Novel Peptide Identification using

ESTs and Genomic Sequence

Novel Peptide Identification using

ESTs and Genomic Sequence

Nathan EdwardsCenter for Bioinformatics and Computational BiologyUniversity 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 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 Digestand

Fractionation

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Single Stage MS

MS

m/z

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Tandem Mass Spectrometry(MS/MS)

Precursor selection

m/z

m/z

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Tandem Mass Spectrometry(MS/MS)

Precursor selection + collision induced dissociation

(CID)

MS/MS

m/z

m/z

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Peptide Identification

• For each (likely) peptide sequence1. Compute fragment masses2. Compare with spectrum3. 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:• 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

2 2

1

2

1

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cSBH-graphs

• Quickly determine those that occur twice

2 2

1

2

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Compressed-SBH-graph

ACDEFGI

2 2

1

2

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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