Unbiased sampling of globular lattice proteins in three dimensions

Abstract

We present a Monte Carlo method that allows efficient and unbiased sampling of Hamiltonian walks on a cubic lattice. Such walks are self-avoiding and visit each lattice site exactly once. They are often used as simple models of globular proteins, upon adding suitable local interactions. Our algorithm can easily be equipped with such interactions, but we study here mainly the flexible homopolymer case where each conformation is generated with uniform probability. We argue that the algorithm is ergodic and has dynamical exponent z=0. We then use it to study polymers of size up to 64^3 = 262144 monomers. Results are presented for the effective interaction between end points, and the interaction with the boundaries of the system.

References in corpus (4)

• Origin of Scaling Behavior of Protein Packing Density: A Sequential Monte Carlo Study of Compact Long Chain Polymers
• Secondary Structures in Long Compact Polymers
• Exact enumeration of Hamiltonian circuits, walks, and chains in two and three dimensions
• Conformal field theory of the Flory model of polymer melting

Cited by in corpus (6)

• Polymer Physics by Quantum Computing
• Worm Monte Carlo study of the honeycomb-lattice loop model
• Polygons in restricted geometries subjected to infinite forces
• Semi-flexible compact polymers in two dimensional nonhomogeneous confinement
• A model of compact polymers on a family of three-dimensional fractal lattices
• First Order Phase Transition of a Long Polymer Chain