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Related Experiment Video

Updated: Mar 3, 2026

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

GASS-WEB: a web server for identifying enzyme active sites based on genetic algorithms.

João P A Moraes¹, Gisele L Pappa², Douglas E V Pires³

¹Department of Computer Engineering, Advanced Campus at Itabira, Universidade Federal de Itajubá - UNIFEI, Itabira, 35903-087, Brazil.

Nucleic Acids Research

|May 2, 2017

Summary

GASS-WEB identifies similar enzyme active sites using an evolutionary algorithm, improving protein function prediction. This tool accurately finds known active sites and performs well in structure prediction challenges.

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Area of Science:

* Structural bioinformatics
* Computational biology
* Protein science

Background:

* Enzyme active sites are crucial for protein function prediction.
* Existing methods for active site identification have limitations, including exact residue matching and inability to find inter-domain sites.

Purpose of the Study:

* To introduce GASS-WEB, a web server for identifying similar enzyme active sites.
* To overcome limitations of existing active site identification methods.

Main Methods:

* GASS-WEB utilizes GASS (Genetic Active Site Search), an evolutionary algorithm.
* The server supports two modes: matching templates to a protein or searching a database for a template.
* It analyzes protein structures and active site conservation.

Main Results:

* GASS-WEB correctly identified over 90% of active sites from the Catalytic Site Atlas.
* Achieved a Matthew correlation coefficient of 0.63 on the CASP 10 dataset.
* GASS ranked fourth out of 18 methods in comparative analysis.

Conclusions:

* GASS-WEB is an effective and user-friendly tool for enzyme active site identification.
* The method demonstrates significant improvements over existing approaches.
* GASS-WEB is freely available, facilitating broader research applications.