Identification of chymotrypsin-like protease inhibitors of SARS-CoV-2 via integrated computational approach
By S. Khan, Komal Zia, Sajda Ashraf, Reaz Uddin, Zaheer Ul‐Haq. Journal of Biomolecular Structure and Dynamics, 2020
324 citations recorded by the source corpus.
Abstract Recently, the world has witnessed outbreak of a novel Coronavirus (SARS-CoV-2), the virus which initially emerged in Wuhan, China has now made its way to a large part of the world, resulting in a public emergency of international concern. The functional importance of Chymotrypsin-like protease (3CLpro) in viral replication and maturation turns it into an attractive target for the development of effective antiviral drugs against SARS and other coronaviruses. At present, there is no standard drug regime nor any vaccine available against the infection. The rapid development and identification of efficient interventions against SARS-CoV-2 remains a major challenge. Based on the available knowledge of closely related coronavirus and their safety profiles, repurposing of existing antiviral drugs and screening of available databases is considered a near term strategic and economic way to contain the SARS-CoV-2 pandemic. Herein, we applied computational drug design methods to identify Chymotrypsin-like protease inhibitors from FDA approved antiviral drugs and our in-house database of natural and drug-like compounds of synthetic origin. As a result three FDA approved drugs (Remdesivir, Saquinavir and Darunavir) and two natural compounds (. flavone and coumarine derivatives) were identified as promising hits. Further, MD simulation and binding free energy calculations were performed to evaluate the dynamic behavior, stability of protein-ligand contact, and binding affinity of the hit compounds. Our results indicate that the identified compounds can inhibit the function of Chymotrypsin-like protease (3CLpro) of Coronavirus. Considering the severity of the spread of coronavirus, the current study is in-line with the concept of finding the new inhibitors against the vital pathway of the corona virus to expedite the process of drug discovery. Communicated by Ramaswamy H. Sarma
Subfields: Computational Theory and Mathematics, Infectious Diseases, Physical and Theoretical Chemistry
View Paper: https://www.semanticscholar.org/paper/adf0fa4c016c9862c103c4f7a9d299422978137c
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In Computational Theory and Mathematics
In Infectious Diseases
Citations in the corpus
- SARS-CoV-2 RNA dependent RNA polymerase (RdRp) targeting: an in silico perspective (2020, background): “…anti-viral drugs and compounds against the coronavirus proteins (Aanouz et al., 2020; Boopathi et al., 2020; Elfiky & Azzam, 2020; Elmezayen et al., 2020; Enayatkhani et al., 2020; Gupta et al., 2020; Khan et al., 2020a, 2020b; Muralidharan et al., 2020; Pant et al., 2020; Sarma et al., 2020).”
- Using X-ray images and deep learning for automated detection of coronavirus disease (2020, background): “…al., 2020; Ghosh et al., 2020; Muralidharan et al., 2020; Phulen et al., 2020; Rajib et al., 2020; Rothan & Byrareddy, 2020; Rowan & Laffey, 2020; Salman et al., 2020; Sourav et al., 2020; Umesh et al., 2020), Covid19 is a respiratory disease that is caused by the new coronavirus SARS-CoV-2…”