From Dietary Proteins to Antiviral Peptides: Machine Learning-Guided Discovery and Structural Evaluation Against H5N1 Influenza Polymerase

Food proteins are well-known bioactive peptides, but their inhibitory activities against the influenza H5N1 polymerase have not been widely studied. In this paper, we applied a computational pipeline to predict the antiviral activities of food-derived peptides against the PB1 subunit of H5N1 polymerase. Nineteen proteins from soybean, cow milk, egg, pea, maize, rice, and cowpea were analyzed in terms of their physiochemical properties, allergenicity, and antimicrobial and hemolytic activities, and then digested in silico with pepsin and chymotrypsin. The peptides generated from each digestion process were ranked according to their antiviral activities using four different machine learning algorithms (SVM, Random Forest, IBk, and K-Star) and prioritized on the basis of consensus across multiple models as well as gastrointestinal release and non-hemolytic properties. Peptides chosen were docked to PB1 protein on three independent docking servers (HawkDock, PatchDock, and pyDockWEB) and further analyzed in terms of interaction analysis and MD simulations with 200.4 ns. Some proteins exhibited a high score for antimicrobial activity, such as legumin A (0.970), β-conglycinin α subunit (0.941), and glutelin type-A 1 (0.911). Most proteins were similar to known allergens. The peptides that showed consistent results in terms of antiviral properties and docking include RIQQAIAAGILPLSPL, SSAVSSVCENPILQPY, and QPYRIQQAI-AAGILPL, even though the ranking of their efficacy by both platforms was different. The peptide SSAVSSVCENPILQPY is the most consistent candidate for further research and has 29 close contacts and four hydrogen bonds. In addition, the complex formed between it and PB1 is stable throughout the simulation period. These results allow identifying peptide candidates for further investigation, but all of them are based only on computational results. They need to be proven experimentally.