Conformational Ensembles from Experimental Data and Computer Simulations

Conformational Ensembles from Experimental Data and Computer Simulations

Poster Abstracts

11-POS Board 11 Density of State Estimates for Conformational Ensembles Using an Improved Wang- Landau Algorithm Augustin Chevallier , Frederic Cazals. INRIA, Sophia Antipolis, France. Generalized ensemble methods have proven effective to explore the energy landscape of complex systems. Among the available options, the Wang-Landau (WL) algorithm implements a random walk in energy space, from which the density of states is obtained. However, even though several improvements have been proposed, notably on the choice of proper bin size and random walk [1], obtaining effective convergence using Wang-Landau is still challenging. Practically, differences in flatness and high dimensional effects (measure concentration) are major hurdles for convergence. To address these problems, we introduce two novel strategies based upon non symmetric random walks and the exploitation by the random walk of local geometric features of the landscape. In addition, there exists very few versatile implementations of the Wang-Landau algorithm. We provide such an implementation, in C++, decoupling all key ingredients of the algorithm (physical system, data structures, flat histogram rule, etc). The code, which is to be released in the Structural Bioinformatics Library [3], is used to obtain results on peptides and a model protein (BLN69), whose energy landscapes have been studied elsewhere [4,5]. Convergence speedups of several orders of magnitude are obtained. [1] Bornn, Jacob, Del Moral and Doucet An adaptive interacting Wang--Landau algorithm for automatic density exploration J. of Computational and Graphical Statistics, 22 (3), 2013 [2] Chevallier and Cazals Exploiting geometric features of the Potential Energy Landscape improves the convergence speed of Wang-Landau Submitted, 2017. [3] The Structural Bioinformatics Library: modeling in biomolecular science and beyond Cazals and Dreyfus Bioinformatics, 7 (33), 2017 [4] Hybridizing rapidly growing random trees and basin hopping yields an improved exploration of energy landscapes Roth, Dreyfus, Robert and Cazals J. of Computational Chemistry, 37 (8), 2016.

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