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. Populationbased 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 (EdaRosec) and an energy-based one (EdaRoseen). We analyze the search dynamics of our new Rosetta protocols and show that EdaRosec is able to provide predictions with lower CaRMSD to the native structure than EdaRoseen and Rosetta AbInitio Relax protocol. Our software is freely available as a C11 patch for the Rosetta suite and can be downloaded from http://www.riken.jp/zhangiru/software/. Our protocols can easily be extended in order to create alternative probability update policies and generate new search dynamics