Theoretical and Computational Chemistry

PASSer2.0: Accurate Prediction of Protein Allosteric Sites Through Automated Machine Learning

Authors

Abstract

Allostery is a fundamental process in regulating proteins’ activity. The discovery, design and development of allosteric drugs demand for better identification of allosteric sites. Several computational methods have been developed previously to predict allosteric sites using static pocket features and protein dynamics. Here, we present a computational model using automated machine learning for allosteric site prediction. Our model, PASSer2.0, advanced the previous results and performed well across multiple indicators with 89.2% of allosteric pockets appeared among the top 3 positions. The trained machine learning model has been integrated with the Protein Allosteric Sites Server (https://passer.smu.edu) to facilitate allosteric drug discovery.

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Supplementary material

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Supporting Infomration
Supporting Information for PASSer2.0: Accurate Prediction of Protein Allosteric Sites Through Automated Machine Learning

Supplementary weblinks

PASSer: Protein Allosteric Sites Server
PASSer is deployed with pretrained models and has been extensively tested to complete prediction within seconds.