Multiscale Computation of a Polypeptide Backbone Model

TL;DR

This paper presents a multiscale Monte-Carlo approach for modeling polypeptide backbones, deriving effective coarse Hamiltonians to accurately replicate local and global structural properties.

Contribution

It introduces a fast Newtonian iterative scheme to derive coarse Hamiltonians that preserve local and global structural features in multiscale simulations.

Findings

Coarse simulations accurately reproduce local structural properties.

Global structural properties are well maintained at convergence.

The method improves efficiency in polypeptide backbone modeling.

Abstract

The multiscale Monte-Carlo algorithm outlined in Bai and Brandt[1] is applied to a simple model of the polypeptide backbone. Effective coarse level Hamiltonians are derived by a fast Newtonian iterative scheme. The coarse Hamiltonian parameters are adjusted so that local structural properties have the same value in both coarse and fine level simulations. It is demonstrated that at convergence of iterations, global structural properties are reproduced very well in coarse level simulations.