In molecular dynamics (MD) simulations the need often arises to maintain such parameters as temperature or pressure rather than energy and volume, or to impose gradients for studying transport properties in nonequilibrium MD. A method is described to realize coupling to an external bath with constant temperature or pressure with adjustable time constants for the coupling. The method is easily extendable to other variables and to gradients, and can be applied also to polyatomic molecules involving internal constraints. The influence of coupling time constants on dynamical variables is evaluated. A leap-frog algorithm is presented for the general case involving constraints with coupling to both a constant temperature and a constant pressure bath.

Successive parameterizations of the GROMOS force field have been used successfully to simulate biomolecular systems over a long period of time. The continuing expansion of computational power with time makes it possible to compute ever more properties for an increasing variety of molecular systems with greater precision. This has led to recurrent parameterizations of the GROMOS force field all aimed at achieving better agreement with experimental data. Here we report the results of the latest, extensive reparameterization of the GROMOS force field. In contrast to the parameterization of other biomolecular force fields, this parameterization of the GROMOS force field is based primarily on reproducing the free enthalpies of hydration and apolar solvation for a range of compounds. This approach was chosen because the relative free enthalpy of solvation between polar and apolar environments is a key property in many biomolecular processes of interest, such as protein folding, biomolecular association, membrane formation, and transport over membranes. The newest parameter sets, 53A5 and 53A6, were optimized by first fitting to reproduce the thermodynamic properties of pure liquids of a range of small polar molecules and the solvation free enthalpies of amino acid analogs in cyclohexane (53A5). The partial charges were then adjusted to reproduce the hydration free enthalpies in water (53A6). Both parameter sets are fully documented, and the differences between these and previous parameter sets are discussed.

New parameter sets of the GROMOS biomolecular force field, 54A7 and 54B7, are introduced. These parameter sets summarise some previously published force field modifications: The 53A6 helical propensities are corrected through new u/w torsional angle terms and a modification of the N-H, C=O repulsion, a new atom type for a charged -CH 3 in the choline moiety is added, the Na ? and Cl -ions are modified to reproduce the free energy of hydration, and additional improper torsional angle types for free energy calculations involving a chirality change are introduced. The new helical propensity modification is tested using the benchmark proteins hen egg-white lysozyme, fox1 RNA binding domain, chorismate mutase and the GCN4-p1 peptide. The stability of the proteins is improved in comparison with the 53A6 force field, and good agreement with a range of primary experimental data is obtained.

Accurate reproduction of the mechanism of peptide folding in solution and conformational preferences as a function of amino acid sequence is possible with atomic level dynamics simulations. For example, the simulations correctly predict a left‐handed 31‐helical fold for the β‐heptapeptide 1 (the molecular model is shown in the picture) and a right‐handed helical fold for the β‐hexapeptide 2, as was confirmed by NMR spectroscopy.

Molecular dynamics simulations of ionic systems require the inclusion of long-range electrostatic forces. We propose an expression for the long-range electrostatic forces based on an analytical solution of the Poisson–Boltzmann equation outside a spherical cutoff, which can easily be implemented in molecular simulation programs. An analytical solution of the linearized Poisson–Boltzmann (PB) equation valid in a spherical region is obtained. From this general solution special expressions are derived for evaluating the electrostatic potential and its derivative at the origin of the sphere. These expressions have been implemented for molecular dynamics (MD) simulations, such that the surface of the cutoff sphere around a charged particle is identified with the spherical boundary of the Poisson–Boltzmann problem. The analytical solution of the Poisson–Boltzmann equation is valid for the cutoff sphere and can be used for calculating the reaction field forces on the central charge, assuming a uniform continuum of given ionic strength beyond the cutoff. MD simulations are performed for a periodic system consisting of 2127 SPC water molecules with 40 NaCl ions (1 molar). We compare the structural and dynamical results obtained from MD simulations in which the long range electrostatic interactions are treated differently; using a cutoff radius, using a cutoff radius and a Poisson–Boltzmann generalized reaction field force, and using the Ewald summation. Application of the Poisson–Boltzmann generalized reaction field gives a dramatic improvement of the structure of the solution compared to a simple cutoff treatment, at no extra computational cost.

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The application of the computer simulation method of molecular dynamics to macromolecules is investigated. The protein trypsin inhibitor (BPTI), consisting of 454 united atoms, is used as an example. Different algorithms for integrating the equations of motion are compared, both theoretically and in practice. It is examined to what extent the chain structure of a macromolecule allows a reduction of the computational effort by the introduction of constraints in the dynamics of the chain.A calculational scheme is proposed, by which constraints can be incorporated in predictor-corrector algorithms. The optimum choice of an algorithm depends on the desired accuracy of the solution and on the character of the forces acting on the molecule, viz. whether these are noisy or not. For nonconstraint dynamics a Gear predictor-corrector algorithm yields the best results, whereas for constraint dynamics the Gear and Verlet algorithms produce comparable results. The application of bond-length constraints reduces the required computer time by a factor of 3. The inclusion of bondangle constraints is not recommended.

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