Otimização Evolutiva Híbrida para Docking Proteína–Peptídeo

Abstract

Protein–peptide interactions play an important role in biological processes associated with molecular recognition, cellular signaling, and the development of therapeutic strategies. However, the computational prediction of these complexes remains challenging, especially in blind docking scenarios, in which the binding site is not previously known and the conformational space to be explored is broad. The central problem investigated in this work is to reduce the computational cost of the conformational search in blind protein–peptide docking without compromising the energetic quality of the final poses. In this context, this dissertation presents the development, implementation, and validation of a hybrid evolutionary optimization approach named ABC–GA–VGOS, applied to protein– peptide molecular docking. The proposed approach combines population-based exploration mechanisms, genetic operators, adaptive local refinement, and a single-objective geometric function based on KD-tree queries, used as a computationally efficient evaluation strategy during the search process. At the end of the optimization, the best pose obtained in each execution is submitted to an energy rescoring step inspired by AutoDock Vina, allowing the separation between fast geometric screening and final energetic evaluation. The experiments were performed with ten protein–peptide complexes obtained from the Protein Data Bank (PDB), considering binding energy, execution time, and comparative statistical analysis among the evaluated algorithms. The results indicated that the hybrid method obtained the lowest mean energies in five evaluated systems and achieved the shortest mean execution time in all cases, maintaining competitive behavior across different complexes. Thus, the main contribution of this work lies in the integration of complementary evolutionary strategies with efficient geometric evaluation, offering an alternative to reduce the computational cost of protein–peptide docking without relying exclusively on more expensive energy functions throughout the entire search.