1. Introduction 2582. Set-up of MD simulations 2602.1 Constant-pressure dynamics 2602.2 Grand-canonical dynamics 2612.3 Boundary conditions 2613. Force fields 2623.1 Proteins 2623.2 Nucleic acids 2653.3 Carbohydrates 2663.4 Phospholipids 2663.5 Polarization 2674. Electrostatics 2674.1 Spherical truncation methods 2684.2 Ewald summation methods 2694.3 Fast multipole (FM) methods 2714.4 Reaction-field methods 2715. Implicit solvation models 2716. Speeding-up the simulation 2736.1 SHAKE and its relatives 2736.2 Multiple time-step algorithms 2746.3 Other algorithms 2757. Conformational space sampling 2757.1 Multiple copy simultaneous search (MCSS) and locally enhanced sampling (LES) 2757.2 Steered or targeted MD 2767.3 Self-guided MD 2767.4 Leaving the standard 3D Cartesian coordinate system: 4D MD and internal coordinate MD 2777.5 Temperature variations 2778. Thermodynamic calculations 2788.1 Lambda (λ) dynamics 2788.2 Extracting thermodynamic information from simulations 2798.3 Non-Boltzmann thermodynamic integration (NBTI) 2798.4 Other methods 2799. QM/MM calculations 28210. MD simulations of protein folding and unfolding 28310.1 High-temperature effects 28410.2 Co-solvent and polarization effects 28810.3 External force effects 28811. On the horizon 29112. Acknowledgements 29213. References 292Molecular dynamics simulations are widely used today to tackle problems in biochemistry and molecular biology. In the 25 years since the first simulation of a protein computers have become faster by many orders of magnitude, algorithms and force fields have been improved, and simulations can now be applied to very large systems, such as protein–nucleic acid complexes and multimeric proteins in aqueous solution. In this review we give a general background about molecular dynamics simulations, and then focus on some recent technical advances, with applications to biologically relevant problems.
Multiple time step algorithms for molecular dynamics simulations of proteins: How good are they?
