A memetic algorithm enables efficient local and global all-atom protein-protein docking with backbone and side-chain flexibility.

Protein complex formation is encoded by specific interactions at the atomic scale, but the computational cost of modeling proteins at this level often requires use of simplified energy models and limited conformational flexibility. In particular, use of all-atom energy functions and backbone and side-chain flexibility results in rugged energy landscapes that are difficult to explore. In this study, we develop a protein-protein docking algorithm, EvoDOCK, that combines the strength of a differential evolution algorithm for efficient exploration of the global search space with the benefits of a local optimization method to refine detailed atomic interactions. EvoDOCK enabled accurate and fast local and global protein-protein docking using an all-atom energy function with side-chain flexibility. Comparison with a standard method built on Monte Carlo optimization demonstrated improved accuracy and increases in computational speed of up to 35 times. The evolutionary algorithm also enabled efficient atomistic docking with backbone flexibility.