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Interplay of G Protein-Coupled Receptors with the Membrane: Insights from Supra-Atomic Coarse Grain Molecular Dynamics Simulations
Open accessAbstract
G protein-coupled receptors (GPCRs) are central to many fundamental cellular signaling pathways. They transduce signals from the outside to the inside of cells in physiological processes ranging from vision to immune response. It is extremely challenging to look at them individually using conventional experimental techniques. Recently, a pseudo atomistic molecular model has emerged as a valuable tool to access information on GPCRs, more specifically on their interactions with their environment in their native cell membrane and the consequences on their supramolecular organization. This approach uses the Martini coarse grain (CG) model to describe the receptors, lipids, and solvent in molecular dynamics (MD) simulations and in enough detail to allow conserving the chemical specificity of the different molecules. The elimination of unnecessary degrees of freedom has opened up large-scale simulations of the lipid-mediated supramolecular organization of GPCRs. Here, after introducing the Martini CGMD method, we review these studies carried out on various members of the GPCR family, including rhodopsin (visual receptor), opioid receptors, adrenergic receptors, adenosine receptors, dopamine receptor, and sphingosine 1-phosphate receptor. These studies have brought to light an interesting set of novel biophysical principles. The insights range from revealing localized and heterogeneous deformations of the membrane bilayer at the surface of the protein, specific interactions of lipid molecules with individual GPCRs, to the effect of the membrane matrix on global GPCR self-assembly. The review ends with an overview of the lessons learned from the use of the CGMD method, the biophysical-chemical findings on lipid-protein interplay.
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“Therefore, investigation of individual GPCRs using conventional experimental techniques is extremely challenging [81].”
From G protein-coupled receptors function as cell membrane receptors for the steroid hormone 20-hydroxyecdysone (2020) · cites this paper as [81]
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Items 201–240 of 240
Complexes between photoactivated rhodopsin and transducin: progress and questions
Jastrzębska, B., Tsybovsky, Y., & Palczewski, K. (2010) · Biochemical Journal · doi:10.1042/bj20100270 · Cited by 46
Dynamic Structure of Retinylidene Ligand of Rhodopsin Probed by Molecular Simulations
Lau, P.-W., Grossfield, A. M., Feller, S. E., Pitman, M. C., & Brown, M. F. (2007) · Journal of Molecular Biology · doi:10.1016/j.jmb.2007.06.047 · Cited by 44
Modulation of the Metarhodopsin I/Metarhodopsin II Equilibrium of Bovine Rhodopsin by Ionic Strength
DeLange, F., Merkx, M., Bovée‐Geurts, P. H. M., Pistorius, A. M. A., & DeGrip, W. J. (1997) · European Journal of Biochemistry · doi:10.1111/j.1432-1033.1997.0174a.x · Cited by 42
A closer look at the cholesterol sensor
Gimpl, G., Bürger, K., & Fahrenholz, F. (2002) · Trends in Biochemical Sciences · doi:10.1016/s0968-0004(02)02224-7 · Cited by 42
Two Dimensional Window Exchange Umbrella Sampling for Transmembrane Helix Assembly
Park, S., & Im, W. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct3008556 · Cited by 42
Provasi, D., Johnston, J. M., & Filizola, M. (2010) · Biochemistry · doi:10.1021/bi100686t · Cited by 41
Contribution of Omega-3 Fatty Acids to the Thermodynamics of Membrane Protein Solvation
Grossfield, A. M., Feller, S. E., & Pitman, M. C. (2006) · The Journal of Physical Chemistry B · doi:10.1021/jp060405r · Cited by 41
