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

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

3 Lost peptide identifications Missing from the sequence database Build exhaustive peptide sequence databases Search prokaryotic genomes Build evidence for unannotated proteins and protein isoforms Search engine strengths, weaknesses, quirks Use multiple search engines and combine results Poor score or statistical significance Use search-engine consensus to boost confidence Use machine-learning to distinguish true from false Thorough search takes too long Harness the power of heterogeneous computational grids

4 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!

5 Unannotated Splice Isoform Human Jurkat leukemia cell-line Lipid-raft extraction protocol, targeting T cells von Haller, et al. MCP 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

6 Unannotated Splice Isoform

7

8

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

10 Halobacterium sp. NRC-1 ORF: GdhA1 Peptide identifications filtered at 10% FDR Consistent/not consistent with NP_279651

11 Halobacterium sp. NRC-1 ORF: GdhA1 Peptide identifications filtered at 20% FDR Consistent/not consistent with NP_279651

12 Translation start-site correction

13 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.

14 Peptide Sequence Databases All amino-acid seqs of at most 30 amino-acids from: IPI and all IPI constituent protein sequences IPI, HInvDB, VEGA, UniProt, EMBL, RefSeq, GenBank SwissProt variants, conflicts, splices, and annotated signal peptide truncations. Genbank and RefSeq mRNA sequence 3 frame translation GenBank EST and HTC sequences 6 frame translation and found in at least 2 sequences Grouped by Gene/UniGene cluster and compressed.

15 Formatted as a FASTA sequence database Easy integration with search engines. One entry per gene/cluster. Automated rebuild every few months. Peptide Sequence Databases OrganismSize (AA)Size (Entries) Human248Mb74,976 Mouse171Mb55,887 Rat 76Mb42,372 Zebra-fish 94Mb40,490

16 Peptide evidence, in context Statistically significant identified peptides can be misleading… Isobaric amino-acid/PTM substitutions Unsubstantiated peptide termini Few b-ions or y-ions suggest “random” mass match Single amino-acids on upstream or downstream exons Peptides in 5’ UTR with no upstream Met Need tools to quickly check the corroborating (genomic, transcript, SNP) evidence

17 PeptideMapper Web Service Counts: by gene and evidence EST, mRNA, Protein Sequences: accessions by gene UniProt variants nucleotide sequence & link to BLAT alignment Genomic Loci: one-click projection to the UCSC genome browser

18 PeptideMapper Web Service I’m Feeling Lucky

19 PeptideMapper Web Service I’m Feeling Lucky

20 PeptideMapper Web Service I’m Feeling Lucky

21 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

22 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), % 14% 28% 14% 3% 2% 1% X! Tandem SEQUEST Mascot

23 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.

24 Supervised Learning

25 Unsupervised Learning

26 PepArML Combining Results Q-TOF LTQ MALDI Edwards, et al., Clin. Prot. 5(1), 2009

27 Unsupervised Learning H C-TMO U-TMO U*-TMO Edwards, et al., Clin. Prot. 5(1), 2009

28 Peptide Atlas A8_IP LTQ Dataset

29 PepArML in the trenches… MALDI spectra of proteolytic peptides in serum Top-down CID spectra after decharging Halobacterium six-frame search PepArML found 389 non-RefSeq peptides Mascot: 173, OMSSA: 168, K-Score: 292 Peptides for GdhA1: PepArML 9(2), K-Score: 6(1) Semi-tryptic searches work particularly well. S17 Spectra at 10% FDR

30 Searching for Consensus Search engine quirks can destroy consensus Initial methionine loss as tryptic peptide Charge state enumeration or guessing X!Tandem's refinement mode Pyro-Gln, Pyro-Glu modifications Difficulty tracking spectrum identifiers Precursor mass tolerance (Da vs ppm) Decoy searches must be identical!

31 Configuring for Consensus 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

32 Peptide Identification Meta-Search Parameters Instrument Precursor Tolerance Fragment Tolerance Max. Charge Sequence Database Target and # of Decoys Modification Fixed/Variable Amino-Acids Position Delta Proteolytic Agent Motif Peptide Candidates Termini Specificity Precursor Tolerance Missed cleavages Charge State Handling # 13 C Peaks Search Engines Mascot, X!Tandem, K-Score, OMSSA, MyriMatch

33 Peptide Identification Meta-Search Simple unified search interface for: Mascot, X!Tandem, K- Score, OMSSA, MyriMatch Automatic decoy searches Automatic spectrum file "chunking" Automatic scheduling Serial, Multi-Processor, Cluster, Grid

34 Peptide Identification Grid-Enabled Meta-Search NSF TeraGrid CPUs UMIACS 250+ CPUs Edwards Lab Scheduler & 48+ CPUs Secure communication Heterogeneous compute resources Single, simple search request Scales easily to 250+ simultaneous searches X!Tandem, KScore, OMSSA, MyriMatch, Mascot (1 core). X!Tandem, KScore, OMSSA. X!Tandem, KScore, OMSSA.

35 Peptide Identification Grid-Enabled Meta-Search NSF TeraGrid CPUs UMIACS 250+ CPUs Edwards Lab Scheduler & 48+ CPUs Secure communication Heterogeneous compute resources Simple search request

36 Peptide Atlas A8_IP LTQ Dataset Tryptic search of Human ESTs using PepSeqDB spectra searched ~ 26 times: Target + 2 decoys, 5 engines, 1+ vs 2+/3+ charge 8685 search jobs 25.7 days of CPU time TeraGrid TKO jobs < 2 hours Using 143 different machines Total elapsed time < 26 hours Bottleneck: Mascot license.

37 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.

38 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

39 Acknowledgements Dr. Catherine Fenselau University of Maryland Biochemistry Dr. Rado Goldman Georgetown University Medical Center Dr. Chau-Wen Tseng & Dr. Xue Wu University of Maryland Computer Science Funding: NIH/NCI