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WIREs Comput Mol Sci
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Polarizable force fields for molecular dynamics simulations of biomolecules

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Molecular dynamics simulations are well established for the study of biomolecular systems. Within these simulations, energy functions known as force fields are used to determine the forces acting on atoms and molecules. While these force fields have been very successful, they contain a number of approximations, included to overcome limitations in computing power. One of the most important of these approximations is the omission of polarizability, the process by which the charge distribution in a molecule changes in response to its environment. Since polarizability is known to be important in many biochemical situations, and since advances in computer hardware have reduced the need for approximations within force fields, there is major interest in the use of force fields that include an explicit representation of polarizability. As such, a number of polarizable force fields have been under development: these have been largely experimental, and their use restricted to specialized researchers. This situation is now changing. Parameters for fully optimized polarizable force fields are being published, and associated code incorporated into standard simulation software. Simulations on the hundred‐nanosecond timescale are being reported, and are now within reach of all simulation scientists. In this overview, I examine the polarizable force fields available for the simulation of biomolecules, the systems to which they have been applied, and the benefits and challenges that polarizability can bring. In considering future directions for development of polarizable force fields, I examine lessons learnt from non‐polarizable force fields, and highlight issues that remain to be addressed. WIREs Comput Mol Sci 2015, 5:241–254. doi: 10.1002/wcms.1215

Protein structures obtained from simulations with the AMOEBA polarizable force field (green) superimposed on reference crystal structures (gray). (a) Crambin, 1EJG. (b) Trp cage, 1L2Y. (c) Villin headpiece (1VII). (d) Ubiquitin, 1UBQ. (e) GB3 domain, 2OED. (f) RD1 antifreeze protein, 1UCS. (g) SUMO‐2 domain, 1WM3. (h) BPTI, 1BPI. (i) FK binding protein, 2PPN. (j) Lysozyme, 6LYT. (Reprinted with permission from Ref . Copyright 2013 American Chemical Society)
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Electrostatic potential energy surfaces around carbohydrate molecules. (a) α‐glucose. (b) β‐glucose. (c) α‐mannose. (d) β‐mannose. Each carbohydrate differs from each of its neighbors only by the inversion of one stereocenter. The surfaces were obtained from quantum mechanics calculations: the structure of each molecule was initially optimized, and the electron density calculated. Surfaces are drawn at electron density contours of 0.002 e/Å3, and are colored according to the electrostatic potential energy at every point on the surface. All calculations were performed at the MP2/6‐31G** level of theory using the program Jaguar.
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The change in electrostatic environment across a lipid bilayer. (a) The structure of a solvated lipid bilayer. (b) The corresponding charge density profile across the membrane. The charge density profile is calculated as described by White and Wimley: initially, absolute partial charge densities are calculated for groups of atoms, with group definitions and volumes taken from Ref and atomic partial charges taken from Ref . The average charge density across the membrane is then obtained by weighting these group charge densities by the number density of groups within the bilayer and the group volume.
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The influence of polarizability on the calculated dipole moment of a guanine base as it undergoes base flipping. (a) Schematic description of the base flipping process. (b) The dipole moment of the guanine base calculated using polarizable (Drude) and non‐polarizable (CHARMM27) force fields; at a pseudo‐dihedral angle of 0° the base is in the canonical Watson–Crick H bonded arrangement, at 60° it is fully exposed to the solvent. (Reproduced by permission of The Royal Society of Chemistry)
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Molecular and Statistical Mechanics > Molecular Mechanics
Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods
Structure and Mechanism > Computational Biochemistry and Biophysics

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