DESP: Deep Enhanced Sampling of Proteins’ Conformation Spaces Using AI-Inspired Biasing Forces - Salawu, Emmanuel Oluwatobi (Frontiers in Molecular Biosciences, 2021)
The molecular structures (conformation spaces, CS) of bio-macromolecules as well as the dynamics that molecules exhibit are crucial to the understanding of the basis of many diseases and in the continuous attempts to retarget known drugs/medications, improve the efficacy of existing drugs, or develop novel drugs. These make a better understanding and the exploration of the CS of molecules a research hotspot. While it is generally easy to computationally explore the CS of small molecules (such as peptides and ligands), the exploration of the CS of larger biomolecules beyond the local energy well around the initial structure of the molecule is generally non-trivial and can often be computationally prohibitive for molecules of considerable size. Therefore, research efforts in this area focus on the development of ways that systematically favors the sampling of new conformations while penalizing the resampling of previously sampled conformations. In this work, we present Deep Enhanced Sampling of Proteins' Conformation Spaces Using AI-Inspired Biasing Forces (DESP), a robust technique for enhanced sampling that combines molecular dynamics (MD) simulations and deep neural networks (DNN), in which biasing potentials for guiding the MD simulations are derived from the KL-divergence of the DNN-learned latent space vectors of the most recently sampled conformation from that of the previously sampled conformations. Overall, DESP efficiently samples wide CS and outperforms conventional MD simulations as well as accelerated MD simulations. We acknowledge that this is an actively evolving research area and we continue to further develop the techniques presented here as well as their derivatives tailored at achieving DNN-enhanced Steered MD Simulations and DNN-enhanced Targeted MD Simulations.