Long Range Moves for High Density Polymer Simulations

TL;DR

This paper introduces long range Monte Carlo moves that significantly improve the efficiency of high-density polymer simulations, enabling faster ground state searches and better folding predictions for complex protein models.

Contribution

It presents a novel set of long range moves for lattice polymer simulations that outperform previous methods in efficiency and effectiveness.

Findings

Long range moves speed up ground state search by about tenfold.

The method outperforms previous fast folding algorithms.

Successful folding of an 80-mer four-helix bundle demonstrated.

Abstract

Monte Carlo simulations of proteins are hindered by the system's high density which often makes local moves ineffective. Here we devise and test a set of long range moves that work well even when all sites of a lattice simulation are filled. We demonstrate that for a 27-mer cube, the ground state of random heteropolymers can quickly be reached. We discuss results for 48-mer systems where the ground state is known exactly. For ten sequences that were examined, the introduction of long range moves speeds up the search for the ground state by about one order of magnitude. The method is compared to a fast folding chain growth algorithm that had previously been used with much success. The new algorithm here appears to be more efficient. The point is illustrated by the folding of an 80-mer four-helix bundle considered previously.