Metaheuristics for protein structure prediction: A comprehensive review and empirical analysis - Sunday O. Oladejo (Scientific African, 2025)
Protein Structure Prediction (PSP), which involves the prediction of a protein’s three-dimensional structure from its amino acid sequence, is a fundamental challenge in computational biology. Accurate prediction is crucial for understanding protein function, drug design, and various biological processes. However, the PSP problem is computationally intensive due to its vast conformational space and the complexity of protein folding dynamics. This work provides a comprehensive review and empirical analysis of metaheuristics in the context of the PSP problem. It explores the application of metaheuristic algorithms to address the PSP problem, focusing on 15 metaheuristics, such as the Genetic Algorithm, Particle Swarm Optimization, Differential Evolution, and Teaching-Learning Based Optimization. Metaheuristics are well-suited for PSP as they provide powerful strategies for navigating large and complex search spaces, enabling the discovery of near-optimal protein conformations within a reasonable computational time. We demonstrate the effectiveness of our approach through extensive Monte Carlo simulations on benchmark protein sequences such as 1CRN, 1CB3, 1BXL, 2ZNF, 1DSQ, and 1TZ4, showing the performance of metaheuristic-based approaches in terms of accuracy and computational efficiency. Furthermore, the Friedman test and Dunn’s post hoc analysis are carried out to determine the statistical significance and ranking of the results obtained.