Efficient Exploration of Protein Conformational Pathways using RRT* and MC

Abstract

The conformational space of proteins is complex and high dimensional, which makes its analysis a highly challenging task. Understanding the structure and dynamics of proteins is essential in order to understand their cellular function. It is often hard to experimentally characterize intermediate structures as well as conformational trajectory, due to the rapid dynamics of some proteins. Conformational pathways, which describe how proteins transition from one conformation to another as a result of a shift in conditions, are hard to describe experimentally. Computationally it is a challenging problem as well since physics-based simulations are time-consuming and often don't span sufficient time scales to allow capturing a full pathway. In previous work, we combined evolutionary information or rigidity analysis obtained from proteins' sequence and structure with an efficient tree based conformational search to elucidate the conformational trajectory of proteins. We incorporated backbone + C - β resolution and helped limit the search space by identifying mobile regions in a molecule. In this work, we use a hybrid algorithm which combines MC sampling and RRT*, a version of the Rapidly Exploring Random Trees (RRT) robotics-based method, to make the search more accurate and efficient, and produce smooth conformational pathways.