title: Inference of functional properties from large-scale analysis of enzyme superfamilies.
abstract: As increasingly large amounts of data from genome and other sequencing projects become available, new approaches are needed to determine the functions of the proteins these genes encode. We show how large-scale computational analysis can help to address this challenge by linking functional information to sequence and structural similarities using protein similarity networks. Network analyses using three functionally diverse enzyme superfamilies illustrate the use of these approaches for facile updating and comparison of available structures for a large superfamily, for creation of functional hypotheses for metagenomic sequences, and to summarize the limits of our functional knowledge about even well studied superfamilies.
authors: Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, 94158-2330.
authors: Shoshana D
authors: SD
authors: Brown
authors: Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, 94158-2330; Pharmaceutical Chemistry, School of Pharmacy; California Institute for Quantitative Biosciences, University of California, San Francisco, California 94158-2330. Electronic address: babbitt@cgl.ucsf.edu.
authors: Patricia C
authors: PC
authors: Babbitt
mesh_terms: D002244:Carbon / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D004798:Enzymes / Q000737:chemistry / Q000378:metabolism*; D056890:Eukaryota / Q000201:enzymology; D006801:Humans; D007422:Intestines / Q000382:microbiology; D054892:Metagenome
chemical_list: D004798:Enzymes; D002244:Carbon