PASSer: prediction of allosteric sites server

Tian, Hao and Jiang, Xi and Tao, Peng (2021) PASSer: prediction of allosteric sites server. Machine Learning: Science and Technology, 2 (3). 035015. ISSN 2632-2153

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Abstract

Allostery is considered important in regulating protein's activity. Drug development depends on the understanding of allosteric mechanisms, especially the identification of allosteric sites, which is a prerequisite in drug discovery and design. Many computational methods have been developed for allosteric site prediction using pocket features and protein dynamics. Here, we present an ensemble learning method, consisting of eXtreme gradient boosting and graph convolutional neural network, to predict allosteric sites. Our model can learn physical properties and topology without any prior information, and shows good performance under multiple indicators. Prediction results showed that 84.9% of allosteric pockets in the test set appeared in the top 3 positions. The PASSer: Protein Allosteric Sites Server (https://passer.smu.edu), along with a command line interface (https://github.com/smutaogroup/passerCLI) provide insights for further analysis in drug discovery.

Item Type: Article
Subjects: West Bengal Archive > Multidisciplinary
Depositing User: Unnamed user with email support@westbengalarchive.com
Date Deposited: 04 Jul 2023 04:30
Last Modified: 28 May 2024 05:40
URI: http://article.stmacademicwriting.com/id/eprint/1215

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