improving the sensitivity of peptide identification for genome annotation
DESCRIPTION
Improving the Sensitivity of Peptide Identification for Genome Annotation. Nathan Edwards Department of Biochemistry and Molecular & Cellular Biology Georgetown University Medical Center. Why Tandem Mass Spectrometry?. - PowerPoint PPT PresentationTRANSCRIPT
Improving the Sensitivityof Peptide Identification for Genome Annotation
Nathan EdwardsDepartment of Biochemistry and
Molecular & Cellular Biology
Georgetown University Medical Center
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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• Electro-Spray
Ionization (ESI)
• Time-Of-Flight (TOF)• Quadrapole• Ion-Trap
• ElectronMultiplier(EM)
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Mass Spectrum
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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 MS/MS
Enzymatic Digestand
Fractionation
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Single Stage MS
MS
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Tandem Mass Spectrometry(MS/MS)
Precursor selection
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Tandem Mass Spectrometry(MS/MS)
Precursor selection + collision induced dissociation
(CID)
MS/MS
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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
762SGFL EEDELKb4
389SGFLEED ELKb7
MWion
633SGFLE EDELKb5
1080S GFLEEDELKb1
1022SG FLEEDELKb2
875SGF LEEDELKb3
504SGFLEE DELKb6
260SGFLEEDE LKb8
147SGFLEEDEL Kb9
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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
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Unannotated Splice Isoform
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Unannotated Splice Isoform
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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)
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Halobacterium sp. NRC-1ORF: GdhA1
K-score E-value vs PepArML @ 10% FDR Many peptides inconsistent with annotated
translation start site of NP_279651
0 40 80 120 160 200 240 280 320 360 400 440
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Translation start-site correction
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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.
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E:\Yersinia Work\yr_inclusion 3/11/2009 3:43:13 PM yrohdei
RT: 19.04 - 25.39
19.5 20.0 20.5 21.0 21.5 22.0 22.5 23.0 23.5 24.0 24.5 25.0
Time (min)
0
20
40
60
80
100
0
20
40
60
80
100
Re
lative
Ab
un
da
nce
25.3619.9919.93
25.2720.04 25.2319.89 23.0322.97 23.08
20.1019.83 23.64 25.1923.7022.88 24.6324.5720.1422.82
20.2019.7822.7220.2519.48
22.5220.41 22.0821.8420.60 21.04
20.00
21.03 21.46
NL: 1.66E8
TIC MS yr_inclusion
NL: 1.01E7
TIC F: FTMS + p ESI d Full ms2 [email protected] [195.00-2000.00] MS yr_inclusion
yr_inclusion #1937-2437 RT: 19.45-24.36 AV: 21 NL: 4.80E4F: FTMS + p ESI d Full ms2 [email protected] [195.00-2000.00]
200 400 600 800 1000 1200 1400 1600 1800 2000
m/z
0
10
20
30
40
50
60
70
80
90
100
Re
lative
Ab
un
da
nce
576.83z=2
840.16z=7
720.39z=2 903.81
z=3785.41
z=4694.62
z=4
584.57z=4
928.49z=4559.55
z=41804.48
z=?992.53
z=3200.78z=?
329.71z=?
1253.14z=?
555.29z=4
1610.27z=?
1883.75z=?
1491.23z=?
1118.93z=?
1666.89z=?
1345.30z=?
461.16z=?
756.70 +8 MW 6044.11
Protein Fragmentation Spectrum
AVQQNKPTRSKRGMRRSHDA
LTTATLSVDKTSGETHLRHH
ITADGFYRGRKVIG
Match to Y. pestis 50S RP L32
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Phyloproteomics
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Phyloproteomics
phylogeny.fr – "One-Click"
Protein Sequence 16S-rRNA Sequence
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Shared "Biomarker" Proteins
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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
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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!
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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.
Peptide Sequence Databases
Organism Size (AA) Size (Entries)Human 248Mb 74,976Mouse 171Mb 55,887
Rat 76Mb 42,372Zebra-fish 94Mb 40,490
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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.
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PeptideMapper Web Service
I’m Feeling Lucky
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PeptideMapper Web Service
I’m Feeling Lucky
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PeptideMapper Web Service
I’m Feeling Lucky
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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
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Comparison of search engine results
No single score is comprehensive
Search engines disagree
Many spectra lack confident peptide assignment
Searle et al. JPR 7(1), 2008
38%
14%28%
14%
3%
2%
1%
X! Tandem
SEQUESTMascot
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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.
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Supervised Learning
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Unsupervised Learning
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Peptide Atlas A8_IP LTQ Dataset
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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
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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
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NSF TeraGrid1000+ CPUs
X!Tandem,KScore,OMSSA,
MyriMatch,Mascot(1 core).
X!Tandem,KScore,OMSSA,
MyriMatch.
PepArML Meta-Search Engine
UMIACS250+ CPUs
Edwards LabScheduler &48+ CPUs
Securecommunication
Heterogeneouscompute resources
Single, simplesearch request
Scales easily to 250+ simultaneous
searches
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PepArML Meta-Search EngineNSF TeraGrid1000+ CPUs
Edwards LabScheduler &80+ CPUs
Securecommunication
Heterogeneouscompute resources
Single, simplesearch request
Scales easily to 250+ simultaneous
searches
X!Tandem,KScore,OMSSA,
MyriMatch,Mascot(1 core).
X!Tandem,KScore,OMSSA,
MyriMatch.
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PepArML Meta-Search Engine
NSF TeraGrid1000+ CPUs
UMIACS250+ CPUs
Edwards LabScheduler &48+ CPUs
Securecommunication
Heterogeneouscompute resources
Simple searchrequest
44
PepArML Meta-Search Engine
NSF TeraGrid1000+ CPUs
UMIACS250+ CPUs
Edwards LabScheduler &48+ CPUs
Securecommunication
Heterogeneouscompute resources
Simple searchrequest
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.
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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
http://edwardslab.bmcb.georgetown.edu
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Acknowledgements
Dr. Catherine Fenselau & students 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