Balancing exploration and exploitation in population-based sampling improves fragment-based de novo protein structure prediction.

Conformational search space exploration remains a major bottleneck for protein structure prediction methods. Population-based meta-heuristics typically enable the possibility to control the search dynamics and to tune the balance between local energy minimization and search space exploration. EdaFold is a fragment-based approach that can guide search by periodically updating the probability distribution over the fragment libraries used during model assembly. We implement the EdaFold algorithm as a Rosetta protocol and provide two different probability update policies: a cluster-based variation (EdaRose(c)) and an energy-based one (EdaRose(en)). We analyze the search dynamics of our new Rosetta protocols and show that EdaRose(c) is able to provide predictions with lower C RMSD to the native structure than EdaRose(en) and Rosetta AbInitio Relax protocol. Our software is freely available as a C++ patch for the Rosetta suite and can be downloaded from . Our protocols can easily be extended in order to create alternative probability update policies and generate new search dynamics. Proteins 2017; 85:852-858. (c) 2016 Wiley Periodicals, Inc.