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Updated: Jun 26, 2025

Thermodynamics of Membrane Protein Folding Measured by Fluorescence Spectroscopy
Published on: April 28, 2011
Helical reorganization in the context of membrane protein folding: Insights from simulations with bacteriorhodopsin
Hindol Chatterjee1, Anshuman J Mahapatra1, Martin Zacharias2
1Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal 741246, India.
Abstract:
Membrane protein folding is distinct from folding of soluble proteins. Conformational acquisition in major membrane protein subclasses can be delineated into insertion and folding processes. An exception to the "two stage" folding, later developed to "three stage" folding, is observed within the last two helices in bacteriorhodopsin (BR), a system that serves as a model membrane protein. We employ a reductionist approach to understand interplay of molecular factors underlying the apparent defiance. Leveraging available solution NMR structures, we construct, sample in silico, and analyze partially (PIn) and fully inserted (FIn) BR membrane states. The membrane lateral C-terminal helix (CH) in PIn is markedly prone to transient structural distortions over microsecond timescales; a disorder prone region (DPR) is thereby identified. While clear transmembrane propensities are not acquired, the distortions induce alterations in local membrane curvature and area per lipid. Importantly, energetic decompositions reveal that overall, the N-terminal helix (NH) is thermodynamically more stable in the PIn. Higher overall stability of the FIn arises from favorable interactions between the NH and the CH. Our results establish lack of spontaneous transition of the PIn to the FIn, and attributes their partitioning to barriers that exceed those accessible with thermal fluctuations. This work paves the way for further detailed studies aimed at determining the thermo-kinetic roles of the initial five helices, or complementary external factors, in complete helical folding and insertion in BR. We comment that complementing such efforts with the growing field of machine learning assisted energy landscape searches may offer unprecedented insights.
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