NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This article, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temperature and pressure controls used. Features for steering the simulation across barriers and for calculating both alchemical and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Finally, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomolecular system, highlighting particular features of NAMD, for example, the Tcl scripting language. The article also provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the molecular graphics/sequence analysis software VMD and the grid computing/collaboratory software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu.

/pdf/scalable-molecular-dynamics-with-namd-51o1z7rwp8.pdf

Scalable molecular dynamics with NAMD

/pdf/scalable-molecular-dynamics-with-namd-3j7xyc70g4.pdf

Scalable Molecular Dynamics with NAMD

CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecu- lar simulation program. It has been developed over the last three decades with a primary focus on molecules of bio- logical interest, including proteins, peptides, lipids, nucleic acids, carbohydrates, and small molecule ligands, as they occur in solution, crystals, and membrane environments. For the study of such systems, the program provides a large suite of computational tools that include numerous conformational and path sampling methods, free energy estima- tors, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. The CHARMM program is applicable to problems involving a much broader class of many-particle systems. Calculations with CHARMM can be performed using a number of different energy functions and models, from mixed quantum mechanical-molecular mechanical force fields, to all-atom classical potential energy functions with explicit solvent and various boundary conditions, to implicit solvent and membrane models. The program has been ported to numer- ous platforms in both serial and parallel architectures. This article provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM article in 1983.

/pdf/charmm-the-biomolecular-simulation-program-4kexem0xkx.pdf

CHARMM: the biomolecular simulation program.

The Weighted Histogram Analysis Method (WHAM), an extension of Ferrenberg and Swendsen's Multiple Histogram Technique, has been applied for the first time on complex biomolecular Hamiltonians. The method is presented here as an extension of the Umbrella Sampling method for free-energy and Potential of Mean Force calculations. This algorithm possesses the following advantages over methods that are currently employed: (1) It provides a built-in estimate of sampling errors thereby yielding objective estimates of the optimal location and length of additional simulations needed to achieve a desired level of precision; (2) it yields the “best” value of free energies by taking into account all the simulations so as to minimize the statistical errors; (3) in addition to optimizing the links between simulations, it also allows multiple overlaps of probability distributions for obtaining better estimates of the free-energy differences. By recasting the Ferrenberg–Swendsen Multiple Histogram equations in a form suitable for molecular mechanics type Hamiltonians, we have demonstrated the feasibility and robustness of this method by applying it to a test problem of the generation of the Potential of Mean Force profile of the pseudorotation phase angle of the sugar ring in deoxyadenosine. © 1992 by John Wiley & Sons, Inc.

The weighted histogram analysis method for free-energy calculations on biomolecules. I: The method

Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling

Monte Carlo free energy estimates using non-Boltzmann sampling: Application to the sub-critical Lennard-Jones fluid

A Monte Carlo method for obtaining the solvent‐averaged interionic potential of mean force is described. The potential of mean force is obtained for two charged hard spheres immersed in a dipolar hard sphere solvent. The ions and solvent particles have the same hard sphere diameters and the ions bear single charges of opposite sign. The solvent particles are characterized by a reduced dipole moment μ*=1.0 which corresponds to a dielectric constant e?7.8. The Monte Carlo result is compared with the ’’primitive model’’ and other approximations which have been suggested for the potential of mean force. These approximations are all found to be inadequate for small ionic separations. Some implications with respect to ’’ion pairing’’ are mentioned.

A Monte Carlo method for obtaining the interionic potential of mean force in ionic solution

Monte Carlo methods are presented for the evaluation of the various components of the force between parallel charged surfaces due to the presence between them of an electrolyte, represented by the restricted primitive model. This is a significant problem because some recent theoretical results, and computations on even simpler models, have put in question the adequacy of traditional Derjaguin–Landau–Verwey–Overbeek (DLVO) theory in offering a basic understanding of colloid stability. The methods are applied principally to 2:2 electrolytes at a variety of surface charge densities. We find, in contrast to DLVO theory, that the force between the surfaces resulting from the electrolyte is almost everywhere attractive; in detail these forces turn out to have a remarkably rich behavior resulting from the interaction of their various contributions. The results bolster the perception that we need to take a new look at some basics of colloid science.

Colloid stability: The forces between charged surfaces in an electrolyte

The effect of surface polarization (i.e., image forces) on the properties of electrical double layers is studied by means of Monte Carlo calculations on a primitive model electrolyte next to a planar charged surface bounding a semi‐infinite dielectric. Two cases are considered, that of a conducting material for which an ion is attracted by its own image and that of an insulator for which the self‐image force is repulsive. At low surface charge densities the image forces cause quite dramatic changes in the ionic densities near the wall; the effect on the electrostatic potential is small but increases with surface charge density. The modified Poisson–Boltzmann theory of Outhwaite is quite successful in describing the Monte Carlo results for the range of parameters studied. A screened self‐image model of image effects is also considered for which both Monte Carlo calculations and numerical solution of the HNC equation have been obtained.

John P. Valleau

Papers

Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling

Monte Carlo free energy estimates using non-Boltzmann sampling: Application to the sub-critical Lennard-Jones fluid

A Monte Carlo method for obtaining the interionic potential of mean force in ionic solution

Colloid stability: The forces between charged surfaces in an electrolyte

Electrical double layers. II. Monte Carlo and HNC studies of image effects