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Multiscale design of coarse-grained elastic network-based potentials for the mu opioid receptor
Stockholm University, Faculty of Science, Department of Materials and Environmental Chemistry (MMK). University of Namur (UNamur), Belgium.
Stockholm University, Faculty of Science, Department of Materials and Environmental Chemistry (MMK).
Number of Authors: 4
2016 (English)In: Journal of Molecular Modeling, ISSN 1610-2940, E-ISSN 0948-5023, Vol. 22, no 9, 227Article in journal (Refereed) Published
Abstract [en]

Despite progress in computer modeling, most biological processes are still out of reach when using all-atom (AA) models. Coarse-grained (CG) models allow classical molecular dynamics (MD) simulations to be accelerated. Although simplification of spatial resolution at different levels is often investigated, simplification of the CG potential in itself has been less common. CG potentials are often similar to AA potentials. In this work, we consider the design and reliability of purely mechanical CG models of the mu opioid receptor (mu OR), a G protein-coupled receptor (GPCR). In this sense, CG force fields (FF) consist of a set of holonomic constraints guided by an elastic network model (ENM). Even though ENMs are used widely to perform normal mode analysis (NMA), they are not often implemented as a single FF in the context of MD simulations. In this work, various ENM-like potentials were investigated by varying their force constant schemes and connectivity patterns. A method was established to systematically parameterize ENM-like potentials at different spatial resolutions by using AA data. To do so, new descriptors were introduced. The choice of conformation descriptors that also include flexibility information is important for a reliable parameterization of ENMs with different degrees of sensitivity. Hence, ENM-like potentials, with specific parameters, can be sufficient to accurately reproduce AA MD simulations of mu OR at highly coarse-grained resolutions. Therefore, the essence of the flexibility properties of mu OR can be captured with simple models at different CG spatial resolutions, opening the way to mechanical approaches to understanding GPCR functions.

Place, publisher, year, edition, pages
2016. Vol. 22, no 9, 227
Keyword [en]
GPCR, Molecular dynamics, Coarse-graining, Multiscale modeling, Elastic network models, Graph theory
National Category
Biological Sciences Chemical Sciences Computer and Information Science
Identifiers
URN: urn:nbn:se:su:diva-134388DOI: 10.1007/s00894-016-3092-zISI: 000382748100032PubMedID: 27566318OAI: oai:DiVA.org:su-134388DiVA: diva2:1040131
Available from: 2016-10-26 Created: 2016-10-06 Last updated: 2016-10-26Bibliographically approved

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