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?:abstract
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SARS-CoV-2 is causative of pandemic COVID-19. There is a sequence similarity between SARS-CoV-2 and SARS-CoV; however, SARS-CoV-2 RBDs (receptor-binding domain) binds 20-fold strongly with human angiotensin-converting enzyme 2 (hACE2) than SARS-CoV. The study aims to investigate protein–protein interactions (PPI) of hACE2 with SARS-CoV-2 RBD between wild and variants to detect the most influential interaction. Variants of hACE2 were retrieved from NCBI and subjected to determine the most pathogenic nsSNPs. Probability of PPIs determines the binding affinity of hACE2 genetic variants with RBD was investigated. Composition variations at the hACE2 and RBD were processed for PatchDock and refined by FireDock for the PPIs. Twelve nsSNPs were identified as the top pathogenic from SNPs (n = 7489) in hACE2 using eight bioinformatics tools. Eight RBD variants were complexed with 12 nSNPS of hACE2, and the global energy scores (Kcal/mol) were calculated and classified as very weak (–3.93 to −18.43), weak (–18.42 to −32.94), moderate (–32.94 to −47.44), strong (–47.44 to −61.95) and very strong (–61.95 to −76.46) zones. Seven composition variants in the very strong zone [G726R-G476S; R768W-V367F; Y252N-V483A; Y252N-V367F; G726R-V367F; N720D-V367F and N720D-F486L], and three in very weak [P263S-S383C; RBD-H378R; G726R-A348T] are significantly (p < 0.00001) varied for global energy score. Zonation of the five zones was established based on the scores to differentiate the effect of hACE2 and RBD variants on the binding affinity. Moreover, our findings support that the combination of hACE2 and RBD is key players for the risk of infection that should be done by further laboratory studies. Communicated by Ramaswamy H. Sarma
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?:doi
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?:doi
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10.1080/07391102.2020.1841032
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Journal_of_biomolecular_structure_&_dynamics
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document_parses/pdf_json/bd22e18825aeb4eba5165752759bcd9fd5a1f881.json
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document_parses/pmc_json/PMC7651216.xml.json
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Emerging of composition variations of SARS-CoV-2 spike protein and human ACE2 contribute to the level of infection: in silico approaches
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