Proteomic Characterization of Alternative Splicing and Coding Polymorphism Nathan Edwards Center for Bioinformatics and Computational Biology University of Maryland, College Park

2 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?

3 Systems Biology Establish relationships by Choosing related samples, Global characterization, and Comparison. Gene / Transcript / Protein MeasurementPredeterminedUnknown Discrete (DNA)GenotypingSequencing ContinuousGene ExpressionProteomics

4 Samples Healthy / Diseased Cancerous / Benign Drug resistant / Drug susceptible Progression or Prognosis Bound / Unbound Tissue specific Cellular location specific Mitochondria, Membrane

5 2D Gel-Electrophoresis Protein separation Molecular weight (MW) Isoelectric point (pI) Staining Birds-eye view of protein abundance

6 2D Gel-Electrophoresis Bécamel et al., Biol. Proced. Online 2002;4:

7 Paradigm Shift Traditional protein chemistry assay methods struggle to establish identity. Identity requires: Specificity of measurement (Precision) A reference for comparison

8 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

9 Mass Spectrometer Ionizer Sample + _ Mass Analyzer Detector MALDI Electro-Spray Ionization (ESI) Time-Of-Flight (TOF) Quadrapole Ion-Trap Electron Multiplier (EM)

10 High Bandwidth

11 Mass is fundamental!

12 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

13 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

14 Sample Preparation for Peptide Identification Enzymatic Digest and Fractionation

15 Single Stage MS MS m/z

16 Tandem Mass Spectrometry (MS/MS) Precursor selection m/z

17 Tandem Mass Spectrometry (MS/MS) Precursor selection + collision induced dissociation (CID) MS/MS m/z

18 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

19 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!

20 What goes missing? Known coding SNPs Novel coding mutations Alternative splicing isoforms Alternative translation start-sites Microexons Alternative translation frames

21 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

22 Novel Splice Isoform Human Jurkat leukemia cell-line Lipid-raft extraction protocol, targeting T cells von Haller, et al. MCP 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

23 Novel Splice Isoform

24 Novel Splice Isoform

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

26 Novel Mutation Ala2→Pro associated with familial amyloid polyneuropathy

27 Novel Mutation

28 Expressed Sequence Tags (ESTs) Cheap, fast, coding Single sequencing reads of mRNA Sequence from 5’ or 3’ end No “assembly”

29 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.

30 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%

31 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 C 2 FASTA database Complete, Correct for amino-acid 30-mers

32 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 C 2 FASTA database Complete, Correct for amino-acid 30-mers

33 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?

34 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!

35 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 spores MALDI Mass Spectrometry

36 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

37 Rapid Microorganism Identification Database ( Protein Sequences 8.1M (2.9M) Species ~ 18K Genbank, Microbial, Virus, Plasmid RefSeq CMR, Swiss-Prot TrEMBL

38 Rapid Microorganism Identification Database (

39 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

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

41 Peptide DLATVYVDVLK

42 Peptide DLATVYVDVLK

43 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

44 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

45 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

46 Future Research Directions Proteomics for Microorganism Identification Specificity of tandem mass spectra Revamp RMIDb prototype Incorporate spectral matching

47 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