Abiri Ardavan, Rezaei Masoud, Zeighami Mohammad Hossein, Vaezpour Younes, Dehghan Leili, KhorramGhahfarokhi Maedeh
Department of Medicinal Chemistry, Faculty of Pharmacy, Kerman University of Medical Sciences, Kerman, Iran.
Faculty of Medicine, Kerman University of Medical Sciences, Kerman, Iran.
Inform Med Unlocked. 2021;27:100787. doi: 10.1016/j.imu.2021.100787. Epub 2021 Nov 15.
Search for new antiviral medications has surged in the past two years due to the COVID-19 crisis. Toll-like receptor 7 (TLR7) is among one of the most important TLR proteins of innate immunity that is responsible for broad antiviral response and immune system control. TLR7 agonists, as both vaccine adjuvants and immune response modulators, are among the top drug candidates for not only our contemporary viral pandemic but also other diseases. The agonists of TLR7 have been utilized as vaccine adjuvants and antiviral agents. In this study, we hybridized a statistical learning-based QSAR model with molecular docking and molecular dynamics simulation to extract new antiviral drugs by drug repurposing of the DrugBank database. First, we manually curated a dataset consisting of TLR7 agonists. The molecular descriptors of these compounds were extracted, and feature engineering was done to restrict the number of features to 45. We applied a statistically inspired modification of the partial least squares (SIMPLS) method to build our QSAR model. In the next stage, the DrugBank database was virtually screened structurally using molecular docking, and the top compounds for the guanosine binding site of TLR were identified. The result of molecular docking was again screened by the ligand-based approach of QSAR to eliminate compounds that do not display strong EC values by the previously trained model. We then subjected the final results to molecular dynamics simulation and compared our compounds with imiquimod (an FDA-approved TLR7 agonist) and compound 1 (the most active compound against TLR7 , EC = 0.2 nM). Our results evidently demonstrate that cephalosporins and nucleotide analogues (especially acyclic nucleotide analogues such as adefovir and cidofovir) are computationally potent agonists of TLR7. We finally reviewed some publications about cephalosporins that, just like pieces of a puzzle, completed our conclusion.
由于新冠疫情危机，在过去两年中，对新型抗病毒药物的研究激增。Toll样受体7（TLR7）是天然免疫中最重要的TLR蛋白之一，负责广泛的抗病毒反应和免疫系统控制。TLR7激动剂作为疫苗佐剂和免疫反应调节剂，不仅是应对当前病毒大流行的顶级候选药物，也是治疗其他疾病的候选药物。TLR7激动剂已被用作疫苗佐剂和抗病毒药物。在本研究中，我们将基于统计学习的定量构效关系（QSAR）模型与分子对接和分子动力学模拟相结合，通过对DrugBank数据库进行药物再利用来提取新型抗病毒药物。首先，我们手动整理了一个由TLR7激动剂组成的数据集。提取了这些化合物的分子描述符，并进行了特征工程，将特征数量限制为45个。我们应用了一种受统计学启发的偏最小二乘法（SIMPLS）改进方法来构建我们的QSAR模型。在下一阶段，使用分子对接对DrugBank数据库进行虚拟结构筛选，确定TLR鸟苷结合位点的顶级化合物。分子对接的结果再次通过基于配体的QSAR方法进行筛选，以消除那些根据先前训练的模型没有显示出强EC值的化合物。然后，我们将最终结果进行分子动力学模拟，并将我们的化合物与咪喹莫特（一种FDA批准的TLR7激动剂）和化合物1（对TLR7最具活性的化合物，EC = 0.2 nM）进行比较。我们的结果清楚地表明，头孢菌素和核苷酸类似物（尤其是无环核苷酸类似物，如阿德福韦和西多福韦）在计算上是有效的TLR7激动剂。我们最后查阅了一些关于头孢菌素的文献，这些文献就像拼图的碎片一样，完善了我们的结论。
