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© Research
Publication : Journal of chemical theory and computation

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

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in Journal of chemical theory and computation - 24 Feb 2017

Koehl P, Poitevin F, Navaza R, Delarue M

Link to Pubmed [PMID] – 28170254

J Chem Theory Comput 2017 Mar;13(3):1424-1438

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.

https://www.ncbi.nlm.nih.gov/pubmed/28170254