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      https://www.ias.ac.in/article/fulltext/pram/094/0047

    • Keywords

       

      Biological membranes; curvature sensing; curvature generation

    • Abstract

       

      The curvature-sensitive localisation of proteins on membranes is vital for many cell biological processes. Coarse-grained models are routinely employed to study the curvature-sensing phenomena and membrane morphology at the length scale of a few micrometres. Two prevalent phenomenological models exist for modelling the experimental observations of curvature sensing: (1) the spontaneous curvature (SC) model and (2) the curvature mismatch (CM) model, which differ in their treatment of the change in elastic energy due to the binding of proteins on the membrane. In this work, the prediction of sensing and generation behaviour by these two models are investigated using analytical calculations as well as dynamic triangulation Monte Carlo simulations of quasispherical vesicles. While the SC model yields a monotonically decreasing sensing curve as a function of the vesicle radius, the CM model results in a non-monotonic sensing curve. We highlight the main differences in the interpretation of the protein-related parameters in the two models. We further propose that the SC model is appropriate for modelling peripheral proteins employing the hydrophobic insertion mechanism, with minimal modification of membrane rigidity, while the CM model is appropriate for modelling curvature generation using scaffolding mechanism where there is significant stiffening of the membrane due to protein binding.

    • Author Affiliations

       

      T V SACHIN KRISHNAN1 SOVAN L DAS2 P B SUNIL KUMAR1 3

      1. Department of Physics, Indian Institute of Technology Madras, Chennai 600 036, India
      2. Department of Mechanical Engineering, Indian Institute of Technology Palakkad, Palakkad 678 557, India
      3. Department of Physics, Indian Institute of Technology Palakkad, Palakkad 678 557, India
    • Dates

       
  • Pramana – Journal of Physics | News

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