CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecular simulation program. It has been developed over the last three decades with a primary focus on molecules of biological 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 estimators, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. In addition, 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 numerous platforms in both serial and parallel architectures. This paper provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM paper in 1983.

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A method for calculating the absolute electrostatic free energy of a titrating system as a function of pH is proposed, and a concise formula for the free energy is presented. Based on the theory of linked functions, the electrostatic free energy is calculated by integration of the titration curve. The approach uses pH = ∞, at which the system is in the unprotonated state, as the reference pH for the integration. The finite-difference Poisson−Boltzmann method is used for the electrostatic free energy, and the titration curve is obtained by the Monte Carlo approach of Beroza et al. The method is applied to the native and denatured state of hen egg-white lysozyme in aqueous solution. A dielectric constant of 20 is assigned to the protein interior, in accord with the work of Antosiewicz et al. X-ray structures are used for the native state, and an extended β-structure is used for the unfolded reference state; good agreement with experiment is obtained. Comparison of the results for the extended β-structure and for a “null” model of noninteracting sites for the unfolded state shows significant differences. This indicates that there are important interactions between titrating sites in the unfolded state, in agreement with recent experimental estimates by Oliveberg et al. A number of structures obtained by in vacuo minimization of the native and unfolded protein are compared, and it is shown that the absolute pH-stability curve has a strong conformational dependence, although the relative stability curve does not. Calculations of absolute free energy differences, rather than relative changes as a function of pH, are of general interest; for example, they can be used to study the binding energy of ligands involving small titratable compounds.

The COP9 signalosome (CSN) is a central component of the activation and remodelling cycle of cullin-RING E3 ubiquitin ligases (CRLs), the largest enzyme family of the ubiquitin–proteasome system in humans. CRLs are implicated in the regulation of numerous cellular processes, including cell cycle progression and apoptosis, and aberrant CRL activity is frequently associated with cancer. Remodelling of CRLs is initiated by CSN-catalysed cleavage of the ubiquitin-like activator NEDD8 from CRLs. Here we describe CSN5i-3, a potent, selective and orally available inhibitor of CSN5, the proteolytic subunit of CSN. The compound traps CRLs in the neddylated state, which leads to inactivation of a subset of CRLs by inducing degradation of their substrate recognition module. CSN5i-3 differentially affects the viability of tumour cell lines and suppresses growth of a human xenograft in mice. Our results provide insights into how CSN regulates CRLs and suggest that CSN5 inhibition has potential for anti-tumour therapy.

Several methods for including the conformational flexibility of proteins in the calculation of titration curves are compared. The methods use the linearized Poisson-Boltzmann equation to calculate the electrostatic free energies of solvation and are applied to bovine pancreatic trypsin inhibitor (BPTI) and hen egg-white lysozyme (HEWL). An ensemble of conformations is generated by a molecular dynamics simulation of the proteins with explicit solvent. The average titration curve of the ensemble is calculated in three different ways: an average structure is used for the pKa calculation; the electrostatic interaction free energies are averaged and used for the pKa calculation; and the titration curve for each structure is calculated and the curves are averaged. The three averaging methods give very similar results and improve the pKa values to approximately the same degree. This suggests, in contrast to implications from other work, that the observed improvement of pKa values in the present studies is due not to averaging over an ensemble of structures, but rather to the generation of a single properly averaged structure for the pKa calculation.

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Knowledge of the pK(a) of phosphoranes is important for the interpretation of phosphate ester hydrolysis. Calculated pK(a)'s of the model phosphorane, ethylene phosphorane, are reported. The method of calculation is based on the use of dimethyl phosphate as a reference state for evaluating relative pK(a) values, and on the optimization of the oxygen and acidic hydrogen van der Waals radii to give reasonable pK(1)(a), pK(2)(a), and pK(3)(a) for phosphoric acid in solution. Density functional theory is employed to calculate the gas-phase protonation energies, and continuum dielectric methods are used to determine the solvation corrections. The calculated pK(1)(a) and p(2)(a) for the model phosphorane are 7.9 and 14.3, respectively. These values are within the range of proposed experimental values, 6.5-11.0 for pK(1)(a), and 11.3-15.0 for pK(2)(a). The mechanistic implications of the calculated pK(a)'s are discussed.