HighRelax: Physics-Based Refinement of Deep Learning Protein Predictions with Noncanonical Amino Acids

Noncanonical amino acids (NCAAs) have emerged as essential building blocks in protein engineering and peptide drug development owing to their advantages in enhancing metabolic stability, membrane permeability, and resistance to proteolytic degradation. Accurate construction of 3D protein structures containing NCAAs is crucial for elucidating their functions, understanding molecular interactions, and enabling rational design. However, integrating NCAAs into state-of-the-art protein structure prediction frameworks─such as AlphaFold3─often results in chirality violations, steric clashes, and local geometric distortions. These issues likely reflect limited parametrization of nonstandard residues within current models. To address these challenges, we expanded the AMBER force field covering 139 NCAAs, and we developed an enhanced Amber-relax protocol named HighRelax. Unlike conventional workflows that are restricted to linear peptides composed of canonical amino acids, HighRelax is compatible with complex systems containing NCAAs and cyclic peptides and can be seamlessly integrated with structures generated by state-of-the-art models such as AlphaFold3. Our results demonstrate that HighRelax effectively reduces steric clashes, restores residue chirality, and improves overall structural quality. This method provides a general postprocessing strategy for refining NCAA-containing structures, facilitating their applications in molecular simulation, peptide drug design, and protein engineering.