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Hot spots, a small area at protein interfaces that plays crucial role in binding free energy. Based on the previous works, sequence, structural and protein-protein interaction features were extracted. In this study, we considered the differences in binding free energies calculated between wild-type and alanine mutated complexes and half sphere amino acid composition (HSAAC) as new features. We then selected features by minimal redundancy maximal relevance (mRMR) combining with Support Vector Machine (SVM) forward. After feature analysis, an optimal 41-dimensional features were selected and then applied to construct a Random Forest (RF) predict model for hot spots in machine learning method. Our method obtained the highest F1 0.625 and Mathew’s correlation coefficient (MCC) value of 0.518 testing on independent test set. New features in this work also showed an importance position after feature selection.

Predicting Hot Spots in Protein Interfaces Based on Feature Selection using Mrmr Combining with SVM Forward