 F 292 G 145 S 88b ions m/z % Intensity y ions y6y6 y7y7 y2y2 y3y3 y4y4 y5y5 y8y8 y9y9 b3b3 b5b5 b6b6 b7b7 b8b8 b9b9 b4b4

33 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!

34 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 amino-acids Must allow for “missed” cleavage sites

35 Peptide Candidate Filtering >ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKA LVLIAFAQYLQQCPFEDHVKLVNEVTEFAK… No missed cleavage sites MK WVTFISLLFLFSSAYSR GVFR R DAHK SEVAHR FK DLGEENFK ALVLIAFAQYLQQCPFEDHVK LVNEVTEFAK …

36 Peptide Candidate Filtering >ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKA LVLIAFAQYLQQCPFEDHVKLVNEVTEFAK… One missed cleavage site MKWVTFISLLFLFSSAYSR WVTFISLLFLFSSAYSRGVFR GVFRR RDAHK DAHKSEVAHR SEVAHRFK FKDLGEENFK DLGEENFKALVLIAFAQYLQQCPFEDHVK ALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK …

37 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

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

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

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

41 Peptide Molecular Weight Peptide sequence WVTFISLLFLFSSAYSR Potential phosphorylation? –S,T,Y + 80 Da WVTFISLLFLFSSAYSR WVTFISLLFLFSSAYSR WVTFISLLFLFSSAYSR WVTFISLLFLFSSAYSR WVTFISLLFLFSSAYSR WVTFISLLFLFSSAYSR WVTFISLLFLFSSAYSR WVTFISLLFLFSSAYSR WVTFISLLFLFSSAYSR …… WVTFISLLFLFSSAYSR Molecular Weights - 64 “Peptides”

42 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

43 Mascot Search Engine

44 Mascot MS/MS Ions Search

45 Mascot MS/MS Search Results

46 Mascot MS/MS Search Results

47 Mascot MS/MS Search Results

48 Mascot MS/MS Search Results

49 Mascot MS/MS Search Results

50 Mascot MS/MS Search Results

51 Mascot MS/MS Search Results

52 Mascot MS/MS Search Results

53 Mascot MS/MS Search Results

54 Mascot MS/MS Search Results

55 Sequence Database Search Traps 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

56 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

57 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

58 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

59 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

60 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!

61 Further Reading Matrix Science (Mascot) Web Site – Seattle Proteome Center (ISB) – Proteomic Mass Spectrometry Lab at The Scripps Research Institute –fields.scripps.edufields.scripps.edu UCSF ProteinProspector –prospector.ucsf.eduprospector.ucsf.edu