Transcript Novel Peptide Identification using ESTs and

Novel Peptide Identification using
ESTs and Genomic Sequence
Nathan Edwards
Center for Bioinformatics and Computational Biology
University of Maryland, College Park
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.
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B.anthracis
MALDI Mass
Spectrometry
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 α/β SASP
• RefSeq/Gb: MVMARN... (7442 Da)
• CMR:
MARN... (7211 Da)
• Need chemistry based protein
classification
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Sample Preparation for
Peptide Identification
Enzymatic Digest
and
Fractionation
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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, NCBI’s nr, ...
• Automated, high-throughput peptide identification
in complex mixtures
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Why don’t we see more
novel peptides?
• Tandem mass spectrometry doesn’t
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:
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•
•
•
Six-frame translation
Eliminate ORFs < 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: < 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 < 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: < 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
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