Solving the HP model with Nested Monte Carlo Search

TL;DR

This paper introduces a new Nested Monte Carlo Search algorithm for finding the ground state energy of proteins in the HP model, comparing it with other algorithms and providing an overview of existing methods.

Contribution

The paper presents a novel Nested Monte Carlo Search algorithm tailored for the HP protein model, expanding the toolkit of algorithms for this problem.

Findings

The new algorithm does not outperform state-of-the-art methods like PERM, REMC, or WLRE.

Comparison with other MCS algorithms highlights strengths and weaknesses.

Provides an overview of algorithms used in the HP model domain.

Abstract

In this paper we present a new Monte Carlo Search (MCS) algorithm for finding the ground state energy of proteins in the HP-model. We also compare it briefly to other MCS algorithms not usually used on the HP-model and provide an overview of the algorithms used on HP-model. The algorithm presented in this paper does not beat state of the art algorithms, see PERM (Hsu and Grassberger 2011), REMC (Thachuk, Shmygelska, and Hoos 2007) or WLRE (W\"ust and Landau 2012) for better results. Hsu, H.-P.; and Grassberger, P. 2011. A review of Monte Carlo simulations of polymers with PERM. Journal of Statistical Physics, 144 (3): 597 to 637. Thachuk, C.; Shmygelska, A.; and Hoos, H. H. 2007. A replica exchange Monte Carlo algorithm for protein folding in the HP model. BMC Bioinformatics, 8(1): 342. W\"ust, T.; and Landau, D. P. 2012. Optimized Wang-Landau sampling of lattice polymers: Ground state search and folding thermodynamics of HP model proteins. The Journal of Chemical Physics, 137(6): 064903.