Integrating Co-Evolutionary Information in Monte Carlo Based Method for Proteins Trajectory Simulation

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 function. Intermediate structures are often hard to capture experimentally, as well as conformational trajectory due to rapid dynamics. How a protein molecule acquires a specific structure remains an open problem, as well as how proteins transition from one conformation to another as a result of a shift in conditions. In this work, we combine evolutionary information obtained from proteins' sequence and structure with conformational search to elucidate the conformational trajectory of proteins. In a previous work we used rigidity analysis to reduce the search space of proteins undergoing large-scale conformational changes. We advance in this work by incorporating the backbone + C-beta resolution for all of the molecules in the experiments and resort to a new way to limit the search space by identifying the co-evolving residues in a molecule. We use a modified version of the Monte Carlo-based tree search algorithm to sample the search space and guide it by expanding the search only in the direction of energetically feasible conformations that are close to the goal structure of the molecule, using perturbation on co-evolving residues as a guide.