Combined deep learning and molecular docking simulations approach identifies potentially effective FDA approved drugs for repurposing against SARS-CoV-2. (February 2022)

• Record Type: Journal Article
• Authors: Anwaar, Muhammad U.; Adnan, Farjad; Abro, Asma; Khan, Rayyan A.; Rehman, Asad U.; Osama, Muhammad; Rainville, Christopher; Kumar, Suresh; Sterner, David E.; Javed, Saad; Jamal, Syed B.; Baig, Ahmadullah; Shabbir, Muhammad R.; Ahsan, Waseh; Butt, Tauseef R.; Assir, Muhammad Z.
• Is Part Of: Computers in biology and medicine. Volume 141(2022)
• Publication Date: 2022-02
• Subjects: SARS-CoV-2 -- Drug repurposing -- Machine learning -- Docking -- Binding affinity; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.105049
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The ongoing pandemic of Coronavirus Disease 2019 (COVID-19) has posed a serious threat to global public health. Drug repurposing is a time-efficient approach to finding effective drugs against SARS-CoV-2 in this emergency. Here, we present a robust experimental design combining deep learning with molecular docking experiments to identify the most promising candidates from the list of FDA-approved drugs that can be repurposed to treat COVID-19. We have employed a deep learning-based Drug Target Interaction (DTI) model, called DeepDTA, with few improvements to predict drug-protein binding affinities, represented as KIBA scores, for 2440 FDA-approved and 8168 investigational drugs against 24 SARS-CoV-2 viral proteins. FDA-approved drugs with the highest KIBA scores were selected for molecular docking simulations. We ran around 50, 000 docking simulations for 168 selected drugs against 285 total predicted and/or experimentally proven active sites of all 24 SARS-CoV-2 viral proteins. A list of 49 most promising FDA-approved drugs with the best consensus KIBA scores and binding affinity values against selected SARS-CoV-2 viral proteins was generated. Most importantly, 16 drugs including anidulafungin, velpatasvir, glecaprevir, rifapentine, flavin adenine dinucleotide (FAD), terlipressin, and selinexor demonstrated the highest predicted inhibitory potential against key SARS-CoV-2 viral proteins. We further measured the inhibitory activity of 5 compounds (rifapentine, velpatasvir, glecaprevir, anidulafungin, and FAD disodium) on SARS-CoV-2 PLpro using Ubiquitin-Rhodamine 110 Gly fluorescent intensity assay. The highest inhibition of PLpro activity was seen with rifapentine (IC50: 15.18 μM) and FAD disodium (IC50: 12.39 μM), the drugs with high predicted KIBA scores and binding affinities. Graphical abstract: Image 1 Highlights: Drug repurposing to treat Covid, combining both deep learning and molecular docking simulations. A deep learning model called DeepDTA with few improvements have been utilized. AutoDock based platform Virtualflow has been employed to execute around 50, 000 docking simulations. Predicted drug-protein binding affinities, represented as KIBA scores, for 2440 FDA approved drugs. Experimentally validated the inhibitory activity of selected drugs against PLpro enzyme.