Multicanonical Chain Growth Algorithm

TL;DR

This paper introduces a temperature-independent Monte Carlo method that efficiently determines the density of states for lattice proteins, enabling comprehensive thermodynamic analysis across all temperatures.

Contribution

The method combines nPERM chain growth with multicanonical reweighting, providing a novel approach applicable to heteropolymers and polymer models.

Findings

Successfully identified state transitions in lattice proteins.

Allows calculation of thermodynamic quantities at all temperatures.

Applicable to a wide range of polymer models.

Abstract

We present a temperature-independent Monte Carlo method for the determination of the density of states of lattice proteins that combines the fast ground-state search strategy of the nPERM chain growth and multicanonical reweighting for sampling the complete energy space. Since the density of states contains all energetic information of a statistical system, we can directly calculate the mean energy, specific heat, Gibbs free energy, and entropy for all temperatures. We apply this method to HP lattice proteins and for the examples of sequences considered, we identify the transitions between native, globule, and random coil states. Since no special properties of heteropolymers are involved in this algorithm, the method applies to polymer models as well.