The cavity method to protein design problem

TL;DR

This paper introduces an analytic statistical mechanics approach using the cavity method to efficiently solve the protein design problem, demonstrating comparable results to Monte Carlo methods for 2D lattice models.

Contribution

It applies the cavity method, an extension of mean-field approximation, to the protein design problem, providing a new analytical tool for biological physics.

Findings

Cavity method yields results similar to Monte Carlo methods.

Approach reduces computational cost for small 2D models.

Demonstrates potential for analytical solutions in protein design.

Abstract

In this study, we propose an analytic statistical mechanics approach to solve a fundamental problem in biological physics called protein design. Protein design is an inverse problem of protein structure prediction, and its solution is the amino acid sequence that best stabilizes a given conformation. Despite recent rapid progress in protein design using deep learning, the challenge of exploring protein design principles remains. Contrary to previous computational physics studies, we used the cavity method, an extension of the mean-field approximation that becomes rigorous when the interaction network is a tree. We found that for small two-dimensional (2D) lattice hydrophobic-polar (HP) protein models, the design by the cavity method yields results almost equivalent to those from the Markov chain Monte Carlo method with lower computational cost.