Macrocyclic peptides are increasingly used to bridge the gap between small molecules and biologics. Their biological activity and pharmacological properties are governed by conformation, making a detailed understanding of the conformational landscape essential for rational design. Although nuclear magnetic resonance (NMR) is the primary method for determining structures in solution, it remains challenging to apply, especially to N-methylated peptides, because of the scarcity of informative observables such as NOEs, J-couplings, and RDCs. To address this challenge, we present a new method for conformational analysis, which we call CSCAN (Chemical Shift Conformational ANalysis), based solely on comparing experimental 1H and 13C NMR chemical shifts with theoretical values computed via an efficient workflow combining extensive conformational searches, deep-learning-based geometry refinement, and DFT calculations. Using two pharmaceutically relevant macrocyclic peptides─Cyclosporin A and Aureobasidin A─as examples, we demonstrate that this approach can reliably identify conformations consistent with both X-ray crystallographic and NMR-derived reference structures. Furthermore, it addresses limitations of conventional conformational analyses by detecting differences between solution conformations and X-ray structures, as well as improving the assignment of solution conformations determined from NOE and RDC data.
Buevich et al. (Mon,) studied this question.