https://www.sandia.gov/~sjplimp/papers/jcompphys95.pdf

Fast parallel algorithms for short-range molecular dynamics

Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

AutoDock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed-up compared with the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multicore machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user.

/pdf/autodock-vina-improving-the-speed-and-accuracy-of-docking-2o77zoffbq.pdf

AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

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 Statistical-Thermodynamic Basis for Computation of Binding Affinities

Although validation studies show that theoretical models for predicting the pKas of ionizable groups in proteins are increasingly accurate, a number of important questions remain:  (1) What factors limit the accuracy of current models? (2) How can conformational flexibility of proteins best be accounted for? (3) Will use of solution structures in the calculations, rather than crystal structures, improve the accuracy of the computed pKas? and (4) Why does accurate prediction of protein pKas seem to require that a high dielectric constant be assigned to the protein interior? This paper addresses these and related issues. Among the conclusions are the following:  (1) computed pKas averaged over NMR structure sets are more accurate than those based upon single crystal structures; (2) use of atomic parameters optimized to reproduce hydration energies of small molecules improves agreement with experiment when a low protein dielectric constant is assumed; (3) despite use of NMR structures and optimized atomic pa...

The determinants of pKas in proteins.

Computer simulation of protein-protein association kinetics: acetylcholinesterase-fasciculin.

Brownian dynamics simulations have been used to calculate the diffusion-controlled rate constants for the binding of a positively charged ligand to several models of acetylcholinesterase (AChE). The crystal structure was used to define the detailed topography and the active sites of the dimeric enzyme. The electric field around AChE was then computed by solving the Poisson equation for different charge distributions in the enzyme at zero ionic strength. These fields were used in turn to calculate the forces on the diffusing ligand. Significant increases in the rate constant resulted in going from a model with no charges to one with the net charges concentrated at the centers of the monomers and then to a model with a realistic distribution of charges throughout the enzyme. The results show that electrostatic steering of ligands contributes to the high rate constants that are observed experimentally for AChE.

Acetylcholinesterase: electrostatic steering increases the rate of ligand binding.

Recent studies on the solvation of atomistic and nanoscale solutes indicate that a strong coupling exists between the hydrophobic, dispersion, and electrostatic contributions to the solvation free energy, a facet not considered in current implicit solvent models. We suggest a theoretical formalism which accounts for coupling by minimizing the Gibbs free energy of the solvent with respect to a solvent volume exclusion function. The resulting differential equation is similar to the Laplace-Young equation for the geometrical description of capillary interfaces but is extended to microscopic scales by explicitly considering curvature corrections as well as dispersion and electrostatic contributions. Unlike existing implicit solvent approaches, the solvent accessible surface is an output of our model. The presented formalism is illustrated on spherically or cylindrically symmetrical systems of neutral or charged solutes on different length scales. The results are in agreement with computer simulations and, most importantly, demonstrate that our method captures the strong sensitivity of solvent expulsion and dewetting to the particular form of the solvent-solute interactions.

/pdf/coupling-nonpolar-and-polar-solvation-free-energies-in-ue7wakxzd1.pdf

James Andrew McCammon

Papers

The Statistical-Thermodynamic Basis for Computation of Binding Affinities

The determinants of pKas in proteins.

Computer simulation of protein-protein association kinetics: acetylcholinesterase-fasciculin.

Acetylcholinesterase: electrostatic steering increases the rate of ligand binding.

Coupling nonpolar and polar solvation free energies in implicit solvent models