Mean Field Approach for a Statistical Mechanical Model of Proteins

TL;DR

This paper evaluates a topology-based protein folding model using three mean-field approaches, demonstrating their effectiveness in approximating thermodynamical properties and aligning with experimental data.

Contribution

It introduces and tests three mean-field methods for a protein folding model, providing reliable alternatives to Monte Carlo simulations.

Findings

Mean-field approaches recover key exact results.

Model aligns well with experimental data when parameters are chosen carefully.

Mean-field methods are effective and computationally simpler.

Abstract

We study the thermodynamical properties of a topology-based model proposed by Galzitskaya and Finkelstein for the description of protein folding. We devise and test three different mean-field approaches for the model, that simplify the treatment without spoiling the description. The validity of the model and its mean-field approximations is checked by applying them to the $\beta$-hairpin fragment of the immunoglobulin-binding protein (GB1) and making a comparison with available experimental data and simulation results. Our results indicate that this model is a rather simple and reasonably good tool for interpreting folding experimental data, provided the parameters of the model are carefully chosen. The mean-field approaches substantially recover all the relevant exact results and represent reliable alternatives to the Monte Carlo simulations.