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Updated: May 26, 2026

Thermodynamics of Membrane Protein Folding Measured by Fluorescence Spectroscopy
Published on: April 28, 2011
Effect of local thermal fluctuations on folding kinetics: a study from the perspective of nonextensive statistical
J P Dal Molin1, M A A da Silva, A Caliri
1Departamento de Física e Química, FCFRP, Universidade de São Paulo, 14040-903 Ribeirão Preto, SP, Brazil. jpdm@fcfrp.usp.br
Abstract:
The search through the proteins conformational space is thought as an early independent stage of the folding process, governed mainly by the hydrophobic effect. Because of the nanoscopic size of proteins, we assume that the effects of local thermal fluctuations work like folding assistants, managed by the nonextensive parameter q. Using a 27-mer heteropolymer on a cubic lattice, we obtained--by Monte Carlo simulations--kinetic and thermodynamic amounts (such as the characteristic folding time and the native stability) as a function of temperature T and q for a few distinct native targets. We found that for each native structure, at a specific system temperature T, there exists an optimum q* that minimizes the folding characteristic time τ(min); for T=1, it is found that q* lies in the interval 1.15±0.05, even for native structures presenting significantly different topological complexities. The distribution of τ(min) obtained for specific q>1 (nonextensive approach) and temperature T can be fully reproduced for q=1 (Boltzmann approach), but only at higher temperatures T'>T. However, assuming that the complete set of proteins of each organism is optimized to work in a narrow range of temperature, we conclude that--for the present problem--the two approaches, namely, (T,q>1) and (T>T',q=1), cannot be equivalent; it is not a simple matter of reparametrization. Finally, by associating the nonextensive parameter q with the instantaneous degree of compactness of the globule, q becomes a dynamic variable, self-adjusted along the simulation. The results obtained through the q-variable approach are utterly consistent with those obtained by using a target-tuned parameter q*. However, in the former approach, q is automatically adjusted by the chain conformational evolution, eliminating the need to seek for a specific optimized value of q for each case. Besides, using the q-variable approach, different target structures are promptly characterized by inherent distributions of q, which reflect the overall complexity of their corresponding native topologies and energy landscapes.
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