LAUSR

Significance Peptide macrocycles are a promising class of drugs, but their weakness is conformational flexibility: target affinity can be limited by an unfavorable transition from a disordered unbound state to an ordered bound state. We introduce general computational methods for stabilizing peptide macrocycles in binding-competent conformations as part of the process of designing for binding to a target protein. As a proof of principle, we apply our methods to create inhibitors of the New Delhi metallo-β-lactamase 1, an antibiotic resistance factor. Predictions of peptide rigidity correlate with experimental success, allowing designs to be prioritized for synthesis and testing. These methods should contribute to the design of peptide macrocycle inhibitors of diverse targets of therapeutic interest. The rise of antibiotic resistance calls for new therapeutics targeting resistance factors such as the New Delhi metallo-β-lactamase 1 (NDM-1), a bacterial enzyme that degrades β-lactam antibiotics. We present structure-guided computational methods for designing peptide macrocycles built from mixtures of l- and d-amino acids that are able to bind to and inhibit targets of therapeutic interest. Our methods explicitly consider the propensity of a peptide to favor a binding-competent conformation, which we found to predict rank order of experimentally observed IC50 values across seven designed NDM-1- inhibiting peptides. We were able to determine X-ray crystal structures of three of the designed inhibitors in complex with NDM-1, and in all three the conformation of the peptide is very close to the computationally designed model. In two of the three structures, the binding mode with NDM-1 is also very similar to the design model, while in the third, we observed an alternative binding mode likely arising from internal symmetry in the shape of the design combined with flexibility of the target. Although challenges remain in robustly predicting target backbone changes, binding mode, and the effects of mutations on binding affinity, our methods for designing ordered, binding-competent macrocycles should have broad applicability to a wide range of therapeutic targets.