Potential drug discovery for COVID-19 treatment targeting Cathepsin L using a deep learning-based strategy

组织蛋白酶L 药物重新定位 药物发现 化学 药理学
作者
Wei-Li Yang,Qi Li,Jing Sun,Sia Huat Tan,Yan-Hong Tang,Miao-Miao Zhao,Yu-Yang Li,Xi Cao,Jin-Cun Zhao,Jin-Kui Yang
出处
期刊:Computational and structural biotechnology journal [Elsevier BV]
卷期号:20: 2442-2454 被引量:1
标识
DOI:10.1016/j.csbj.2022.05.023
摘要

• Cathepsin L(CTSL) is a promising therapeutic target for COVID-19. • A new deep learning model was used to predict CTSL inhibitor based on structure. • 5 molecules for inhibiting CTSL and treating COVID-19 at nmol level were identified. • Daptomycin can distinctly inhibit CTSL and has potential for COVID-19 treatment. Cathepsin L (CTSL), a cysteine protease that can cleave and activate the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein, could be a promising therapeutic target for coronavirus disease 2019 (COVID-19). However, there is still no clinically available CTSL inhibitor that can be used. Here, we applied Chemprop, a newly trained directed-message passing deep neural network approach, to identify small molecules and FDA-approved drugs that can block CTSL activity to expand the discovery of CTSL inhibitors for drug development and repurposing for COVID-19. We found 5 molecules (Mg-132, Z-FA-FMK, leupeptin hemisulfate, Mg-101 and calpeptin) that were able to significantly inhibit the activity of CTSL in the nanomolar range and inhibit the infection of both pseudotype and live SARS-CoV-2. Notably, we discovered that daptomycin, an FDA-approved antibiotic, has a prominent CTSL inhibitory effect and can inhibit SARS-CoV-2 pseudovirus infection. Further, molecular docking calculation showed stable and robust binding of these compounds with CTSL. In conclusion, this study suggested for the first time that Chemprop is ideally suited to predict additional inhibitors of enzymes and revealed the noteworthy strategy for screening novel molecules and drugs for the treatment of COVID-19 and other diseases with unmet needs.

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