A Conformational Search Method for Protein Systems Using Genetic Crossover and Metropolis Criterion

TL;DR

This paper introduces a novel conformational search method for proteins combining genetic crossover and Metropolis criterion, effectively identifying stable protein states and aligning well with experimental data.

Contribution

The study presents a new protein conformational search technique that integrates genetic algorithms with the Metropolis criterion, improving sampling of stable states.

Findings

Conformations obtained match experimental results.

Method effectively explores energy landscape with multiple minima.

Applicable to alpha-helical proteins.

Abstract

Many proteins carry out their biological functions by forming the characteristic tertiary structures. Therefore, the search of the stable states of proteins by molecular simulations is important to understand their functions and stabilities. However, getting the stable state by conformational search is difficult, because the energy landscape of the system is characterized by many local minima separated by high energy barriers. In order to overcome this difficulty, various sampling and optimization methods for conformations of proteins have been proposed. In this study, we propose a new conformational search method for proteins by using genetic crossover and Metropolis criterion. We applied this method to an $\alpha$-helical protein. The conformations obtained from the simulations are in good agreement with the experimental results.