Biophysical and Computational Chemistry Laboratory, Department of Chemistry, National Institute of Technology Karnataka, Mangalore, India.
J Biomol Struct Dyn. 2022 Jul;40(11):5112-5127. doi: 10.1080/07391102.2020.1868337. Epub 2021 Jan 5.
Novel coronavirus (COVID-19) responsible for viral pneumonia which emerged in late 2019 has badly affected the world. No clinically proven drugs are available yet as the targeted therapeutic agents for the treatment of this disease. The viral main protease which helps in replication and transcription inside the host can be an effective drug target. In the present study, we aimed to discover the potential of β-adrenoceptor agonists and adenosine deaminase inhibitors which are used in asthma and cancer/inflammatory disorders, respectively, as repurposing drugs against protease inhibitor by ligand-based and structure-based virtual screening using COVID-19 protease-N3 complex. The AARRR pharmacophore model was used to screen a set of 22,621 molecules to obtain hits, which were subjected to high-throughput virtual screening. Extra precision docking identified four top-scored molecules such as +/--fenoterol, FR236913 and FR230513 with lower binding energy from both categories. Docking identified three major hydrogen bonds with Gly143, Glu166 and Gln189 residues. 100 ns MD simulation was performed for four top-scored molecules to analyze the stability, molecular mechanism and energy requirements. MM/PBSA energy calculation suggested that van der Waals and electrostatic energy components are the main reasons for the stability of complexes. Water-mediated hydrogen bonds between protein-ligand and flexibility of the ligand are found to be responsible for providing extra stability to the complexes. The insights gained from this combinatorial approach can be used to design more potent and bio-available protease inhibitors against novel coronavirus.Communicated by Ramaswamy H. Sarma.
新型冠状病毒(COVID-19)引起的病毒性肺炎于 2019 年末出现,严重影响了世界。目前尚无针对该疾病的临床验证药物,病毒主要蛋白酶可作为有效的药物靶点,有助于在宿主内进行复制和转录。在本研究中,我们旨在通过 COVID-19 蛋白酶-N3 复合物的基于配体和基于结构的虚拟筛选,发现β-肾上腺素能受体激动剂和腺苷脱氨酶抑制剂(分别用于哮喘和癌症/炎症性疾病)作为重新利用药物针对蛋白酶抑制剂的潜力。AARRR 药效团模型用于筛选一组 22621 个分子以获得命中,然后对其进行高通量虚拟筛选。精准对接确定了四个评分最高的分子,如+/--非诺特罗、FR236913 和 FR230513,它们的结合能较低,属于两类。对接确定了与 Gly143、Glu166 和 Gln189 残基的三个主要氢键。对四个评分最高的分子进行了 100nsMD 模拟,以分析稳定性、分子机制和能量需求。MM/PBSA 能量计算表明范德华和静电能成分是复合物稳定性的主要原因。发现蛋白-配体之间的水介导氢键和配体的灵活性是为复合物提供额外稳定性的原因。这种组合方法获得的见解可用于设计针对新型冠状病毒的更有效和生物可利用的蛋白酶抑制剂。由 Ramaswamy H. Sarma 交流。