Crystal packing analysis of Rhodopsin crystals
Lodowski, D. T., Salom, D., Trong, I. L., Teller, D. C., Ballesteros, J. A., Palczewski, K., & Stenkamp, R. E. (2007) · Journal of Structural Biology · doi:10.1016/j.jsb.2007.01.017 · Cited by 41
Li, P., Miyashita, N., Im, W., Ishido, S., & Sugita, Y. (2013) · Journal of Computational Chemistry · doi:10.1002/jcc.23494 · Cited by 40
Sphingolipid-Binding Domain in the Serotonin1A Receptor
Chattopadhyay, A., Paila, Y. D., Shrivastava, S. K., Tiwari, S., Singh, P., & Fantini, J. (2012) · Advances in experimental medicine and biology · doi:10.1007/978-1-4614-3381-1_19 · Cited by 39
Molecular simulations of glycolipids: Towards mammalian cell membrane models
Shorthouse, D., Hedger, G., Koldsø, H., & Sansom, M. S. P. (2015) · Biochimie · doi:10.1016/j.biochi.2015.09.033 · Cited by 35
Olausson, B. E. S., Grossfield, A. M., Pitman, M. C., Brown, M. F., Feller, S. E., & Vogel, A. (2012) · Journal of the American Chemical Society · doi:10.1021/ja2108382 · Cited by 35
A comprehensive model of the phototransduction cascade in mouse rod cells
Invergo, B. M., Dell’Orco, D., Montanucci, L., Koch, K., & Bertranpetit, J. (2014) · Molecular BioSystems · doi:10.1039/c3mb70584f · Cited by 35
The Free Energy Landscape of Dimerization of a Membrane Protein, NanC
Dunton, T. A., Goose, J. E., Gavaghan, D. J., Sansom, M. S. P., & Osborne, J. M. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003417 · Cited by 34
Computational methods for studying G protein-coupled receptors (GPCRs)
Kaczor, A. A., Rutkowska, E. E., Bartuzi, D., Targowska‐Duda, K. M., Matosiuk, D., & Selent, J. (2015) · Methods in cell biology · doi:10.1016/bs.mcb.2015.11.002 · Cited by 34
Huber, T., & Sakmar, T. P. (2011) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2011.03.004 · Cited by 32
Jörg, M., Scammells, P. J., & Capuano, B. (2014) · Current Medicinal Chemistry · doi:10.2174/1389200215666140217110716 · Cited by 32
Mathematical Simulation of Membrane Protein Clustering for Efficient Signal Transduction
RADHAKRISHNAN, K., Halász, Á., McCabe, M. M., Edwards, J. S., & Wilson, B. S. (2012) · Annals of Biomedical Engineering · doi:10.1007/s10439-012-0599-z · Cited by 30
Horn, J. N., Kao, T., & Grossfield, A. M. (2013) · Advances in experimental medicine and biology · doi:10.1007/978-94-007-7423-0_5 · Cited by 29
Ghosh, A., Sonavane, U., & Joshi, R. (2013) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2013.11.002 · Cited by 29
Dell’Orco, D. (2013) · FEBS Letters · doi:10.1016/j.febslet.2013.05.017 · Cited by 28
Detecting single photons: A supramolecular matter?
Cangiano, L., & Dell’Orco, D. (2012) · FEBS Letters · doi:10.1016/j.febslet.2012.11.015 · Cited by 27
Cholesterol‐β1AR interaction versus cholesterol‐β2AR interaction
Cang, X., Yang, L.-L., Yang, J., Luo, C., Zheng, M., Yu, K.-Q., Yang, H., & Jiang, H.-L. (2014) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.24456 · Cited by 26
Baltoumas, F. A., Theodoropoulou, M. C., & Hamodrakas, S. J. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9919-y · Cited by 26
Siuda, I., & Thøgersen, L. (2013) · Journal of Molecular Modeling · doi:10.1007/s00894-013-1991-9 · Cited by 25
Mondal, S., Khelashvili, G., Johner, N. B., & Weinstein, H. (2013) · Advances in experimental medicine and biology · doi:10.1007/978-94-007-7423-0_4 · Cited by 24
Differential Stability of the Crystallographic Interfaces of Mu- and Kappa-Opioid Receptors
Johnston, J. M., & Filizola, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0090694 · Cited by 24
What can simulations tell us about GPCRs
Sengupta, D., Joshi, M., Athale, C. A., & Chattopadhyay, A. (2015) · Methods in cell biology · doi:10.1016/bs.mcb.2015.11.007 · Cited by 24
G protein-coupled receptor oligomerization for what?
