A theoretical and computational study of the interaction between biomembranes and curved proteins by Caterina Tozzi

Caterina Tozzi defended her PhD thesis A theoretical and computational study of the interaction between biomembranes and curved proteins (pdf), supervised by Professor Marino Arroyo Balaguer, on April 30, 2021, within the UPC doctoral program in Applied Mathematics. Currently, she is a Postdoctoral Researcher at the Oncology department of the Vall d’Hebron Hospital of Barcelona where she is working on a project that aims to integrate advanced imaging analysis with 3D molecular characterization under the supervision of Raquel Perez-Lopez.

Thesis summary

Organelles are the smallest functional parts of eukaryotic cells. Among them, some are membrane-bound such as the nucleus, the endoplasmic reticulum, or the Golgi apparatus, each of them with essential biological functions. In order to accomplish cell functions, membranes enclosing these organelles continuously adapt their shapes through the out-of-equilibrium interaction with macro-molecules, notably proteins. During the life of cells, proteins are main actors in membrane bending dynamics since they have the ability to impinge their curvature onto the membrane, and generate transiently highly curved structures, such as tubes and spherical buds. How proteins can remodel the different organelles has been broadly studied in equilibrium, but a clear understanding of the complex chemo-mechanical problem that drives membrane reshaping out-of-equilibrium is still lacking.

In the first Part of the thesis we develop a general theoretical and computational framework for the dynamics of curved proteins adhered to lipid membranes. The theory is based on a nonlinear Onsager’s principle, a variational method for irreversible thermodynamics. The resulting governing equations and numerical simulations provide a foundation to understand the dynamics of curvature sensing, curvature generation, and more generally membrane curvature mechanochemistry, as illustrated by a selection of test cases. We show that continuum modeling is a powerful instrument to describe the protein-membrane interaction [1]. However, this model does not account for the orientational order of proteins and its derivation lacks a microscopic basis.

To address these limitations, in the second Part of the thesis we develop a mean-field density functional theory to predict the orientational order and evaluate the free-energy of ensembles of elongated and curved objects, such as BAR proteins, on curved membranes. This kind of protein may adopt different states of orientational order, from isotropic to nematic. The theory is tightly coupled to the microscopic properties of the proteins and explains how a density-dependent isotropic-to-nematic transition is modified by the anisotropic underlying curvature of the membrane [2]. This work lays the ground to understand the interplay between the molecular organization of proteins and the membrane shape dynamics. We explore the coexistence of isotropic and nematic phases on differently curved lipid membranes. We explain, both experimentally and through modelling, how a BAR protein binds on differently curved membrane templates and reshapes them based while modifying their microscopic organization. Our results broaden our understanding of the reshaping dynamics by BAR proteins on mechanically constrained membranes, and provide a framework to understand biological responses involving BAR proteins to membrane-mediated mechanical stimuli [3].

Highlighted publication: [3]

References
[1] Tozzi, Caterina, Nikhil Walani, and Marino Arroyo. «Out-of-equilibrium mechanochemistry and self-organization of fluid membranes interacting with curved proteins.» New journal of physics 21.9 (2019): 093004.

[2] Tozzi, Caterina, et al. «A theory of ordering of elongated and curved proteins on membranes driven by density and curvature.» Soft matter 17.12 (2021): 3367-3379.

[3] Le Roux, Anabel-Lise, Tozzi, Caterina et al. «Dynamic mechanochemical feedback between curved membranes and BAR protein self-organization.» Nature Communications 12.1 (2021): 1-12.

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