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Multiscale design of coarse-grained elastic network-based potentials for the μ opioid receptor

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Abstract

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 μ opioid receptor (μOR), 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 μOR at highly coarse-grained resolutions. Therefore, the essence of the flexibility properties of μOR can be captured with simple models at different CG spatial resolutions, opening the way to mechanical approaches to understanding GPCR functions.

All atom structure, residue interaction network and coarse-grained elastic network models of the μ opioid receptor (μOR)

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Acknowledgments

M.F., L.L., and D.P.V. acknowledge Doctor François Meurant, anesthetist at the Centre Hospitalier Tubize-Nivelles (Nivelles, Belgium) for fruitful discussions. All authors acknowledge the Swedish National Infrastructure for Computing (SNIC, Sweden), the Consortium des Equipements de Calcul Intensif (C.E.C.I., supported by the F.R.S.-FNRS, Belgium), and the Plateforme Technologique de Calcul Intensif (P.T.C.I., supported by the F.R.S.-FNRS, Belgium) for computing resources. M.F. thanks the Belgian National Fund for Research (F.R.S.-FNRS) for his F.R.I.A. doctoral scholarship. M.F., L.L., and D.P.V. also thank the Interuniversity Attraction Poles Programmes n° 7/05: ‘Functional supramolecular systems’ initiated by the Belgian Science Policy Office for partial financial support. A.L. wishes to thank the Swedish Science Council (VR) for financial support.

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Authors and Affiliations

  1. Laboratoire de Physico-Chimie Informatique, Unité de Chimie Physique Théorique et Structurale, Namur Medicine and Drug Innovation Center (NAMEDIC), Namur Research Institute for Life Science (NARILIS), University of Namur (UNamur), 61 Rue de Bruxelles, 5000, Namur, Belgium

    Mathieu Fossépré, Laurence Leherte & Daniel P. Vercauteren

  2. Arrhenius Laboratory, Division of Physical Chemistry, Stockholm University, Svante Arrhenius väg 10-18, 106 91, Stockholm, Sweden

    Mathieu Fossépré & Aatto Laaksonen

  3. Stellenbosch Institute of Advanced Study (STIAS), Wallenberg Research Centre at Stellenbosch University, 10 Marais Road, 7600, Stellenbosch, South Africa

    Aatto Laaksonen

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  1. Mathieu Fossépré
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  2. Laurence Leherte
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  3. Aatto Laaksonen
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  4. Daniel P. Vercauteren
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Correspondence to Mathieu Fossépré.

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Fossépré, M., Leherte, L., Laaksonen, A. et al. Multiscale design of coarse-grained elastic network-based potentials for the μ opioid receptor. J Mol Model 22, 227 (2016). https://doi.org/10.1007/s00894-016-3092-z

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