De Novo Design of Cyclic Peptide Binders Based on Fragment Docking and Assembling

Cyclic peptides offer distinct advantages in modulating protein-protein interactions (PPIs), including enhanced target specificity, structural stability, reduced toxicity, and minimal immunogenicity. However, most cyclic peptide therapeutics currently in clinical development are derived from natural products or the cyclization of protein loops, with few methodologies available for de novo cyclic peptide design based on target protein structures. To fill this gap, we introduce CycDockAssem, an integrative computational platform that facilitates the systematic generation of head-to-tail cyclic peptides made entirely of natural - or -amino acid residues. The cyclic peptide binders are constructed from oligopeptide fragments containing 3-5 amino acids. A fragment library comprising 15 million fragments was created from the Protein Data Bank. The assembly workflow involves dividing the targeted protein surface into two docking boxes; the updated protein-protein docking program SDOCK2.0 is then utilized to identify the best binding fragments for these boxes. The fragments binding in different boxes are concatenated into a ring using two additional peptide fragments as linkers. A ROSETTA script is employed for sequence redesign, while molecular dynamics simulations and MM-PBSA calculations assess the conformational stability and binding free energy. To enhance docking performance, cation-π interactions, backbone hydrogen bonding potential, and explicit water exclusion energy were incorporated into the docking score function of SDOCK2.0, resulting in a significantly improved performance on the updated test set. A mirror design strategy was developed for cyclic peptides composed of -amino acids, where natural amino acid cyclic peptide binders are first designed for the mirror image of the target protein and the resulting complexes are then mirrored back. CycDockAssem was experimentally validated using tumor necrosis factor α (TNFα) as the target. Surface plasmon resonance experiments demonstrated that six of the seven designed cyclic peptides bind TNFα with micromolar affinity, two of which significantly inhibit TNFα downstream gene expression. Overall, CycDockAssem provides a robust strategy for targeted de novo cyclic peptide drug discovery.