Discriminative structural approaches for enzyme active-site prediction
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Updated: Jun 4, 2026

Modeling an Enzyme Active Site using Molecular Visualization Freeware
Published on: December 25, 2021
1Graduate school of Engineering, Gunma University, Tenjin-cho 1-5-1, Kiryu, Gunma 376-8515, Japan. kato-tsuyoshi@aist.go.jp
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.
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