Accounting for electronic polarization in nonpolarizable force fields

TL;DR

This paper discusses how electronic polarizability affects nonpolarizable force fields in molecular dynamics, proposing charge scaling and explaining water dipole discrepancies to improve simulation accuracy.

Contribution

It introduces a model for including electronic screening in nonpolarizable force fields, explaining water dipole values and their impact on protein dynamics.

Findings

Charges should be scaled by about 0.7 in nonpolarizable force fields.

Electronic screening significantly alters protein dynamics.

Water's effective dipole can be explained by a simple scaling law.

Abstract

The issues of electronic polarizability in molecular dynamics simulations are discussed. We argue that the charges of ionized groups in proteins, and charges of ions in conventional non-polarizable force fields such as CHARMM, AMBER, GROMOS, etc should be scaled by a factor about 0.7. Our model explains why a neglect of electronic solvation energy, which typically amounts to about a half of total solvation energy, in non-polarizable simulations with un-scaled charges can produce a correct result; however, the correct solvation energy of ions does not guarantee the correctness of ion-ion pair interactions in many non-polarizable simulations. The inclusion of electronic screening for charged moieties is shown to result in significant changes in protein dynamics and can give rise to new qualitative results compared with the traditional non-polarizable force field simulations. The model also explains the striking difference between the value of water dipole $\mu$~3D reported in recent ab initio and experimental studies with the value $\mu_{eff}$~2.3D typically used in the empirical potentials, such as TIP3P or SPC/E. It is shown that the effective dipole of water can be understood as a scaled value $\mu_{eff}=\mu/\sqrt{\epsilon_{el}}$, where $\epsilon_{el}$=1.78 is the electronic (high-frequency) dielectric constant of water. This simple theoretical framework provides important insights into the nature of the effective parameters, which is crucial when the computational models of liquid water are used for simulations in different environments, such as proteins, or for interaction with solutes.