Clustering Molecular Dynamics trajectories with a univariate estimation of distribution algorithm

Molecular Dynamics simulations of protein receptors are an emergent tool in rational drug discovery. Nevertheless, employing Molecular Dynamics trajectories in virtual screening of large repositories is a very costly procedure, which ultimately may become unfeasible. Data clustering have been applied in this context with the goal of reducing the overall computational cost in order to make this task feasible. In this paper, we develop a novel estimation of distribution algorithm called Clus-EDA for clustering entire trajectories using structural features from the substrate-binding cavity of the protein receptor. This novel approach is capable of reducing the original trajectory to about 4% of its original size whilst keeping all relevant information for the analysis of receptor-ligand binding. The resulting partition generated by the estimation of distribution algorithm is further validated by analyzing the interactions between 20 ligands and a Fully-Flexible Receptor model containing a 20 ns Molecular Dynamics simulation trajectory. Results show that Clus-EDA is capable of outperforming traditional clustering algorithms such as k-means and hierarchical clustering by providing the smallest variance of the free energy of binding within the conformations in each cluster.