Accelerator-based look-up table for coarse-grained molecular dynamics computations

Molecular Dynamics (MD) is a simulation technique widely used by computational chemists and biologists to simulate and observe the physical properties of a system of particles or molecules. The method provides invaluable three-dimensional structural and transport property data for macromolecules that can be used in applications such as the study of protein folding and drug design. The most time-consuming and inefficient routines in MD packages, particularly for large systems, are the ones involving the computation of intermolecular energy and forces for each molecule. Many fully atomistic systems such as CHARMM and NAMD have been refined over the years to improve their efficiency. But, simulating complex long-time events such as protein folding remains out reach for atomistic simulations. The consensus view amongst computational chemists and biologists is that the development of a coarse-grained (CG) MD package will make the long timescales required for protein folding simulations possible. The shortcoming of this method remains an inability to produce accurate dynamics and results that are comparable with atomistic simulations. It is the objective of this dissertation to develop a coarse-grained method that is computationally faster than atomistic simulations, while being dynamically accurate enough to produce structural and transport property data comparable to results from the latter. Firstly, the accuracy of the Gay-Berne potential in modelling liquid benzene in comparison to fully atomistic simulations was investigated. Following this, the speed of a course-grained condensed phase benzene simulation employing a Gay-Berne potential was compared with that of a fully atomistic simulation. While coarse-graining algorithmically reduces the total number of particles in consideration, the execution time and efficiency scales poorly for large systems. Both fully-atomistic and coarse-grained developers have accelerated packages using high-performance parallel computing platforms such as multi-core CPU clusters, Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs). GPUs have especially gained popularity in recent years due to their massively parallel architecture on a single chip, making them a cheaper alternative to a CPU cluster. Their relatively shorter development time also gives them an advantage over FPGAs. NAMD is perhaps the most popular MD package that employs efficient use of a single GPU or a multi-GPU cluster to conduct simulations. The Scientific Computing Research Unit’s in-house generalised CG code, the Free Energy Force Induced (FEFI) coarse-grained MD package, was accelerated using a GPU to investigate the achievable speed-up in comparison to the CPU algorithm. To achieve this, a parallel version of the sequential force routine, i.e. the computation of the energy, force and torque per molecule, was developed and implemented on a GPU. The GPU-accelerated FEFI package was then used to simulate benzene, which is almost exclusively governed by van der Waal’s forces (i.e. dispersion effects), using the parameters for the Gay-Berne potential from a study by Golubkov and Ren in their work “Generalized coarse-grained model based on point multipole and Gay-Berne potentials”. The coarse-grained condensed phase structural properties, such as the radial and orientational distribution functions, proved to be inaccurate. Further, the transport properties such as diffusion were significantly more unsatisfactory compared to a CHARMM simulation. From this, a conclusion was reached that the Gay-Berne potential was not able to model the subtle effects of dispersion as observed in liquid benzene. In place of the analytic Gay-Berne potential, a more accurate approach would be to use a multidimensional free energy-based potential. Using the Free Energy from Adaptive Reaction Coordinate Forces (FEARCF) method, a four-dimensional Free Energy Volume (FEV) for two interacting benzene molecules was computed for liquid benzene. The focal point of this dissertation was to use this FEV as the coarse-grained interaction potential in FEFI to conduct CG simulations of condensed phase liquid benzene. The FEV can act as a numerical potential or Look-Up Table (LUT) from which the interaction energy and four partial derivatives required to compute the forces and torques can be obtained via numerical methods at each step of the CG MD simulation. A significant component of this dissertation was the development and implementation of four-dimensional LUT routines to use the FEV for accurate condensed phase coarse-grained simulations. To compute the energy and partial derivatives between the grid points of the surface, an interpolation algorithm was required. A four-dimensional cubic B-spline interpolation was developed because of the method’s superior accuracy and resistance to oscillations compared with other polynomial interpolation methods. When The algorithm’s introduction into the FEFI CG MD package for CPUs exhausted the single-core CPU architecture with its large number of interpolations for each MD step. It was therefore impractical for the high throughput interpolation required for MD simulations. The 4D cubic B-spline algorithm and the LUT routine were then developed and implemented on a GPU. Following evaluation, the LUT was integrated into the FEFI MD simulation package. A FEFI CG simulation of liquid benzene was run using the 4D FEV for a benzene molecular pair as the numerical potential. The structural and transport properties outperformed the analytical Gay-Berne CG potential, more closely approximating the atomistic predicted properties. The work done in this dissertation demonstrates the feasibility of a coarse-grained simulation using a free energy volume as a numerical potential to accurately simulate dispersion effects, a key feature needed for protein folding