1. Peptide Identification via Tandem Mass Spectrometry Sorin Istrail

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

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

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

5. Tandem MS for Peptide ID 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 The peptide backbone breaks to form fragments with characteristic masses.

6. Tandem MS for Peptide ID m/z KLEDEELFGS 147260389504633762875102210801166 102090777866353440529214588 % Relative Abundance 100 0 2505007501000

7. Tandem MS for Peptide ID -HN-CH-CO-NH-CH-CO-NH- RiRi CH-R’ aiai bibi cici x n-i y n-i z n-i y n-i-1 b i+1 R” d i+1 v n-i w n-i i+1 low energy fragmentshigh energy fragments Peptide fragmentation possibilities

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

9. De novo Spectrum Interpretation m/z % Relative Abundance 100 0 2505007501000 ELF KL SGF G E D E L E E D E L

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. Putative Fragment Comparison

12. Candidate peptide sequenceOutput: Peptide mass, tryptic digestion properties, compositional information… Input: 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