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Novel Peptide Identification using ESTs and Genomic Sequence

Novel Peptide Identification using ESTs and Genomic Sequence. Nathan Edwards Center for Bioinformatics and Computational Biology University of Maryland, College Park. Mass Spectrometry for Proteomics. Measure mass of many (bio)molecules simultaneously High bandwidth

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Novel Peptide Identification using ESTs and Genomic Sequence

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  1. Novel Peptide Identification using ESTs and Genomic Sequence Nathan Edwards Center for Bioinformatics and Computational Biology University of Maryland, College Park

  2. Mass Spectrometry for Proteomics • Measure mass of many (bio)molecules simultaneously • High bandwidth • Mass is an intrinsic property of all (bio)molecules • No prior knowledge required

  3. Mass Spectrometry for Proteomics • Measure mass of many molecules simultaneously • ...but not too many, abundance bias • Mass is an intrinsic property of all (bio)molecules • ...but need a reference to compare to

  4. Mass Spectrometry for Proteomics • Mass spectrometry has been around since the turn of the century... • ...why is MS Proteomics so new? • Ionization methods • MALDI, Electrospray • Protein chemistry & automation • Chromatography, Gels, Computers • Protein sequence databases • A reference for comparison

  5. Direct observation of microorganism biomarkers in the field. Peaks represent masses of abundant proteins. Statistical models assess identification significance. Microorganism Identification by MALDI Mass Spectrometry B.anthracis MALDI Mass Spectrometry

  6. Key Principles • Protein mass from protein sequence • No introns, few PTMs • Specificity of single mass is very weak • Statistical significance from many peaks • Not all proteins are equally likely to be observed • Ribosomal proteins, SASPs

  7. Protein Sequences 5.3M (1.9M) Species ~ 15K Genbank, RefSeq CMR, Swiss-Prot TrEMBL Rapid Microorganism Identification Database (www.RMIDb.org)

  8. Rapid Microorganism Identification Database (www.RMIDb.org)

  9. Informatics Issues • Need good species / strain annotation • B.anthracis vs B.thuringiensis  • Need correct protein sequence • B.anthracis Sterne α/β SASP • RefSeq/Gb: MVMARN... (7442 Da) • CMR: MARN... (7211 Da) • Need chemistry based protein classification

  10. Enzymatic Digest and Fractionation Sample Preparation for Peptide Identification

  11. Single Stage MS MS m/z

  12. Tandem Mass Spectrometry(MS/MS) m/z Precursor selection m/z

  13. Tandem Mass Spectrometry(MS/MS) Precursor selection + collision induced dissociation (CID) m/z MS/MS m/z

  14. Peptide Identification • For each (likely) peptide sequence 1. Compute fragment masses 2. Compare with spectrum 3. Retain those that match well • Peptide sequences from protein sequence databases • Swiss-Prot, IPI, NCBI’s nr, ... • Automated, high-throughput peptide identification in complex mixtures

  15. Why don’t we see more novel peptides? • Tandem mass spectrometry doesn’t discriminate against novel peptides......but protein sequence databases do! • Searching traditional protein sequence databases biases the results towards well-understood protein isoforms!

  16. What goes missing? • Known coding SNPs • Novel coding mutations • Alternative splicing isoforms • Alternative translation start-sites • Microexons • Alternative translation frames

  17. Why should we care? • Alternative splicing is the norm! • Only 20-25K human genes • Each gene makes many proteins • Proteins have clinical implications • Biomarker discovery • Evidence for SNPs and alternative splicing stops with transcription • Genomic assays, ESTs, mRNA sequence. • Little hard evidence for translation start site

  18. Novel Splice Isoform

  19. Novel Splice Isoform

  20. Novel Frame

  21. Novel Frame

  22. Novel Mutation Ala2→Pro associated with familial amyloid polyneuropathy

  23. Novel Mutation

  24. Searching ESTs • Proposed long ago: • Yates, Eng, and McCormack; Anal Chem, ’95. • Now: • Protein sequences are sufficient for protein identification • Computationally expensive/infeasible • Difficult to interpret • Make EST searching feasible for routine searching to discover novel peptides.

  25. Pros No introns! Primary splicing evidence for annotation pipelines Evidence for dbSNP Often derived from clinical cancer samples Cons No frame Large (8Gb) “Untrusted” by annotation pipelines Highly redundant Nucleotide error rate ~ 1% Searching Expressed Sequence Tags (ESTs)

  26. Compressed EST Peptide Sequence Database • For all ESTs mapped to a UniGene gene: • Six-frame translation • Eliminate ORFs < 30 amino-acids • Eliminate amino-acid 30-mers observed once • Compress to C2 FASTA database • Complete, Correct for amino-acid 30-mers • Gene-centric peptide sequence database: • Size: < 3% of naïve enumeration, 20774 FASTA entries • Running time: ~ 1% of naïve enumeration search • E-values: ~ 2% of naïve enumeration search results

  27. Compressed EST Peptide Sequence Database • For all ESTs mapped to a UniGene gene: • Six-frame translation • Eliminate ORFs < 30 amino-acids • Eliminate amino-acid 30-mers observed once • Compress to C2 FASTA database • Complete, Correct for amino-acid 30-mers • Gene-centric peptide sequence database: • Size: < 3% of naïve enumeration, 20774 FASTA entries • Running time: ~ 1% of naïve enumeration search • E-values: ~ 2% of naïve enumeration search results

  28. SBH-graph ACDEFGI, ACDEFACG, DEFGEFGI

  29. Compressed SBH-graph ACDEFGI, ACDEFACG, DEFGEFGI

  30. Sequence Databases & CSBH-graphs • Original sequences correspond to paths ACDEFGI, ACDEFACG, DEFGEFGI

  31. Sequence Databases & CSBH-graphs • All k-mers represented by an edge have the same count 1 2 2 1 2

  32. cSBH-graphs • Quickly determine those that occur twice 2 2 1 2

  33. Compressed-SBH-graph 2 2 1 2 ACDEFGI

  34. Compressed EST Database • Gene centric compressed EST peptide sequence database • 20,774 sequence entries • ~8Gb vs 223 Mb • ~35 fold compression • 22 hours becomes 15 minutes • E-values improve by similar factor! • Makes routine EST searching feasible • Search ESTs instead of IPI?

  35. Back to the lab... • Current LC/MS/MS workflows identify a few peptides per protein • ...not sufficient for protein isoforms • Need to raise the sequence coverage to (say) 80% • ...protein separation prior to LC/MS/MS analysis • Potential for database of splice sites of (functional) proteins!

  36. Conclusions • Good informatics gets the most out of proteomics data • Proteomics may be useful for genome annotation • Peptides identify more than just proteins • Compressed peptide sequence databases make routine EST searching feasible

  37. Acknowledgements • Chau-Wen Tseng, Xue Wu • UMCP Computer Science • Catherine Fenselau • UMCP Biochemistry • Calibrant Biosystems • PeptideAtlas, HUPO PPP, X!Tandem • Funding: National Cancer Institute

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