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A computational approach to curvature sensing in lipid bilayers
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.ORCID iD: 0000-0003-4114-8768
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Local curvature is a key driving force for spatial organization of cellular membranes, via a phenomenon known as membrane curvature sensing, where the binding energy of membrane associated macromolecules depends on the local membrane shape. However, the microscopic mechanisms of curvature sensing are not well understood. Molecular dynamics simulations offer a powerful complement to biochemical experiments, yet their contribution to the study of curvature sensing has been limited, due in part to the lack of efficient methods, not least because of methodological difficulties in dealing with curved membranes. We develop a method based on simulated buckling, which has been previously employed to study mechanical properties of membranes. Here, we describe, validate and evaluate this method. We then apply to study curvature sensing properties of three model systems, using coarse-grained simulations. On the first system, we study lipid sorting in a three-component lipid mixture with emphasis on cardiolipin. We find that if curvature is high, curvature sensing is strong enough to drive cardiolipin molecules to negative curvature regions, outcompeting other lipids, without the need of external interactions or cooperative effects. We then simulated three systems consisting of a short amphipathic peptide attached to the surface of a buckled membrane. All three peptides localize to positive curvature, in agreement with the so-called cylindrical hydrophobic insertion mechanism. Their orientational preferences, however, defy the prediction of alignment perpendicular to the direction of maximum curvature. They also fail to show expected symmetries, indicating there is more to the picture than purely shape-based effects. The curvature sensing probe of the next system is a transmembrane trimeric protein, which shows preference to intermediate curvature, in agreement with theoretical predictions. But the lack of an expected 2-fold rotation symmetry indicates that the trimer senses the local curvature gradient, and not just the point-wise local curvature. Finally, dispensing with the buckling methodology, we simulated a series of symmetric transmembrane multimers embedded in cylindrical bilayers. Based on the results of these simulations and theoretical arguments, we discuss the relationship between structural symmetry and curvature sensitivity. We conclude that anisotropic (i.e. orientation-dependent) curvature sensing is strongly limited by odd and high order rotational symmetries. However, measurements of in-plane orientation on peptides and asymmetric proteins, as well as dimers and tetramers, should yield valuable information. Our method, along with our initial conclusions, provides an useful tool for the understanding of the relationship between membrane shape and membrane protein function, and should prove useful to biophysicists in the design and interpretation of experimental curvature sensing assays.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University , 2018. , p. 60
Keywords [en]
curvature sensing, membrane curvature, cardiolipin, amphipathic helix, symmetric multimers, lipid bilayer, molecular dynamics
National Category
Biophysics
Research subject
Biophysics
Identifiers
URN: urn:nbn:se:su:diva-157417ISBN: 978-91-7797-332-4 (print)ISBN: 978-91-7797-333-1 (electronic)OAI: oai:DiVA.org:su-157417DiVA, id: diva2:1220118
Public defence
2018-09-07, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 14:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Submitted. Paper 4: Manuscript.

Available from: 2018-08-15 Created: 2018-06-18 Last updated: 2022-02-26Bibliographically approved
List of papers
1. Computing Curvature Sensitivity of Biomolecules in Membranes by Simulated Buckling
Open this publication in new window or tab >>Computing Curvature Sensitivity of Biomolecules in Membranes by Simulated Buckling
2018 (English)In: Journal of Chemical Theory and Computation, ISSN 1549-9618, E-ISSN 1549-9626, Vol. 14, no 3, p. 1643-1655Article in journal (Refereed) 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.

National Category
Biophysics
Research subject
Biophysics
Identifiers
urn:nbn:se:su:diva-154791 (URN)10.1021/acs.jctc.7b00878 (DOI)000427661400043 ()29350922 (PubMedID)
Available from: 2018-04-17 Created: 2018-04-17 Last updated: 2022-02-26Bibliographically approved
2. Curvature sensing by cardiolipin in simulated buckled membranes
Open this publication in new window or tab >>Curvature sensing by cardiolipin in simulated buckled membranes
(English)In: Article in journal (Refereed) Submitted
National Category
Biophysics
Research subject
Biophysics
Identifiers
urn:nbn:se:su:diva-157413 (URN)
Funder
Swedish Research CouncilSwedish National Infrastructure for Computing (SNIC)EU, Horizon 2020Swedish Foundation for Strategic Research
Available from: 2018-06-18 Created: 2018-06-18 Last updated: 2022-02-26Bibliographically approved
3. Anisotropic Membrane Curvature Sensing by Amphipathic Peptides
Open this publication in new window or tab >>Anisotropic Membrane Curvature Sensing by Amphipathic Peptides
2016 (English)In: Biophysical Journal, ISSN 0006-3495, E-ISSN 1542-0086, Vol. 110, no 1, p. 197-204Article in journal (Refereed) Published
Abstract [en]

Many proteins and peptides have an intrinsic capacity to sense and induce membrane curvature, and play crucial roles for organizing and remodeling cell membranes. However, the molecular driving forces behind these processes are not well understood. Here, we describe an approach to study curvature sensing by simulating the interactions of single molecules with a buckled lipid bilayer. We analyze three amphipathic antimicrobial peptides, a class of membrane-associated molecules that specifically target and destabilize bacterial membranes, and find qualitatively different sensing characteristics that would be difficult to resolve with other methods. Our findings provide evidence for direction-dependent curvature sensing mechanisms in amphipathic peptides and challenge existing theories of hydrophobic insertion. The buckling approach is generally applicable to a wide range of curvature-sensing molecules, and our results provide strong motivation to develop new experimental methods to track position and orientation of membrane proteins.

National Category
Biophysics
Research subject
Biophysics
Identifiers
urn:nbn:se:su:diva-126377 (URN)10.1016/j.bpj.2015.11.3512 (DOI)000367783900012 ()26745422 (PubMedID)
Available from: 2016-02-12 Created: 2016-02-01 Last updated: 2022-02-23Bibliographically approved
4. Curvature sensing by multimeric proteins
Open this publication in new window or tab >>Curvature sensing by multimeric proteins
(English)Manuscript (preprint) (Other academic)
National Category
Biophysics
Research subject
Biophysics
Identifiers
urn:nbn:se:su:diva-157407 (URN)
Available from: 2018-06-18 Created: 2018-06-18 Last updated: 2022-02-26Bibliographically approved

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Elías-Wolff, Federico

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