Finding the minimum energy conformation of protein-like heteropolymers by Greedy Neighborhood Search

TL;DR

This paper introduces Greedy Neighborhood Search (GNS), a new global optimization method, and a spherical sampling technique to find the lowest energy conformations of protein-like heteropolymers modeled as AB chains, outperforming existing methods.

Contribution

It presents a novel GNS algorithm and a spherical sampling approach for accurately locating minimum energy states in protein-like heteropolymer models.

Findings

GNS finds lower energy conformations than previous methods.

Minimum energy structures resemble real proteins with hydrophobic cores.

Effective for sequences up to 55 monomers.

Abstract

A global optimization method called Greedy Neighborhood Search (GNS) and a novel conformational sampling method using a spherical distribution is proposed to find the minimum energy conformation of a protein-like heteropolymer model called AB model. The AB model consists of hydrophobic (A) and hydrophilic (B) monomers analogous to the real proteins. The AB model in three-dimensional space is represented by simple bead-rod chain system which is identical to the one-bead protein model. The minimum energy conformations of four different sequences consisting of 13, 21, 34, and 55 monomers are obtained by the GNS method. The minimum energies found are lower than those obtained by other methods. Also the minimum energy conformations found have a similarity with the real proteins forming a single hydrophobic core.