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Predicting protein curvature sorting across membrane compositions.

Yiben Fu1, David H Johnson2, Andrew H Beaven3

  • 1School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, P.R. China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, P.R. China; Guangdong Provincial Key Laboratory of Biomedical Engineering, South China University of Technology, Guangzhou, P.R. China; Key Laboratory of Biomedical Materials and Engineering of the Ministry of Education, South China University of Technology, Guangzhou, P.R. China.

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Summary
This summary is machine-generated.

This study introduces a new membrane model to understand how lipid composition affects protein curvature sorting. The model accurately predicts how membrane properties influence protein recruitment to curved surfaces, aiding in understanding protein targeting.

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Area of Science:

  • Biophysics
  • Computational Biology
  • Cell Biology

Background:

  • Cytoplasmic proteins require membrane recruitment for essential cellular functions like endocytosis and cell division.
  • Protein recruitment is often influenced by membrane lipid composition and surface curvature, a phenomenon known as curvature sorting.
  • Previous models struggled to systematically characterize lipid composition effects on curvature preferences due to simultaneous alterations in membrane properties.

Purpose of the Study:

  • To develop and apply a bilayer continuum membrane model for systematically analyzing how lipid composition impacts protein curvature sorting.
  • To quantify the influence of controlled changes in membrane material properties (thickness, spontaneous curvature, leaflet symmetry) on protein binding preferences.
  • To provide a predictive tool for understanding protein-membrane interactions across various membrane curvatures.

Main Methods:

  • Development of a bilayer continuum membrane model using continuous triangular meshes to represent membrane monolayers.
  • Introduction of a coupling energy term to account for membrane incompressibility and lipid tilt energetics.
  • Validation of the model against in vitro experiments and all-atom molecular dynamics (MD) simulations.

Main Results:

  • The model accurately predicts stronger curvature sorting in membranes with distinct lipid tail groups (e.g., POPC vs. DLPC), attributing weaker sorting in DLPC to its reduced thickness and wedge shape.
  • Membrane thickness and lipid shape were identified as key factors influencing curvature sorting, more so than spontaneous curvature or leaflet asymmetry.
  • The model demonstrated strong agreement with experimental and simulation data regarding energetic and structural changes upon protein insertion.

Conclusions:

  • The developed multi-scale membrane model effectively predicts how lipid composition and material properties govern protein curvature sorting.
  • This computational approach enables efficient and quantitative analysis of protein-membrane interactions, crucial for understanding spatiotemporal protein targeting.
  • Findings highlight the importance of membrane thickness and lipid shape in directing protein recruitment to specific membrane regions.