(all co-investigators in the Enzyme Function Initiative)
Patricia C. Babbitt, University of California, San Francisco
John A. Gerlt, University of Illinois
Matthew P. Jacobson, University of California, San Francisco
Andrej Sali, University of California, San Francisco
Brian K. Shoichet, University of California, San Francisco
For more details, go to the ASBMB Meeting 2013 program page and click to expland “Workshops.”
As of July, the nonredundant TrEMBL protein database contained 23,165,610 nonredundant sequences; a conservative estimate is that one half of these proteins have unknown, uncertain or incorrect functional annotations. Without correct annotations, the unlimited potential for medicine, chemistry and industry that could be obtained from functional and mechanistic understanding of nature’s complete repertoire of enzymes and metabolic pathways cannot be realized.
The Enzyme Function Initiative (supported by NIH U54GM093342) is devising an integrated sequence/structure based strategy for predicting and assigning functions to previously unknown enzymes discovered in genome projects to meet this challenge.
To accomplish this goal, the EFI has brought together multidisciplinary expertise in bioinformatics, experimental structural biology and structural modeling/docking so that predictions of in vitro enzymatic functions can be made and experimental enzymology, microbiology and metabolomics studies can be pursued. The goal is to validate and confirm enzymatic functions found in vitro as the actual physiological functions of the enzymes in vivo.
This workshop will feature presentations describing the development and application of high-throughput computational tools to facilitate functional assignment of unknown enzymes:
- 1. Bioinformatic analyses can cluster sequences into probable isofunctional groups, thereby assigning tentative functions to be investigated by structure determination, structural modeling and docking, and biochemical experimentation.
- 2. Homology modeling methods can expand the use of structural modeling to guide function assignment to proteins without structures.
- 3. Computational docking methods can leverage structure to guide functional assignment by suggesting substrates for biochemical experimentation.
The presentations will be followed by a question-and-answer session to identify potential collaborations between the audience and the EFI.
John A. Gerlt (email@example.com) is a professor at the University of Illinois at Urbana–Champaign. Patricia C. Babbitt (firstname.lastname@example.org) is a professor at the University of California, San Francisco.