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The Enzyme Function Initiative

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‡ †Departments of Biochemistry and Chemistry and ‡Institute for Genomic Biology, University of Illinois, Urbana, Illinois 61801, United States
§ Department of Chemistry, Boston University, Boston, Massachusetts 02215, United States
∥ Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York 10461, United States
⊥ Department of Biochemistry, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
@ Department of Bioengineering and Therapeutic Sciences and Department of Pharmaceutical Chemistry, California Institute of Quantitative Biosciences, University of California, San Francisco, California 94143, United States
# Departments of Microbiology and Biochemistry, University of Illinois, Urbana, Illinois 61801, United States
∇ Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
○ Department of Pharmaceutical Chemistry, University of California, San Francisco, California 94143, United States
◆ Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia 22908, United States
+ Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
▲ Department of Chemistry, Texas A&M University, College Station, Texas 77843, United States
△ Department of Chemistry, University of Illinois, Urbana, Illinois 61801, United States
Institute for Genomic Biology, University of Illinois, 1206 W. Gregory Dr., Urbana, IL 61801. Phone: (217) 244-7414. Fax: (217) 333-0508. E-mail: [email protected]
Cite this: Biochemistry 2011, 50, 46, 9950–9962
Publication Date (Web):October 14, 2011
https://doi.org/10.1021/bi201312u
Copyright © 2011 American Chemical Society
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    Abstract

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    The Enzyme Function Initiative (EFI) was recently established to address the challenge of assigning reliable functions to enzymes discovered in bacterial genome projects; in this Current Topic, we review the structure and operations of the EFI. The EFI includes the Superfamily/Genome, Protein, Structure, Computation, and Data/Dissemination Cores that provide the infrastructure for reliably predicting the in vitro functions of unknown enzymes. The initial targets for functional assignment are selected from five functionally diverse superfamilies (amidohydrolase, enolase, glutathione transferase, haloalkanoic acid dehalogenase, and isoprenoid synthase), with five superfamily specific Bridging Projects experimentally testing the predicted in vitro enzymatic activities. The EFI also includes the Microbiology Core that evaluates the in vivo context of in vitro enzymatic functions and confirms the functional predictions of the EFI. The deliverables of the EFI to the scientific community include (1) development of a large-scale, multidisciplinary sequence/structure-based strategy for functional assignment of unknown enzymes discovered in genome projects (target selection, protein production, structure determination, computation, experimental enzymology, microbiology, and structure-based annotation), (2) dissemination of the strategy to the community via publications, collaborations, workshops, and symposia, (3) computational and bioinformatic tools for using the strategy, (4) provision of experimental protocols and/or reagents for enzyme production and characterization, and (5) dissemination of data via the EFI’s Website, http://enzymefunction.org. The realization of multidisciplinary strategies for functional assignment will begin to define the full metabolic diversity that exists in nature and will impact basic biochemical and evolutionary understanding, as well as a wide range of applications of central importance to industrial, medicinal, and pharmaceutical efforts.

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    The SFLD was developed by the NIH National Center for Research Resources for Biocomputing, Visualization, and Informatics (supported by NIH Grant P41 RR-01081) as well as NIH Grant R01GM60595 and National Science Foundation Grants DBI 0234768 and DBI 0640476 (to P.C.B.).

    Nonredundant sequences are those obtained by excluding those that share >98% sequenced identity over 95% of the length of the functional domain.

    T. Lukk, A. Sakai, C. Kalyanaraman, S. Brown, H. J. Imker, L. Song, A. A. Fedorov, E. V. Fedorov, R. Toro, B. Hillerich, R. Seidel, Y. Patskovsky, M. V. Vetting, S. K. Nair, P. C. Babbitt, S. C. Almo, J. A. Gerlt, and M. P. Jacobson, manuscript submitted for publication.

    T. J. Erb, K. Choi, B. S. Evans, J. Singh, B. M. Wood, J. V. Sweedler, J. E. Cronan, R. F. Tabita, and J. A. Gerlt, manuscript submitted for publication.

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