Prediction of Protein Allosteric Sites with Transfer Entropy and Spatial Neighbor-Based Evolutionary Information Learned by an Ensemble Model

Allostery is one of the most direct and efficient ways to regulate protein functions. The diverse allosteric sites make it possible to design allosteric modulators of differential selectivity and improved safety compared with those of orthosteric drugs targeting conserved orthosteric sites. Here, we develop an ensemble machine learning method AllosES to predict protein allosteric sites in which the new and effective features are utilized, including the entropy transfer-based dynamic property, secondary structure features, and our previously proposed spatial neighbor-based evolutionary information besides the traditional physicochemical properties. To overcome the class imbalance problem, the multiple grouping strategy is proposed, which is applied to feature selection and model construction. The ensemble model is constructed where multiple submodels are trained on multiple training subsets, respectively, and their results are then integrated to be the final output. AllosES achieves a prediction performance of 0.556 MCC on the independent test set D24, and additionally, AllosES can rank the real allosteric sites in the top three for 83.3/89.3% of allosteric proteins from the test set D24/D28, outperforming the state-of-the-art peer methods. The comprehensive results demonstrate that AllosES is a promising method for protein allosteric site prediction. The source code is available at https://github.com/ChunhuaLab/AllosES.