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Molecular Dynamics Simulation to Compute Free Energies for Real Systems

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Molecular Dynamics (MD) simulations can be used to study a wide range of problems, but arguably one of the most important applications is the use of MD simulations to compute free energy (FE) for many problems of interest this is the most important thermodynamic variable. Computing FE is critical to understanding many phenomena in biological systems.

 

 

An interactive steered molecular dynamics simulation in progress. The red arrow-headed lines represent the forces that are applied to the end residues of the DNA to guide and speed up its translocation through the pore.

 

The use of MD simulations to compute FE (MDFE) in real systems is not trivial - FE calculations are amongst the most computationally intensive science problems. The use of scalable atomistic MD codes running on state-of-the-art high performance computers (HPC) enables us to compute biomolecular properties deemed inaccessible some years ago. HPC resources enable us to simulate larger, more realistic biological systems for longer durations without the imposition of artificial constraints. To benefit from the potential advantages of the SMD-JE approach and to facilitate its implementation at all levels, we use grid infrastructure to optimally utilise the computational resources of a federated transatlantic grid. The grid infrastructure enables us to circumvent the traditional constraints of a batch computing style by performing distributed large-scale interactive simulations (by coupling geographically distributed visualisation and computational resources) as well as providing the additional computational resources in a uniform, easy-to-access fashion. Grid techniques also facilitate the use of advanced optical networks, which in turn make interactive simulations feasible. HPCx is one of several resources which are being connected to the optically networked Global Lambda Infrastructure Facility (GLIF) via UKLight.

Shantenu Jha, Shunzhou Wan, and Peter V. Coveney, Centre for Computational Science, University College London

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