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? 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
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
Mass Spectrometer Ionizer Sample Mass Analyzer Detector MALDI + _ Mass Analyzer Detector MALDI Electro-Spray Ionization (ESI) Time-Of-Flight (TOF) Quadrapole Ion-Trap Electron Multiplier (EM)
Mass Spectrum
Mass is fundamental
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
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
Sample Preparation for MS/MS Enzymatic Digest and Fractionation
Single Stage MS MS
Tandem Mass Spectrometry (MS/MS) Precursor selection
Tandem Mass Spectrometry (MS/MS) Precursor selection + collision induced dissociation (CID) MS/MS
Peptide Fragmentation Peptide: S-G-F-L-E-E-D-E-L-K y1 y2 y3 y4 y5 y6 y7 y8 y9 ion 1020 907 778 663 534 405 292 145 88 MW 762 SGFL EEDELK b4 389 SGFLEED ELK b7 633 SGFLE EDELK b5 1080 S GFLEEDELK b1 1022 SG FLEEDELK b2 875 SGF LEEDELK b3 504 SGFLEE DELK b6 260 SGFLEEDE LK b8 147 SGFLEEDEL K b9
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
Unannotated Splice Isoform
Unannotated Splice Isoform
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)
Halobacterium sp. NRC-1 ORF: GdhA1 K-score E-value vs PepArML @ 10% FDR Many peptides inconsistent with annotated translation start site of NP_279651
Translation start-site correction
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.
Protein Fragmentation Spectrum Match to Y. pestis 50S RP L32 AVQQNKPTRSKRGMRRSHDA LTTATLSVDKTSGETHLRHH ITADGFYRGRKVIG
Phyloproteomics
phylogeny.fr – "One-Click" Phyloproteomics Protein Sequence 16S-rRNA Sequence phylogeny.fr – "One-Click"
Shared "Biomarker" Proteins
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?
Lost peptide identifications Missing from the sequence database Search engine strengths, weaknesses, quirks Poor score or statistical significance Thorough search takes too long
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 of well-understood and/or computationally predicted proteins and protein isoforms!
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. Organism Size (AA) Size (Entries) Human 248Mb 74,976 Mouse 171Mb 55,887 Rat 76Mb 42,372 Zebra-fish 94Mb 40,490
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.
PeptideMapper Web Service I’m Feeling Lucky
PeptideMapper Web Service I’m Feeling Lucky
PeptideMapper Web Service I’m Feeling Lucky
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
Comparison of search engine results No single score is comprehensive Search engines disagree Many spectra lack confident peptide assignment 38% 14% 28% 3% 2% 1% X! Tandem SEQUEST Mascot Here is way, no single one gives the best results Q: after improvement, what is the percentage of identified spectra, how is the improvement? 25 – 30% Searle et al. JPR 7(1), 2008
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.
Supervised Learning
Unsupervised Learning
Peptide Atlas A8_IP LTQ Dataset
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
Peptide Identification Meta-Search Simple unified search interface for: Mascot, X!Tandem, K-Score, OMSSA, MyriMatch, S-Score, InsPecT, KM-Score Automatic decoy searches Automatic spectrum file "chunking" Automatic scheduling Serial, Multi-Processor, Cluster, Grid
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+ simultaneous searches Single, simple search request UMIACS 250+ CPUs
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+ simultaneous searches Single, simple search request
PepArML Meta-Search Engine Heterogeneous compute resources NSF TeraGrid 1000+ CPUs Edwards Lab Scheduler & 48+ CPUs Secure communication Simple search request UMIACS 250+ CPUs
PepArML Meta-Search Engine Heterogeneous compute resources NSF TeraGrid 1000+ CPUs Edwards Lab Scheduler & 48+ CPUs Secure communication Simple search request UMIACS 250+ CPUs
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.
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
Acknowledgements Dr. Catherine Fenselau & students Dr. Yan Wang 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