利用大规模药物筛选鉴定针对 COVID-19 的药物候选物。
Identifying Drug Candidates for COVID-19 with Large-Scale Drug Screening.
机构信息
School of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, GA 30602, USA.
Division of Biomedical Sciences, School of Medicine, University of California Riverside, Riverside, CA 92521, USA.
出版信息
Int J Mol Sci. 2023 Feb 23;24(5):4397. doi: 10.3390/ijms24054397.
Papain-like protease (PL) is critical to COVID-19 infection. Therefore, it is a significant target protein for drug development. We virtually screened a 26,193 compound library against the PL of SARS-CoV-2 and identified several drug candidates with convincing binding affinities. The three best compounds all had better estimated binding energy than those of the drug candidates proposed in previous studies. By analyzing the docking results for the drug candidates identified in this and previous studies, we demonstrate that the critical interactions between the compounds and PL proposed by the computational approaches are consistent with those proposed by the biological experiments. In addition, the predicted binding energies of the compounds in the dataset showed a similar trend as their IC values. The predicted ADME and drug-likeness properties also suggested that these identified compounds can be used for COVID-19 treatment.
木瓜蛋白酶样蛋白酶 (PL) 对 COVID-19 感染至关重要。因此，它是药物开发的重要靶标蛋白。我们通过虚拟筛选针对 SARS-CoV-2 的 PL 的 26193 种化合物库，鉴定出了几种具有令人信服的结合亲和力的候选药物。这三种最佳化合物的估计结合能都优于先前研究中提出的候选药物。通过分析本研究和先前研究中鉴定的候选药物的对接结果，我们证明了计算方法提出的化合物与 PL 之间的关键相互作用与生物实验提出的一致。此外，数据集中化合物的预测结合能与它们的 IC 值呈现出相似的趋势。预测的 ADME 和类药性特性也表明这些鉴定出的化合物可用于 COVID-19 的治疗。
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