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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.
SUBMITTER: Kato T
PROVIDER: S-EPMC3044306 | biostudies-literature | 2011 Feb
REPOSITORIES: biostudies-literature
Kato Tsuyoshi T Nagano Nozomi N
BMC bioinformatics 20110215
<h4>Background</h4>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.<h4>Results</h4>This paper introduces new ...[more]