Background: There is currently no effective dengue virus (DENV) therapeutic. We aim to develop a genetic algorithm-based framework for the design of peptides with possible DENV inhibitory activity. Methods & results: A Python-based tool (denominated AutoPepGEN) based on a DENV support vector machine classifier as the objective function was implemented. AutoPepGEN was applied to the design of three- to seven-amino acid sequences and ten peptides were selected. Peptide-protease (DENV) docking and Molecular Mechanics-Generalized Born Surface Area calculations were performed for the selected sequences and favorable binding energies were observed. Conclusion: It is hoped that AutoPepGEN will serve as an in silico alternative to the experimental design of positional scanning combinatorial libraries, known to be prone to a combinatorial explosion. AutoPepGEN is available at: https://github.com/sjbarigye/AutoPepGEN.
Keywords: MM-GBSA; dengue virus; genetic algorithm; peptide-protein docking; peptides.