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Since its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published. They include the following: new faster simulation engines, accessible user interfaces for convenient workflows, and a vast array of simulation and analysis methods that encompass quantum mechanical, atomistic, and coarse-grained levels, as well as extensive coverage of force fields. In addition to providing the current snapshot of the CHARMM development, this review may serve as a starting point for exploring relevant theories and computational methods for tackling contemporary and emerging problems in biomolecular systems. CHARMM is freely available for academic and nonprofit research at https://academiccharmm.org/program.

Recent advances in improved force fields and sampling methods have made it possible for the accurate calculation of protein–ligand binding free energies. Alchemical free energy perturbation (FEP) using an explicit solvent model is one of the most rigorous methods to calculate relative binding free energies. However, for cases where there are high energy barriers separating the relevant conformations that are important for ligand binding, the calculated free energy may depend on the initial conformation used in the simulation due to the lack of complete sampling of all the important regions in phase space. This is particularly true for ligands with multiple possible binding modes separated by high energy barriers, making it difficult to sample all relevant binding modes even with modern enhanced sampling methods. In this paper, we apply a previously developed method that provides a corrected binding free energy for ligands with multiple binding modes by combining the free energy results from multiple alchemical FEP calculations starting from all enumerated poses, and the results are compared with Glide docking and MM-GBSA calculations. From these calculations, the dominant ligand binding mode can also be predicted. We apply this method to a series of ligands that bind to c-Jun N-terminal kinase-1 (JNK1) and obtain improved free energy results. The dominant ligand binding modes predicted by this method agree with the available crystallography, while both Glide docking and MM-GBSA calculations incorrectly predict the binding modes for some ligands. The method also helps separate the force field error from the ligand sampling error, such that deviations in the predicted binding free energy from the experimental values likely indicate possible inaccuracies in the force field. An error in the force field for a subset of the ligands studied was identified using this method, and improved free energy results were obtained by correcting the partial charges assigned to the ligands. This improved the root-mean-square error (RMSE) for the predicted binding free energy from 1.9 kcal/mol with the original partial charges to 1.3 kcal/mol with the corrected partial charges.

The results of molecular dynamics simulations of the bovine pancreatic trypsin inhibitor (BPTI) in water with force fields that explicitly include polarization for both the protein and the water are presented. The findings reveal that the solvating water's relaxation is most affected when both the protein and the water models are polarizable.

The microscopic origin of the interface potential calculated from computer simulations is elucidated by considering a simple model of molecules near an interface. The model posits that molecules are isotropically oriented and their charge density is Gaussian distributed. Molecules that have a charge density that is more negative toward their interior tend to give rise to a negative interface potential relative to the gaseous phase, while charge densities more positive toward their interior give rise to a positive interface potential. The interface potential for the model is compared to the interface potential computed from molecular dynamics simulations of the nonpolar vacuum-methane system and the polar vacuum-water interface system. The computed vacuum-methane interface potential from a molecular dynamics simulation (-220 mV) is captured with quantitative precision by the model. For the vacuum-water interface system, the model predicts a potential of -400 mV compared to -510 mV, calculated from a molecular dynamics simulation. The physical implications of this isotropic contribution to the interface potential is examined using the example of ion solvation in liquid methane. [ABSTRACT FROM AUTHOR]

The particle–particle particle–mesh (P3M) method for calculating long-range electrostatic forces in molecular simulations is modified and combined with the reversible reference system propagator algorithm (RESPA) for treating the multiple time scale problems in the molecular dynamics of complex systems with multiple time scales and long-range forces. The resulting particle–particle particle–mesh Ewald RESPA (P3ME/RESPA) method provides a fast and accurate representation of the long-range electrostatic interactions for biomolecular systems such as protein solutions. The method presented here uses a different breakup of the electrostatic forces than was used by other authors when they combined the Particle Mesh Ewald method with RESPA. The usual breakup is inefficient because it treats the reciprocal space forces in an outer loop even though they contain a part that changes rapidly in time. This does not allow use of a large time step for the outer loop. Here, we capture the short-range contributions in the reciprocal space forces and include them in the inner loop, thereby allowing for larger outer loop time steps and thus for a much more efficient RESPA implementation. The new approach has been applied to both regular Ewald and P3ME. The timings of Ewald/RESPA and P3ME/RESPA are compared in detail with the previous approach for protein water solutions as a function of number of atoms in the system, and significant speedups are reported. © 2001 American Institute of Physics. [ABSTRACT FROM AUTHOR]

Molecular dynamics simulations of a lipid monolayer at a water-air interface are used for examining the dipole potential arising at the water-lipid interface. The results have shown the utility of the inclusion of many-body polarization effects in empirical force field models of lipids.

A study was conducted to examine the role played by electronic polarization in the dielectric properties of liquid N-methyl acetamide (NMA). The findings provided insight into two factors related to electronic polarizability, namely mean amide molecular dipole magnitude and strong hydrogen bonding between molecular neighbors which contribute to large dielectric constant.

Hydrogen-bond (H-bond) dynamics in the air-water interface is studied by molecular dynamics simulations. The dynamics of breaking and forming hydrogen bonds in the air-water interface is revealed to be faster than in bulk water for the polarizable water models.

A general method to calculate the self-diffusion coefficient tensor from molecular dynamics (MD) in a finite region is proposed. The results revealed that far from the interface the diffusion tensor is found of distance as expected, and the diffusion coefficient has the value D approximately is 0.22 A degree 2/ps, in agreement with what is found in the bulk liquid.