Self-Learning Adaptive Umbrella Sampling Method for the Determination of Free Energy Landscapes in Multiple Dimensions

Wojciech Wojtas-Niziurski, Yilin Meng, Benoı̂t Roux, Simon Bernèche
2013 Journal of Chemical Theory and Computation  
The potential of mean force describing conformational changes of biomolecules is a central quantity that determines the function of biomolecular systems. Calculating an energy landscape of a process that depends on three or more reaction coordinates might require a lot of computational power, making some of multidimensional calculations practically impossible. Here, we present an efficient automatized umbrella sampling strategy for calculating multidimensional potential of mean force. The
more » ... progressively learns by itself, through a feedback mechanism, which regions of a multidimensional space are worth exploring and automatically generates a set of umbrella sampling windows that is adapted to the system. The self-learning adaptive umbrella sampling method is first explained with illustrative examples based on simplified reduced model systems, and then applied to two non-trivial situations: the conformational equilibrium of the pentapeptide Met-enkephalin in solution and ion permeation in the KcsA potassium channel. With this method, it is demonstrated that a significant smaller number of umbrella windows needs to be employed to characterize the free energy landscape over the most relevant regions without any loss in accuracy. * Corresponding authors: simon.berneche@unibas.ch, roux@uchicago.edu. † Those two authors contributed equally to this work Application of the self-learning adaptive US approach to an analytical potential defined as a Fermat spiral and to a model system consisted of Lennard-Jones particles. This information is available free of charge via the Internet at
doi:10.1021/ct300978b pmid:23814508 pmcid:PMC3694627 fatcat:rzsbjx6aefcofptwbuc5lkjvfe