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Accelerating the Generalized Born with Molecular Volume and Solvent Accessible Surface Area Implicit Solvent Model

Xiping Gong1, Mara Chiricotto1, Xiaorong Liu1

  • 1Department of Chemistry, University of Massachusetts, Amherst, Amherst, Massachusetts, 01003.

Journal of Computational Chemistry
|December 26, 2019
PubMed
Summary
This summary is machine-generated.

We developed a faster GPU-accelerated implicit solvent model (GBMV2/SA) for biomolecular simulations. This computational enhancement significantly speeds up molecular dynamics, enabling more accurate protein behavior analysis.

Keywords:
CHARMMOpenMMgeneralized Bornprotein conformationsolvation free energy

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Area of Science:

  • Computational chemistry
  • Biophysics
  • Molecular modeling

Background:

  • The generalized Born with molecular volume and solvent accessible surface area (GBMV2/SA) model accurately describes molecular volume and protein conformational equilibria.
  • Wider application of GBMV2/SA is hindered by high computational cost and poor parallel computing scalability.

Purpose of the Study:

  • To develop an efficient graphics processing unit (GPU) implementation of the GBMV2/SA implicit solvent model.
  • To accelerate biomolecular simulations using the GBMV2/SA model.

Main Methods:

  • Implemented electrostatic and nonpolar components of GBMV2/SA on GPUs using CHARMM/OpenMM.
  • Validated numerical equivalence between GPU-GBMV2/SA and CPU-GBMV2/SA.
  • Assessed performance on molecular dynamics simulations of proteins.

Main Results:

  • Achieved ~60- to 70-fold speedup on a single NVIDIA TITAN X (Pascal) GPU.
  • Demonstrated numerical equivalence between GPU and CPU implementations.
  • Confirmed applicability to both folded and unstructured proteins of various sizes.

Conclusions:

  • The GPU-accelerated GBMV2/SA model significantly enhances computational efficiency for biomolecular simulations.
  • This advancement facilitates broader application of implicit solvent models in studying protein dynamics.
  • Paves the way for future improvements in implicit solvent methodologies.