1. Common parameters At the beginning one need to set up the parameters. http://human.thegpm.org

2. Common parameters Most important: the input experimental spectra – Self-explaining. SequestMascotX!Tandem.DTAxXX.RAWX.MGFXXX.PKLXX.PKSX.mzDataXXX.mzXMLXXX.mzMLX

3. Common parameters Taxon, and database – Self-explaining. – E.g. samples form human cells should be queried against human protein database. – Sometimes Protein Sequence libraries are available.

4. Common parameters Parent mass tolerance If it is much smaller than the optimal would be: – the correct peptide can be eliminated from the search space – Execution time decreases Spectra comparison

5. Common parameters Parent mass tolerance If it is much bigger than the optimal would be: – decreases the significance of the scores, – makes execution time longer Spectra comparison

6. Common parameters Parent mass tolerance Usually is around 1Da. Spectra comparison

7. Common parameters Fragment ion match tolerance – Depends on the instrument accuracy. – If it is mach small than the optimum: matches will be lost 100% 0% 1 0

8. Common parameters Fragment ion match tolerance – If it is much smaller than the optimal would be: Correct matched peaks will be lost. Increases the FDR, increases the false negatives, decreases the sensitivity,

9. Common parameters If the fragment ion match tolerance is much bigger than the optimal would be: – Many theoretical peaks will match to an experimental peak – Increases the random scores and it decreases the statistical significance

10. Common parameters

11. Fragment ion tolerance (T) T = 0.4Da (correct) T = 0.05Da (too small)T = 2.0Da (too large)

12. Fragment ion tolerance (T) T = 0.4 (correct) T = 0.05 (too small)T = 2.0 (too large) 217 proteins 713 homologs 930 proteins 132 proteins 406 homologs 538 proteins 197 proteins 589 homologs 786 proteins

13. Common parameters Instrument – Some database search software's allow you to select the type of the instruments like ESI QUAD or Quad-TOF – This fine-tunes the search engine according to which fragment ion series will be used for scoring. – E.g.: Immonium ions, a series ions, b-, c-, x-, a- NH 3,z+H series, y-H 2 O etc.

14. Common parameters Enzyme, – the enzyme used for enzymatic digestion in the biological sample preparation. – This will be used for the in silico digestion of protein sequences for peptide generation.

15. Common parameters E-value cut off

16. Common parameters Ion mass search type – Monoisotopic (default) More accurate, – Average Might need larger fragment ion tolerance,

17. Common parameters Charge state – Too high charge state increases the FDR.

18. Common parameters Decoy search – Includes reversed dataset in the peptide identification. – Provides more accurate p-value and FDR estimation – Can double the search time

19. Common parameters Error tolerant search. Large number of spectra remain without significant score. Reasonable number of fragment ion peaks might have not match. – Underestimated mass measurement error (should be seen in peptide view graphs, – Incorrect determination of precursor charge state – Peptide sequence is not in the database. – Missed cleavage & unexpected cleavage, – Unexpected chemical & post-translational modification.

20. Scores: 13.15 6.4 1.4 9.3 4.3 3.2 7.2 11.2 8.1 10.1 2.1 5.1 12.1 Scores: 13.15 6.4 1.4 9.3 4.3 3.2 7.2 11.2 8.1 10.1 2.1 5.1 12.1 Input data Experimental Spectra Protein sequence DB Score: 4 Peptide: AELDLNMTR Score: 32 Peptide: SHLITLLLFLFHSETICR Score: 3 Peptide: MEICRGLR Score: 15 Peptide: LLHGDPGEEDK Score: 4 Peptide: MDHPEDESHSEK Score: 5 Peptide: SAEDLEADK Score: 3 Peptide: SIEAKLTLR Input data Peptide assignment Validation Protein inference Quantitation Interpretation  Cn=(32-4)/32=0.875  Cn=(4-4)/4=0  Cn=(3-3)/3=0  Cn=(15-4)/15=0.733 Keep the peptide assignment that exceeds a certain limit.

21. >IPI:IPI00000044.1|SWISS-PROT:P01127 MNRTFGQVVARLVSAEGDPIPEELYEMLSDHSIRSFDDLQRLLHGDPGEEDKAELDLNMTRSHSG GELESLARGRRSLGSLTIAEPAMIAECKTRTEVFEISRRLIDRTNANFLVWPPCVEVQRCSGCCNNR NVQCRPTQVQLRPVQVRKIEIVRKKPIFKKATVTLEDHLACKCETVAAARPVTRSPGGSQEQRAKT PQTRVTIRTVRVRRPPKGKHRKFKHTHDKTALKETLGA Input data Experimental Spectra Scores: 1.2 Scores: 1.2 Input data Peptide assignment Validation Protein inference Quantitation Interpretation Spectra comparison: Protein sequence DB TFGQVVAR FGQVVAR GQVVAR QVVAR VVAR VAR AR TFGQVVA TFGQVV TFGQV TFGQ TFG TF Unexpected cleavages

