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Updated: Sep 23, 2025
09:49
Sedimentation Equilibrium of a Small Oligomer-forming Membrane Protein: Effect of Histidine Protonation on Pentameric Stability
Published on: April 2, 2015
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On Computing Equilibrium Binding Constants for Protein-Protein Association in Membranes.
Ayan Majumder1, Seulki Kwon1, John E Straub1
1Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States.
Journal of Chemical Theory and Computation
|May 17, 2022
Summary
Accurately simulating protein association in lipid membranes requires advanced methods. Two-dimensional collective variables improve sampling of protein conformations, overcoming limitations of one-dimensional approaches for accurate binding equilibrium calculations.
Area of Science:
- Biophysics
- Computational Biology
- Membrane Protein Dynamics
Background:
- Protein-protein interactions in lipid membranes are crucial for cellular functions and disease.
- Accurate computation of association constants and dimerization free energy is essential but challenging.
- Slow protein diffusion in membranes hinders conformational sampling for equilibrium studies.
Purpose of the Study:
- To evaluate the effectiveness of one-dimensional versus two-dimensional collective variables for sampling protein homodimerization.
- To improve the accuracy of free energy landscapes for membrane protein association.
- To address challenges in characterizing binding equilibria involving multiple protein poses.
Main Methods:
- Utilized umbrella sampling simulations with the MARTINI v2.2 force field.
- Compared conformational sampling using one-dimensional collective variables with two-dimensional collective variable space.
- Focused on the homodimerization equilibrium of the transmembrane (TM) region of glycophorin A.
Main Results:
- One-dimensional collective variables resulted in restricted sampling and a biased free energy landscape.
- Two-dimensional collective variable simulations effectively captured native and non-native interactions.
- Demonstrated improved characterization of association equilibrium and binding poses.
Conclusions:
- Two-dimensional collective variables are superior for accurately modeling membrane protein homodimerization.
- Overcoming sampling limitations is key to understanding protein association in lipid bilayers.
- Accurate binding equilibrium characterization requires robust sampling of diverse bound states.
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