A Random Forest Model for Predicting Allosteric and Functional Sites on Proteins

Abstract

We created a computational method to identify allosteric sites using a machine learning method trained and tested on protein structures containing bound ligand molecules. The Random Forest machine learning approach was adopted to build our three-way predictive model. Based on descriptors collated for each ligand and binding site, the classification model allows us to assign protein cavities as allosteric, regular or orthosteric, and hence to identify allosteric sites. 43 structural descriptors per complex were derived and were used to characterize individual protein-ligand binding sites belonging to the three classes, allosteric, regular and orthosteric. We carried out a separate validation on a further unseen set of protein structures containing the ligand 2-(N-cyclohexylamino) ethane sulfonic acid (CHES).

Keywords: Allosteric site; Cheminformatics; Drug Design; Machine Learning; Random Forest.