Wei Lin, Chen Yaru, Liu Jiaqi, Rao Li, Ren Yanliang, Xu Xin, Wan Jian
Hubei International Scientific and Technological Cooperation Base of Pesticide and Green Synthesis, Key Laboratory of Pesticide & Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 43009, China.
Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Ministry of Education (MOE) Laboratory for Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, People's Republic of China.
J Med Chem. 2022 Apr 14;65(7):5528-5538. doi: 10.1021/acs.jmedchem.1c02007. Epub 2022 Mar 30.
A handful of molecular docking tools have been extended to enable a covalent docking. However, all of them face the challenge brought by the covalent bond between proteins and ligands. Many covalent drug design scenarios still heavily rely on demanding crystallographic experiments for accurate binding structures. Aiming at filling the gap between covalent dockings and crystallographic experiments, we develop and validate a hybrid method, dubbed as Cov_DOX, in this work. Cov_DOX achieves an overall success rate of 81% with RMSD < 2 Å for the Top 1 pose prediction in the validation against a test set including 405 crystal structures for covalent protein-ligand complexes, covering various types of the warhead chemistry and receptors. Such accuracy is not far from the much more demanding crystallographic experiments, in sharp contrast to the performance of the covalent docking front runners (success rate: 40-60%).
少数分子对接工具已得到扩展以实现共价对接。然而,它们都面临着蛋白质与配体之间共价键带来的挑战。许多共价药物设计方案仍然严重依赖要求苛刻的晶体学实验来获得精确的结合结构。为了填补共价对接与晶体学实验之间的差距,我们在这项工作中开发并验证了一种名为Cov_DOX的混合方法。在针对包含405个共价蛋白质 - 配体复合物晶体结构的测试集进行验证时,Cov_DOX对于排名第一的构象预测实现了81%的总体成功率,且均方根偏差(RMSD)< 2 Å,涵盖了各种类型的弹头化学和受体。这样的准确度与要求更高的晶体学实验相差不远,这与共价对接领先工具的性能形成鲜明对比(成功率:40 - 60%)。