Abstract
The molecular structures (i.e., conformation spaces, CS) of bio-macromolecules and 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 a larger biomolecule beyond the local energy well and beyond the initial equilibrium structure of the molecule is generally nontrivial 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 favor 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 technique for enhanced sampling that combines molecular dynamics (MD) simulations and deep neural networks (DNNs), in which biasing potentials for guiding the MD simulations are derived from the KL divergence between the DNN-learned latent space vectors of [a] the most recently sampled conformation and those of [b] 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 and their derivatives tailored at achieving DNN-enhanced steered MD simulations and DNN-enhanced targeted MD simulations.
Highlights
The functions of biomolecules are encoded in their structures and dynamics (Council and others 1989; Karplus and Kuriyan, 2005; Yang et al, 2014)
These challenges have led to the growing roles and the increasing importance of computational approaches, such as molecular dynamics (MD) simulations, that are often used for studying the dynamic behaviors of molecules and their interactions with other molecules as well as for exploring much wider CS of the molecules of interest
We present Deep Enhanced Sampling of Proteins’ Conformation Spaces Using AI-Inspired Biasing Forces (DESP), which combines deep neural networks (DNNs) and MD simulations to create a robust technique for enhanced sampling of CS of molecules
Summary
The functions of biomolecules are encoded in their structures and dynamics (Council and others 1989; Karplus and Kuriyan, 2005; Yang et al, 2014). AI-Inspired Proteins’ Conformation Space Sampling importance in deciphering of many diseases (Salawu, 2018a; Salawu, 2018b) but are crucial in the drug development efforts targeted at curing or managing many diseases (Carlson and McCammon, 2000; Lee et al, 2018; Pawełand and Caflisch, 2018; Wang et al, 2018; Lin et al, 2020) These recognitions have motivated extensive efforts in the field of structural biochemistry and form the rationale for many structural biology studies (such as through X-ray crystallography, NMR, and Cryo-EM) and the creation of the Protein Data Bank (Berman et al, 2000) as well as other databases for molecular structures. Considerable challenges exist because the solely static molecular structures obtained through the wet laboratory approaches alone (such as the ones listed above) often fall short of providing enough insights into the dynamics of the molecules of interest. These challenges have led to the growing roles and the increasing importance of computational approaches, such as molecular dynamics (MD) simulations, that are often used for studying the dynamic behaviors of molecules and their interactions with other molecules as well as for exploring much wider CS of the molecules of interest
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