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Improving the Sensitivity of Peptide Identification for Genome Annotation

Improving the Sensitivity of Peptide Identification for Genome Annotation. Nathan Edwards Department of Biochemistry and Molecular & Cellular Biology Georgetown University Medical Center. Why Tandem Mass Spectrometry?.

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Improving the Sensitivity of Peptide Identification for Genome Annotation

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  1. Improving the Sensitivityof Peptide Identification for Genome Annotation Nathan Edwards Department of Biochemistry and Molecular & Cellular Biology Georgetown University Medical Center

  2. Why Tandem Mass Spectrometry? • MS/MS spectra provide evidence for the amino-acid sequence of functional proteins. • Key concepts: • Spectrum acquisition is unbiased • Direct observation of amino-acid sequence • Sensitive to small sequence variations

  3. 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

  4. Sample + _ Detector Ionizer Mass Analyzer Mass Spectrometer ElectronMultiplier(EM) Time-Of-Flight (TOF) Quadrapole Ion-Trap MALDI Electro-SprayIonization (ESI)

  5. Mass Spectrum

  6. Mass is fundamental

  7. 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

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

  9. Enzymatic Digest and Fractionation Sample Preparation for MS/MS

  10. Single Stage MS MS

  11. Tandem Mass Spectrometry(MS/MS) Precursor selection

  12. Tandem Mass Spectrometry(MS/MS) Precursor selection + collision induced dissociation (CID) MS/MS

  13. Peptide: S-G-F-L-E-E-D-E-L-K MW ion ion MW 88 b1 S GFLEEDELK y9 1080 145 b2 SG FLEEDELK y8 1022 292 b3 SGF LEEDELK y7 875 405 b4 SGFL EEDELK y6 762 534 b5 SGFLE EDELK y5 633 663 b6 SGFLEE DELK y4 504 778 b7 SGFLEED ELK y3 389 907 b8 SGFLEEDE LK y2 260 1020 b9 SGFLEEDEL K y1 147 Peptide Fragmentation

  14. Unannotated Splice Isoform • Human Jurkat leukemia cell-line • Lipid-raft extraction protocol, targeting T cells • von Haller, et al. MCP 2003. • LIME1 gene: • LCK interacting transmembrane adaptor 1 • LCK gene: • Leukocyte-specific protein tyrosine kinase • Proto-oncogene • Chromosomal aberration involving LCK in leukemias. • Multiple significant peptide identifications

  15. Unannotated Splice Isoform

  16. Unannotated Splice Isoform

  17. Translation start-site correction • Halobacterium sp. NRC-1 • Extreme halophilic Archaeon, insoluble membrane and soluble cytoplasmic proteins • Goo, et al. MCP 2003. • GdhA1 gene: • Glutamate dehydrogenase A1 • Multiple significant peptide identifications • Observed start is consistent with Glimmer 3.0 prediction(s)

  18. Halobacterium sp. NRC-1ORF: GdhA1 • K-score E-value vs PepArML @ 10% FDR • Many peptides inconsistent with annotated translation start site of NP_279651

  19. Translation start-site correction

  20. Phyloproteomics • Tandem mass-spectra of proteins (top-down) • High-accuracy instrument (Orbitrap, UMD Core) • Proteins from unsequenced bacteria matching identical proteins in related organisms • Demonstration using Y.rohdei.

  21. A­V­Q­Q­N­K­P­T­R­S­K­R­G­M­R­R­S­H­D­A­ L­T­T­A­T­L­S­V­D­K­T­S­G­E­T­H­L­R­H­H­ I­T­A­D­G­F­Y­R­G­R­K­V­I­G Protein Fragmentation Spectrum Match to Y. pestis 50S RP L32

  22. Phyloproteomics

  23. Phyloproteomics Protein Sequence 16S-rRNA Sequence phylogeny.fr – "One-Click"

  24. Shared "Biomarker" Proteins

  25. Phyloproteomics • Recent extension to highly homologous proteins in related organisms • Merely require N- and/or C-terminus in common • Broadens applicability considerably • Phyloproteomic trees for E.herbicola and Enterocloacae, neither sequenced. • New paradigm for phylogenetic analysis?

  26. Lost peptide identifications • Missing from the sequence database • Search engine strengths, weaknesses, quirks • Poor score or statistical significance • Thorough search takes too long

  27. Searching under the street-light… • Tandem mass spectrometry doesn’t discriminate against novel peptides......but protein sequence databases do! • Searching traditional protein sequence databases biases the results in favor ofwell-understoodand/orcomputationally predicted proteins and protein isoforms!

