The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Emerging Diversity in Lipid–Protein Interactions
Open accessAbstract
Membrane lipids interact with proteins in a variety of ways, ranging from providing a stable membrane environment for proteins to being embedded in to detailed roles in complicated and well-regulated protein functions. Experimental and computational advances are converging in a rapidly expanding research area of lipid-protein interactions. Experimentally, the database of high-resolution membrane protein structures is growing, as are capabilities to identify the complex lipid composition of different membranes, to probe the challenging time and length scales of lipid-protein interactions, and to link lipid-protein interactions to protein function in a variety of proteins. Computationally, more accurate membrane models and more powerful computers now enable a detailed look at lipid-protein interactions and increasing overlap with experimental observations for validation and joint interpretation of simulation and experiment. Here we review papers that use computational approaches to study detailed lipid-protein interactions, together with brief experimental and physiological contexts, aiming at comprehensive coverage of simulation papers in the last five years. Overall, a complex picture of lipid-protein interactions emerges, through a range of mechanisms including modulation of the physical properties of the lipid environment, detailed chemical interactions between lipids and proteins, and key functional roles of very specific lipids binding to well-defined binding sites on proteins. Computationally, despite important limitations, molecular dynamics simulations with current computer power and theoretical models are now in an excellent position to answer detailed questions about lipid-protein interactions.
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Citations
Cites
Scalable molecular dynamics with NAMD
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The Amber biomolecular simulation programs
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Cellular and Molecular Mechanisms of Pain
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Molecular Basis of Bacterial Outer Membrane Permeability Revisited
Nikaido, H. (2003) · Microbiology and Molecular Biology Reviews · doi:10.1128/mmbr.67.4.593-656.2003 · Cited by 4,316
Crystal structure of the β2 adrenergic receptor–Gs protein complex
Rasmussen, S. G. F., DeVree, B. T., Zou, Y., Kruse, A. C., Chung, K. Y., Kobilka, T. S., Thian, F. S., Chae, P. S., Pardon, E., Calinski, D., et al. (2011) · Nature · doi:10.1038/nature10361 · Cited by 3,009
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Molecular Physiology of P2X Receptors
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‘Improved parameters for the Martini coarse-grained protein force field’
de Jong, D. H., Singh, G., Bennett, W. F. D., Arnarez, C., Wassenaar, T. A., Schäfer, L. V., Periole, X., Tieleman, D. P., & Marrink, S. J. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct300646g · Cited by 1,486
Self-citationThe Retinal Conformation and its Environment in Rhodopsin in Light of a New 2.2 Å Crystal Structure
Okada, T., Sugihara, M., Bondar, A.-N., Elstner, M., Entel, P., & Buss, V. (2004) · Journal of Molecular Biology · doi:10.1016/j.jmb.2004.07.044 · Cited by 1,049
Mechanistic diversity in ATP-binding cassette (ABC) transporters.
Locher, K. P. (2016) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.3216 · Cited by 804
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Prebiotic Peptides: Molecular Hubs in the Origin of Life
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Two decades of Martini: Better beads, broader scope
Marrink, S. J., Monticelli, L., Melo, M. N., Alessandri, R., Tieleman, D. P., & Souza, P. C. T. (2023) · WIREs Computational Molecular Science · doi:10.1002/wcms.1620 · Cited by 255
Self-citationMartini 3 Coarse-Grained Force Field for Carbohydrates
Grünewald, F., Punt, M. H., Jefferys, E. E., Vainikka, P. A., König, M., Virtanen, V., Meyer, T. A., Pezeshkian, W., Gormley, A. J., Karonen, M., et al. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00757 · Cited by 116
Structural and mechanistic basis of the EMC-dependent biogenesis of distinct transmembrane clients
Miller-Vedam, L. E., Bräuning, B., Popova, K. D., Schirle Oakdale, N. T., Bonnar, J. L., Prabu, J. R., Boydston, E. A., Sevillano, N., Shurtleff, M. J., Stroud, R. M., et al. (2020) · eLife · doi:10.7554/elife.62611 · Cited by 87
Guellec, J., Elbahnsi, A., Tertre, M. L., Uguen, K., Gourlaouen, I., Férec, C., Ka, C., Callebaut, I., & Gac, G. L. (2019) · The FASEB Journal · doi:10.1096/fj.201901857r · Cited by 21
A novel model for proton transport mediated by uncoupling protein 1
Jacobsen, L., Menon, S., Gaudry, M. J., Hakami Zanjani, A. A., Reinholdt, P., Jastroch, M., & Khandelia, H. (2025) · Protein Science · doi:10.1002/pro.70357 · Cited by 3
mPPases create a conserved anionic membrane fingerprint as identified via multi-scale simulations
Holmes, A. O. M., Goldman, A., & Kalli, A. C. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010578 · Cited by 3
Metadata
- Authors
- Corradi, V., Sejdiu, B. I., Mesa-Galloso, H., Abdizadeh, H., Noskov, S. Y., Marrink, S. J., & Tieleman, D. P.
- Venue
- Chemical Reviews
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-02-13
- Volume
- 119
- Issue
- 9
- Pages
- 5775-5848
- ISSN
- 0009-2665 (ISSN-L)
- 1520-6890
- OpenAlex
- W2912735727
- Cited by
- 459
- References
- 27
- FWCI
- 3.60
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