Ciruela, F., Vallano, A., Arnau, J. M., Sánchez, S., Borroto‐Escuela, D. O., Agnati, L. F., Fuxé, K., & Fernández‐Dueñas, V. (2010) · Journal of Receptors and Signal Transduction · doi:10.3109/10799893.2010.508166 · Cited by 23
Johnston, J. M., & Filizola, M. (2013) · Advances in experimental medicine and biology · doi:10.1007/978-94-007-7423-0_6 · Cited by 22
Rhodopsin–Lipid Interactions Studied by NMR
Soubias, O., & Gawrisch, K. (2013) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-407865-9.00012-1 · Cited by 22
G Protein-Coupled Receptors - Modeling and Simulation
Filizola, M. (2013) · Advances in experimental medicine and biology · doi:10.1007/978-94-007-7423-0 · Cited by 20
Protein and Lipid Interactions Driving Molecular Mechanisms of in meso Crystallization
Johner, N. B., Mondal, S., Morra, G., Caffrey, M., Weinstein, H., & Khelashvili, G. (2014) · Journal of the American Chemical Society · doi:10.1021/ja4129839 · Cited by 20
Sgourakis, N. G., & Garcı́a, A. E. (2010) · Journal of Molecular Biology · doi:10.1016/j.jmb.2010.02.032 · Cited by 19
Tian, H., Sakmar, T. P., & Huber, T. (2013) · Methods in cell biology · doi:10.1016/b978-0-12-408143-7.00015-3 · Cited by 15
Cutolo, P., Basdevant, N., Bernadat, G., Bachelerie, F., & Ha‐Duong, T. (2016) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2016.1145142 · Cited by 13
SAHBNET, an Accessible Surface-Based Elastic Network: An Application to Membrane Protein
Dony, N., Crowet, J., Joris, B., Brasseur, R., & Lins, L. (2013) · International Journal of Molecular Sciences · doi:10.3390/ijms140611510 · Cited by 9
Multiscale design of coarse-grained elastic network-based potentials for the μ opioid receptor
Fossepré, M., Leherte, L., Laaksonen, A., & Vercauteren, D. P. (2016) · Journal of Molecular Modeling · doi:10.1007/s00894-016-3092-z · Cited by 7
A Structurally Flexible Protein Backbone for the MARTINI Coarse Grained Force Field
Périole, X., Marrink, . J., & Tieleman, P. D. (2011) · Biophysical Journal · doi:10.1016/j.bpj.2010.12.3533 · Cited by 1
Mukhopadhyay, P., Huber, T., & Sakmar, T. P. (2011) · RSC drug discovery/RSC drug discovery series · doi:10.1039/9781849733441-00375 · Cited by 1
Research works printed but not linked: 2
- Bowers K., 2006, Proceedings of ACM/IEEE Conference on Supercomputing (SC06) Tampa
- 10.1016/0005-2736(79)90284-0 · DOI
Cited by
Sorted by citations
Computational Modeling of Realistic Cell Membranes
Marrink, . J., Corradi, V., Souza, P. C. T., Ingólfsson, H. I., Tieleman, D. P., & Sansom, M. S. P. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00460 · Cited by 715
Yao, N., Chen, X., Fu, Z., & Zhang, Q. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00904 · Cited by 578
Emerging Diversity in Lipid–Protein Interactions
Corradi, V., Sejdiu, B. I., Galloso, H. M., Abdizadeh, H., Noskov, S. Y., Marrink, . J., & Tieleman, D. P. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00451 · Cited by 470
Excessive aggregation of membrane proteins in the Martini model
Javanainen, M., Martinez‐Seara, H., & Vattulainen, I. (2017) · PLoS ONE · doi:10.1371/journal.pone.0187936 · Cited by 183
Rhodopsin, light-sensor of vision
Hofmann, K. P., & Lamb, T. D. (2022) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2022.101116 · Cited by 134
Metadata
- Authors
- Periole, X.
- Affiliations
- Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 7, 9747AG Groningen, The Netherlands
- Venue
- Chemical Reviews
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-11-29
- Volume
- 117
- Issue
- 1
- Pages
- 156-185
- ISSN
- 0009-2665 (ISSN-L)
- 1520-6890
- OpenAlex
- W2559129809
- Cited by
- 58
- References
- 238
- Citations by year
- 2026: 5 · 2025: 1 · 2024: 3 · 2023: 3 · 2022: 3 · 2021: 4 · 2020: 6 · 2019: 15 · 2018: 7 · 2017: 11
- FWCI
- 0.99
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by
- Locations
- Chemical Reviews (publishedVersion)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Lipid Membrane Structure and Behavior
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- G protein-coupled receptors, Martini coarse-grained model, protein-lipid interactions, rhodopsin, opioid receptors, adrenergic receptors, adenosine receptors, dopamine receptors, sphingosine-1-phosphate receptor, supramolecular organization
- MeSH terms
- Cell Membrane, Receptors, G-Protein-Coupled, Molecular Dynamics Simulation
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