1. Protein Sequence Databases, Peptides to Proteins, and Statistical Significance Nathan Edwards Department of Biochemistry and Mol. & Cell. Biology Georgetown University Medical Center

2. 2 Protein Sequence Databases Link between mass spectra and proteins A protein’s amino-acid sequence provides a basis for interpreting Enzymatic digestion Separation protocols Fragmentation Peptide ion masses We must interpret database information as carefully as mass spectra.

3. 3 More than sequence… Protein sequence databases provide much more than sequence: Names Descriptions Facts Predictions Links to other information sources Protein databases provide a link to the current state of our understanding about a protein.

4. 4 Much more than sequence Names Accession, Name, Description Biological Source Organism, Source, Taxonomy Literature Function Biological process, molecular function, cellular component Known and predicted Features Polymorphism, Isoforms, PTMs, Domains Derived Data Molecular weight, pI

5. 5 Database types Curated Swiss-Prot UniProt RefSeq NP Translated TrEMBL RefSeq XP, ZP Omnibus NCBI’s nr MSDB IPI Other PDB HPRD EST Genomic

6. 6 SwissProt From ExPASy Expert Protein Analysis System Swiss Institute of Bioinformatics ~ 515,000 protein sequence “entries” ~ 12,000 species represented ~ 20,000 Human proteins Highly curated Minimal redundancy Part of UniProt Consortium

7. 7 TrEMBL Translated EMBL nucleotide sequences European Molecular Biology Laboratory European Bioinformatics Institute (EBI) Computer annotated Only sequences absent from SwissProt ~ 10.5 M protein sequence “entries” ~ 230,000 species ~ 75,000 Human proteins Part of UniProt Consortium

8. 8 UniProt Universal Protein Resource Combination of sequences from Swiss-Prot TrEMBL Mixture of highly curated/reviewed (SwissProt) and computer annotation (TrEMBL) “Similar sequence” clusters are available 50%, 90%, 100% sequence similarity

9. 9 RefSeq Reference Sequence From NCBI (National Center for Biotechnology Information), NLM, NIH Integrated genomic, transcript, and protein sequences. Varying levels of curation Reviewed, Validated, …, Predicted, … ~ 9.7 M protein sequence “entries” ~ 209,000 reviewed, ~ 90,000 validated ~ 39,000 Human proteins

10. 10 RefSeq Particular focus on major research organisms Tightly integrated with genome projects. Curated entries: NP accessions Predicted entries: XP accessions Others: YP, ZP, AP

11. 11 IPI International Protein Index From EBI For a specific species, combines UniProt, RefSeq, Ensembl Species specific databases: HInv-DB, VEGA, TAIR ~ 87,000 (from ~ 307,000 ) human protein sequence entries Human, mouse, rat, zebra fish, arabidopsis, chicken, cow Slated for closure November 2010, but still going…

12. 12 MSDB From the Imperial College (London) Combines PIR, TrEMBL, GenBank, SwissProt Distributed with Mascot …so well integrated with Mascot ~ 3.2M protein sequence entries “Similar sequences” suppressed 100% sequence similarity Not updated since September 2006 (obsolete)

13. 13 NCBI’s nr “non-redundant” Contains GenBank CDS translations RefSeq Proteins Protein Data Bank (PDB) SwissProt, TrEMBL, PIR Others “Similar sequences” suppressed 100% sequence similarity ~ 10.5 M protein sequence “entries”

14. 14 Human Sequences Number of Human genes is believed to be between 20,000 and 25,000 SwissProt~ 20,000 RefSeq~ 39,000 TrEMBL~ 75,000 IPI-HUMAN~ 87,000 MSDB~130,000 nr~230,000

15. 15 DNA to Protein Sequence Derived from http://online.itp.ucsb.edu/online/infobio01/burge

16. 16 UCSC Genome Browser Shows many sources of protein sequence evidence in a unified display

17. 17 Accessions Permanent labels Short, machine readable Enable precise communication Typos render them unusable! Each database uses a different format Swiss-Prot: P17947 Ensembl: ENSG00000066336 PIR: S60367; S60367 GO: GO:0003700;

18. 18 Names / IDs Compact mnemonic labels Not guaranteed permanent Require careful curation Conceptual objects ALBU_HUMAN Serum Albumin RT30_HUMAN Mitochondrial 28S ribosomal protein S30 CP3A7_HUMAN Cytochrome P450 3A7

19. 19 Description / Name Free text description Human readable Space limited Hard for computers to interpret! No standard nomenclature or format Often abused…. COX7R_HUMAN Cytochrome c oxidase subunit VIIa- related protein, mitochondrial [Precursor]

20. 20 FASTA Format > Accession number No uniform format Multiple accessions separated by | One line of description Usually pretty cryptic Organism of sequence? No uniform format Official latin name not necessarily used Amino-acid sequence in single-letter code Usually spread over multiple lines.

21. 21 FASTA Format

22. 22 Organism / Species / Taxonomy The protein’s organism… …or the source of the biological sample The most reliable sequence annotation available Useful only to the extent that it is correct NCBI’s taxonomy is widely used Provides a standard of sorts; Heirachical Other databases don’t necessarily keep up Organism specific sequence databases starting to become available.

23. 23 Organism / Species / Taxonomy Buffalo rat Gunn rats Norway rat Rattus PC12 clone IS Rattus norvegicus Rattus norvegicus8 Rattus norwegicus Rattus rattiscus Rattus sp. Rattus sp. strain Wistar Sprague-Dawley rat Wistar rats brown rat laboratory rat rat rats zitter rats

24. 24 Controlled Vocabulary Middle ground between computers and people Provides precision for concepts Searching, sorting, browsing Concept relationships Vocabulary / Ontology must be established Human curation Link between concept and object: Manually curated Automatic / Predicted

25. 25 Gene Ontology Hierarchical Molecular function Biological process Cellular component Describes the vocabulary only! Protein families provide GO association Not necessarily any appropriate GO category. Not necessarily in all three hierarchies. Sometimes general categories are used because none of the specific categories are correct.

