Growth Algorithms for Lattice Heteropolymers at Low Temperatures

TL;DR

This paper introduces two improved stochastic algorithms based on PERM for lattice heteropolymer models, outperforming previous methods in efficiency and accuracy, and capable of general-purpose thermodynamic sampling.

Contribution

The paper presents two novel enhancements to PERM that outperform earlier stochastic algorithms and provide a fully general, thermodynamically consistent sampling method.

Findings

The new algorithms outperform previous PERM versions and other stochastic methods in finding low-energy states.

They are faster in most test cases and discover new lowest energy configurations.

The methods are fully general and capable of providing correct thermodynamic properties.

Abstract

Two improved versions of the pruned-enriched-Rosenbluth method (PERM) are proposed and tested on simple models of lattice heteropolymers. Both are found to outperform not only the previous version of PERM, but also all other stochastic algorithms which have been employed on this problem, except for the core directed chain growth method (CG) of Beutler & Dill. In nearly all test cases they are faster in finding low-energy states, and in many cases they found new lowest energy states missed in previous papers. The CG method is superior to our method in some cases, but less efficient in others. On the other hand, the CG method uses heavily heuristics based on presumptions about the hydrophobic core and does not give thermodynamic properties, while the present method is a fully blind general purpose algorithm giving correct Boltzmann-Gibbs weights, and can be applied in principle to any stochastic sampling problem.