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

2. 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

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

4. 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

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

6. High Bandwidth
100
250
500
750
1000
m/z
% Intensity

7. Mass is fundamental!

8. 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

9. 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

10. Sample Preparation for Peptide Identification
Enzymatic Digest
and
Fractionation

11. Single Stage MS MS
m/z

12. Tandem Mass Spectrometry (MS/MS)
m/z
Precursor selection
m/z

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

14. 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

15. 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!

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

17. 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

18. Novel Splice Isoform Human Jurkat leukemia cell-line LIME1 gene:
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

19. Novel Splice Isoform

20. Novel Splice Isoform

21. Novel Mutation HUPO Plasma Proteome Project TTR gene
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

22. Ala2→Pro associated with familial amyloid polyneuropathy
Novel Mutation
Ala2→Pro associated with familial amyloid polyneuropathy

23. Novel Mutation

24. Translation Start-Site
Human erythroleukemia K562 cell-line
Depth of coverage study
Resing et al. Anal. Chem
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

25. Translation Start-Site

26. Translation Start-Site

27. Translation Start-Site

28. Translation Start-Site

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

30. Searching ESTs Proposed long ago: Now:
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.

31. 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%

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 C2 FASTA database
Complete, Correct for amino-acid 30-mers
Gene-centric peptide sequence database:
Size: < 3% of naïve enumeration, FASTA entries
Running time: ~ 1% of naïve enumeration search
E-values: ~ 2% of naïve enumeration search results

33. 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, FASTA entries
Running time: ~ 1% of naïve enumeration search
E-values: ~ 2% of naïve enumeration search results

34. SBH-graph
ACDEFGI, ACDEFACG, DEFGEFGI

35. Compressed SBH-graph
ACDEFGI, ACDEFACG, DEFGEFGI

36. Sequence Databases & CSBH-graphs
Original sequences correspond to paths
ACDEFGI, ACDEFACG, DEFGEFGI

37. Sequence Databases & CSBH-graphs
All k-mers represented by an edge have the same count 1 2 2 1 2

38. cSBH-graphs
Quickly determine those that occur twice 2 2 1 2

39. Correct, Complete (C2) Enumeration
Set of paths that use each edge at least once
ACDEFGEFGI, DEFACG

40. 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?

41. 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 z=+2 → z=+3
Nonsense AA masses sum to precursor
Need to ensure:
fragment ions define novel sequence
sequence evidence is strong
other plausible explanations can be eliminated

42. Significant False Positives
DFLAGGLAAAISK 2.2x10-8
2 ESTs
DFLAGGIAAAISK x10-8
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

43. Significant False Positives

44. 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!

45. Spectral Matching for Peptide Identification
Detection vs. identification
Increased sensitivity & specificity
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

46. NIST MS Search: Peptides

47. Peptide DLATVYVDVLK

48. Protein Families

49. Protein Families

50. Peptide DLATVYVDVLK

51. 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 1000’s of additional spectra (p-value < 10-5)

52. (m/z,int) pair emitted by ion & insert states
Hidden Markov Model
Delete
Insert
Ion
(m/z,int) pair emitted by ion & insert states

53. Spectral Matching of Peptide Variants
DFLAGGIAAAISK
DFLAGGVAAAISK

54. Spectral Matching of Peptide Variants
AVMDDFAAFVEK
AVM*DDFAAFVEK

55. HMM model extrapolation

56. 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

57. 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