Automatic Learning of Hydrogen-Bond Fixes in the AMBER RNA Force Field.

ABSTRACT: The capability of current force fields to reproduce RNA structural dynamics is limited. Several methods have been developed to take advantage of experimental data in order to enforce agreement with experiments. Here, we extend an existing framework which allows arbitrarily chosen force-field correction terms to be fitted by quantification of the discrepancy between observables back-calculated from simulation and corresponding experiments. We apply a robust regularization protocol to avoid overfitting and additionally introduce and compare a number of different regularization strategies, namely, L1, L2, Kish size, relative Kish size, and relative entropy penalties. The training set includes a GACC tetramer as well as more challenging systems, namely, gcGAGAgc and gcUUCGgc RNA tetraloops. Sp

SUBMITTER: Frohlking T 

PROVIDER: S-EPMC9281393 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature