CHARMM: the biomolecular simulation program.

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. 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 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.

Dopaminergic pharmacophore of ergoline and its analogues. A molecular electrostatic potential study.

Spatial correspondence between apomorphine, a prototype dopaminergic (DA) drug, and ergoline and some of its (partial) analogues were derived by matching their molecular electrostatic potential (MEP) patterns surrounding the aromatic moieties with respect to the coincident aliphatic N atoms. The MEP patterns were calculated from ab initio wave functions of model molecules. The congruent superimpositions of the molecular frameworks obtained between apomorphine and DA active ergoline analogues might corroborate the hypothesis that they bind with the same receptor sites when activating certain subtypes of the DA receptor.

Catalytic mechanism of aldose reductase studied by the combined potentials of quantum mechanics and molecular mechanics.

The catalytic reduction of D-glyceraldehyde to glycerol by aldose reductase has been investigated with the combined potentials of quantum mechanics (QM) and molecular mechanics (MM) to resolve the question of whether Tyr48 or His110 serves as the proton donor during catalysis. Site directed mutagenesis studies favor Tyr48 as the proton donor while the presence of a water channel linking the N delta 1 of His110 to the bulk solvent suggests that His110 is the proton donor. Utilizing the combined potentials of QM and MM, the binding mode of substrate D-glyceraldehyde was investigated by optimizing the local geometry of Asp43, Lys77, Tyr48, His110 and NADPH at the active site of aldose reductase. Reaction pathways for the reduction of D-glyceraldehyde to glycerol were then constructed by treating both Tyr48 and His110 as proton donors. Comparison of energetics obtained from the reaction pathways suggests His110 to be the proton donor. Based on these findings, a reduction mechanism of D-glyceraldehyde to glycerol is described.

Strategies in the rational drug design.

Rational design is applied in the discovery of novel lead drugs. Its rapid development is mainly attributed to the tremendous advancements in the computer science, statistics, molecular biology, biophysics, biochemistry, medicinal chemistry, pharmacokinetics and pharmacodynamics experienced in the last few decades. The promising feature that characterizes the application of rational drug design is that it uses for developing potential leads in drug discovery all known theoretical and experimental knowledge of the system under study. The utilization of the knowledge of the molecular basis of the system ultimately aims to reduce human power cost, time saving and laboratory expenses in the drug discovery. In this review paper various strategies applied for systems which include: (i) absence of knowledge of the receptor active site; (ii) the knowledge of a homology model of a receptor, (iii) the knowledge of the experimentally determined (i.e. X-ray crystallography, NMR spectroscopy) coordinates of the active site of the protein in absence and (iv) the presence of the ligand will be analyzed.

Prediction of enzyme binding: human thrombin inhibition study by quantum chemical and artificial intelligence methods based on X-ray structures.

Thrombin is a serine protease which plays important roles in the human body, the key one being the control of thrombus formation. The inhibition of thrombin has become a target for new antithrombotics. The aim of our work was to (i) construct a model which would enable us to predict Ki values for the binding of an inhibitor into the active site of thrombin based on a database of known X-ray structures of inhibitor-enzyme complexes and (ii) to identify the structural and electrostatic characteristics of inhibitor molecules crucially important to their effective binding. To retain as much of the 3D structural information of the bound inhibitor as possible, we implemented the quantum mechanical/molecular mechanical (QM/MM) procedure for calculating the molecular electrostatic potential (MEP) at the van der Waals surfaces of atoms in the protein's active site. The inhibitor was treated quantum mechanically, while the rest of the complex was treated by classical means. The obtained MEP values served as inputs into the counter-propagation artificial neural network (CP-ANN), and a genetic algorithm was subsequently used to search for the combination of atoms that predominantly influences the binding. The constructed CP-ANN model yielded Ki values predictions with a correlation coefficient of 0.96, with Ki values extended over 7 orders of magnitude. Our approach also shows the relative importance of the various amino acid residues present in the active site of the enzyme for inhibitor binding. The list of residues selected by our automatic procedure is in good correlation with the current consensus regarding the importance of certain crucial residues in thrombin's active site.

Computational chemistry on commodity-type computers.

A number of inexpensive computers were benchmarked with the ab initio program Gaussian 94, using both small standard test jobs and larger density functional (DFT) calculations. Several varieties of Pentium (x86) and Alpha CPU based systems were tested. Most of them were running under the open source code operating system Linux. They were compared with several workstations and supercomputers. The most powerful of today's commodity-type processors surpassed current supercomputers in speed. The choice of compilers and compilation options was often found to have a larger influence on job CPU times than details of the hardware. Especially on the x86 type machines, the jobs always ran faster the less memory (RAM) they were given. The fastest machine on a per-CPU basis was an Alpha/Linux system. For the DFT calculation, it was close to twice as fast as a Cray J90 supercomputer.