1. Improving the Sensitivity of Peptide Identification for Genome Annotation
Nathan Edwards
Department of Biochemistry and Molecular & Cellular Biology
Georgetown University Medical Center

2. Why Tandem Mass Spectrometry?
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 small sequence variations

3. 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

4. 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)

5. Mass Spectrum

6. Mass is fundamental

7. 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

8. 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

9. Sample Preparation for MS/MS
Enzymatic Digest
and
Fractionation

10. Single Stage MS MS

11. Tandem Mass Spectrometry (MS/MS)
Precursor selection

12. Tandem Mass Spectrometry (MS/MS)
Precursor selection +
collision induced dissociation
(CID)
MS/MS

13. Peptide Fragmentation
Peptide: S-G-F-L-E-E-D-E-L-K y1 y2 y3 y4 y5 y6 y7 y8 y9
ion
1020
907
778
663
534
405
292
145 88 MW
762
SGFL EEDELK b4
389
SGFLEED ELK b7
633
SGFLE EDELK b5
1080
S GFLEEDELK b1
1022
SG FLEEDELK b2
875
SGF LEEDELK b3
504
SGFLEE DELK b6
260
SGFLEEDE LK b8
147
SGFLEEDEL K b9

14. Unannotated 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

15. Unannotated Splice Isoform

16. Unannotated Splice Isoform

17. Translation start-site correction
Halobacterium sp. NRC-1
Extreme halophilic Archaeon, insoluble membrane and soluble cytoplasmic proteins
Goo, et al. MCP 2003.
GdhA1 gene:
Glutamate dehydrogenase A1
Multiple significant peptide identifications
Observed start is consistent with Glimmer 3.0 prediction(s)

18. Halobacterium sp. NRC-1 ORF: GdhA1
K-score E-value vs 10% FDR
Many peptides inconsistent with annotated translation start site of NP_279651

19. Translation start-site correction

20. Phyloproteomics Tandem mass-spectra of proteins (top-down)
High-accuracy instrument (Orbitrap, UMD Core)
Proteins from unsequenced bacteria matching identical proteins in related organisms
Demonstration using Y.rohdei.

21. Protein Fragmentation Spectrum
Match to Y. pestis 50S RP L32
A­V­Q­Q­N­K­P­T­R­S­K­R­G­M­R­R­S­H­D­A­
L­T­T­A­T­L­S­V­D­K­T­S­G­E­T­H­L­R­H­H­
I­T­A­D­G­F­Y­R­G­R­K­V­I­G

22. Phyloproteomics

23. phylogeny.fr – "One-Click"
Phyloproteomics
Protein Sequence
16S-rRNA Sequence
phylogeny.fr – "One-Click"

24. Shared "Biomarker" Proteins

25. Phyloproteomics
Recent extension to highly homologous proteins in related organisms
Merely require N- and/or C-terminus in common
Broadens applicability considerably
Phyloproteomic trees for E.herbicola and Enterocloacae, neither sequenced.
New paradigm for phylogenetic analysis?

26. Lost peptide identifications
Missing from the sequence database
Search engine strengths, weaknesses, quirks
Poor score or statistical significance
Thorough search takes too long

27. Searching under the street-light…
Tandem mass spectrometry doesn’t discriminate against novel peptides but protein sequence databases do!
Searching traditional protein sequence databases biases the results in favor of well-understood and/or computationally predicted proteins and protein isoforms!

28. Peptide Sequence Databases
All amino-acid 30-mers, no redundancy
From ESTs, Proteins, mRNAs
30-40 fold size, search time reduction
Formatted as a FASTA sequence database
One entry per gene/cluster.
Organism
Size (AA)
Size (Entries)
Human
248Mb
74,976
Mouse
171Mb
55,887
Rat
76Mb
42,372
Zebra-fish
94Mb
40,490

29. We can observe evidence for…
Known coding SNPs
Unannotated coding mutations
Alternate splicing isoforms
Alternate/Incorrect translation start-sites
Microexons
Alternate/Incorrect translation frames
…though it must be treated thoughtfully.

