AUTHOR=Garg Priyanka , Vanamamalai Venkata Krishna , Jali Itishree , Sharma Shailesh 

TITLE=In silico prediction of the animal susceptibility and virtual screening of natural compounds against SARS-CoV-2: Molecular dynamics simulation based analysis

JOURNAL=Frontiers in Genetics

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.906955

DOI=10.3389/fgene.2022.906955

ISSN=1664-8021

ABSTRACT=COVID-19 is an infectious disease caused by SARS-CoV-2 virus. It has 6 open reading frames (orf1ab, orf3a, orf6, orf7a, orf8 and orf10), a spike protein, a membrane protein, an envelope small membrane protein, a nucleocapsid protein. Out of which orf1ab is the largest ORF coding different important non-structural proteins. In this study, an effort was made to evaluate the susceptibility of different animals against SARS-CoV-2 by analyzing the interactions of Spike and ACE2 protein of the animals and propose a list of potential natural compounds binding to orf1ab of SARS-CoV-2. Here, we analyzed structural interactions between spike protein of SARS-CoV-2 and ACE2 receptor of 16 different hosts. A simulation for 50 ns was performed on these complexes. Based on post-simulation analysis, Chelonia mydas was found to be having more stable complex while, Bubalus bubalis, Aquila chrysaetos chrysaetos, Crocodylus porosus, Loxodonta africana were found to be least stable complexes with more fluctuations than all other organisms. Apart from that, we performed domain assignment of orf1ab of SARS-CoV-2 and identified 14 distinct domains. Out of these, Domain 3 (DNA/RNA polymerases) was selected as target, as it showed no similarities with host proteomes and was validated in-silico. Then, top 10 molecules were selected from the virtual screening of ~1.8 lakh molecules from ZINC database, based on binding energy and validated for ADME and toxicological properties. Three molecules were selected and analyzed further. The structural analysis showed that these molecules were residing within the pocket of receptor. Finally, a simulation for 200 ns was performed on complexes with 3 selected molecules. Based on post-simulation analysis (RMSD, RMSD, Rg, SASA and energies), the molecule ZINC000103666966 was found as the most suitable inhibitory compound against Domain 3. As this is an in-silico prediction, further experimental studies could unravel the potential of the proposed molecule against SARS-CoV-2.