1. How to identify peptides October 2013 Gustavo de Souza IMM, OUS

2. Peptide or Proteins?

3. Bottom-up Proteomics

4. 2DE-based approach

5. Peptide Mass Fingerprinting MALDI (Matrix Assisted Laser Desorption Ionization)

6. Peptide Mass Fingerprinting m/z Intensity

7. MS/MS Ion Source Mass Analyzer Detector Mass Analyzer Mass Analyzer Collision cell

8. MS/MS 899.013

9. Fragmentation Nomenclature for peptide sequence-ions: Collision-Induced Dissociation (CID): MH n n+ * + N 2 --> b + y Electron Capture Dissociation (ECD): MH n n+ + e - --> MH n (n-1)+ · --> c + z·

10. Fragmentation H 2 N N H H N N H H N N H R 1 R 2 R 3 R 4 R 5 H N R 6 N H R 7 R 8 O O O O O O O O OH y 7 b 1 y 6 b 2 y 4 b 4 y 5 b 3 y 2 b 6 y 3 b 5 y 1 b 7 Roepstorff-Fohlmann-Biemann-Nomenclature

11. Fragmentation 12 aa …… b ionsy ions

12. MS/MS of a peptide P y13 y12 y11 y10 y9 y8 y7 y6 y5y4 y3 y2 b13 b12 b11 b10 b9 b8 b7 b6 b5 b4 b3 P y++13 VPTVDVSVVDLTVK

13. How to Identify MS/MS Stenn and Mann, 2004. Peptide Sequence Tags Autocorrelation Probability based match

14. Submitting to Search

15. How identification happen? Your data Protein database (fasta) Step 1: which theoretical peptides has the same mass of the observed ion? Step 2: From those, which one have the most similar fragmentation pattern? x x x

16. High mass accuracy – what is it good for? All theoretical tryptic peptide masses from human IPI database Example Tryptic HSP-70 peptide: ELEEIVQPIISK, mass 1396.7813 Da 11 Ext. 2 ppm LTQ-FT 93352344 # of tryptic peptides for m/z 1396.7813 Ext-SIMInt.Ext.Ext.Calibration 1 ppm 10 ppm 20 ppm 500 Mass Accuracy LTQ-FTQSTARQSTARLTQInstrument 3 Int. 0.5 ppm LTQ-FT

17. Defining the “Search Space”

18. The “Search Space” 0 mcl 1 2 3 4 5 6 1/2 1 2 3 4 5 6 2/3 3/4 4/5 5/6 1 mcl 1/2 1 2 3 4 5 6 2/3 3/4 4/5 5/6 2 mcl 1/2/3 2/3/4 3/4/5 4/5/6

19. Importance of Search Space Size Search tool does not identify a peptide. It only reports the statiscally most suitable theoretical sequence related with the experimental data. If you increase the size of the database too much, or the size of the search space, false-positive rates also increase.

20. Steen and Mann, 2004 Defining FDRs

21. Chance that two peptides with different sequences but approximate Mr and sharing MS/MS similarities. More variables inserted during search  Higher chance to get random events  Higher MOWSE score threshold Parameters that can modify the MOWSE calculation: -Database size; -MMD (measured mass deviation); -Number of PTMs choosen; -Data quality. MOWSE

22. Mycoplasma sp. sample (Munich 2006): -Database had ~ 700 entries; -Data accuracy had 0.7ppm average; -MMD used during search: 3 ppm. Probability Based Mowse Score Ions score is -10*Log(P), where P is the probability that the observed match is a random event. Individual ions scores > 7 indicate identity or extensive homology (p<0.05). Protein scores are derived from ions scores as a non-probabilistic basis for ranking protein hits. Example of MMD issue

23. Peng et al (2003). Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome. J Prot Res 2, 43-50. Reversed database sequence Strategies to Visualize FDRs

24. False positive identification using reversed database

25. Typical Result

26. Are there any Reversed hit protein with 2 peptides above MOWSE score? -No: All proteins identified with 2 peptides score higher than p<0.05 are good -Yes: Repeat mascot search with more stringent parameters. What about 1-hit wonders? (Proteins identified with only 1 peptide) How to Validate the Data

27. Basically, the idea is to ”play around” with the statistics to make your result more reliable. How to Validate the Data

28. Take home message 1.Data quality (mass accuracy) and a well-defined search space are key for reliable peptide identification 2.Reliable identification is an interplay between asking enough without asking too much (careful when trying to get “as many IDs as I can”!)

29. PTMs October 2013 Gustavo de Souza IMM, OUS

30. PTMs in biology

32. Complexity of Protein Samples in Eukaryotes Modifications are specific to a group of amino acids

33. What difference to expect at MS level? Larsen MR et al, 2006.

34. Defining the “Search Space”

35. PTM abundance in a cell Total peptides in a sample Modified peptides Number of Peptides Abundance level Differences from 10e2 to 10e4

36. PTM abundance in a cell

37. Stable vs. Labile PTMs Larsen MR et al, 2006.

38. Neutral loss Boersema PJ et al, 2009.

39. Identifying Labile PTMs Larsen MR et al, 2006.

40. HCD fragmentation Larsen MR et al, 2006.

41. Status of PTM coverage Lemeer and Heck, 2009.

42. Status of PTM coverage Derouiche A et al, 2012.

43. Take home message - Dependent on stability under fragmentation and abundance in the sample - ID improvement was mostly defined by instrumentation improvements (sensitivity etc) - Depending on PTM, identification can be very easy or very hard