Protein-protein Docking Using Information from Native Interaction Interfaces

We present a probabilistic search algorithm for rigid-body protein-protein docking. The algorithm is a realization of the basin hopping framework for sampling low-energy local minima of a given energy function. To save computational resources, the algorithm employs a machine learning model to score bound configurations prior to subjecting promising configurations to local optimization with a sophisticated force field. The machine learning model is a decision tree trained on known native dimers to learn features that constitute true interaction interfaces. The FoldX force field is employed only on sampled dimeric configurations determined by the decision tree model to contain true interaction interfaces. The preliminary results are promising and motivate us to further investigate such an informatics-driven approach to protein-protein docking.