Enzyme Function Initiative

Enzyme Function Initiative (EFI)

Formation	2010
Purpose	Develop and disseminate a robust strategy to determine enzyme function
Headquarters	University of Illinois, Urbana-Champaign
Principal Investigator	John A. Gerlt, Ph.D.
Budget	Five-year NIGMS Glue Grant
Website	www.enzymefunction.org

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.

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.