Machine learning-guided rational engineering of ACE2-derived peptides for broad-spectrum neutralization of SARS-CoV-2 variants.

The dynamic mutational landscape of SARS-CoV-2, especially within the spike glycoprotein's RBD, continues to undermine the effectiveness of available therapeutics. Although ACE2-derived peptides have previously been explored as spike inhibitors, their rational optimization has largely lacked systematic, data-driven selection strategies. This article describes an integrative computer-aided approach involving molecular dynamics (MD) simulations and machine learning-assisted peptide engineering to engineer potent peptide-based inhibitors targeting the RBD-ACE2 interface. Molecular dynamics simulations of wild-type and variant RBD-ACE2 complexes were first employed to identify energetically unfavorable interfacial residues, enabling the rational extraction and refinement of a 19-residue ACE2-derived peptide. A supervised regression model of the type XGBoost, trained using experimentally obtained peptide-protein affinity matrices (R2 = 0.6958), estimated binding potentials for 54 synthetically designed peptide variants. The best lead, M1 (HAHTFLETFNYEAQTLSYE), exhibited improved docking energies (-257.35 kcal/mol for Omicron and -234.24 kcal/mol for wild-type) and improved free-binding energies (-35.67 kcal/mol and -21.63 kcal/mol, respectively), relative to the native peptide, with binding energies comparable to or exceeding reported RBD-hACE2 interaction benchmarks (- 13 to - 18 kcal/mol). Molecular dynamics analyses further confirmed enhanced conformational stability, sustained hydrogen bonding, and favourable energetic landscapes for the M1-RBD complexes. Collectively, this study demonstrates that integrating energetic decomposition with ML-driven peptide optimization provides a scalable and mechanistically informed strategy for developing peptide-based inhibitors against rapidly evolving viral pathogens.