The Laboratoire International Associé between the Centre National de la Recherche Scientifique and the University of Illinois at Urbana-Champaign was launched at the end of 2012. Its primary objective is to develop methods for high-performance molecular simulation with the aim of understanding the function of complex biological assemblies, transcending the frontiers of traditional disciplines by uniting mathematicians, physicists, theoretical chemists and biologists on both sides of the Atlantic. In France, the major contributors are located at the Université de Lorraine, the École des Ponts ParisTech, the Institut de Biologie et Chimie des Protéines-Université Claude Bernard and the Laboratoire d'Ingénierie des Systèmes Macromoléculaires-Université d'Aix-Marseille. In the United States, the contributors belong to the NIH Resource for Macromolecular Modeling and Bioinformatics. In Nancy, the partner is a theoretical chemistry and biophysics group incepted in 2003. Its expertise lies in describing the structure and the dynamic properties of the biological membrane and elucidating the mechanisms of the cell machinery. To attain this goal, its members leverage numerical simulations over size and timescales commensurate with the biological process at hand. Over the years, the team has gleaned milestone results in such diverse research areas as membrane transport, interaction with the biological membrane, membrane protein structure and function, as well as self-organized molecular systems. They also develop original approaches in the field of free-energy calculations, as well as that of intermolecular potentials.
The adaptive biasing force method: Everything you always wanted to know but were afraid to ask. In the host of numerical schemes devised to calculate free energy differences by way of geometric transformations, the adaptive biasing force algorithm has emerged as a promising route to map complex free-energy landscapes. It relies upon the simple concept that as a simulation progresses, a continuously updated biasing force is added to the equations of motion, such that in the long-time limit it yields a Hamiltonian devoid of an average force acting along the transition coordinate of interest. This means that sampling proceeds uniformly on a flat free-energy surface, thus providing reliable free-energy estimates. Much of the appeal of the algorithm to the practitioner is in its physically intuitive underlying ideas and the absence of any requirements for prior knowledge about free-energy landscapes. Since its inception in 2001, the adaptive biasing force scheme has been the subject of considerable attention, from in-depth mathematical analysis of convergence properties to novel developments and extensions. The method has also been successfully applied to many challenging problems in chemistry and biology. In this contribution, the method is presented in a comprehensive, self-contained fashion, discussing with a critical eye its properties, applicability, and inherent limitations, as well as introducing novel extensions. Through free-energy calculations of prototypical molecular systems, many methodological aspects are examined, from stratification strategies to overcoming the so-called hidden barriers in orthogonal space, relevant not only to the adaptive biasing force algorithm but also to other importance-sampling schemes. On the basis of the discussions in this paper, a number of good practices for improving the efficiency and reliability of the computed free-energy differences are proposed. Journal of Chemical Theory and Computation, 2015.
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