Enhanced Conformational Sampling with an Adaptive Coarse-Grained Elastic Network Model Using Short-Time All-Atom Molecular Dynamics br

Compared to all-atom molecular dynamics (AA-MD) simulations, coarse-grained (CG) MD simulations can significantly reduce calculation costs. However, existing CG-MDmethods are unsuitable for sampling structures that departsignificantly from the initial structure without any biased force.In this study, we developed a new adaptive CG elastic networkmodel (ENM), in which the dynamic cross-correlation coefficientbased on short-time AA-MD of at most ns order is considered. Byapplying Bayesian optimization to search for a suitable parameteramong the vast parameter space of adaptive CG-ENM, wesucceeded in reducing the searching cost to approximately 10% of those for random sampling and exhaustive sampling. Toevaluate the performance of adaptive CG-ENM, we applied the new methodology to adenylate kinase (ADK) and glutamine bindingprotein (GBP) in the apo state. The results showed that the structural ensembles explored by adaptive CG-ENM could beconsiderably more diverse than those by conventional ENMs with enhanced sampling such as temperature replica exchange MD andlong-time AA-MD of 1 mu s. In particular, some of the structures sampled by adaptive ENM are relatively close to the holo-types tructures of ADK and GBP. Furthermore, as a challenging task, to demonstrate the advantages of the CG model with lower calculation cost, we applied our new methodology to a larger biomolecule, integrin (alpha V) in the inactive state. Then, we sampled various structural ensembles, including extended structures that are apparently different from inactive ones.