One of the computational grand challenge problems is to develop methodology capa¬ble of sampling conformational equilibria in systems with rough energy landscapes. If met, many important problems, most notably protein structure prediction, could be signi?cantly impacted. In this talk, I will present an approach [1, 2] in which molecular dynamics is combined with a novel variable transformation designed to warp con?guration space in such a way that barriers are reduced and attractive basins stretched. The new method rigorously preserves equilibrium properties while leading to very large enhancements in sampling e?¬ciency in high-dimensional subspaces of collective variables. The performance of the method is demonstrated on long polymer chains and simple protein models and is shown to out¬perform replica-exchange Monte Carlo signi?cantly with only one trajectory[2]. I will also discuss the use of adiabatic dynamics to generate multi-dimensional free-energy surfaces [3,4], our recent development[5] that circumvents the need for explicit coordinate transformations based on recent work of Maragliano and Vanden-Eijnden[6], and the possibility of adapting this approach to help ease the implementation of spatial-warping transformations.
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