Recent Applications of Replica-Exchange Molecular Dynamics Simulations of Biomolecules
Publisher:
Bentham Science Publishers
E-ISSN:
1877-9476|2|4|401-412
ISSN:
1877-9468
Source:
Current Physical Chemistry,
Vol.2,
Iss.4, 2012-09,
pp. : 401-412
Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.
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Abstract
Replica-exchange molecular dynamics (REMD) method is one of the enhanced conformational samplingtechniques in MD simulations of proteins or other systems with rugged-energy landscapes. In REMD method, copies oforiginal simulation system at different temperatures are simulated separately and simultaneously. Every few steps,temperatures between neighboring replicas are exchanged if the Metropolis criteria for their instantaneous potentialenergies are satisfied. Due to its simplicity and high efficiency in parallel computers, the method has been applied to manybiological problems including protein folding, aggregation, receptor-ligand binding, and so on. In the last ten years,continuous effort to improve sampling efficiency of REMD simulations for larger biological systems has been carried outby us and other theoretical scientists. In this review article, we introduce two different approaches in REMD simulationsto reduce the computational cost. One is the multicanonical replica-exchange method (MUCAREM) for reducing thenumber of replicas. In this method, each replica has a different multicanonical weight factor and takes a flat energydistribution to cover a wider potential energy space. Another approach is to employ implicit solvent/membrane models forrepresenting surrounding environments of target proteins in REMD simulations. We show two applications of proteinfoldingsimulations in explicit solvent using the former approach and a structural prediction of a transmembrane proteindimer using the latter. Finally, we discuss possibilities of REMD method to simulate a large-scale conformational changeof protein systems using massively parallel supercomputers.