Improving the Sensitivity of Peptide Identification

1
Improving the Sensitivityof Peptide
Identification

• Nathan Edwards
• Department of Biochemistry and Molecular
  Cellular Biology
• Georgetown University Medical Center
• Xue Wu, Chau-Wen Tseng
• Department of Computer Science
• University of Maryland, College Park

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 engine strengths, weaknesses, quirks
• Use multiple search engines and combine results
• Poor score or statistical significance
• Use spectral-matching to identify weak spectra
• 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
Peptide Sequence Databases

• All peptides at most 30 amino-acids long from
• IPI and all IPI constituent protein sequences
• IPI, HInvDB, VEGA, UniProt, EMBL, RefSeq, GenBank
• SwissProt variants, conflicts, splices, and
  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 UniGene cluster and compressed.

5
Peptide Sequence Databases

• Formatted as a FASTA sequence database
• Easy integration with search engines.
• One entry per gene/cluster.
• Automated rebuild every few months.

Organism Size (AA) Size (Entries)
Human 209Mb 75,043
Mouse 151Mb 55,929
Rat 67Mb 43,211
Zebra-fish 90Mb 47,922
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Spectral Matching with HMMs
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Spectral Matching with HMMs
8
Hidden Markov Model
Delete
Insert
Ion
(m/z,int) pair emitted by ion insert states
9
Boosting Identification Sensitivity
10
Spectral Matching of Peptide Variants
DFLAGGIAAAISK
DFLAGGVAAAISK
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Spectral Matching Extrapolation
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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
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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.

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Supervised Learning
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Unsupervised Learning
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PepArML Combining Results
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Unsupervised Learning
U-TMO
U-TMO
C-TMO
H
False Positive Rate
Iteration
18
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!

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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

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Peptide Identification Meta-Search Parameters

• Instrument
• Precursor Tolerance
• Fragment Tolerance
• Max. Charge
• Sequence Database
• Target/Decoy
• Modification
• Fixed/Variable
• Amino-Acids
• Position
• Delta

• Proteolytic Agent
• Motif
• Peptide Candidates
• Termini Specificity
• Precursor Tolerance
• Missed cleavages
• Charge State Handling
• 13C Peaks
• Search Engines
• Mascot, X!Tandem
• OMSSA, MyriMatch

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Peptide Identification Meta-Search

• Simple unified search interface for
• Mascot, X!Tandem
• OMSSA, Myrimatch
• Automatic decoy searches
• Automatic spectrumfile "chunking"
• Automatic scheduling
• Serial, Multi-Processor,
• Cluster, Grid

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Peptide Identification Meta-Search
Heterogeneous compute resources
NSF TeraGrid 1000 CPUs
Edwards Lab Scheduler 48 CPUs
Secure communication
Simple searchrequest
UMIACS 250 CPUs
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Conclusions

• Improve sensitivity of peptide identification
• Exhaustive peptide sequence databases
• Machine-learning for matching and combining
• Meta-search tools maximize consensus
• Grid-computing to achieve thorough search

24
Acknowledgements

• Catherine Fenselau
• University of Maryland Biochemistry
• Funding NIH/NCI, USDA/ARS