Abstract

Macromolecular structure prediction has become one of the most challenging and computationally demanding problems in bioinformatics structural biology and molecular engineering. Understanding the three-dimensional configuration of proteins nucleic acids and other complex biological macromolecules is essential for advancements in drug design biomaterial development disease analysis and synthetic biology. Traditional experimental methods such as X-ray crystallography cryo-electron microscopy and nuclear magnetic resonance spectroscopy although accurate are expensive time-consuming and often limited by molecular size and crystallization constraints. Consequently computational methods have emerged as powerful alternatives that enable predictive modelling of biomolecular structures. Particle Swarm Optimisation (PSO) a population-based metaheuristic inspired by social behaviour in birds and fish has gained prominence for solving high-dimensional nonlinear and multimodal optimisation problems