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Computing Curvature Sensitivity of Biomolecules in Membranes by Simulated Buckling
Stockholm Univ, Dept Biochem & Biophys, Stockholm, Sweden; Stockholm Univ, Dept Mat & Environm Chem, Stockholm, Sweden.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Molekylär systembiologi.ORCID-id: 0000-0003-4200-0191
Stockholm Univ, Dept Mat & Environm Chem, Stockholm, Sweden.
Stockholm Univ, Dept Mat & Environm Chem, Stockholm, Sweden.ORCID-id: 0000-0002-5496-4695
2018 (engelsk)Inngår i: Journal of Chemical Theory and Computation, ISSN 1549-9618, E-ISSN 1549-9626, Vol. 14, nr 3, s. 1643-1655Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Membrane curvature sensing, where the binding free energies of membrane-associated molecules depend on the local membrane curvature, is a key factor to modulate and maintain the shape and organization of cell membranes. However, the microscopic mechanisms are not well understood, partly due to absence of efficient simulation methods. Here, we describe a method to compute the curvature dependence of the binding free energy of a membrane associated probe molecule that interacts with a buckled membrane, which has been created by lateral compression of a flat bilayer patch. This buckling approach samples a wide range of curvatures in a single simulation, and anisotropic effects can be extracted from the orientation statistics. We develop an efficient and robust algorithm to extract the motion of the probe along the buckled membrane surface, and evaluate its numerical properties by extensive sampling of three coarse-grained model systems: local lipid density in a curved environment for single-component bilayers, curvature preferences of individual lipids in two-component membranes, and curvature sensing by a homotrimeric transmembrane protein. The method can be used to complement experimental data from curvature partition assays and provides additional insight into mesoscopic theories and molecular mechanisms for curvature sensing.

sted, utgiver, år, opplag, sider
2018. Vol. 14, nr 3, s. 1643-1655
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Identifikatorer
URN: urn:nbn:se:uu:diva-351091DOI: 10.1021/acs.jctc.7b00878ISI: 000427661400043PubMedID: 29350922OAI: oai:DiVA.org:uu-351091DiVA, id: diva2:1208722
Forskningsfinansiär
Swedish Research CouncilEU, Horizon 2020Tilgjengelig fra: 2018-05-18 Laget: 2018-05-18 Sist oppdatert: 2018-05-18bibliografisk kontrollert

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