title: Metric learning for enzyme active-site search.
abstract: BACKGROUND
  Finding functionally analogous enzymes based on the local structures of active sites is an important problem. Conventional methods use templates of local structures to search for analogous sites, but their performance depends on the selection of atoms for inclusion in the templates.
  
  
  RESULTS
  The automatic selection of atoms so that site matches can be discriminated from mismatches. The algorithm provides not only good predictions, but also some insights into which atoms are important for the prediction. Our experimental results suggest that the metric learning automatically provides more effective templates than those whose atoms are selected manually.
  
  
  BACKGROUND
  Online software is available at http://www.net-machine.net/∼kato/lpmetric1/
authors.affiliation: GSFS, University of Tokyo, 5-1-5 Kashiwahoha, Kashiwa, Chiba, Japan. kato-tsuyoshi@k.u-tokyo.ac.jp
authors.forename: Tsuyoshi
authors.initials: T
authors.lastname: Kato
authors.forename: Nozomi
authors.initials: N
authors.lastname: Nagano
mesh_terms: D000465:Algorithms; D020134:Catalytic Domain; D030562:Databases, Protein; D004798:Enzymes / Q000737:chemistry*
chemical_list: D004798:Enzymes