A Configurational Bias Monte Carlo Method for Linear and Cyclic Peptides

Abstract

In this manuscript, we describe a new configurational bias Monte Carlo technique for the simulation of peptides. We focus on the biologically relevant cases of linear and cyclic peptides. Our approach leads to an efficient, Boltzmann-weighted sampling of the torsional degrees of freedom in these biological molecules, a feat not possible with previous Monte Carlo and molecular dynamics methods.

19 pages, LaTeX. Figures 3-9 available in PostScript

Cited by in corpus (2)

• Monte Carlo Update for Chain Molecules: Biased Gaussian Steps in Torsional Space
• Analytical Rebridging Monte Carlo: Application to cis/trans Isomerization in Proline-Containing, Cyclic Peptides