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Protein Dynamics in Living Cells

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ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale.

M J Harvey¹, G Giupponi¹, G De Fabritiis¹

¹Information and Communications Technologies, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom, Department de Fisica Fundamental, Universitat de Barcelona, Carrer Marti i Franques 1, 08028 Barcelona, Spain, and Computational Biochemistry and Biophysics Lab (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C/ Doctor Aiguader 88, 08003 Barcelona, Spain.

Journal of Chemical Theory and Computation

|November 27, 2015

Summary

This summary is machine-generated.

Graphical processing units (GPUs) accelerate molecular dynamics (MD) simulations. The ACEMD software achieves high performance on GPUs, enabling microsecond-scale simulations on affordable hardware for broader scientific impact.

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Area of Science:

Computational chemistry
Biophysics
Molecular modeling

Background:

Recent advancements in graphical processing units (GPUs) offer significant computational power.
Molecular dynamics (MD) simulations are crucial for understanding biological systems.
Existing MD software may not fully leverage GPU capabilities.

Purpose of the Study:

To evaluate the performance and validate the ACEMD biomolecular dynamics engine.
To demonstrate the feasibility of microsecond-scale MD simulations on cost-effective hardware.

Main Methods:

Utilized the ACEMD software, designed for GPU acceleration.
Performed all-atom simulations of protein systems with over 23,000 atoms.
Ran a microsecond-long trajectory using 3 GPUs in explicit TIP3P water.

Main Results:

ACEMD achieved production-class performance, reaching 40 ns/day for large all-atom protein systems.
Successfully generated a microsecond-scale trajectory on a single workstation with 3 GPUs.
Validated the accuracy and efficiency of the ACEMD code.

Conclusions:

GPU-accelerated MD simulations with ACEMD offer supercomputing scale performance.
Microsecond time-scale simulations are achievable on cost-effective hardware.
This advancement has significant implications for both methodology and scientific discovery in molecular dynamics.