Polarizable Mean-Field Model of Water for Biological Simulations with Amber and Charmm force fields
arXiv:1505.01454 · doi:10.1021/ct300011h
Abstract
Although a great number of computational models of water are available today, the majority of current biological simulations are done with simple models, such as TIP3P and SPC, developed almost thirty years ago and only slightly modified since then. The reason is that the non-polarizable force fields that are mostly used to describe proteins and other biological molecules are incompatible with more sophisticated modern polarizable models of water. The issue is electronic polarizability: in liquid state, in protein, and in vacuum the water molecule is polarized differently, and therefore has different properties; thus the only way to describe all these different media with the same model is to use a polarizable water model. However, to be compatible with the force field of the rest of the system, e.g. a protein, the latter should be polarizable as well. Here we describe a novel model of water that is in effect polarizable, and yet compatible with the standard non-polarizable force fields such as AMBER, CHARMM, GROMOS, OPLS, etc. Thus the model resolves the outstanding problem of incompatibility.
38 pages, 5 figures, 2 tables
Cited by in corpus (9)
- A force field of Li , Na , K, Mg, Ca, Cl, and SO in aqueous solution based on the TIP4P/2005 water model and scaled charges for the ions
- The Madrid-2019 force field for electrolytes in water using TIP4P/2005 and scaled charges: extension to the ions F, Br, I, Rb, Cs
- Polarizable molecular interactions in condensed phase and their equivalent nonpolarizable models
- Scaled charges for ions: an improvement but not the final word for modeling electrolytes in water
- Computation of Electrical Conductivities of Aqueous Electrolyte Solutions: Two Surfaces , One Property
- Structural and Transport Properties of Li/S Battery Electrolytes: Role of the Polysulfide Species
- Understanding the Anomalous Diffusion of Water in Aqueous Electrolytes Using Machine Learned Potentials
- Accurate Evaluation of Charge Asymmetry in Aqueous Solvation
- Experimental Evidence of Quantum Drude Oscillator Behavior in Liquids Revealed with Probabilistic Iterative Boltzmann Inversion