Identification of Influenza PAN Endonuclease Inhibitors via 3D-QSAR Modeling and Docking-Based Virtual Screening

作者：Chao Zhang, Jun-Jie Xiang, Qian Xie, Jing Zhao, Hong Zhang, Er-Fang Huang, Pang‐Chui Shaw, Xiaoping Liu, Chun Hu · 发表于：Molecules · 年份：2021 · DOI：10.3390/molecules26237129 · 被引用次数：15 · 研究领域：RNA and protein synthesis mechanisms、HIV/AIDS drug development and treatment、Computational Drug Discovery Methods

Structural and biochemical studies elucidate that PAN may contribute to the host protein shutdown observed during influenza A infection. Thus, inhibition of the endonuclease activity of viral RdRP is an attractive approach for novel antiviral therapy. In order to envisage structurally diverse novel compounds with better efficacy as PAN endonuclease inhibitors, a ligand-based-pharmacophore model was developed using 3D-QSAR pharmacophore generation (HypoGen algorithm) methodology in Discovery Studio. As the training set, 25 compounds were taken to generate a significant pharmacophore model. The selected pharmacophore Hypo1 was further validated by 12 compounds in the test set and was used as a query model for further screening of 1916 compounds containing 71 HIV-1 integrase inhibitors, 37 antibacterial inhibitors, 131 antiviral inhibitors and other 1677 approved drugs by the FDA. Then, six compounds (Hit01–Hit06) with estimated activity values less than 10 μM were subjected to ADMET study and toxicity assessment. Only one potential inhibitory ‘hit’ molecule (Hit01, raltegravir’s derivative) was further scrutinized by molecular docking analysis on the active site of PAN endonuclease (PDB ID: 6E6W). Hit01 was utilized for designing novel potential PAN endonuclease inhibitors through lead optimization, and then compounds were screened by pharmacophore Hypo1 and docking studies. Six raltegravir’s derivatives with significant estimated activity values and docking scores were obtaine...