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Force Field X (FFX) is an open-source software package for atomic resolution modeling of genetic variants and organic crystals that leverages advanced potential energy functions and experimental data. FFX currently consists of nine modular packages with novel algorithms that include global optimization via a many-body expansion, acid–base chemistry using polarizable constant-pH molecular dynamics, estimation of free energy differences, generalized Kirkwood implicit solvent models, and many more. Applications of FFX focus on the use and development of a crystal structure prediction pipeline, biomolecular structure refinement against experimental datasets, and estimation of the thermodynamic effects of genetic variants on both proteins and nucleic acids. The use of Parallel Java and OpenMM combines to offer shared memory, message passing, and graphics processing unit parallelization for high performance simulations. Overall, the FFX platform serves as a computational microscope to study systems ranging from organic crystals to solvated biomolecular systems.
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Accelerating the Generalized Born with Molecular Volume and Solvent Accessible Surface Area Implicit Solvent Model Using Graphics Processing Units
The generalized Born with molecular volume and solvent accessible surface area (GBMV2/SA) implicit solvent model provides an accurate description of molecular volume and has the potential to accurately describe the conformational equilibria of structured and disordered proteins. However, its broader application has been limited by the computational cost and poor scaling in parallel computing. Here, we report an efficient implementation of both the electrostatic and nonpolar components of GBMV2/SA on graphics processing unit (GPU) within the CHARMM/OpenMM module. The GPU‐GBMV2/SA is numerically equivalent to the original CPU‐GBMV2/SA. The GPU acceleration offers ~60‐ to 70‐fold speedup on a single NVIDIA TITAN X (Pascal) graphics card for molecular dynamic simulations of both folded and unstructured proteins of various sizes. The current implementation can be further optimized to achieve even greater acceleration with minimal reduction on the numerical accuracy. The successful development of GPU‐GBMV2/SA greatly facilitates its application to biomolecular simulations and paves the way for further development of the implicit solvent methodology. © 2019 Wiley Periodicals, Inc.
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- Award ID(s):
- 1817332
- PAR ID:
- 10458938
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Journal of Computational Chemistry
- Volume:
- 41
- Issue:
- 8
- ISSN:
- 0192-8651
- Page Range / eLocation ID:
- p. 830-838
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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