Prediction of Peptide Conformation by Multicanonical Algorithm: A New Approach to the Multiple-Minima Problem

TL;DR

This paper demonstrates that the multicanonical algorithm effectively predicts peptide structures by overcoming the multiple-minima problem, providing accurate conformations and thermodynamic data from a single simulation run.

Contribution

The study introduces the multicanonical algorithm as a new approach for peptide structure prediction, maintaining exact ensemble control and enabling efficient thermodynamic calculations.

Findings

Successfully predicted the lowest-energy conformation of Met-enkephalin.

Overcame the ergodicity and multiple-minima problems in peptide folding.

Enabled calculation of thermodynamic quantities from a single simulation.

Abstract

We apply a recently developed method, multicanonical algorithm, to the problem of tertiary structure prediction of peptides and proteins. As a simple example to test the effectiveness of the algorithm, Met-enkephalin is studied and the ergodicity problem, or multiple-minima problem, is shown to be overcome by this algorithm. The lowest-energy conformation obtained agrees with that determined by other efficient methods such as Monte Carlo simulated annealing. The superiority of the present method to simulated annealing lies in the fact that the relationship to the canonical ensemble remains exactly controlled. Once the multicanonical parameters are determined, only one simulation run is necessary to obtain the lowest-energy conformation and furthermore the results of this one run can be used to calculate various thermodynamic quantities at any temperature. The latter point is demonstrated by the calculation of the average potential energy and specific heat as functions of temperature.