title: Discriminative structural approaches for enzyme active-site prediction.
abstract: BACKGROUND
  Predicting enzyme active-sites in proteins is an important issue not only for protein sciences but also for a variety of practical applications such as drug design. Because enzyme reaction mechanisms are based on the local structures of enzyme active-sites, various template-based methods that compare local structures in proteins have been developed to date. In comparing such local sites, a simple measurement, RMSD, has been used so far.
  
  
  RESULTS
  This paper introduces new machine learning algorithms that refine the similarity/deviation for comparison of local structures. The similarity/deviation is applied to two types of applications, single template analysis and multiple template analysis. In the single template analysis, a single template is used as a query to search proteins for active sites, whereas a protein structure is examined as a query to discover the possible active-sites using a set of templates in the multiple template analysis.
  
  
  CONCLUSIONS
  This paper experimentally illustrates that the machine learning algorithms effectively improve the similarity/deviation measurements for both the analyses.
authors.affiliation: Graduate school of Engineering, Gunma University, Tenjin-cho 1-5-1, Kiryu, Gunma 376-8515, Japan. kato-tsuyoshi@aist.go.jp
authors.forename: Tsuyoshi
authors.initials: T
authors.lastname: Kato
authors.forename: Nozomi
authors.initials: N
authors.lastname: Nagano
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D020134:Catalytic Domain; D019295:Computational Biology / Q000379:methods; D004798:Enzymes / Q000737:chemistry*; D011487:Protein Conformation
chemical_list: D004798:Enzymes