Zhang Haiping, Gong Xiaohua, Peng Yun, Saravanan Konda Mani, Bian Hengwei, Zhang John Z H, Wei Yanjie, Pan Yi, Yang Yang
Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for Infectious Disease, State Key Discipline of Infectious Disease, Shenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China.
Front Chem. 2022 Jul 12;10:933102. doi: 10.3389/fchem.2022.933102. eCollection 2022.
Desired drug candidates should have both a high potential binding chance and high specificity. Recently, many drug screening strategies have been developed to screen compounds with high possible binding chances or high binding affinity. However, there is still no good solution to detect whether those selected compounds possess high specificity. Here, we developed a reverse DFCNN (Dense Fully Connected Neural Network) and a reverse docking protocol to check a given compound's ability to bind diversified targets and estimate its specificity with homemade formulas. We used the RNA-dependent RNA polymerase (RdRp) target as a proof-of-concept example to identify drug candidates with high selectivity and high specificity. We first used a previously developed hybrid screening method to find drug candidates from an 8888-size compound database. The hybrid screening method takes advantage of the deep learning-based method, traditional molecular docking, molecular dynamics simulation, and binding free energy calculated by metadynamics, which should be powerful in selecting high binding affinity candidates. Also, we integrated the reverse DFCNN and reversed docking against a diversified 102 proteins to the pipeline for assessing the specificity of those selected candidates, and finally got compounds that have both predicted selectivity and specificity. Among the eight selected candidates, Platycodin D and Tubeimoside III were confirmed to effectively inhibit SARS-CoV-2 replication with EC values of 619.5 and 265.5 nM, respectively. Our study discovered that Tubeimoside III could inhibit SARS-CoV-2 replication potently for the first time. Furthermore, the underlying mechanisms of Platycodin D and Tubeimoside III inhibiting SARS-CoV-2 are highly possible by blocking the RdRp cavity according to our screening procedure. In addition, the careful analysis predicted common critical residues involved in the binding with active inhibitors Platycodin D and Tubeimoside III, Azithromycin, and Pralatrexate, which hopefully promote the development of non-covalent binding inhibitors against RdRp.
理想的候选药物应具有高潜在结合机会和高特异性。最近，已经开发了许多药物筛选策略来筛选具有高可能结合机会或高结合亲和力的化合物。然而，对于检测那些选定的化合物是否具有高特异性，仍然没有好的解决方案。在此，我们开发了一种反向深度全连接神经网络（DFCNN）和一种反向对接协议，以检查给定化合物与多种靶点结合的能力，并使用自制公式估计其特异性。我们以RNA依赖性RNA聚合酶（RdRp）靶点作为概念验证示例，来识别具有高选择性和高特异性的候选药物。我们首先使用先前开发的混合筛选方法从一个包含8888种化合物的数据库中寻找候选药物。该混合筛选方法利用了基于深度学习的方法、传统分子对接、分子动力学模拟以及通过元动力学计算的结合自由能，这在选择高结合亲和力的候选药物方面应该很强大。此外，我们将针对102种不同蛋白质的反向DFCNN和反向对接整合到评估那些选定候选药物特异性的流程中，最终得到了具有预测选择性和特异性的化合物。在八个选定的候选药物中，桔梗皂苷D和土贝母苷III被证实能有效抑制SARS-CoV-2复制，其半数有效浓度（EC）值分别为619.5和265.5 nM。我们的研究首次发现土贝母苷III能有效抑制SARS-CoV-2复制。此外，根据我们的筛选程序，桔梗皂苷D和土贝母苷III抑制SARS-CoV-2的潜在机制很可能是通过阻断RdRp腔。另外，仔细分析预测了与活性抑制剂桔梗皂苷D、土贝母苷III、阿奇霉素和普拉曲沙结合所涉及的常见关键残基，这有望促进针对RdRp的非共价结合抑制剂的开发。