  28. Peptide Sequence Databases • All amino-acid 30-mers, no redundancy • From ESTs, Proteins, mRNAs • 30-40 fold size, search time reduction • Formatted as a FASTA sequence database • One entry per gene/cluster.

  29. We can observe evidence for… • Known coding SNPs • Unannotated coding mutations • Alternate splicing isoforms • Alternate/Incorrect translation start-sites • Microexons • Alternate/Incorrect translation frames …though it must be treated thoughtfully.

  30. PeptideMapper Web Service I’m Feeling Lucky

  31. PeptideMapper Web Service I’m Feeling Lucky

  32. PeptideMapper Web Service I’m Feeling Lucky

  33. PeptideMapper Web Service • Suffix-tree index on peptide sequence database • Fast peptide to gene/cluster mapping • “Compression” makes this feasible • Peptide alignment with cluster evidence • Amino-acid or nucleotide; exact & near-exact • Genomic-loci mapping via • UCSC “known-gene” transcripts, and • Predetermined, embedded genomic coordinates

  34. SEQUEST Mascot 28% 14% 14% 38% 1% 3% 2% X! Tandem Comparison of search engine results • No single score is comprehensive • Search engines disagree • Many spectra lack confident peptide assignment Searle et al. JPR 7(1), 2008

  35. Combining search engine results – harder than it looks! • Consensus boosts confidence, but... • How to assess statistical significance? • Gain specificity, but lose sensitivity! • Incorrect identifications are correlated too! • How to handle weak identifications? • Consensus vs disagreement vs abstention • Threshold at some significance? • We apply unsupervised machine-learning.... • Lots of related work unified in a single framework.

  36. Supervised Learning

  37. Unsupervised Learning

  38. Peptide Atlas A8_IP LTQ Dataset

  39. Running many search engines Search engine configuration can be difficult: • Correct spectral format • Search parameter files and command-line • Pre-processed sequence databases. • Tracking spectrum identifiers • Extracting peptide identifications, especially modifications and protein identifiers

  40. Simple unified search interface for: Mascot, X!Tandem, K-Score, OMSSA, MyriMatch, S-Score, InsPecT, KM-Score Automatic decoy searches Automatic spectrumfile "chunking" Automatic scheduling Serial, Multi-Processor, Cluster, Grid Peptide Identification Meta-Search

  41. PepArML Meta-Search Engine X!Tandem, KScore, OMSSA, MyriMatch, Mascot (1 core). NSF TeraGrid 1000+ CPUs Heterogeneous compute resources X!Tandem, KScore, OMSSA, MyriMatch. Secure communication Edwards Lab Scheduler & 48+ CPUs Scales easily to 250+ simultaneoussearches Single, simplesearch request UMIACS 250+ CPUs

  42. PepArML Meta-Search Engine X!Tandem, KScore, OMSSA, MyriMatch, Mascot (1 core). NSF TeraGrid 1000+ CPUs Heterogeneous compute resources X!Tandem, KScore, OMSSA, MyriMatch. Secure communication Edwards Lab Scheduler & 80+ CPUs Scales easily to 250+ simultaneoussearches Single, simplesearch request

  43. PepArML Meta-Search Engine Heterogeneous compute resources NSF TeraGrid 1000+ CPUs Edwards Lab Scheduler & 48+ CPUs Secure communication Simple searchrequest UMIACS 250+ CPUs

  44. PepArML Meta-Search Engine Heterogeneous compute resources NSF TeraGrid 1000+ CPUs Edwards Lab Scheduler & 48+ CPUs Secure communication Simple searchrequest UMIACS 250+ CPUs

  45. Peptide Identification Grid-Enabled Meta-Search • Access to high-performance computing resources for the proteomics community • NSF TeraGrid Community Portal • University/Institute HPC clusters • Individual lab compute resources • Contribute cycles to the community and get access to others’ cycles in return. • Centralized scheduler • Compute capacity can still be exclusive, or prioritized. • Compute client plays well with HPC grid schedulers.

  46. Conclusions Improve the scope and sensitivity of peptide identification for genome annotation, using • Exhaustive peptide sequence databases • Machine-learning for combining • Meta-search tools to maximize consensus • Grid-computing for thorough search http://edwardslab.bmcb.georgetown.edu

  47. Acknowledgements • Dr. Catherine Fenselau & students • University of Maryland Biochemistry • Dr. Yan Wang • University of Maryland Proteomics Core • Dr. Art Delcher • University of Maryland CBCB • Dr. Chau-Wen Tseng & Dr. Xue Wu • University of Maryland Computer Science • Funding: NIH/NCI

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