Application of the parallel fast messy genetic algorithm to the protein folding problem (original) (raw)

A parallel implementation of the fast messy genetic algorithm (PFMGA) is developed for addressing the protein folding problem, which involves predicting the tertiary structure of polypeptides based on their amino acid sequences. This research focuses on the global minimization of a semi-empirical energy model, with particular attention to the computational challenges associated with high-dimensional conformational spaces. The method's effectiveness is demonstrated through its application to the pentapeptide Met-enkephalin, showcasing significant efficiencies in execution times and energy minimization outcomes.