Reactivity-guided de novo molecular design and high throughput virtual screening of a targeted library of peptidomimetic compounds reveals charge-based structure-activity relationship of potential covalent inhibitors of the main protease of SARS-CoV-2
DOI:
https://doi.org/10.47611/jsrhs.v9i2.1082Keywords:
COVID-19, SARS-CoV-2, Protease Inhibitors, Computer-Guided Drug Design, Organic Chemistry, Medicinal Chemistry, Antiviral Drugs, Molecular DockingAbstract
In December of 2019, a novel coronavirus was first identified in Wuhan, China, and has since spread around the world, leaving a largely unsolved biomedical problem in its wake. Upon entry into host cells, the main protease is essential for the replication of viral RNA, which is what allows the virus to replicate inside humans. Inhibition of the main protease has been investigated as a potential strategy for inhibition of the viral replication cycle. Here, we designed a combinatorial library of small molecules and performed high-throughput virtual screening to identify a series of hit compounds that may serve as potential inhibitors of the main protease. In our design of covalent inhibitors of the coronavirus protease, we modeled a library of 361 peptidomimetic Michael acceptor small molecules, which are designed to engage the nucleophilic cysteine residue in the active site of the protease in an irreversible 1,4-conjugate addition. We then employed a variety of computational tools to determine the binding affinity of our designed compounds when bound to the protease active site, where we determined that cationic side chains are potentially beneficial for inhibition of SARS-CoV-2.
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Copyright (c) 2020 Stephanie Sun, Kavya Anand , Ishani Ashok, Bhavesh Ashok, Ayush Bajaj, Varsha Beldona , Kushal Chattopadhyay, Audrey Kwan, Karankumar Mageswaran, Anvi Surapaneni, Atri Surapaneni, Pranjal Verma, Allen Chen, Ria Kolala, Andrew Liang, Ayeeshi Poosarla, Krithikaa Premnath, Karthikha Sri Indran, Jeslyn Wu, Aishwarya Yuvaraj, Harsha Raj, Tanish Sathish, Aashi Shah, Sarah Su, Kara Tran, Edward Njoo
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