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PASSer: Prediction of Allosteric Sites Server
preprintrevised 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.
Probing Hidden Conformational Space and Dynamical States of Circadian Clock Proteins through Rigid Residue Scan and Machine Learning
National Institute of General Medical SciencesFind out more...