Novel Peptide Identification using ESTs and Genomic Sequence

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Novel Peptide Identification using ESTs and
Genomic Sequence

• Nathan Edwards
• Center for Bioinformatics and Computational
  Biology
• University of Maryland, College Park

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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

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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

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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

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Microorganism Identification by MALDI Mass
Spectrometry

• Direct observation of microorganism biomarkers in
  the field.
• Peaks represent masses of abundant proteins.
• Statistical models assess identification
  significance.

B.anthracis
MALDI Mass Spectrometry
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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

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Rapid Microorganism Identification Database
(www.RMIDb.org)

• Protein Sequences
• 5.3M (1.9M)
• Species
• 15K
• Genbank,
• RefSeq
• CMR,
• Swiss-Prot
• TrEMBL

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Rapid Microorganism Identification Database
(www.RMIDb.org)
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Informatics Issues

• Need good species / strain annotation
• B.anthracis vs B.thuringiensis 
• Need correct protein sequence
• B.anthracis Sterne a/ß SASP
• RefSeq/Gb MVMARN... (7442 Da)
• CMR MARN... (7211 Da)
• Need chemistry based protein classification

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Sample Preparation for Peptide Identification
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Single Stage MS
MS
m/z
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Tandem Mass Spectrometry(MS/MS)
m/z
Precursor selection
m/z
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Tandem Mass Spectrometry(MS/MS)
Precursor selection collision induced
dissociation (CID)
m/z
MS/MS
m/z
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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, NCBIs nr, ...
• Automated, high-throughput peptide identification
  in complex mixtures

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Why dont we see more novel peptides?

• Tandem mass spectrometry doesnt discriminate
  against novel peptides......but protein
  sequence databases do!
• Searching traditional protein sequence databases
  biases the results towards well-understood
  protein isoforms!

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What goes missing?

• Known coding SNPs
• Novel coding mutations
• Alternative splicing isoforms
• Alternative translation start-sites
• Microexons
• Alternative translation frames

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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

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Novel Splice Isoform
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Novel Splice Isoform
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Novel Frame
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Novel Frame
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Novel Mutation
Ala2?Pro associated with familial amyloid
polyneuropathy
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Novel Mutation
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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.

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Searching Expressed Sequence Tags (ESTs)

• 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

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Compressed EST Peptide Sequence Database

• For all ESTs mapped to a UniGene gene
• Six-frame translation
• Eliminate ORFs lt 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 lt 3 of naïve enumeration, 20774 FASTA
  entries
• Running time 1 of naïve enumeration search
• E-values 2 of naïve enumeration search results

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Compressed EST Peptide Sequence Database

• For all ESTs mapped to a UniGene gene
• Six-frame translation
• Eliminate ORFs lt 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 lt 3 of naïve enumeration, 20774 FASTA
  entries
• Running time 1 of naïve enumeration search
• E-values 2 of naïve enumeration search results

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SBH-graph
ACDEFGI, ACDEFACG, DEFGEFGI
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Compressed SBH-graph
ACDEFGI, ACDEFACG, DEFGEFGI
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Sequence Databases CSBH-graphs

• Original sequences correspond to paths

ACDEFGI, ACDEFACG, DEFGEFGI
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Sequence Databases CSBH-graphs

• All k-mers represented by an edge have the same
  count

1
2
2
1
2
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cSBH-graphs

• Quickly determine those that occur twice

2
2
1
2
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Compressed-SBH-graph
2
2
1
2
ACDEFGI
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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?

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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!

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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

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Acknowledgements

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