Proteomic Characterization

of Alternative Splicing and

Coding Polymorphism

Proteomic Characterization

of Alternative Splicing and

Coding PolymorphismNathan EdwardsCenter for Bioinformatics and Computational BiologyUniversity of Maryland, College Park

2

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

3

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

4

High Bandwidth

100

0250 500 750 1000

m/z

% I

nte

nsit

y

5

Mass is fundamental!

6

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

7

Sample Preparation for Peptide Identification

Enzymatic Digestand

Fractionation

8

Single Stage MS

MS

m/z

9

Tandem Mass Spectrometry(MS/MS)

Precursor selection

m/z

m/z

10

Tandem Mass Spectrometry(MS/MS)

Precursor selection + collision induced dissociation

(CID)

MS/MS

m/z

m/z

11

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

12

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!

13

What goes missing?

• Known coding SNPs

• Novel coding mutations

• Alternative splicing isoforms

• Alternative translation start-sites

• Microexons

• Alternative translation frames

14

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

15

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

16

Novel Splice Isoform

17

Novel Splice Isoform

18

Novel Frame

19

Novel Frame

20

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

21

Novel Mutation

Ala2→Pro associated with familial amyloid polyneuropathy

22

Novel Mutation

23

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.

24

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%

25

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

26

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

27

SBH-graph

ACDEFGI, ACDEFACG, DEFGEFGI

28

Compressed SBH-graph

ACDEFGI, ACDEFACG, DEFGEFGI

29

Sequence Databases & CSBH-graphs

• Original sequences correspond to paths

ACDEFGI, ACDEFACG, DEFGEFGI

30

Sequence Databases & CSBH-graphs

• All k-mers represented by an edge have the same count

2 2

1

2

1

31

cSBH-graphs

• Quickly determine those that occur twice

2 2

1

2

32

Correct, Complete, Compact (C3) Enumeration

• Set of paths that use each edge exactly once

ACDEFGEFGI, DEFACG

33

Correct, Complete (C2) Enumeration

• Set of paths that use each edge at least once

ACDEFGEFGI, DEFACG

34

Patching the CSBH-graph

• Use artificial edges to fix unbalanced nodes

35

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?

36

“Novel Peptide” Computational Infrastructure

• Binaries (C++)• cSBH-graph construction

• Condor grid-enabled• Eulerian path k-mer enumeration

• Suitable for large graphs

• Data-model for peptide identification• Spectra (>5 million)• Peptide identifications

• Mascot, SEQUEST, X!Tandem, NIST • Genomic context of peptides

37

“Novel Peptide” Computational Infrastructure

• Condor grid-enabled MS/MS search• Mascot, X!Tandem, (Inspect, OMSSA)

• TurboGears python web-stack• SQLObject Object-Relational-Manager• MVC web-application framework• Suitable for AJAX & web-services too

• Integration with UCSC genome browser• caBIG compatible web-services

• Java applet for viewing spectra

38

Peptide Identification Navigator

39

Peptide Identification Navigator

40

Spectrum Viewer

41

Spectrum Viewer

42

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!

43

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.anthracisspores

MALDI Mass Spectrometry

44

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

45

Rapid Microorganism Identification Database (www.RMIDb.org)

• Protein Sequences• 8.1M (2.9M)

• Species• ~ 18K

• Genbank,• Microbial, Virus, Plasmid

• RefSeq• CMR,• Swiss-Prot• TrEMBL

46

Rapid Microorganism Identification Database (www.RMIDb.org)

47

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

48

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

49

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• Aggressive peptide sequence enumeration• dbPep for genomic annotation• Open, flexible informatics infrastructure for

peptide identification

50

Future Research Directions

• Proteomics for Microorganism Identification• Specificity of tandem mass spectra• Revamp RMIDb prototype• Incorporate spectral matching

• Primer design• k-mer sets as FASTA sequence databases• Uniqueness oracle for exact and inexact match• Integration with Primer3• Tiling, multiplexing, pooling, & tag arrays

51

Acknowledgements

• Chau-Wen Tseng, Xue Wu• UMCP Computer Science

• Catherine Fenselau, Steve Swatkoski• UMCP Biochemistry

• Calibrant Biosystems

• PeptideAtlas, HUPO PPP, X!Tandem

• Funding: National Cancer Institute