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Comput Methods Programs Biomed . Computational investigation reveals that the mutant strains of SARS-CoV2 have differential structural and binding

tetano

Editor, Senior Moderator
Comput Methods Programs Biomed


. 2021 Dec 20;215:106594.
doi: 10.1016/j.cmpb.2021.106594. Online ahead of print.
Computational investigation reveals that the mutant strains of SARS-CoV2 have differential structural and binding properties


Rakesh Kumar[SUP] 1 [/SUP], Rahul Kumar[SUP] 1 [/SUP], Harsh Goel[SUP] 1 [/SUP], Pranay Tanwar[SUP] 2 [/SUP]



Affiliations

Abstract

Background and objectives: Remarkable infectivity of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV2) is due to the rapid emergence of various strains which enable the virus to ruling the world. Over the course of SARS-CoV2 pandemic, the scientific communities worldwide are responding to newly emerging genetic variants. However, mechanism behind the persistent infection of these variants is still not known due to the paucity of study of these variants at molecular level. In this scenario, computational methods have immense utility in understanding the molecular and functional properties of different variants.
Methods: The various mutants (MTs) of SpikeS1 receptor binding domain (RBD) of highly infectious SARS-CoV2 strains were manifested and elucidated the protein structure and binding strength using molecular dynamics (MD) simulation and protein-protein docking approaches.
Results: MD simulation study showed that all MTs exhibited stable structures with altered functional properties. Furthermore, the binding strength of different MTs along with WT (wildtype) was revealed that MTs showed differential binding affinities to host protein with high binding strength exhibited by V367F and V483A MTs.
Conclusion: Hence, this study shed light on the molecular basis of infection caused by different variants of SARS-CoV2, which might play an important role in to cease the transmission and pathogenesis of virus and also implicate in rational designing of a specific drug.

Keywords: Molecular dynamics; Mutant strains; Network analysis; Protein-protein interaction; SARS-CoV2.
 
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