School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
Bioinformatics. 2022 Sep 2;38(17):4109-4116. doi: 10.1093/bioinformatics/btac486.
Cyclization is a common strategy to enhance the therapeutic potential of peptides. Many cyclic peptide drugs have been approved for clinical use, in which the disulfide-driven cyclic peptide is one of the most prevalent categories. Molecular docking is a powerful computational method to predict the binding modes of molecules. For protein-cyclic peptide docking, a big challenge is considering the flexibility of peptides with conformers constrained by cyclization.
Integrating our efficient peptide 3D conformation sampling algorithm MODPEP2.0 and knowledge-based scoring function ITScorePP, we have proposed an extended version of our hierarchical peptide docking algorithm, named HPEPDOCK2.0, to predict the binding modes of the peptide cyclized through a disulfide against a protein. Our HPEPDOCK2.0 approach was extensively evaluated on diverse test sets and compared with the state-of-the-art cyclic peptide docking program AutoDock CrankPep (ADCP). On a benchmark dataset of 18 cyclic peptide-protein complexes, HPEPDOCK2.0 obtained a native contact fraction of above 0.5 for 61% of the cases when the top prediction was considered, compared with 39% for ADCP. On a larger test set of 25 cyclic peptide-protein complexes, HPEPDOCK2.0 yielded a success rate of 44% for the top prediction, compared with 20% for ADCP. In addition, HPEPDOCK2.0 was also validated on two other test sets of 10 and 11 complexes with apo and predicted receptor structures, respectively. HPEPDOCK2.0 is computationally efficient and the average running time for docking a cyclic peptide is about 34 min on a single CPU core, compared with 496 min for ADCP. HPEPDOCK2.0 will facilitate the study of the interaction between cyclic peptides and proteins and the development of therapeutic cyclic peptide drugs.
http://huanglab.phys.hust.edu.cn/hpepdock/.
Supplementary data are available at Bioinformatics online.
环化是增强肽类治疗潜力的常用策略。许多环状肽药物已被批准用于临床，其中二硫键驱动的环状肽是最常见的类别之一。分子对接是一种强大的计算方法，可用于预测分子的结合模式。对于蛋白质-环状肽对接，一个很大的挑战是考虑到通过环化约束构象的肽的灵活性。
我们整合了高效的肽 3D 构象采样算法 MODPEP2.0 和基于知识的评分函数 ITScorePP，提出了我们的分层肽对接算法的扩展版本，命名为 HPEPDOCK2.0，用于预测通过二硫键环化的肽与蛋白质的结合模式。我们的 HPEPDOCK2.0 方法在不同的测试集上进行了广泛评估，并与最先进的环状肽对接程序 AutoDock CrankPep (ADCP) 进行了比较。在 18 个环状肽-蛋白质复合物的基准数据集上，当考虑最高预测时，HPEPDOCK2.0 对于 61%的情况获得了高于 0.5 的天然接触分数，而 ADCP 为 39%。在更大的 25 个环状肽-蛋白质复合物测试集上，HPEPDOCK2.0 的最高预测成功率为 44%，而 ADCP 为 20%。此外，HPEPDOCK2.0 还在另外两个具有 apo 和预测受体结构的 10 个和 11 个复合物测试集上进行了验证。HPEPDOCK2.0 计算效率高，对接环状肽的平均运行时间约为 34 分钟在单个 CPU 内核上，而 ADCP 为 496 分钟。HPEPDOCK2.0 将促进环状肽与蛋白质相互作用的研究和治疗性环状肽药物的开发。
http://huanglab.phys.hust.edu.cn/hpepdock/。
补充数据可在 Bioinformatics 在线获得。
