Transcript Faster, More Sensitive Peptide ID by Sequence DB Compression

Proteomic Characterization
of Alternative Splicing and
Coding Polymorphism
Nathan Edwards
Center for Bioinformatics and Computational Biology
University of Maryland, College Park
Proteomics
• Proteins are the machines that drive
much of biology
• Genes are merely the recipe
• The direct characterization of a
sample’s proteins en masse.
• What proteins are present?
• How much of each protein is present?
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Systems Biology
• Establish relationships by
• Choosing related samples,
• Global characterization, and
• Comparison.
Gene / Transcript / Protein
Measurement
Discrete (DNA)
Continuous
Predetermined
Unknown
Genotyping
Sequencing
Gene Expression
Proteomics
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Samples
• Healthy / Diseased
• Cancerous / Benign
• Drug resistant / Drug susceptible
• Progression or Prognosis
• Bound / Unbound
• Tissue specific
• Cellular location specific
• Mitochondria, Membrane
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2D Gel-Electrophoresis
• Protein separation
• Molecular weight (MW)
• Isoelectric point (pI)
• Staining
• Birds-eye view of
protein abundance
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2D Gel-Electrophoresis
Bécamel et al., Biol. Proced. Online 2002;4:94-104.
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Paradigm Shift
• Traditional protein chemistry assay
methods struggle to establish identity.
• Identity requires:
• Specificity of measurement (Precision)
• A reference for comparison
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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
Sample
+
_
Ionizer
• MALDI
• Electro-Spray
Ionization (ESI)
Mass Analyzer
• Time-Of-Flight (TOF)
• Quadrapole
• Ion-Trap
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Detector
• Electron
Multiplier
(EM)
High Bandwidth
% Intensity
100
0
250
500
750
10
1000
m/z
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
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
• 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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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:
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•
•
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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
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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
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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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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
spores
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
• 8.1M (2.9M)
• Species
• ~ 18K
• Genbank,
• Microbial, Virus, Plasmid
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•
•
•
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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Spectral Matching
• Detection vs. identification
• Increased sensitivity
• No novel peptides
• NIST GC/MS Spectral Library
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•
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Identifies small molecules,
100,000’s of (consensus) spectra
Bundled/Sold with many instruments
“Dot-product” spectral comparison
Current project: Peptide MS/MS
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Peptide DLATVYVDVLK
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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
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Conclusions
• Molecular biology & bioinformatics provide
a reference for biotechnologies
• Foundation of systems biology
• Peptides identify more than just proteins
• Untapped source of disease biomarkers
• Compressed peptide sequence databases
make routine EST searching feasible
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Future Research Directions
• Identification of protein isoforms:
• Optimize proteomics workflow for isoform
detection
• Identify splice variants in cancer cell-lines
(MCF-7) and clinical brain tumor samples
• dbPep for genomic annotation
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Future Research Directions
• Proteomics for Microorganism Identification
• Specificity of tandem mass spectra
• Revamp RMIDb prototype
• Incorporate spectral matching
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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
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