A community-driven annotation platform for structural genomics
Workshop on the Biological Annotation of Novel Proteins, March 7-8, 2008
Biomedical theme: Central Machinery of Life -proteins conserved in all kingdoms of lifeBiological theme: Complete coverage of Thermotoga maritima
Adam Godzikand the JCSG Bioinformatics Team
Science is all about communication• Since late XIX century, a dominant way of communicating scientific
results is through peer-reviewed manuscripts• Pro
– Peer review ensures quality– Enforces a “publishable unit” – decreases noise in the
“communication space”– Authorship rules ensure proper distribution of credit in a
system that is well integrated with system of promotions and evaluations
• Con– Significant time lag and additional costs– Enforces a “publishable unit” – below the threshold
results are lost – Not scalable with high throughput data production
Increasingly, it’s not the only game in town
• Databases and automated annotation protocols– pro: fast, machine searchable, scalable– con: difficult to ensure quality and assign credit, put the
burden of expertise on the user
• Wikipedia– pro: harnesses power of community, scalable– con: unreliable, difficult to ensure quality and assign
credit
Can we have the best of all worlds?
Peer-reviewed manuscript
Automated database annotationWikipedia
entry
Fast, accurate,scalable
purificationexpressioncloning
struc. refinementstruc. validationannotationpublication
phasingdata collectionxtal screening tracing xtal mounting
crystallizationimagingharvesting
targetselection
PDB
Structure determination in PSI centers is done on a semi-automated assembly line
• Joint Center for Structural Genomics• One of four large scale (production) centers of PSI2• ~ 600 structures deposited in the PDB• Sustained pace of ~15 PDB depositions per month
…and the pace of structure determination far outstrips the pace of our publications…
PSI-1
PSI-2
Structure collage: PSIPublication statistics : http://olenka.med.virginia.edu/psi/
Why?• Speed: 2-3 months from target selection to
structure, 2-3 structures per week in each center• Assembly line process, no time to develop
“special relationship” with each protein• Structures are not associated with ongoing
biochemical and biological research.• Targets selected based on novelty, no expertise
available anywhere, difficult to reach “publishable unit”
We are not alone …• Bacterial genomes
– 1995-2000 - every new sequenced genome led to a Nature/Science publication
– with 500+ genomes, an increasing percentage of them never become a single focus of a specific publication
– Community based annotation efforts become the best source of information (SEED)
WEB 2.0 is reshaping how we share information:
Communities of globally distributed peers (Networks) built around rich, collaborative environments.
WikipediaCitizendiumScholarpediaGoogle Knols
OpenWetwareWikiomicsWikiProteinsTOPSAN
How can we tap into an ultimate research tool?
• Search engines are becoming serious research tools
• Google indexes research papers, books, wikipedia pages
• Semi-natural language searches
0
10
20
30
40
50
60
70
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1980 1985 1990 1995 2000 2007
year
% c
ove
red
in PDB
sequence identity > 30%
blast e-value < 0.001
FFAS score < -9.5
Structural coverage of many genomes (here T.maritima) approaches completeness
~73% of feasible targets
http://research.calit2.net/metagenomics/thermotoga
Which brings attention from broader communities
Which brings attention from broader communities
We can utilize other information
• Metabolic reconstruction of T.maritima was done in collaboration with UCSD Systems Biology Lab (Bernard Palsson)
• Model is consistent with all the published experimental data on TM (see Ines Thiele poster)
• First generation model covers 479 genes (1398 are not in the model), 492 metabolites
– Proteins coded by 113 of these genes have been solved (71 at JCSG, 28 at other PSI centers)
– 320 have be modeled– We know at least approximate
structure of ALL the proteins in the reconstruction
And bring it together to help make sense of the structures and see them in the full context
All available information about a protein on one page
We try to combine automated, database driven annotations with expert curated input.
Annotation:• Feeds from public databases• Expert-curated informationContent management:• Wiki-style editing (WYSIWYG editor)• Page-level access control• Structured fields + free text• Instant publication• Always open for comments and editsQuality control & authorship:• Encourage community collaboration• JCSG scientists & invited peers• Many authors - no contribution too
small• Lead authors (editors) in charge of
releases
TOPSAN content:
Protein Groups
Browsing Options
JCSG: Structures / Structure Notes / TOPSAN
278 of 593 structures have an annotation on TOPSAN
Members of the biological community can utilize PSI structures only when they are aware of them
Functionally well characterized enzyme and is also a new fold.PDB ID: 3C8WTargetDB: 376561
TOPSAN access statistics- Jan to Mar 08
1143 visits from rest of the world.
Google sent us the largest number of visitors.
UCSD & BurnhamBioinformatics CoreJohn WooleyAdam GodzikLukasz JaroszewskiSlawomir GrzechnikSri Krishna SubramanianAndrew MorseTamara AstakhovaLian DuanPiotr KozbialDana WeekesNatasha SefcovicJosie Alaoen
GNF & TSRICrystallomics CoreScott LesleyMark KnuthHeath KlockMarc DellerDennis CarltonPolat Abdubek Sanjay AgarwallaConnie ChenThomas ClaytonDustin ErnstJulie FeuerhelmRegina GorskiAnna GrzechnikJoanna C. HaleThamara JanaratneHope JohnsonSachin KaleDaniel McMullanEdward NigoghossianAmanda NopakunLinda OkachJessica PaulsenChristina PuckettSebastian SudekJessica Canseco
Scientific Advisory Board
Sir Tom BlundellUniv. CambridgeHomme Hellinga
Duke University Medical CenterJames Naismith
The Scottish Structural Proteomics facility Univ. St. AndrewsJames Paulson
Consortium for Functional Glycomics,The Scripps Research Institute
Robert StroudCenter for Structure of Membrane Proteins,
Membrane Protein Expression Center UC San FranciscoSoichi Wakatsuki
Photon Factory, KEK, JapanJames Wells
UC San FranciscoTodd Yeates
UCLA-DOE, Inst. for Genomics and Proteomics
TSRINMR CoreKurt Wüthrich Reto Horst Margaret JohnsonAmaranth ChatterjeeMichael GeraltWojtek AugustyniakJin-Kyu RheeBiswaranjan MohantyBill PedriniPedro Serrano
TSRI Administrative CoreIan WilsonMarc ElsligerGye Won HanDavid MarcianoHenry TienXiaoping DaiLisa van Veen
Stanford /SSRLStructure Determination CoreKeith HodgsonAshley DeaconMitchell Miller Hsiu-Ju (Jessica) ChiuDebanu Das Kevin JinAbhinav KumarWinnie LamSilvya OommachenChristopher RifeScott TalafuseChristine TrameQingping XuHenry van den BedemRonald Reyes
The JCSG is supported by the NIH Protein Structure Initiative grant U54 GM074898 from the National Institute of General Medical Sciences (www.nigms.nih.gov).
Thermotoga browser acknowledgments• Co-PI of the project - Andrei Osterman (the biochemistry side, specific examples)• The JCSG team - for all the structures, focus on Thermotoga and CML• Bernard Palsson group and Ines Thiele for work with Thermotoga reconstruction and
model simulations• The JCMM team for structure modeling• Krzysztof Ginalski and bioinfor server team for assistance with “borderline” predictions
• Ying Zhang (JCMM) - finalizing the metabolic reconstruction, network and fold distribution analysis
• Dana Weekes (JCSG) - first pass on the Thermotoga metabolic reconstruction, TM TOPSAN pages
• Craig Shepherd (JCMM) - network visualization• Zhanwen Li (JCMM) - modeling and fold assignments
