Integrating Physical Chemistry and AI to Design Next-Generation Peptide Therapeutics:

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Peptide-protein interactions (PPIs) are vital regulatory hubs driving biomolecular processes, making them premier targets for therapeutic intervention. Small peptides represent a promising class of therapeutics due to their high specificity and low toxicity. To efficiently target PPIs, amino acid side chains are frequently cross-linked—“stapled”—to rigidify the peptide backbone into a stable, bioactive conformation. Fully exploiting these scaffolds requires a comprehensive understanding of their structural physics and binding mechanisms. In this work, we integrate artificial intelligence (AI)-driven design frameworks with molecular simulations to evaluate and optimize stapled p53 peptides targeting the mdm2 protein. Our simulations demonstrate that the stapling agent robustly shields the peptide’s secondary structure against thermal and chemical denaturation. Crucially, while the hydrocarbon staple increases overall hydrophobicity, it reduces the baseline enthalpic stabilization upon binding. However, this enthalpic loss is completely offset by a significant reduction in the conformational entropic penalty, thereby enhancing the net binding affinity. By bridging AI-based sequence optimization with deep mechanistic insights, this dual computational approach offers a powerful pipeline for the rational design of next-generation stapled peptide therapeutics. More recently, we have elucidated the role of water entropy in protein-peptide binding in this context.