26. 26 Gene Ontology

27. 27 Protein Families Similar sequence implies similar function Similar structure implies similar function Common domains imply similar function Bootstrap up from small sets of proteins/domains with well understood characteristics Usually a hybrid manual / automatic approach

28. 28 Protein Families

29. 29 Protein Families

30. 30 Sequence Variants Protein sequence can vary due to Polymorphism Alternative splicing Post-translational modification Sequence databases typically do not capture all versions of a protein’s sequence

31. 31 Swiss-Prot Variant Annotations

32. 32 Swiss-Prot Variant Annotations

33. 33 Omnibus Database Redundancy Elimination Source databases often contain the same sequences with different descriptions Omnibus databases keep one copy of the sequence, and An arbitrary description, or All descriptions, or Particular description, based on source preference Good definitions can be lost, including taxonomy

34. 34 Description Elimination gi|12053249|emb|CAB66806.1| hypothetical protein [Homo sapiens] gi|46255828|gb|AAH68998.1| COMMD4 protein [Homo sapiens] gi|42632621|gb|AAS22242.1| COMMD4 [Homo sapiens] gi|21361661|ref|NP_060298.2| COMM domain containing 4 [Homo sapiens] gi|51316094|sp|Q9H0A8| COM4_HUMAN COMM domain containing protein 4 gi|49065330|emb|CAG38483.1| COMMD4 [Homo sapiens]

35. 35 Peptides to Proteins Nesvizhskii et al., Anal. Chem. 2003

36. 36 Peptides to Proteins

37. 37 Peptides to Proteins A peptide sequence may occur in many different protein sequences Variants, paralogues, protein families Separation, digestion and ionization is not well understood Proteins in sequence database are extremely non-random, and very dependent

38. Indistinguishable Protein Sequences 38 Nesvizhskii, Aebersold, Mol Cell Proteomics, 2005

39. Indistinguishable Protein Sequences 39 Nesvizhskii, Aebersold, Mol Cell Proteomics, 2005

40. Protein Families 40 Nesvizhskii, Aebersold, Mol Cell Proteomics, 2005

41. Protein Grouping Scenarios Parsimony Minimum # of proteins Weighted Choose proteins with the most confident peptides (ProteinProphet) Show all Mark repeated peptides Often no (ideal) resolution is possible! 41 Nesvizhskii, Aebersold, Mol Cell Proteomics, 2005

42. 42 High Quality Peptide Identification: E-value < 10 -8

43. 43 Moderate quality peptide identification: E-value < 10 -3

44. 44 Peptide Identification Peptide fragmentation by CID is poorly understood MS/MS spectra represent incomplete information about amino-acid sequence I/L, K/Q, GG/N, … Correct identifications don’t come with a certificate!

45. 45 Peptide Identification High-throughput workflows demand we analyze all spectra, all the time. Spectra may not contain enough information to be interpreted correctly …bad static on a cell phone Peptides may not match our assumptions …its all Greek to me “Don’t know” is an acceptable answer!

46. 46 What scores do “wrong” peptides get? Generate random peptide sequences Real looking fragment masses Empirical distribution Require similar precursor mass Arbitrary score function can model anything we like!

47. 47 Random Peptide Scores Fenyo & Beavis, Anal. Chem., 2003

48. 48 Random Peptide Scores Fenyo & Beavis, Anal. Chem., 2003

49. 49 Random Peptide Scores Truly random peptides don’t look much like real peptides Just use peptides from the sequence database! Assumptions: IID sampling of “score” values per spectra Caveats: Correct peptide (non-random) may be included Peptides are not independent

50. 50 Extrapolating from the Empirical Distribution Often, the empirical shape is consistent with a theoretical model Geer et al., J. Proteome Research, 2004 Fenyo & Beavis, Anal. Chem., 2003

51. E-values vs p-values Need to adjust for the size of the sequence database Best false/random score goes up with number of trials E-value makes this adjustment Expected number of incorrect peptides (with this score) from this sequence database. E-value = # Trials * p-value (to 1 st approx.) 51

52. 52 False Discovery Rate Which peptide IDs to accept? E-value only provides a per-spectrum statistic With enough spectra, even these can be misleading! Decide which spectra (w/ scores) will be accepted: SEQUEST Xcorr, E-value, Score, etc., plus... Threshold on identification criteria Control the proportion of incorrect identifications in the result for entire dataset

53. Distribution of scores over all spectra 53 Brian Searle, Proteome Software

54. Distribution of scores over all spectra 54 False True Brian Searle, Proteome Software

55. 55 False Discovery Rate FDR score ≥ x = # false ids with score ≥ x # all ids with score ≥ x Need to estimate numerator! Assumes the false (and true) scores, sampled over spectra, are IID Not true for some peptide-spectrum scores (Mostly) true for E-values Can compute the # false ids using a decoy search…

56. 56 Peptide Prophet Distribution of spectral scores in the results Keller et al., Anal. Chem. 2002

57. Decoy searches Shuffle or reverse sequence database Same size as original Known false identifications Estimate “False” distribution Alternatively, merge target+decoy results: Competition between target and decoy scores Assume false target and false decoys each win half the time FDR score ≥ x = 2 * # decoy ids with score ≥ x # target ids with score ≥ x 57

58. Summary Protein sequence databases have varying characteristics, choose wisely! Inferring proteins from peptides can be (very) tricky! Statistical significance can help control the proportion of errors in the (peptide- level) results. 58