| Issue |
ESAIM: Proc.
Volume 48, January 2015
CEMRACS 2013 - Modelling and simulation of complex systems: stochastic and deterministic approaches
|
|
|---|---|---|
| Page(s) | 215 - 225 | |
| DOI | https://doi.org/10.1051/proc/201448009 | |
| Published online | 09 March 2015 | |
Adaptive Multilevel Splitting in Molecular Dynamics Simulations*
1 Department of Mathematics, University of Minnesota, USA
2 CERMICS, École des Ponts ParisTech,
France
3 Beckman Institute, University of Illinois at
Urbana-Champaign, USA
Adaptive Multilevel Splitting (AMS) is a replica-based rare event sampling method that has been used successfully in high-dimensional stochastic simulations to identify trajectories across a high potential barrier separating one metastable state from another, and to estimate the probability of observing such a trajectory. An attractive feature of AMS is that, in the limit of a large number of replicas, it remains valid regardless of the choice of reaction coordinate used to characterize the trajectories. Previous studies have shown AMS to be accurate in Monte Carlo simulations. In this study, we extend the application of AMS to molecular dynamics simulations and demonstrate its effectiveness using a simple test system. Our conclusion paves the way for useful applications, such as molecular dynamics calculations of the characteristic time of drug dissociation from a protein target.
The Laboratoire International Associé between the Centre National de la Recherche Scientifique (CNRS) and the University of Illinois at Urbana-Champaign (UIUC) is gratefully acknowledged. We acknowledge the financial support of Sanofi R&D and beneficial discussions with Dr. Marc Bianciotto, Dr. Claire Minoletti and Dr. Hervé Minoux, from Sanofi-Aventis. This work has also received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking, K4DD grant n° 115366. The work of T. Lelièvre is supported by the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement number 614492. The work of C. Mayne and I. Teo was supported by the National Institutes of Health (NIH) Grant 9P41GM104601. Last but not least, we are grateful to Dr. Mahmoud Moradi for the invaluable discussions about technical aspects of the NAMD colvars module, and to Dr. James Phillips for his assistance in modifying the NAMD code for implementation of the AMS algorithm.
© EDP Sciences, SMAI 2015
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