Enzyme Function Initiative
|Purpose||Develop and disseminate a robust strategy to determine enzyme function|
|Headquarters||University of Illinois, Urbana-Champaign|
|John A. Gerlt, Ph.D.|
|Five-year NIGMS Glue Grant|
The Enzyme Function Initiative (EFI) is a large-scale collaborative project aiming to develop and disseminate a robust strategy to determine enzyme function through an integrated sequence–structure-based approach. The project was funded in May 2010 by the National Institute of General Medical Sciences as a Glue Grant which supports the research of complex biological problems that cannot be solved by a single research group. The EFI was largely spurred by the need to develop methods to identify the functions of the enormous number proteins discovered through genomic sequencing projects.
The dramatic increase in genome sequencing technology has caused the number of protein sequences deposited into public databases to grow apparently exponentially. To cope with the influx of sequences, databases use computational predictions to auto-annotate individual protein's functions. While these computational methods offer the advantages of being extremely high-throughput and generally provide accurate broad classifications, exclusive use has led to a significant level of misannotation of enzyme function in protein databases. Thus although the information now available represents an unprecedented opportunity to understand cellular metabolism across a wide variety of organisms, which includes the ability to identify molecules and/or reactions that may benefit human quality of life, the potential has not been fully actualized. The biological community's ability to characterize newly discovered proteins has been outstripped by the rate of genome sequencing, and the task of assigning function is now considered the rate-limiting step in understanding biological systems in detail.
Integrated strategy for functional assignment
The EFI is developing an integrated sequence-structure based strategy for functional assignment by predicting the substrate specificities of unknown members of mechanistically diverse enzyme superfamilies. The approach leverages conserved features within a given superfamily such as known chemistry, identity of active site functional groups, and composition of specificity-determining residues, motifs, or structures to predict function but relies on multidisciplinary expertise to streamline, refine, and test the predictions. The integrated sequence-strategy under development will be generally applicable to deciphering the ligand specificities of any functionally unknown protein.
By NIGMS program mandate, Glue Grant consortia must contain core resources and bridging projects. The EFI consists of six scientific cores which provide bioinformatic, structural, computational, and data management expertise to facilitate functional predictions for enzymes of unknown function targeted by the EFI. These predictions are then tested by five Bridging Projects representing the amidohydrolase, enolase, GST, HAD, and isoprenoid synthase enzyme superfamilies.
The superfamily/genome core contributes bioinformatic analysis by collecting and curating complete sequence data sets, generating sequence similarity networks, and classification of superfamily members into subgroups and families for subsequent annotation transfer and evaluation as targets for functional characterization.
The protein core develops cloning, expression, and protein purification strategies for the enzymes targeted for study.
The structure core fulfills the structural biology component for EFI by providing high resolution structures of targeted enzymes.
The computation core performs in silico docking to generate rank-ordered lists of predicted substrates for targeted enzymes using both experimentally determined and/or homology modeled protein structures.
The amidohydrolase superfamily contains evolutionarily related enzymes with a distorted (β/α)8 barrel fold which primarily catalyze metal-assisted deamination, decarboxylation, isomerization, hydration, or retroaldol cleavage reactions.
The enolase superfamily contains evolutionarily related enzymes with a (β/α)7β‑barrel (TIM‑barrel) fold which primarily catalyze metal-assisted epimerization/racemization or β-elimination of carboxylate substrates.
The GST superfamily contains evolutionarily related enzymes with a modified thioredoxin fold and an additional all α-helical domain which primarily catalyze nucleophilic attack of reduced glutathione (GSH) on electrophlic substrates.
The HAD superfamily contains evolutionarily related enzymes with a Rosmmannoid α/β fold with an inserted "cap" region which primarily catalyze metal-assisted nucleophilic catalysis, most frequently resulting in phosphoryl group transfer.
The isoprenoid synthase (I) superfamily contains evolutionarily related enzymes with a mostly all α-helical fold and primarily catalyze trans-prenyl transfer reactions to form elongated or cyclized isoprene products.
Sixteen investigators with expertise in various disciplines make up the EFI.
|Gerlt, John A.||University of Illinois, Urbana-Champaign||EFI Director, Director of the Enolase Bridging Project|
|Allen, Karen N.||Boston University||Co-Director of the HAD Bridging Project|
|Almo, Steven C.||Albert Einstein College of Medicine||Director of the Protein, Director of the Structure Core|
|Armstrong, Richard N.||Vanderbilt University School of Medicine||Director of the GST Bridging Project|
|Babbitt, Patricia C.||University of California, San Francisco||Director of the Superfamily/Genome Core, Co-Director of the Data and Dissemination Core|
|Cronan, John E.||University of Illinois, Urbana-Champaign||Co-Director of the Microbiology Core|
|Dunaway‑Mariano, Debra||University of New Mexico||Co-Director of the HAD Bridging Project|
|Jacobson, Matthew P.||University of California, San Francisco||Co-Director of the Computation Core|
|Minor, Wladek||University of Virginia||Co-Director of Data and Dissemination Core|
|Poulter, C. Dale||University of Utah||Director of the Isoprenoid Synthase Bridging Project|
|Raushel, Frank M.||Texas A&M University||Director of the Amidohydrolase Bridging Project|
|Sali, Andrej||University of California, San Francisco||Co-Director of the Computation Core|
|Shoichet, Brian K.||University of California, San Francisco||Co-Director of the Computation Core|
|Sweedler, Jonathan V.||University of Illinois, Urbana-Champaign||Co-Director of the Microbiology Core|
|Pollard, Katherine S.||Gladstone Institutes||Director of the Sifting Families Pilot Project|
|Booker, Squire J.||Pennsylvania State University||Director of the Radical SAM Pilot Project|
The EFI's primary deliverable is development and dissemination of an integrated sequence/structure strategy for functional assignment. As the strategy is developed, data and clones generated by the EFI are made freely available via several online resources.
The EFI was established in May 2010 with $33.9 million in funding over a five-year period (grant number GM093342). Pending project success and assessment of the Glue Grant funding mechanism, the grant may be renewed for an additional 5 years in 2015.
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