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Cyclic peptides (macrocycles) possess head-to-tail cyclic or partially cyclized substructures, which have received more and more attention in developing new drugs recently, since they have unique advantages in regulating protein-protein interactions (PPIs). Comparing to small-molecule compounds, it is easier to design cyclic peptide molecules that bind to target sites with high affinity and specificity, due to the broad and flat interfaces of PPIs and their large surfaces. Moreover, cyclic peptides are generally more rigid and difficult for digestion by proteases than their linear counterparts, making them more stable than linear peptides or proteins. Meanwhile, cyclic peptides are easier for modifications to increase transmembrane activity, targeting intracellular proteins through conformation adaptation or chemical modifications. 3D structure data and structure modeling technics are basis for designing structure based cyclic-peptide drugs. In this review, we assess the structures of cyclic peptides and target proteins available in protein structure database (PDB). Then, we review the algorithms of conformation generation or structure prediction for cyclic peptides, including homologous modeling, secondary structure prediction and optimization, backbone torsion sampling, and distance geometry method. We also summarize progress in target structure based computational design for cyclic peptides, including structure-based virtual screening, molecular dynamic simulation aided methods, de novo design algorithms, and the transmembrane cyclic peptide design. However, more generalized structure-based de novo design algorithms remains to be further explored, and methods to adopt unnatural amino acids or chemical modifications are also needs to be developed. It's worth noting that, with the increase of data for cyclic peptide 3D structures, the data-driven machine learning method may provide a more promising solution for improving the efficiency and effectiveness of structure based cyclic peptide de novo design and conformation generation to develop cyclic peptide drugs in the future.

Keywords： cyclic peptides ; peptide drugs ; peptidedesign ; conformation generation