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

2 Outline Proteomics Mass Spectrometry Protein Identification Peptide Mass Fingerprint Tandem Mass Spectrometry

3 Proteomics Proteins are the machines that drive much of biology Genes are merely the recipe The direct characterization of a sample’s proteins en masse. What proteins are present? How much of each protein is present?

4 2D Gel-Electrophoresis Protein separation Molecular weight (MW) Isoelectric point (pI) Staining Birds-eye view of protein abundance

5 2D Gel-Electrophoresis Bécamel et al., Biol. Proced. Online 2002;4:

6 Paradigm Shift Traditional protein chemistry assay methods struggle to establish identity. Identity requires: Specificity of measurement (Precision) Mass spectrometry A reference for comparison (Measurement → Identity) Protein sequence databases

7 Mass Spectrometer Ionizer Sample + _ Mass Analyzer Detector MALDI Electro-Spray Ionization (ESI) Time-Of-Flight (TOF) Quadrapole Ion-Trap Electron Multiplier (EM)

8 Mass Spectrometer (MALDI-TOF) Source Length = s Field-free drift zone Length = D E d = 0 Microchannel plate detector Backing plate (grounded) Extraction grid (source voltage -V s ) UV (337 nm) Detector grid -V s Pulse voltage Analyte/ matrix

9 Mass Spectrum

10 Mass is fundamental

11 Peptide Mass Fingerprint Cut out 2D-Gel Spot

12 Peptide Mass Fingerprint Trypsin Digest

13 Peptide Mass Fingerprint MS

14 Peptide Mass Fingerprint

15 Peptide Mass Fingerprint 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

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

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

18 Amino-Acid Masses 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

19 Peptide Mass & m/z Peptide Molecular Weight: N-terminal-mass (0.00) + Sum (AA masses) + C-terminal-mass ( ) Observed Peptide m/z: (Peptide Molecular Weight + z * Proton-mass ( )) / z Monoisotopic mass values!

20 Peptide Masses GLSDGEWQQVLNVWGK VEADIAGHGQEVLIR LFTGHPETLEK HGTVVLTALGGILK KGHHEAELKPLAQSHATK GHHEAELKPLAQSHATK YLEFISDAIIHVLHSK HPGDFGADAQGAMTK ALELFR

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

22 Sample Preparation for Tandem Mass Spectrometry Enzymatic Digest and Fractionation

23 Single Stage MS MS

24 Tandem Mass Spectrometry (MS/MS) MS/MS

25 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.

26 Peptide Fragmentation

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

28 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

29 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

30 Peptide Fragmentation

31 Peptide Fragmentation

32 Peptide Fragmentation

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

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

35 De Novo Interpretation

36 De Novo Interpretation

37 De Novo Interpretation

38 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

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

40 De Novo Interpretation

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

42 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

43 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

44 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

45 Sequence Database Search

46 Sequence Database Search

47 Sequence Database Search

48 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!

49 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

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

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

52 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

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

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

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

56 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”

57 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

58 Peptide Identification High-throughput workflows demand we analyze all spectra, all the time. Spectra may not contain enough information to be interpreted correctly …fading in and out on a cell phone Spectra may contain too many peaks …static or background noise Peptides may not match our assumptions …its all Greek to me “Don’t know” is an acceptable answer!

59 Peptide Identification Rank the best peptide identifications Is the top ranked peptide correct?

60 Peptide Identification Rank the best peptide identifications Is the top ranked peptide correct?

61 Peptide Identification Rank the best peptide identifications Is the top ranked peptide correct?

62 Peptide Identification Incorrect peptide has best score Correct peptide is missing? Potential for incorrect conclusion What score ensures no incorrect peptides? Correct peptide has weak score Insufficient fragmentation, poor score Potential for weakened conclusion What score ensures we find all correct peptides?

63 Statistical Significance Can’t prove particular identifications are right or wrong......need to know fragmentation in advance! A minimal standard for identification scores......better than guessing. p-value, E-value, statistical significance

64 Mascot MS/MS Ions Search

65 Mascot MS/MS Search Results

66 Mascot MS/MS Search Results

67 Mascot MS/MS Search Results

68 Mascot MS/MS Search Results

69 Mascot MS/MS Search Results

70 Mascot MS/MS Search Results

71 Mascot MS/MS Search Results

72 Sequence Database Search Traps and Pitfalls Search options may eliminate the correct peptide Precursor mass 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

73 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

74 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? Incorrect instrument settings or fragment tolerance can render scores non-specific. 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

75 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

76 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

77 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.

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