Enhancing Biomolecular Simulations with Hybrid Potentials Incorporating NMR Data.

ABSTRACT: Some recent advances in biomolecular simulation and global optimization have used hybrid restraint potentials, where harmonic restraints that penalize conformations inconsistent with experimental data are combined with molecular mechanics force fields. These hybrid potentials can be used to improve the performance of molecular dynamics, structure prediction, energy landscape sampling, and other computational methods that rely on the accuracy of the underlying force field. Here, we develop a hybrid restraint potential based on NapShift, an artificial neural network trained to predict protein nuclear magnetic resonance (NMR) chemical shifts from sequence and structure. In addition to providing accurate predictions of experimental chemical shifts, NapShift is fully differentiable with respect

SUBMITTER: Qi G 

PROVIDER: S-EPMC9753583 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature