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Peptide Identification via Tandem Mass Spectrometry Sorin Istrail PowerPoint Presentation
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Peptide Identification via Tandem Mass Spectrometry Sorin Istrail

Peptide Identification via Tandem Mass Spectrometry Sorin Istrail

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Peptide Identification via Tandem Mass Spectrometry Sorin Istrail

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  1. Peptide Identification via Tandem Mass Spectrometry Sorin Istrail

  2. Enzymatic Digestion (Trypsin) + Fractionation Sample Preparation for MS

  3. Single Stage MS Mass Spectrometry LC-MS: 1 MS spectrum / second

  4. Tandem MS LC-MS/MS: 2-3 spectra / second Secondary Fragmentation

  5. H...-HN-CH-CO-NH-CH-CO-NH-CH-CO-…OH Ri-1 Ri Ri+1 Tandem MS for Peptide ID The peptide backbone breaks to form fragments with characteristic masses. C-terminus N-terminus AA residuei-1 AA residuei AA residuei+1

  6. S G F L E E D E L K Tandem MS for Peptide ID 88 145 292 405 534 663 778 907 1020 1166 1166 1080 1022 875 762 633 504 389 260 147 100 % Relative Abundance 0 250 500 750 1000 m/z

  7. xn-i yn-i yn-i-1 vn-i wn-i zn-i -HN-CH-CO-NH-CH-CO-NH- CH-R’ Ri i+1 ai R” i+1 bi bi+1 ci di+1 low energy fragments high energy fragments Tandem MS for Peptide ID Peptide fragmentation possibilities

  8. Input: Mass of parent peptide, Tandem MS spectrum Output: Peptide sequence Tandem MS Spectrum Interpretation • De novo • Putative fragment comparison • Combinatorial enumeration • Sequence database

  9. SGF L L L F De novo Spectrum Interpretation 100 % Relative Abundance G E E E D E KL E E D 0 250 500 750 1000 m/z

  10. De novo Spectrum Interpretation • Works best for spectra with simple, well formed fragment ladders. • Missing fragments create ambiguity. • Noise or unexpected fragments create ambiguity. • Many fragment types create ambiguity. • “Best” de novo interpretation may have no biological relevance.

  11. b1 b2 b3 b4 b5 b6 b7 b8 b9 M+H 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 y9 y8 y7 y6 y5 y4 y3 y2 y1 M+H y6 100 y7 [M+2H]2+ % Relative Abundance y5 b3 b4 y2 y3 b5 y4 y8 b8 b9 b6 b7 y9 0 250 500 750 1000 m/z Putative Fragment Comparison

  12. Input: Peptide mass, tryptic digestion properties, compositional information… Output: Candidate peptide sequence Putative Fragment Comparison Generating candidate peptide sequences • Combinatorial enumeration • Sequence database

  13. Putative Fragment Comparison • Combinatorial enumeration • All possible sequences can be checked • Too many candidates • Many candidates are equally plausible. • “Best” candidate may have no biological relevance • Sequence database • Sequences with no biological relevance are eliminated • Few candidates to evaluate • Sequence permutations eliminated • Correct candidate might be missing from database • All candidates have some biological relevance

  14. Candidate Peptide Evaluation • Score functions for candidate peptide evaluation • Shared peak count • Correlation • Pr [ spectrum | peptide ] By itself, the score of a peptide candidate is meaningless!

  15. Candidate Peptide Evaluation 1 83.5 TCVADESAENCDK ALBU_HUMAN,ALBU_MACMU,ALBU_PIG 2 109.4 KCAADESAENCDK ALBU_HORSE 3 115.3 FKKCDGDTVWDK SRB9_YEAST 4 121.7 SGKAPILIATDVASR DD17_HUMAN 5 124.1 MGFINLSLFDVDK RRPO_RCNMV 6 126.4 QSDEDCVEIYIK LEM2_BOVIN 7 127.8 MLDQSTDFEERK SMOO_HUMAN 8 128.1 NFEMDTLTLLSSK DHAS_BACSU 9 129.3 DNIAKEYENKFK HPAA_HELNE 10 129.6 VEHVAFGLVLGDDK SYR_CAEEL 11 129.6 LVEVSHDAEDEQK DYHC_NEUCR 12 129.9 KTGYAHFFSRER HIS2_THEMA 13 130.2 DYTLFALQEGDVK RK27_PLECA,RK27_PLEHA 14 130.3 FNVTISLTDFITK SYK_CAEEL 15 130.4 ENCQTLDNYVSR GS27_CAEEL

  16. Candidate Peptide Evaluation