Biased decoy sampling to aid the selection of near-native protein conformations

Abstract

A central challenge in ab-initio protein structure prediction is the selection of low-resolution decoy conformations whose subsequent refinement leads to high-resolution near-native conformations. Successful selection strategies are tightly coupled with the exploration method employed to obtain decoys. Density-based clustering is often used to identify regions of the energy surface that are highly sampled by exploration trajectories. The trajectories are often numerous and long, because the goal is to obtain both a broad view of the energy surface and to converge to regions that are promising for further refinement. In this paper we separate this into two subgoals. We first investigate a robotics-inspired exploration framework and demonstrate its ability to steer sampling towards diverse decoy conformations. Once a broad view of the energy surface is obtained, Metropolis Monte Carlo trajectories continue the exploration from selected decoys. Density-based clustering then identifies regions where trajectories converge. The two exploration stages both employ molecular fragment replacement but gradually add more detail through different fragment lengths. Results on a diverse list of proteins show that highly-sampled regions contain near-native conformations that are worthy of further refinement for use in a blind prediction setting.