About: A computational prediction of SARS-CoV-2 structural protein inhibitors from Azadirachta indica (Neem)

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About: A computational prediction of SARS-CoV-2 structural protein inhibitors from Azadirachta indica (Neem) Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A computational prediction of SARS-CoV-2 structural protein inhibitors from Azadirachta indica (Neem)
Creator	Banerjee, Manidipa Borkotoky, Subhomoi
Source	Medline; PMC
abstract	The rapid global spread of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has created an unprecedented healthcare crisis. The treatment for the severe respiratory illness caused by this virus is primarily symptomatic at this point, although the usage of a broad antiviral drug Remdesivir has been allowed on emergency basis by the Food and Drug Administration (FDA). The ever-increasing death toll highlights an urgent need for development of specific antivirals. In this work, we have utilized docking and simulation methods to identify small molecule inhibitors of SARS-CoV-2 Membrane (M) and Envelope (E) proteins, which are essential for virus assembly and budding. A total of 70 compounds from an Indian medicinal plant source (Azadirachta indica or Neem) were virtually screened against these two proteins and further analyzed with molecular dynamics simulations, which resulted in the identification of a few common compounds with strong binding to both structural proteins. The compounds bind to biologically critical regions of M and E, indicating their potential to inhibit the functionality of these components. We hope that our computational approach may result in the identification of effective inhibitors of SARS-CoV-2 assembly. Communicated by Ramaswamy H. Sarma
has issue date	2020-06-11(xsd:dateTime)
bibo:doi	10.1080/07391102.2020.1774419
bibo:pmid	32462988
has license	no-cc
sha1sum (hex)	ab44ded344939c5b6b546f96279143fafb7394a1
schema:url	https://doi.org/10.1080/07391102.2020.1774419
resource representing a document's title	A computational prediction of SARS-CoV-2 structural protein inhibitors from Azadirachta indica (Neem)
has PubMed Central identifier	PMC7311162
has PubMed identifier	32462988
schema:publication	J Biomol Struct Dyn
resource representing a document's body	covid:ab44ded344939c5b6b546f96279143fafb7394a1#body_text
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