PASSer2.0: Accurate Prediction of Protein Allosteric Sites Through Automated Machine Learning - Xiao, Sian; Tian, Hao; Tao, Peng (Frontiers in Molecular Biosciences, 2022)
Allostery is a fundamental process in regulating protein activities. The discovery, design and development of allosteric drugs demand 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 define a baseline model for allosteric site prediction and present a computational model using automated machine learning. Our model, PASSer2.0, advanced the previous results and performed well across multiple indicators with 82.7% of allosteric pockets appeared among the top 3 positions. The trained machine learning model has been integrated with the Protein Allosteric Sites Server (PASSer) to facilitate allosteric drug discovery.