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The Renormalization Group and Its Applications to Generating Coarse-Grained Models of Large Biological Molecular Systems

Abstract : Understanding the dynamics of biomolecules is the key to understanding their biological activities. Computational methods ranging from all-atom molecular dynamics simulations to coarse-grained normal-mode analyses based on simplified elastic networks provide a general framework to studying these dynamics. Despite recent successes in studying very large systems with up to a 100,000,000 atoms, those methods are currently limited to studying small- to medium-sized molecular systems due to computational limitations. One solution to circumvent these limitations is to reduce the size of the system under study. In this paper, we argue that coarse-graining, the standard approach to such size reduction, must define a hierarchy of models of decreasing sizes that are consistent with each other, i.e., that each model contains the information of the dynamics of its predecessor. We propose a new method, Decimate, for generating such a hierarchy within the context of elastic networks for normal-mode analysis. This method is based on the concept of the renormalization group developed in statistical physics. We highlight the details of its implementation, with a special focus on its scalability to large systems of up to millions of atoms. We illustrate its application on two large systems, the capsid of a virus and the ribosome translation complex. We show that highly decimated representations of those systems, containing down to 1% of their original number of atoms, still capture qualitatively and quantitatively their dynamics. Decimate is available as an OpenSource resource.
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Submitted on : Friday, July 5, 2019 - 11:01:36 AM
Last modification on : Thursday, April 7, 2022 - 10:10:36 AM




Patrice Koehl, Frédéric Poitevin, Rafael Navaza, Marc Delarue. The Renormalization Group and Its Applications to Generating Coarse-Grained Models of Large Biological Molecular Systems. Journal of Chemical Theory and Computation, American Chemical Society, 2017, 13 (3), pp.1424-1438. ⟨10.1021/acs.jctc.6b01136⟩. ⟨pasteur-02174591⟩



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