Improved Energy Selection of Nativelike Protein Loops from Loop Decoys.

Abstract

We demonstrate the performance of a new implicit solvent model on native protein loop prediction from a large set of loop decoys of 4- to 12-residue in length. The physics-based energy function combines a hydrophobic potential of mean force (HPMF) description with a Generalized Born model for polarization of protein charge by the high dielectric solvent, which we combine with AMBER force field for the protein chain. The novelty of our energy function is the stabilizing effect of hydrophobic exposure to aqueous solvent that defines the HPMF hydration physics, which in principle should be an important stabilizing factor for loop conformations of a protein that typically are more solvent exposed. While our results for short loop decoy sets are comparably good to existing energy functions, we find demonstrable superiority for loop lengths of 8-residue and greater, and the quality of our predictions is largely insensitive to the length of the target loop on a filtered set of decoys. Given that the current weakness in loop modeling is the ability to select the most nativelike loop conformers from loop ensembles, this energy function provides a means for greater prediction accuracy in structure prediction of homologous and distantly related proteins, thereby aiding large-scale genomics efforts in comparative modeling.