Proteomic Characterization of Alternative Splicing and Coding Polymorphism

1
Proteomic Characterization of Alternative
Splicing and Coding Polymorphism

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

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Synopsis

• MS/MS spectra provide evidence for the amino-acid
  sequence of functional proteins.
• Key concepts
• Spectrum acquisition is unbiased
• Direct observation of amino-acid sequence
• Sensitive to minor sequence variation
• Observed peptides represent folded proteins

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Synopsis

• MS/MS spectra provide evidence for the amino-acid
  sequence of functional proteins.
• Applications
• Cancer biomarkers
• Genome annotation

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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 Spectrometer

• ElectronMultiplier(EM)

• Time-Of-Flight (TOF)
• Quadrapole
• Ion-Trap

• MALDI
• Electro-SprayIonization (ESI)

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High Bandwidth
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Mass is fundamental!
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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 based Proteomics so new?
• Ionization methods
• MALDI, Electrospray
• Protein chemistry automation
• Chromatography, Gels, Computers
• Protein sequence databases
• A reference for comparison

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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

• Human Jurkat leukemia cell-line
• Lipid-raft extraction protocol, targeting T cells
• von Haller, et al. MCP 2003.
• 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

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Novel Splice Isoform
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Novel Splice Isoform
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Novel Mutation

• HUPO Plasma Proteome Project
• Pooled samples from 10 male 10 female healthy
  Chinese subjects
• Plasma/EDTA sample protocol
• Li, et al. Proteomics 2005. (Lab 29)
• TTR gene
• Transthyretin (pre-albumin)
• Defects in TTR are a cause of amyloidosis.
• Familial amyloidotic polyneuropathy
• late-onset, dominant inheritance

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Novel Mutation
Ala2?Pro associated with familial amyloid
polyneuropathy
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Novel Mutation
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Translation Start-Site

• Human erythroleukemia K562 cell-line
• Depth of coverage study
• Resing et al. Anal. Chem. 2004.
• THOC2 gene
• Part of the heteromultimeric THO/TREX complex.
• Initially believed to be a novel ORF
• RefSeq mRNA in Jun 2007, no RefSeq protein
• TrEMBL entry Feb 2005, no SwissProt entry
• Genbank mRNA in May 2002 (complete CDS)
• Plenty of EST support
• 100,000 bases upstream of other isoforms

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Translation Start-Site
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Translation Start-Site
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Translation Start-Site
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Translation Start-Site
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Expressed Sequence Tags (ESTs)

• Cheap, fast, coding
• Single sequencing reads of mRNA
• Sequence from 5 or 3 end
• No assembly

http//www.ncbi.nlm.nih.gov/About/primer/est.html
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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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Correct, Complete (C2) Enumeration

• Set of paths that use each edge at least once

ACDEFGEFGI, DEFACG
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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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Significant False Positives

• E-values are not enough!
• Random guessers are easy to beat.
• Post-translational modifications vs. amino-acid
  substitution
• methylation (on I/L, Q, R, C, H, K, S, T, N) 14
• D ? E, G ? A, V ? I/L, N ? Q, S ? T 14
• Peptide extension z2 ? z3
• Nonsense AA masses sum to precursor
• Need to ensure
• fragment ions define novel sequence
• sequence evidence is strong
• other plausible explanations can be eliminated

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Significant False Positives

• DFLAGGLAAAISK 2.2x10-8
• 2 ESTs
• DFLAGGIAAAISK 2.2x10-8
• IPI (2), RefSeq, mRNA, 1400 ESTs
• DFLAGGVAAAISK 3.7x10-8
• IPI, RefSeq, mRNA, 700 ESTs
• DFLAGGVAAAISKMAVVPI 3.5x10-5
• Genscan exon
• AISFAKDFLAGGIAAAISK 3.3x10-4
• Genscan exon

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Significant False Positives
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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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Spectral Matching for Peptide Identification

• Detection vs. identification
• Increased sensitivity specificity
• No novel peptides
• NIST GC/MS Spectral Library
• Identifies small molecules,
• 100,000s of (consensus) spectra
• Bundled/Sold with many instruments
• Dot-product spectral comparison
• Current project Peptide MS/MS

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NIST MS Search Peptides
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Peptide DLATVYVDVLK
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Protein Families
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Protein Families
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Peptide DLATVYVDVLK
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Hidden Markov Models for Spectral Matching

• Capture statistical variation and consensus in
  peak intensity
• Capture semantics of peaks
• Extrapolate model to other peptides
• Good specificity with superior sensitivity for
  peptide detection
• Assign 1000s of additional spectra (p-value lt
  10-5)

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Hidden Markov Model
Delete
Insert
Ion
(m/z,int) pair emitted by ion insert states
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Spectral Matching of Peptide Variants
DFLAGGIAAAISK
DFLAGGVAAAISK
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Spectral Matching of Peptide Variants
AVMDDFAAFVEK
AVMDDFAAFVEK
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HMM model extrapolation
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Conclusions

• Proteomics can inform genome annotation
• Eukaryotic and prokaryotic
• Functional vs silencing variants
• Peptides identify more than just proteins
• Untapped source of disease biomarkers
• Compressed peptide sequence databases make
  routine EST searching feasible
• Novel spectral matching technique using HMMs
  looks very promising

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Acknowledgements

• Catherine Fenselau, Steve Swatkoski
• UMCP Biochemistry
• Chau-Wen Tseng, Xue Wu
• UMCP Computer Science
• Cheng Lee
• Calibrant Biosystems
• PeptideAtlas, HUPO PPP, X!Tandem
• Funding NIH/NCI, USDA/ARS