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- Blanchard, H
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Professor Peter V. Coveney, BA MA DPhil (Oxon) CChem CPhys FRSC FInstPhys holds a Chair in Physical Chemistry, is Director of the Centre for Computational Science (CCS) and an Honorary Professor in Computer Science at UCL.
His group performs research in atomistic, mesoscale and multiscale modeling, including quantum and classical molecular dynamics, dissipative particle dynamics, lattice gas and lattice-Boltzmann techniques, and exploits state of the art high performance computing and visualisation methods.
Coveney has been leading the large EPSRC RealityGrid e-Science Pilot Project (2001-05) which is funded from 2005 through to 2009 as a Platform Grant; he is also the PI and co-Investigator on several other current grants funded by EPSRC, BBSRC and the U.K. Open Middleware Infrastructure Institute (OMII) which involve grid computing and/or high performance computing (HPC) research. He has held several major NSF funded supercomputing grants (under the PACI and NRAC programs), and currently holds an MRAC allocation under the same program which provides roaming access to the entire set of computational resources on the US TeraGrid. Coveney is the recipient of an HPC Challenge Award at Supercomputing 2003 for the TeraGyroid Project, an inaugural HPC Analytics Challenge Award at SC05 for the SPICE Project, and International Supercomputing Conference Awards in 2004 and 2006, which have helped to promote the global competitiveness of the UK in high performance computing. TeraGyroid and SPICE were jointly funded by NSF & EPSRC, and have involved collaborations with several of the partners who are involved in this proposal. Coveney is Chairman of the UK Collaborative Computational Projects (CCP) Steering Panel and is a member of the UK High-End Computing Strategy Committee, for which he chaired a Working Group that produced a new HEC Strategic Framework. He was a partner in the EU's 6th Framework Programme STEP (Strategy for the EuroPhysiome) Project, a Coordination Action which wrote the Road Map for the development of the Virtual Physiological Human (VPH). VPH represents a methodological and technical framework that will enable the investigation of the human body as a single, integrated, complex system. The VPH Initiative, now underway within the EU's 7th Framework Programme (2007-20014), aims at developing this ambitious goal for integrative biomedicine; he is leading the VPH Network of Excellenece within this initiative, which is designed to coordinate the entire set of activities within the Initiative.
Professor Coveney's research interests are very cross-disciplinary and involve collaborations with chemists, physicists, mathematicians, materials scientists, engineers, and computer scientists. His work has a substantial high performance computing element, but it also includes theory as well as close collaborations with experimentalists in relevant areas.
He has close collaborations with various industrial and high-tech companies, including Schlumberger, Accelrys, Silicon Graphics Inc. (SGI), British Telecom and MI-SWACO.
His group has access to substantial supercomputing resources made available through international grid projects, including TeraGrid, the UK NGS and DEISA.
The following two popular science books give a good introduction to Professor Coveney's research interests:
1. PV Coveney and RR Highfield, The Arrow of Time (WH Allen, London, 1990; Ballantine, New York, 1991).
2. PV Coveney and RR Highfield, Frontiers of Complexity (Fawcett, New York, 1995; Faber and Faber, London, 1995).
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Rapid and Accurate Determination of Binding Free Energies in Protein-Drug Systems using Automated Workflows across Federated Intercontinental Supercomputing Grids
Peter V. Coveney, University College London, UK
Medical practitioners have limited ways of matching a drug to the unique genetic profile of a virus population as it mutates within a patient under drug-related selective pressure. Currently, knowledge based decision support software, making use of existing clinical records and associated viral genotypic data, is used to aid inhibitor selection [1].
Our aim is to explain and accurately quantify the effects of resistance mutations on drug binding using fully-atomistic molecular dynamics (MD) simulations. Furthermore, we aim to demonstrate how the development of automating software which utilises suitable High Performance Computing (HPC) and grid distributed computational infrastructure can be employed to rapidly turn around large numbers of molecular dynamics-based binding affinity calculations. This makes the potential for implementing patient-specific decision support realizable [2] as well as relating molecular level insight directly to the clinical domain. In such a scenario the exact viral-genotypic sequence of a patient is used to deduce inhibitor efficacy across an array of inhibitors, using multiple binding affinity calculations in MD simulations, which return in clinically relevant timescales to confer decision support.
We have recently shown that it is possible, in principle, to quantitatively predict the differences in strength of inhibitors binding to wildtype and mutant HIV-1 protease (PR) enzymes using the single-trajectory MMPBSA and configurational entropy methods [3]. This allows the resistance conferred by an array of mutations to be ranked with respect to a given inhibitor. Computational models of the PR enzyme were fully atomistic, including solvent atoms with production runs of 10 ns duration. Absolute and relative binding affinities for three resistant HIV-1 protease mutants (L90M, G48V, and G48V/L90M) in complex with the inhibitor saquinavir were in excellent agreement to those obtained experimentally. A cross correlation coefficient of 0.96 was obtained for relative ranking of all variants and absolute affinities were all within 0.5 kcal/mol of experimental values.
In general, the approach I describe requires a highly distributed computational infrastructure as well as substantial automation in order to make such studies feasible. To this end, we have developed a tool, the Binding Affinity Calculator (BAC), for the rapid and automated construction, deployment, implementation and post-processing stages of the molecular dynamics simulations [4]. BAC makes use of the Application Hosting Environment (AHE) [5] to execute its constituent components and thus implement the various stages of the workflow involved in the calculation, in general, across multiple HPC and grid-based resources. In particular, the study presented here made use of the compute nodes of the UK National Grid Service (NGS) as well as the US TeraGrid, including the petascale machine, Ranger, at the Texas Advanced Compute Center (TACC).
References
1. Kantor, R., R. Machekano, M. J. Gonzales, K. Dupnik, J. M. Schapiro and R. W. Shafer, 2001. Human immunodeficiency virus reverse transcriptase and protease sequence database: an expanded data model integrating natural language text and sequence analysis programs. Nucleic Acids Research 29(1):296–299.
2. Sadiq, S. K., Mazzeo, M. D., Zasada, S. J., Manos, S., Stoica, I., Gale, C. V., Watson, S. J., Kellam, P., Brew, S. and P. V. Coveney. Patient-specific simulation as a basis for clinical decision making. Preprint.
3. Stoica, I., Sadiq, S. K. and P. V. Coveney (2008). Rapid and accurate prediction of binding free energies for saquinavir-bound HIV-1 proteases. Journal of the American Chemical Society, 130, 2639-2648.
4. Sadiq, S. K., Zasada, S. J., Wright, D., Stoica, I. and P. V. Coveney. An automated molecular simulation-based binding affinity calculator for ligand-bound HIV-1 proteases. Preprint.
5. Coveney, P. V., R. S. Saksena, S. J. Zasada, M. McKeown and S. Pickles, 2007. The application hosting environment: Lightweight middleware for grid-based computational science. Computer Physics Communications 176:406–418.
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