Controlling secondary structures—such as alpha-helices, beta-sheets, and turns—is essential for designing high-affinity peptide drugs. The bioactive conformation of a peptide is often a specific secondary structure, and stabilizing that structure can dramatically improve binding affinity and selectivity.
Alpha-helical peptides are common in protein-protein interactions, where the helix binds to a complementary surface on the target protein. Stabilizing the helical conformation through stapling or cyclization can enhance binding affinity by reducing the entropic cost of binding. Hydrocarbon stapling is a powerful technique for stabilizing alpha-helical peptides, constraining them into the bioactive conformation.
Beta-sheet peptides are another important class, with applications in antimicrobial therapy and protein aggregation research. Stabilizing beta-sheet conformations can enhance activity and reduce aggregation. Cyclization and other constraints can promote beta-sheet formation in synthetic peptides.
Turn structures are involved in many biological processes, including receptor binding and enzyme inhibition. Stabilizing turn conformations can enhance activity and selectivity. Proline residues and other turn-inducing motifs are often used to promote turn formation.
Computational approaches are increasingly used to design peptides with specific secondary structures. Molecular dynamics simulations can predict the preferred conformations of peptide sequences, guiding the design of constrained peptides. Machine learning can predict the effects of modifications on secondary structure, enabling more efficient optimization. At PeptideHub, our computational and experimental approaches guide the design of conformationally constrained peptides with high affinity and selectivity.