Environment-Conditioned Design of α-Helical Peptides

Abstract

Abstract Peptides at biological interfaces often encode function through preferential compatibility with chemical environments rather than through binding to a single molecular target. We report a physically motivated, knowledge-based Hamiltonian for programming such preferences in α-helical peptides. The model maps amino-acid sequences onto a normalized energy landscape that combines helix propensity, local sequence context, residue contacts, solvent exposure, membrane charge coupling, and electrostatic screening. Random-reference Z-scores place different lengths, residue sets, and environments on a common scale. In homogeneous benchmarks, the Hamiltonian separates experimentally characterized alanine-rich Glu/Lys water-soluble helices from WALP/GWALP transmembrane model helices. In interfacial benchmarks, it recognizes canonical membrane-active antimicrobial peptides as preferentially stabilized at anionic interfaces over neutral, cationic, aqueous, and apolar alternatives, while rejecting non-antimicrobial helical controls. The same score generates sequence libraries for environment-specific compatibility, polar–apolar robustness, and one-vs-rest anionic selectivity. Low-energy ensembles display amphipathic segregation, charge regulation, and hydrophobicity periodicity at sequence separations of 3 and 4 residues, linking the designed sequences to interpretable chemical organization. Because the objective is a discrete Hamiltonian, it can be explored by exact search, classical heuristics, and binary-encoded quantum-compatible workflows without changing the scoring function. This framework provides a transparent route to design α-helical peptides whose environmental preferences are specified directly in the molecular objective.