A Hybrid Local Search for Simplified Protein Structure Prediction

TL;DR

This paper introduces a hybrid local search algorithm for simplified protein structure prediction that effectively escapes local minima to find more optimal conformations, outperforming existing methods on benchmark proteins.

Contribution

The paper presents a novel hybrid local search approach combining exhaustive search and heuristics to improve protein structure prediction accuracy.

Findings

Achieved significantly better results than state-of-the-art methods on benchmark proteins.

Effectively escapes local minima to find more compact hydrophobic cores.

Demonstrates the effectiveness of combining exhaustive search with heuristics.

Abstract

Protein structure prediction based on Hydrophobic-Polar energy model essentially becomes searching for a conformation having a compact hydrophobic core at the center. The hydrophobic core minimizes the interaction energy between the amino acids of the given protein. Local search algorithms can quickly find very good conformations by moving repeatedly from the current solution to its "best" neighbor. However, once such a compact hydrophobic core is found, the search stagnates and spends enormous effort in quest of an alternative core. In this paper, we attempt to restructure segments of a conformation with such compact core. We select one large segment or a number of small segments and apply exhaustive local search. We also apply a mix of heuristics so that one heuristic can help escape local minima of another. We evaluated our algorithm by using Face Centered Cubic (FCC) Lattice on a set of standard benchmark proteins and obtain significantly better results than that of the state-of-the-art methods.