GPU-Accelerated Exploration of Biomolecular Energy Landscapes

We present graphics processing unit (GPU)-acceleration of various computational energy landscape methods for biomolecular systems. Basin-hopping global optimization, the doubly nudged elastic band method (DNEB), hybrid eigenvector-following (EF), and a local rigid body framework are described, including details of GPU implementations. We analyze the results for eight different system sizes, and consider the effects of history size for minimization and local rigidification on the overall efficiency. We demonstrate improvement relative to CPU performance of up to 2 orders of magnitude for the largest systems.

Keywords

Aldehyde Reductase, Algorithms, Apoptosis Regulatory Proteins, Humans, Molecular Dynamics Simulation, Myoglobin, Protein Structure, Tertiary, Proteins, Thermodynamics

Journal Title

Journal of Chemical Theory and Computation

Journal ISSN

1549-9618
1549-9626

Volume Title

12

Publisher

American Chemical Society

Publisher DOI

https://doi.org/10.1021/acs.jctc.6b00934

Rights and licensing

Except where otherwised noted, this item's license is described as Attribution 4.0 International

Sponsorship

Engineering and Physical Sciences Research Council (EP/L504920/1)

EPSRC (Grant ID: EP/L504920/1)

Relationships

Is supplemented by:

https://doi.org/10.5281/zenodo.163633

Collections

Scholarly Works - Chemistry
Symplectic mapped items for data match