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Protein Identification by Sequence Database Search
Nathan Edwards Department of Biochemistry and Mol. & Cell. Biology Georgetown University Medical Center
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Peptide Mass Fingerprint
Cut out 2D-Gel Spot
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Peptide Mass Fingerprint
Trypsin Digest
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Peptide Mass Fingerprint
MS
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Peptide Mass Fingerprint
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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
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Protein Sequence Myoglobin GLSDGEWQQV LNVWGKVEAD IAGHGQEVLI RLFTGHPETL EKFDKFKHLK TEAEMKASED LKKHGTVVLT ALGGILKKKG HHEAELKPLA QSHATKHKIP IKYLEFISDA IIHVLHSKHP GDFGADAQGA MTKALELFRN DIAAKYKELG FQG
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Protein Sequence Myoglobin GLSDGEWQQV LNVWGKVEAD IAGHGQEVLI RLFTGHPETL EKFDKFKHLK TEAEMKASED LKKHGTVVLT ALGGILKKKG HHEAELKPLA QSHATKHKIP IKYLEFISDA IIHVLHSKHP GDFGADAQGA MTKALELFRN DIAAKYKELG FQG
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Amino-Acid Masses Amino-Acid Residual MW A Alanine 71.03712 M
Methionine C Cysteine N Asparagine D Aspartic acid P Proline E Glutamic acid Q Glutamine F Phenylalanine R Arginine G Glycine S Serine H Histidine T Threonine I Isoleucine V Valine K Lysine W Tryptophan L Leucine Y Tyrosine
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Peptide Masses 1811.90 GLSDGEWQQVLNVWGK 1606.85 VEADIAGHGQEVLIR
LFTGHPETLEK HGTVVLTALGGILK KGHHEAELKPLAQSHATK GHHEAELKPLAQSHATK YLEFISDAIIHVLHSK HPGDFGADAQGAMTK ALELFR
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Peptide Mass Fingerprint
YLEFISDAIIHVLHSK GLSDGEWQQVLNVWGK GHHEAELKPLAQSHATK HGTVVLTALGGILK HPGDFGADAQGAMTK VEADIAGHGQEVLIR KGHHEAELKPLAQSHATK ALELFR LFTGHPETLEK
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Sample Preparation for Tandem Mass Spectrometry
Enzymatic Digest and Fractionation
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Single Stage MS MS
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Tandem Mass Spectrometry (MS/MS)
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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
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Peptide Fragmentation
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Peptide Fragmentation
bi yn-i yn-i-1 -HN-CH-CO-NH-CH-CO-NH- Ri Ri+1 bi+1
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Peptide Fragmentation
-HN-CH-CO-NH-CH-CO-NH- Ri CH-R’ bi yn-i yn-i-1 bi+1 R” i+1 ai xn-i ci zn-i
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Peptide Fragmentation
Peptide: S-G-F-L-E-E-D-E-L-K MW ion 88 b1 S GFLEEDELK y9 1080 145 b2 SG FLEEDELK y8 1022 292 b3 SGF LEEDELK y7 875 405 b4 SGFL EEDELK y6 762 534 b5 SGFLE EDELK y5 633 663 b6 SGFLEE DELK y4 504 778 b7 SGFLEED ELK y3 389 907 b8 SGFLEEDE LK y2 260 1020 b9 SGFLEEDEL K y1 147
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Peptide Fragmentation
100 250 500 750 1000 m/z % Intensity K 1166 L 1020 E 907 D 778 663 534 405 F 292 G 145 S 88 b ions 147 260 389 504 633 762 875 1022 1080 y ions
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Peptide Fragmentation
K 1166 L 1020 E 907 D 778 663 534 405 F 292 G 145 S 88 b ions 100 250 500 750 1000 m/z % Intensity 147 260 389 504 633 762 875 1022 1080 y ions y6 y7 y2 y3 y4 y5 y8 y9
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Peptide Fragmentation
K 1166 L 1020 E 907 D 778 663 534 405 F 292 G 145 S 88 b ions 100 250 500 750 1000 m/z % Intensity 147 260 389 504 633 762 875 1022 1080 y ions y6 y7 y2 y3 y4 y5 y8 y9 b3 b5 b6 b7 b8 b9 b4
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Peptide Identification
Given: The mass of the precursor ion, and The MS/MS spectrum Output: The amino-acid sequence of the peptide
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Peptide Identification
Two paradigms: De novo interpretation Sequence database search
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De Novo Interpretation
100 250 500 750 1000 m/z % Intensity
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De Novo Interpretation
100 250 500 750 1000 m/z % Intensity E L
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De Novo Interpretation
100 250 500 750 1000 m/z % Intensity E L F KL SGF G D
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De Novo Interpretation
Amino-Acid Residual MW A Alanine M Methionine C Cysteine N Asparagine D Aspartic acid P Proline E Glutamic acid Q Glutamine F Phenylalanine R Arginine G Glycine S Serine H Histidine T Threonine I Isoleucine V Valine K Lysine W Tryptophan L Leucine Y Tyrosine
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De Novo Interpretation
…from Lu and Chen (2003), JCB 10:1
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De Novo Interpretation
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De Novo Interpretation
…from Lu and Chen (2003), JCB 10:1
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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
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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
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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
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Sequence Database Search
100 250 500 750 1000 m/z % Intensity K L E D F G S
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Sequence Database Search
100 250 500 750 1000 m/z % Intensity K 1166 L 1020 E 907 D 778 663 534 405 F 292 G 145 S 88 b ions 147 260 389 504 633 762 875 1022 1080 y ions
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Sequence Database Search
K 1166 L 1020 E 907 D 778 663 534 405 F 292 G 145 S 88 b ions 100 250 500 750 1000 m/z % Intensity 147 260 389 504 633 762 875 1022 1080 y ions y6 y7 y2 y3 y4 y5 y8 y9 b3 b5 b6 b7 b8 b9 b4
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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!
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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
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Peptide Candidate Filtering
>ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK… No missed cleavage sites MK WVTFISLLFLFSSAYSR GVFR R DAHK SEVAHR FK DLGEENFK ALVLIAFAQYLQQCPFEDHVK LVNEVTEFAK …
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Peptide Candidate Filtering
>ALBU_HUMAN MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK… One missed cleavage site MKWVTFISLLFLFSSAYSR WVTFISLLFLFSSAYSRGVFR GVFRR RDAHK DAHKSEVAHR SEVAHRFK FKDLGEENFK DLGEENFKALVLIAFAQYLQQCPFEDHVK ALVLIAFAQYLQQCPFEDHVKLVNEVTEFAK …
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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
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Peptide Molecular Weight
Same peptide, i = # of C13 isotope i=0 i=1 i=2 i=3 i=4
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Peptide Molecular Weight
Same peptide, i = # of C13 isotope i=0 i=1 i=2 i=3 i=4
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Peptide Molecular Weight
…from “Isotopes” – An IonSource.Com Tutorial
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Peptide Molecular Weight
Peptide sequence WVTFISLLFLFSSAYSR Potential phosphorylation? S,T,Y + 80 Da WVTFISLLFLFSSAYSR … 7 Molecular Weights 64 “Peptides”
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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
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Mascot Search Engine
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Mascot Peptide Mass Fingerprint
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Mascot MS/MS Ions Search
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Mascot Sequence Query
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Mascot MS/MS Search Results
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Mascot MS/MS Search Results
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Mascot MS/MS Search Results
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Mascot MS/MS Search Results
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Mascot MS/MS Search Results
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Mascot MS/MS Search Results
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Mascot MS/MS Search Results
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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
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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
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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
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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
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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
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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!
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