Evaluation of the coarse-grained OPEP force field for protein-protein docking.

BACKGROUND: Knowing the binding site of protein-protein complexes helps understand their function and shows possible regulation sites. The ultimate goal of protein-protein docking is the prediction of the three-dimensional structure of a protein-protein complex. Docking itself only produces plausible candidate structures, which must be ranked using scoring functions to identify the structures that are most likely to occur in nature.

METHODS: In this work, we rescore rigid body protein-protein predictions using the optimized potential for efficient structure prediction (OPEP), which is a coarse-grained force field. Using a force field based on continuous functions rather than a grid-based scoring function allows the introduction of protein flexibility during the docking procedure. First, we produce protein-protein predictions using ZDOCK, and after energy minimization via OPEP we rank them using an OPEP-based soft rescoring function. We also train the rescoring function for different complex classes and demonstrate its improved performance for an independent dataset.

RESULTS: The trained rescoring function produces a better ranking than ZDOCK for more than 50 % of targets, rising to over 70 % when considering only enzyme/inhibitor complexes.

CONCLUSIONS: This study demonstrates for the first time that energy functions derived from the coarse-grained OPEP force field can be employed to rescore predictions for protein-protein complexes.