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BMC Bioinformatics. 2011 Feb 15;12 Suppl 1:S49. doi: 10.1186/1471-2105-12-S1-S49.

Discriminative structural approaches for enzyme active-site prediction.

Author information

1
Graduate school of Engineering, Gunma University, Tenjin-cho 1-5-1, Kiryu, Gunma 376-8515, Japan. kato-tsuyoshi@aist.go.jp

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.

PMID:
21342581
PMCID:
PMC3044306
DOI:
10.1186/1471-2105-12-S1-S49
[Indexed for MEDLINE]
Free PMC Article

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