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W-RESP: Well-Restrained Electrostatic Potential-Derived Charges. Revisiting the Charge Derivation Model.

Michal JanečekPetra KührováVojtěch MlýnskýMichal OtyepkaJiřı ŠponerPavel Banáš
Published in: Journal of chemical theory and computation (2021)
Representation of electrostatic interactions by a Coulombic pairwise potential between atom-centered partial charges is a fundamental and crucial part of empirical force fields used in classical molecular dynamics simulations. The broad success of the AMBER force-field family originates mainly from the restrained electrostatic potential (RESP) charge model, which derives partial charges to reproduce the electrostatic field around the molecules. However, the description of the electrostatic potential around molecules by standard RESP may be biased for some types of molecules. In this study, we modified the RESP charge derivation model to improve its description of the electrostatic potential around molecules and thus electrostatic interactions in the force field. In particular, we reoptimized the atomic radii for definition of the grid points around the molecule, redesigned the restraining scheme, and included extra point (EP) charges. The RESP fitting was significantly improved for aromatic heterocyclic molecules. Thus, the suggested W-RESP(-EP) charge derivation model shows some potential for improving the performance of the nucleic acid force fields, for which the poor description of nonbonded interactions, such as the underestimated stability of base pairing, is well-established. We also report some preliminary simulation tests (around 1 ms of simulation data) on A-RNA duplexes, tetranucleotides, and tetraloops. The simulations reveal no adverse effects, while the description of base-pairing interactions might be improved. The new charges can thus be used in future attempts to improve the nucleic acid simulation force fields, in combination with reparametrization of the other terms.
Keyphrases
  • molecular dynamics simulations
  • nucleic acid
  • single molecule
  • human health
  • multiple sclerosis
  • gene expression
  • mass spectrometry
  • risk assessment
  • ms ms
  • deep learning
  • electronic health record