TopoGromacs: Automated Topology Conversion from CHARMM to GROMACS within VMD

Molecular dynamics (MD) simulation engines use a variety of different approaches for modeling molecular systems with force fields that govern their dynamics and describe their topology. These different approaches introduce incompatibilities between engines, and previously published software bridges the gaps between many popular MD packages, such as between CHARMM and AMBER or GROMACS and LAMMPS. While there are many structure building tools available that generate topologies and structures in CHARMM format, only recently have mechanisms been developed to convert their results into GROMACS input. We present an approach to convert CHARMM-formatted topology and parameters into a format suitable for simulation with GROMACS by expanding the functionality of TopoTools, a plugin integrated within the widely used molecular visualization and analysis software VMD. The conversion process was diligently tested on a comprehensive set of biological molecules <i>in vacuo</i>. The resulting comparison between energy terms shows that the translation performed was lossless as the energies were unchanged for identical starting configurations. By applying the conversion process to conventional benchmark systems that mimic typical modestly sized MD systems, we explore the effect of the implementation choices made in CHARMM, NAMD, and GROMACS. The newly available automatic conversion capability breaks down barriers between simulation tools and user communities and allows users to easily compare simulation programs and leverage their unique features without the tedium of constructing a topology twice

Reconsidering Dispersion Potentials: Reduced Cutoffs in Mesh-Based Ewald Solvers Can Be Faster Than Truncation

Long-range dispersion interactions have a critical influence on physical quantities in simulations of inhomogeneous systems. However, the perceived computational overhead of long-range solvers has until recently discouraged their implementation in molecular dynamics packages. Here, we demonstrate that reducing the cutoff radius for local interactions in the recently introduced particle–particle particle−mesh (PPPM) method for dispersion [Isele-Holder et al., <i>J. Chem. Phys.</i>, <b>2012</b>, <i>137</i>, 174107] can actually often be faster than truncating dispersion interactions. In addition, because all long-range dispersion interactions are incorporated, physical inaccuracies that arise from truncating the potential can be avoided. Simulations using PPPM or other mesh Ewald solvers for dispersion can provide results more accurately and more efficiently than simulations that truncate dispersion interactions. The use of mesh-based approaches for dispersion is now a viable alternative for all simulations containing dispersion interactions and not merely those where inhomogeneities were motivating factors for their use. We provide a set of parameters for the dispersion PPPM method using either <i>i</i><b>k</b> or analytic differentiation that we recommend for future use and demonstrate increased simulation efficiency by using the long-range dispersion solver in a series of performance tests on massively parallel computers

Reconsidering Dispersion Potentials: Reduced Cutoffs in Mesh-Based Ewald Solvers Can Be Faster Than Truncation

Long-range dispersion interactions have a critical influence on physical quantities in simulations of inhomogeneous systems. However, the perceived computational overhead of long-range solvers has until recently discouraged their implementation in molecular dynamics packages. Here, we demonstrate that reducing the cutoff radius for local interactions in the recently introduced particle–particle particle−mesh (PPPM) method for dispersion [Isele-Holder et al., <i>J. Chem. Phys.</i>, <b>2012</b>, <i>137</i>, 174107] can actually often be faster than truncating dispersion interactions. In addition, because all long-range dispersion interactions are incorporated, physical inaccuracies that arise from truncating the potential can be avoided. Simulations using PPPM or other mesh Ewald solvers for dispersion can provide results more accurately and more efficiently than simulations that truncate dispersion interactions. The use of mesh-based approaches for dispersion is now a viable alternative for all simulations containing dispersion interactions and not merely those where inhomogeneities were motivating factors for their use. We provide a set of parameters for the dispersion PPPM method using either <i>i</i><b>k</b> or analytic differentiation that we recommend for future use and demonstrate increased simulation efficiency by using the long-range dispersion solver in a series of performance tests on massively parallel computers

Reconsidering Dispersion Potentials: Reduced Cutoffs in Mesh-Based Ewald Solvers Can Be Faster Than Truncation

Long-range dispersion interactions have a critical influence on physical quantities in simulations of inhomogeneous systems. However, the perceived computational overhead of long-range solvers has until recently discouraged their implementation in molecular dynamics packages. Here, we demonstrate that reducing the cutoff radius for local interactions in the recently introduced particle–particle particle−mesh (PPPM) method for dispersion [Isele-Holder et al., <i>J. Chem. Phys.</i>, <b>2012</b>, <i>137</i>, 174107] can actually often be faster than truncating dispersion interactions. In addition, because all long-range dispersion interactions are incorporated, physical inaccuracies that arise from truncating the potential can be avoided. Simulations using PPPM or other mesh Ewald solvers for dispersion can provide results more accurately and more efficiently than simulations that truncate dispersion interactions. The use of mesh-based approaches for dispersion is now a viable alternative for all simulations containing dispersion interactions and not merely those where inhomogeneities were motivating factors for their use. We provide a set of parameters for the dispersion PPPM method using either <i>i</i><b>k</b> or analytic differentiation that we recommend for future use and demonstrate increased simulation efficiency by using the long-range dispersion solver in a series of performance tests on massively parallel computers

Micellization Studied by GPU-Accelerated Coarse-Grained Molecular Dynamics

The computational design of advanced materials based on surfactant self-assembly without ever stepping foot in the laboratory is an important goal, but there are significant barriers to this approach, because of the limited spatial and temporal scales accessible by computer simulations. In this paper, we report our work to bridge the gap between laboratory and computational time scales by implementing the coarse-grained (CG) force field previously reported by Shinoda et al. [Shinoda, W.; DeVane, R.; Klein, M. L. <i>Mol. Simul</i>. <b>2007</b>, <i>33</i>, 27–36] into the HOOMD-Blue graphical processing unit (GPU)-accelerated molecular dynamics (MD) software package previously reported by Anderson et al. [Anderson, J. A.; Lorenz, C. D.; Travesset, A. <i>J. Comput. Phys</i>. <b>2008</b>, <i>227</i>, 5342–5359]. For a system of 25 750 particles, this implementation provides performance on a single GPU, which is superior to that of a widely used parallel MD simulation code running on an optimally sized CPU-based cluster. Using our GPU setup, we have collected 0.6 ms of MD trajectory data for aqueous solutions of 7 different nonionic polyethylene glycol (PEG) surfactants, with most of the systems studied representing ∼1 000 000 atoms. From this data, we calculated various properties as a function of the length of the hydrophobic tails and PEG head groups. Specifically, we determined critical micelle concentrations (CMCs), which are in good agreement with experimental data, and characterized the size and shape of micelles. However, even with the microsecond trajectories employed in this study, we observed that the micelles composed of relatively hydrophobic surfactants are continuing to grow at the end of our simulations. This suggests that the final micelle size distributions of these systems are strongly dependent on initial conditions and that either longer simulations or advanced sampling techniques are needed to properly sample their equilibrium distributions. Nonetheless, the combination of coarse-grained modeling and GPU acceleration marks a significant step toward the computational prediction of the thermodynamic properties of slowly evolving surfactant systems