Zhang Changsheng, Wang Fanhao, Zhang Tiantian, Yang Yang, Wang Liying, Zhang Xiaoling, Lai Luhua
BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.
Center for Quantitative Biology, Peking University, Beijing 100871, China.
J Chem Inf Model. 2025 Apr 28;65(8):4206-4218. doi: 10.1021/acs.jcim.5c00088. Epub 2025 Apr 14.
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 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 cyclic peptide drug discovery.
环肽在调节蛋白质-蛋白质相互作用（PPI）方面具有独特优势，包括增强的靶标特异性、结构稳定性、降低的毒性和最小的免疫原性。然而，目前处于临床开发阶段的大多数环肽疗法都源自天然产物或蛋白质环的环化，基于靶标蛋白质结构进行环肽设计的方法很少。为了填补这一空白，我们引入了CycDockAssem，这是一个综合计算平台，有助于系统地生成完全由天然α-氨基酸残基组成的头对尾环肽。环肽结合剂由包含3-5个氨基酸的寡肽片段构建而成。从蛋白质数据库创建了一个包含1500万个片段的片段库。组装工作流程包括将目标蛋白质表面划分为两个对接框；然后使用更新后的蛋白质-蛋白质对接程序SDOCK2.0来识别这些框的最佳结合片段。使用另外两个肽片段作为接头，将在不同框中结合的片段连接成一个环。采用ROSETTA脚本进行序列重新设计，同时通过分子动力学模拟和MM-PBSA计算评估构象稳定性和结合自由能。为了提高对接性能，将阳离子-π相互作用、主链氢键潜力和明确的水排斥能纳入SDOCK2.0的对接评分函数，从而在更新后的测试集上显著提高了性能。针对由β-氨基酸组成的环肽开发了一种镜像设计策略，首先为靶标蛋白质的镜像设计天然氨基酸环肽结合剂，然后将所得复合物镜像回来。以肿瘤坏死因子α（TNFα）为靶标对CycDockAssem进行了实验验证。表面等离子体共振实验表明，七个设计的环肽中有六个以微摩尔亲和力结合TNFα，其中两个显著抑制TNFα下游基因表达。总体而言，CycDockAssem为靶向环肽药物发现提供了一种强大的策略。
