AUTHOR=Xiao Sian , Tian Hao , Tao Peng 

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

JOURNAL=Frontiers in Molecular Biosciences

VOLUME=9

YEAR=2022

URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2022.879251

DOI=10.3389/fmolb.2022.879251

ISSN=2296-889X

ABSTRACT=<p>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 appearing among the top three positions. The trained machine learning model has been integrated with the <ext-link ext-link-type="uri" xlink:href="https://passer.smu.edu/" xmlns:xlink="http://www.w3.org/1999/xlink">Protein Allosteric Sites Server (PASSer)</ext-link> to facilitate allosteric drug discovery.</p>