1. ADVANCEMENT IN PROTEIN INFERENCE FROM SHOTGUN PROTEOMICS USING PEPTIDE DETECTABILITY PEDRO ALVES Advisor: Predrag Radivojac School of Informatics BLOOMINGTON

2. Overview Shotgun Proteomics Protein Inference Problem Protein Identification Using Peptide Detectability Results Limitations and Improvements

5. Degenerate Peptides Rat Sample/Rat IPI Database 60% Nesvizhskii, A.I. and Aebersold, R. (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell Proteomics, 4, 1419–1440.

6. Protein Inference Problem Solution 1 * (A, E) * Solution 2 * (B, C, D) * * * * Minimum Protein Set 11 Possible Solutions Nesvizhskii, A.I. and Aebersold, R. (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell Proteomics, 4, 1419–1440.

7. Identified Peptides 1 2 3 4 5 6 7 8 9 10 Proteins 1 2 3 4 2 6 1 2 3 5 4 1 7 8 10 9 6 6 9 10 GMPSA 5 33 1 3 2 3 22 2 0 0 1 Greedy Minimum Protein Set Algorithm Nesvizhskii, A.I. and Aebersold, R. (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell Proteomics, 4, 1419–1440.

8. Resolving Ambiguity detectability of a peptide – the probability that the peptide will be observed in a standard sample analyzed by a standard proteomics routine Tang, H., Arnold, R. J., Alves, P., Xun, Z., Clemmer, D. E., Novotny, M. V., Reilly, J. P. & Radivojac, P. (2006). A computational approach toward label-free protein quantification using predicted peptide detectability. Bioinformatics, (2006) 22 (14): e481-e488

9. Factors affecting Peptide Detection Four classes of factors 1)Chemical properties of the peptide (and parent protein) 2)Limitations of peptide identification protocol 3)Abundance of the peptide in the sample 4)Presence of other peptides that compete for detection Mean Accuracy :71% Mean AUC :78% Synthetic : ~30% of peptides identified Real :~10% of peptides identified Peptide Detectability Prediction

10. Identified Peptides 1 2 3 4 5 6 7 8 9 10 Proteins Minimum Missed Peptides 12 45 4 2 6 1457814578 9 27 10 24 53 23 17 55 6 9 10 14 1 17 2 3 1 2 15 3 24 01 Missed peptide MDAP

11. Identified Peptides 1 2 3 4 5 6 7 8 9 10 ProteinsLDFA 12 45 4 2 6 1457814578 9 27 10 24 53 23 17 55 6 9 10 1 4 5 7 6 2 9 8 3 14 1 17 2 3 1 2 15 3 24 2 1 0 2 1 0

12. RESULTS GMPSALDFA Synthetic Sample with 12 Proteins 7 correct proteins 10 correct proteins 5 tied proteins 1 tied protein 1 incorrect tied protein

13. GMPSA vs LDFA in a R. norvegicus sample GMPSALDFA Rat Sample/Rat IPI Database 2346 94 Indistinguishable pairs

14. GMPSA vs LDFA GMPSALDFA 247275 Total proteins identified 62%81% Percent of proteins assigned with no ties 153 224 Total assignments with no ties 149 Proteins assigned due to unique peptides 4 75 Total unambiguous assignments excluding the proteins with unique peptides Identified Proteins Unambiguously Identified Proteins

15. Limitations and Improvements Include missed-cleavage peptides Include lower scoring peptides to aid in the differentiation of tied proteins Include peptides identified with charges +1 and +3 Train on other analytical platforms Study the effects of detectability prediction on algorithm results

16. Publications PSB 2007 –Alves, P., Arnold, R., Novotny, M., Radivojac, P., Reilly, J., Tang, H. (2007). Advancement in Protein Inference from Shotgun Proteomics Using Peptide Detectability. Pac. Symp. Biocomput., (2007) 12: 409-420 ISMB 2006 –Tang, H., Arnold, R. J., Alves, P., Xun, Z., Clemmer, D. E., Novotny, M. V., Reilly, J. P. & Radivojac, P. (2006). A computational approach toward label-free protein quantification using predicted peptide detectability. Bioinformatics, (2006) 22 (14): e481-e488.

17. Acknowledgements Predrag Radivojac Haixu Tang Randy Arnold IU School of Informatics IU Chemistry Dept.