Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C1175175 (SARS)
 19,188 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Since the outbreak of the 2019 novel coronavirus disease (COVID-19), the medical research community is vigorously seeking a treatment to control the infection and save the lives of severely infected patients. The main potential candidates for the control of viruses are virally targeted agents. In this short letter, we report our calculations on the inhibitors for the SARS-CoV-2 3CL protease and the spike protein for the potential treatment of COVID-19. The results show that the most potent inhibitors of the SARS-CoV-2 3CL protease include saquinavir, tadalafil, rivaroxaban, sildenafil, dasatinib, etc. Ergotamine, amphotericin b, and vancomycin are most promising to block the interaction of the SARS-CoV-2 S-protein with human ACE-2.
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PMID:Computational View toward the Inhibition of SARS-CoV-2 Spike Glycoprotein and the 3CL Protease. 3266 94

Computer-aided drug screening by molecular docking, molecular dynamics (MD) and structural-activity relationship (SAR) can offer an efficient approach to identify promising drug repurposing candidates for COVID-19 treatment. In this study, computational screening is performed by molecular docking of 1615 Food and Drug Administration (FDA) approved drugs against the main protease (Mpro) of SARS-CoV-2. Several promising approved drugs, including Simeprevir, Ergotamine, Bromocriptine and Tadalafil, stand out as the best candidates based on their binding energy, fitting score and noncovalent interactions at the binding sites of the receptor. All selected drugs interact with the key active site residues, including His41 and Cys145. Various noncovalent interactions including hydrogen bonding, hydrophobic interactions, pi-sulfur and pi-pi interactions appear to be dominant in drug-Mpro complexes. MD simulations are applied for the most promising drugs. Structural stability and compactness are observed for the drug-Mpro complexes. The protein shows low flexibility in both apo and holo form during MD simulations. The MM/PBSA binding free energies are also measured for the selected drugs. For pattern recognition, structural similarity and binding energy prediction, multiple linear regression (MLR) models are used for the quantitative structural-activity relationship. The binding energy predicted by MLR model shows an 82% accuracy with the binding energy determined by molecular docking. Our details results can facilitate rational drug design targeting the SARS-CoV-2 main protease. Communicated by Ramaswamy H. Sarma.
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PMID:Virtual screening, molecular dynamics and structure-activity relationship studies to identify potent approved drugs for Covid-19 treatment. 3269 6

The World Health Organization has declared COVID-19 as a global health emergency. COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and highlights an urgent need for therapeutics. Here, we have employed a series of computer-aided drug repurposing campaign to discover inhibitors of RNA dependent RNA polymerase (RdRp) and Nsp15/EndoU. Subsequently, MD simulation has been performed to observe dynamic behavior of identified leads at the active site of RdRp and Nsp15. We successfully identified novel lead molecule such as Alectinib for RdRp while Naldemedine and Ergotamine for NSP15. These lead molecules were accommodated in the active site of the enzyme and stabilized by the networks of the hydrogen bond, pi type and hydrophobic interaction with key residues of either target. Interestingly, identified compounds show molecular mimicry in terms of molecular interactions with key residues of RdRp and Nsp15 essential for catalysis and substrate interaction. Previously, Alectinib, Naldemedine and Ergotamine were used as drug in different diseases might be repurposed against selected protein targets of COVID19. Finally, we propose that the identified inhibitors represent a novel lead molecule to design a more effective inhibitor to stop the progress of pathogen. Communicated by Ramaswamy H. Sarma.
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PMID:Identification and characterization of novel RdRp and Nsp15 inhibitors for SARS-COV2 using computational approach. 3315 31