Protein folding simulations in the hydrophobic-polar model using a hybrid cuckoo search algorithm

TL;DR

This paper introduces a novel hybrid algorithm combining cuckoo search and hill climbing to efficiently predict protein native structures in the hydrophobic-polar model, demonstrating improved results on benchmark sequences.

Contribution

The paper presents a new hybrid cuckoo search algorithm that enhances protein folding prediction accuracy in the 3D-HP model by integrating global and local search techniques.

Findings

Outperforms existing algorithms on benchmark sequences

Effectively finds low-energy conformations

Demonstrates robustness across different protein sizes

Abstract

A protein is a linear chain containing a set of amino acids, which folds on itself to create a specific native structure, also called the minimum energy conformation. It is the native structure that determines the functionality of each protein. The protein folding problem (PFP) remains one of the more difficult problems in computational and chemical biology. The principal challenge of PFP is to predict the optimal conformation of a given protein by considering only its amino acid sequence. As the conformational space contains a very large number of conformations, even when addressing short sequences, different simplified models have been developed and applied to make the PFP less complex. In the last few years, many computational approaches have been proposed to solve the PFP. They are based on simplified lattice models such as the hydrophobic-polar model. In this paper, we present a new Hybrid Cuckoo Search Algorithm (HCSA) to solve the 3D-HP protein folding optimization problem. Our proposed algorithm consists of combining the Cuckoo Search Algorithm (CSA) with the Hill Climbing (HC) algorithm. Simulation results on different benchmark sequences are presented and compared to the state-of-the-art algorithms.