Literature DB >> 33554747

Targeting the N-terminal domain of the RNA-binding protein of the SARS-CoV-2 with high affinity natural compounds to abrogate the protein-RNA interaction: a molecular dynamics study.

Sohail Khan1, Zahid Hussain2, Muhammad Safdar3, Abbas Khan4, Dong-Qing Wei4,5,6.   

Abstract

The emergence of COVID-19 took the world by shock in December 2019, starting from Wuhan, China and swiftly spreading across the globe. The number of COVID-19 cases continues to rise which is a global burden on the health care system worldwide. Efforts are continuing to come up with a solution either to develop a small molecular inhibitor or vaccine, but still no success. In the fight against SARS-CoV-2, targeting a different protein of the SARS-CoV-2 is the need of the hour to impede and relinquish the current pandemic. Therefore, in this study, computational modelling and simulation approaches are used to target the N-terminal domain of the phosphor-nucleoprotein (RNA binding protein), which is primarily responsible for binding and packing the viral genome to get ribonucleoprotein complex (RNP). Our multi-step drug screening approach shortlisted potential drugs. These top hits were confirmed by re-docking which revealed that the interacting molecules block the key residues i.e. Thr57, His59, Ser105, Arg107, and Arg177 and thus ultimately block the NTD from RNA recognition. Furthermore, the activity of the top four hits was also confirmed by using molecular dynamics simulation and free energy calculation. Our analysis suggests that these top hits possess strong inhibitory properties and should be tested experimentally. In conclusion, we hope these top hits would abrogate the binding of RNA and the NTD of the SARS-CoV-2, which might be helpful to combat COVID-19.Communicated by Ramaswamy H. Sarma.

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Keywords:  COVID-19; docking; interactions; molecular dynamics; natural compounds

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Year:  2021        PMID: 33554747     DOI: 10.1080/07391102.2021.1882337

Source DB:  PubMed          Journal:  J Biomol Struct Dyn        ISSN: 0739-1102            Impact factor:   5.235


  1 in total

1.  Discovery of Potential Therapeutic Drugs for COVID-19 Through Logistic Matrix Factorization With Kernel Diffusion.

Authors:  Xiongfei Tian; Ling Shen; Pengfei Gao; Li Huang; Guangyi Liu; Liqian Zhou; Lihong Peng
Journal:  Front Microbiol       Date:  2022-02-28       Impact factor: 5.640

  1 in total

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