Summary
Protein complex formation is encoded by specific interactions at the atomic scale, but the computational cost of modeling proteins at this level often requires the use of simplified energy models and limited conformational flexibility. In particular, the use of all-atom energy functions, backbone and sidechain flexibility results in rugged energy landscapes that are difficult to explore. In this study we develop a protein-protein docking algorithm, EvoDOCK, that combine 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 with increases in computational speed of up to 35 times. The evolutionary algorithm also enabled efficient atomistic docking with backbone flexibility.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
The efficiency improvements extends to backbone flexibility, enabling more intensive sampling of backbone conformations during docking
