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PASSer: Prediction of Allosteric Sites Server

preprint
revised on 14.12.2020, 21:36 and posted on 16.12.2020, 11:12 by Hao Tian, Xi Jiang, Peng Tao
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 prerequisite in drug discovery and design. Many computational methods have been developed for allosteric site prediction using pocket features and dynamics information. Here, we provide a novel ensembled model, consisting of eXtreme gradient boosting (XGBoost) and graph convolutional neural network (GCNN) to predict allosteric sites. Our model can learn both physical properties and topology structure without any prior information and exhibited good performance under several indicators. Prediction results have shown that 84.9% of allosteric pockets in the testing proteins appeared in the top 3 positions. The PASSer: Protein Allosteric Sites Server (https://passer.smu.edu), along with a command line interface (CLI, https://github.com/smutaogroup/passerCLI) provide insights for further analysis in drug discovery.

Funding

Probing Hidden Conformational Space and Dynamical States of Circadian Clock Proteins through Rigid Residue Scan and Machine Learning

National Institute of General Medical Sciences

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History

Email Address of Submitting Author

haot@smu.edu

Institution

Southern Methodist University

Country

USA

ORCID For Submitting Author

0000-0002-0186-9811

Declaration of Conflict of Interest

The authors declare no competing interests

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