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Updated: Jul 3, 2025

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

Modeling membrane geometries implicitly in Rosetta.

Hope Woods^1,2, Julia Koehler Leman³, Jens Meiler^1,4,5

¹Center of Structural Biology, Vanderbilt University, Nashville, Tennessee, USA.

Protein Science : a Publication of the Protein Society

|February 15, 2024

Summary

This summary is machine-generated.

Rosetta can now model membrane proteins in curved or complex membrane shapes, improving structural accuracy. This enhances protein modeling for biological research and drug discovery.

Keywords:

Rosetta implicit membrane membrane proteins protein structure

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Published on: January 16, 2016

Area of Science:

Computational Biology
Structural Biology
Biophysics

Background:

Membrane protein (MP) interactions with lipid bilayers are crucial for cellular functions.
The Rosetta modeling suite traditionally uses a flat
slab
model for implicit membrane energy calculations.
Natural membranes exhibit curvature, and experimental studies often use diverse model systems (micelles, bicelles, nanodiscs, liposomes).

Purpose of the Study:

To modify Rosetta's membrane energy potentials to accommodate various membrane geometries.
To enable more accurate modeling of membrane proteins in curved and complex lipid environments.
To improve the quality and discrimination of membrane protein models.

Main Methods:

Adapted Rosetta's implicit membrane energy function to support non-planar membrane geometries.
Integrated modified potentials into RosettaMP framework for core applications.
Tested modifications in structure refinement, protein-protein docking, and protein design.

Main Results:

Refining MP structures in curved implicit membranes yielded higher quality models closer to experimental data.
Modeling MPs within geometries mimicking experimental systems (e.g., micelles, liposomes) improved model accuracy.
Representing multiple membranes in simulations resulted in more favorable energy scores.

Conclusions:

Modified Rosetta potentials allow modeling of MPs in diverse membrane geometries, including curved and complex systems.
This advancement improves the accuracy of computational models for membrane proteins.
The approach enhances structure refinement, docking, and design applications, better reflecting biological and experimental realities.