22. >IPI:IPI00000044.1|SWISS-PROT:P01127 MNRCWALFLSLCCYLRLVSAEGDPIPEELYEMLSDHSIRSFDDLQRLLHGDPGEEDKAELDLNMTR SHSGGELESLARGRRSLGSLTIAEPAMIAECKTRTEVFEISRRLIDRTNANFLVWPPCVEVQRCSGC CNNRNVQCRPTQVQLRPVQVRKIEIVRKKPIFKKATVTLEDHLACKCETVAAARPVTRSPGGSQE QRAKTPQTRVTIRTVRVRRPPKGKHRKFKHTHDKTALKETLGA Input data Experimental Spectra Scores: 1.2 Scores: 1.2 Input data Peptide assignment Validation Protein inference Quantitation Interpretation Spectra comparison: Protein sequence DB Missed cleavages

23. >IPI:IPI00000044.1|SWISS-PROT:P01127 MNRCWALFLSLCCYLRLVSAEGDPIPEELYEMLSDHSIRSFDDLQRLLHGDPGEEDKAELDLNMTR SHSGGELESLARGRRSLGSLTIAEPAMIAECKTRTEVFEISRRLIDRTNANFLVWPPCVEVQRCSGC CNNRNVQCRPTQVQLRPVQVRKIEIVRKKPIFKKATVTLEDHLACKCETVAAARPVTRSPGGSQE QRAKTPQTRVTIRTVRVRRPPKGKHRKFKHTHDKTALKETLGA Input data Experimental Spectra Scores: 1.2 2.2 Scores: 1.2 2.2 Input data Peptide assignment Validation Protein inference Quantitation Interpretation Spectra comparison: Protein sequence DB Missed cleavages

24. >IPI:IPI00000044.1|SWISS-PROT:P01127 MNRCWALFLSLCCYLRLVSAEGDPIPEELYEMLSDHSIRSFDDLQRLLHGDPGEEDKAELDLNMTR SHSGGELESLARGRRSLGSLTIAEPAMIAECKTRTEVFEISRRLIDRTNANFLVWPPCVEVQRCSGC CNNRNVQCRPTQVQLRPVQVRKIEIVRKKPIFKKATVTLEDHLACKCETVAAARPVTRSPGGSQE QRAKTPQTRVTIRTVRVRRPPKGKHRKFKHTHDKTALKETLGA Input data Experimental Spectra Scores: 1.2 2.2 3.1 Scores: 1.2 2.2 3.1 Input data Peptide assignment Validation Protein inference Quantitation Interpretation Spectra comparison: Protein sequence DB Missed cleavages

25. Common parameters Automatic error tolerant search. Chemical and Post-Translational Modifications (PTMs) Fixed modification (simply modifies the mass of the Amino Acid) Variable modifications (can modify the mass) Search engines iteratively insert all combination of the possible PTMs.

26. Common parameters Automatic error tolerant search. – more peptides can be indentified. –  enlarges the search space much more Increases the execution time Decreases the statistical significance, increases the FDR.

27. Common parameters Automatic error tolerant search. In order to reduce the search space two pass approach is applied. – 1 st pass: Identification of perfect peptides (no PTMs, perfect digestion) – 2 nd pass: Pass the proteins whose one of the peptides was identified in the 1 st pass. Extensive search in the reduced protein sequence, including missed and unexpected cleavage, PTMs, point mutations, etc.

28. Common parameters Output parameters – Mainly about formatting the results files. What and how many details want to see.

29. Common parameters Other program specific parameters. Different for X!tandem, Mascot, Sequest, etc.

30. X!Tandem

31. Outputs – Browsing the results

36. OMSSA’s search engine

37. OMSSA’s output

38. OMSSA’s result

39. Good spectrum, good score, bad annotation – Rare if the p-value is significant Good spectrum, bad score, bad annotation – Peptide might be modified, non-perfect digestion, not in the database.

40. Bad spectrum, bad score, bad annotation

41. Good spectrum, good score, good annotation

42. Trans-Proteomic Pipeline (TPP) Trans-Proteomic Pipeline (TPP) is a data analysis pipeline for the analysis of LC/MS/MS proteomics data. TPP includes modules for validation of database search results, quantitation of isotopically labeled samples, and validation of protein identifications, as well as tools for viewing raw LC/MS data, peptide identification results, and protein identification results. The XML backbone of this pipeline enables a uniform analysis for LC/MS/MS data generated by a wide variety of mass spectrometer types, and assigned peptides using a wide variety of database search engines.

43. Trans-Proteomic Pipeline (TPP)

44. Summary Protein identification from MS/MS data is not a black box. Always look at the results and understand how it