title: Representing structure-function relationships in mechanistically diverse enzyme superfamilies.
abstract: The prediction of protein function from structure or sequence data remains a problem best addressed by leveraging information available from previously determined structure-function relationships. In the case of enzymes, the study of mechanistically diverse superfamilies can provide a rich source of structure-function information useful in functional determination and enzyme engineering. To access these relationships using a computational resource, several issues must be addressed regarding the representation of enzyme function, the organization of structure-function relationships in the superfamily context, the handling of misannotations, and reliability of classifications and evidence. We discuss here our approaches to solving these problems in the development of a Structure-Function Linkage Database (SFLD) (online at http://sfld.rbvi.ucsf.edu).
authors.affiliation: Dept of Biopharmaceutical Sciences, University of California, San Francisco 94143, USA.
authors.forename: Scott C H
authors.initials: SC
authors.lastname: Pegg
authors.forename: Shoshana
authors.initials: S
authors.lastname: Brown
authors.forename: Sunil
authors.initials: S
authors.lastname: Ojha
authors.forename: Conrad C
authors.initials: CC
authors.lastname: Huang
authors.forename: Thomas E
authors.initials: TE
authors.lastname: Ferrin
authors.forename: Patricia C
authors.initials: PC
authors.lastname: Babbitt
mesh_terms: D000595:Amino Acid Sequence; D002384:Catalysis; D019295:Computational Biology / Q000379:methods; D017124:Conserved Sequence; D004798:Enzymes / Q000737:chemistry* / Q000145:classification / Q000378:metabolism*; D007700:Kinetics; D015202:Protein Engineering / Q000379:methods; D012984:Software; D013329:Structure-Activity Relationship
chemical_list: D004798:Enzymes