An emerging class of therapeutic molecules are cyclic peptides with over 40 cyclic peptide drugs currently in clinical use. Their mode of action is, however, not fully understood, impeding rational drug design. Computational techniques could positively impact their design, but modeling them and their interactions remains challenging due to their cyclic nature and their flexibility. This study presents a step-by-step protocol for generating cyclic peptide conformations and docking them to their protein target using HADDOCK2.4. A dataset of 30 cyclic peptide–protein complexes was used to optimize both cyclization and docking protocols. It supports peptides cyclized via an N- and C-terminus peptide bond and/or a disulfide bond. An ensemble of cyclic peptide conformations is then used in HADDOCK to dock them onto their target protein using knowledge of the binding site on the protein side to drive the modeling. The presented protocol predicts at least one acceptable model according to the critical assessment of prediction of interaction criteria for each complex of the dataset when the top 10 HADDOCK-ranked single structures are considered (100% success rate top 10) both in the bound and unbound docking scenarios. Moreover, its performance in both bound and fully unbound docking is similar to the state-of-the-art software in the field, Autodock CrankPep. The presented cyclization and docking protocol should make HADDOCK a valuable tool for rational cyclic peptide-based drug design and high-throughput screening.

中文翻译：

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使用 HADDOCK2.4 进行环状肽-蛋白质建模的环化和对接协议

一类新兴的治疗分子是环肽，目前临床使用的环肽药物超过 40 种。然而，它们的作用方式尚未完全了解，阻碍了合理的药物设计。计算技术可能会对它们的设计产生积极影响，但由于它们的循环性质和灵活性，对它们及其交互进行建模仍然具有挑战性。本研究提出了一个分步协议，用于生成环肽构象并将它们与使用 HADDOCK2.4 的蛋白质靶标对接。30 个环状肽-蛋白质复合物的数据集用于优化环化和对接方案。它支持通过 N 和 C 末端肽键和/或二硫键环化的肽。然后在 HADDOCK 中使用一组环状肽构象，利用蛋白质侧结合位点的知识将它们停靠到它们的靶蛋白上，以驱动建模。当在绑定和未绑定中考虑前 10 个 HADDOCK 排名的单个结构（100% 成功率前 10）时，所提出的协议根据对数据集每个复合体的交互标准预测的关键评估预测至少一个可接受的模型对接场景。此外，它在绑定和完全非绑定对接方面的性能与该领域最先进的软件 Autodock CrankPep 相似。所提出的环化和对接方案应使 HADDOCK 成为合理的基于环肽的药物设计和高通量筛选的宝贵工具。当在绑定和未绑定中考虑前 10 个 HADDOCK 排名的单个结构（100% 成功率前 10）时，所提出的协议根据对数据集每个复合体的交互标准预测的关键评估预测至少一个可接受的模型对接场景。此外，它在绑定和完全非绑定对接方面的性能与该领域最先进的软件 Autodock CrankPep 相似。所提出的环化和对接方案应使 HADDOCK 成为合理的基于环肽的药物设计和高通量筛选的宝贵工具。当在绑定和未绑定中考虑前 10 个 HADDOCK 排名的单个结构（100% 成功率前 10）时，所提出的协议根据对数据集每个复合体的交互标准预测的关键评估预测至少一个可接受的模型对接场景。此外，它在绑定和完全非绑定对接方面的性能与该领域最先进的软件 Autodock CrankPep 相似。所提出的环化和对接方案应使 HADDOCK 成为合理的基于环肽的药物设计和高通量筛选的宝贵工具。