30. PeptideMapper Web Service
I’m Feeling Lucky

31. PeptideMapper Web Service
I’m Feeling Lucky

32. PeptideMapper Web Service
I’m Feeling Lucky

33. PeptideMapper Web Service
Suffix-tree index on peptide sequence database
Fast peptide to gene/cluster mapping
“Compression” makes this feasible
Peptide alignment with cluster evidence
Amino-acid or nucleotide; exact & near-exact
Genomic-loci mapping via
UCSC “known-gene” transcripts, and
Predetermined, embedded genomic coordinates

34. Comparison of search engine results
No single score is comprehensive
Search engines disagree
Many spectra lack confident peptide assignment
38%
14%
28% 3% 2% 1%
X! Tandem
SEQUEST
Mascot
Here is way, no single one gives the best results
Q: after improvement, what is the percentage of identified spectra, how is the improvement? 25 – 30%
Searle et al. JPR 7(1), 2008

35. Combining search engine results – harder than it looks!
Consensus boosts confidence, but...
How to assess statistical significance?
Gain specificity, but lose sensitivity!
Incorrect identifications are correlated too!
How to handle weak identifications?
Consensus vs disagreement vs abstention
Threshold at some significance?
We apply unsupervised machine-learning....
Lots of related work unified in a single framework.

36. Supervised Learning

37. Unsupervised Learning

38. Peptide Atlas A8_IP LTQ Dataset

39. Running many search engines
Search engine configuration can be difficult:
Correct spectral format
Search parameter files and command-line
Pre-processed sequence databases.
Tracking spectrum identifiers
Extracting peptide identifications, especially modifications and protein identifiers

40. Peptide Identification Meta-Search
Simple unified search interface for:
Mascot, X!Tandem, K-Score, OMSSA, MyriMatch, S-Score, InsPecT, KM-Score
Automatic decoy searches
Automatic spectrum file "chunking"
Automatic scheduling
Serial, Multi-Processor, Cluster, Grid

41. PepArML Meta-Search Engine
X!Tandem,
KScore,
OMSSA,
MyriMatch,
Mascot
(1 core).
NSF TeraGrid
1000+ CPUs
Heterogeneous
compute resources
X!Tandem,
KScore,
OMSSA, MyriMatch.
Secure
communication
Edwards Lab
Scheduler &
48+ CPUs
Scales easily to simultaneous searches
Single, simple search request
UMIACS
250+ CPUs

42. PepArML Meta-Search Engine
X!Tandem,
KScore,
OMSSA,
MyriMatch,
Mascot
(1 core).
NSF TeraGrid
1000+ CPUs
Heterogeneous
compute resources
X!Tandem,
KScore,
OMSSA, MyriMatch.
Secure
communication
Edwards Lab
Scheduler &
80+ CPUs
Scales easily to simultaneous searches
Single, simple search request

43. PepArML Meta-Search Engine
Heterogeneous
compute resources
NSF TeraGrid
1000+ CPUs
Edwards Lab
Scheduler &
48+ CPUs
Secure
communication
Simple search request
UMIACS
250+ CPUs

44. PepArML Meta-Search Engine
Heterogeneous
compute resources
NSF TeraGrid
1000+ CPUs
Edwards Lab
Scheduler &
48+ CPUs
Secure
communication
Simple search request
UMIACS
250+ CPUs

45. Peptide Identification Grid-Enabled Meta-Search
Access to high-performance computing resources for the proteomics community
NSF TeraGrid Community Portal
University/Institute HPC clusters
Individual lab compute resources
Contribute cycles to the community and get access to others’ cycles in return.
Centralized scheduler
Compute capacity can still be exclusive, or prioritized.
Compute client plays well with HPC grid schedulers.

46. Conclusions
Improve the scope and sensitivity of peptide identification for genome annotation, using
Exhaustive peptide sequence databases
Machine-learning for combining
Meta-search tools to maximize consensus
Grid-computing for thorough search

47. Acknowledgements Dr. Catherine Fenselau & students Dr. Yan Wang
University of Maryland Biochemistry
Dr. Yan Wang
University of Maryland Proteomics Core
Dr. Art Delcher
University of Maryland CBCB
Dr. Chau-Wen Tseng & Dr. Xue Wu
University of Maryland Computer Science
Funding: NIH/NCI