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Research Article
Darren Fayne
Trinity College Dublin
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2417-5406
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Declarations:
Declarations
- The author has confirmed that a statement listing potential conflicts of interest or lack thereof is included in the text.
The COVID-19 pandemic has negatively affected human life globally. It has led to economic crises and health emergencies across the world, spreading rapidly among the human population and has caused many deaths. Currently, there are no treatments available for COVID-19 so there is an urgent need to develop therapeutic interventions that could be used against the novel coronavirus infection. In this research, we used computational drug design technologies to repurpose existing drugs as inhibitors of SARS-CoV-2 viral proteins. The Broad Institute’s Drug Repurposing Hub consists of in-development/approved drugs and was computationally screened to identify potential hits which could inhibit protein targets encoded by the SARS-CoV-2 genome. By virtually screening the Broad collection, using rationally designed pharmacophore features, we identified molecules which may be repurposed against viral nucleocapsid and non-structural proteins. The pharmacophore features were generated after careful visualisation of the interactions between co-crystalised ligands and the protein binding site. The ChEMBL database was used to determine the compound’s level of inhibition of SARS-CoV-2 and correlate the predicted viral protein target with whole virus in vitro data. The results from this study may help to accelerate drug development against COVID-19 and the hit compounds should be progressed through further in vitro and in vivo studies on SARS-CoV-2.
Keywords
SARS-CoV-2, COVID-19, drug repurposing, pharmacophore, virtual screening, nucleocapsid protein, non-structural proteins
Figure 1
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Figure 4
This is a list of supplementary files associated with this preprint. Click to download.
- FigureS1.jpg
A) 2D representation of the nsp7-nsp8-nsp12 ligand-binding pocket showing hydrogen bond interaction between F86 and amino acid residues. (B) 3D representation of ligand F86 (pink colour) interactions with amino acids. (C) Ligand inside the binding pocket with surface representation. (D) Superposition of 7BV2 and 7BW4 showing the structural similarity between them (Maroon colour- 7BW4, Orange colour- 7BV2).
- FigureS2.jpg
(A) First Pharmacophore features on F86 ligand (pink colour) of 7BV2 protein structure. (B) Structures of selected hit (Coenzyme-I, adenosine triphosphate and nadide) molecules obtained after VS.
- supplement3.jpg
(A) Second Pharmacophore features on F86 ligand (pink colour) from 7BV2 protein structure. (B) Structures of select hits (Procyanidin-b-2, rutin, kuromanin and epigallocatechin-gallate-(-)) obtained after VS
- FigureS4.png
(A) First pharmacophore features around the hit molecule Coenzyme-I (brown colour) and interactions with the amino acids of the binding pocket. (B) Second pharmacophore features around the hit molecule rutin (dark blue colour) and its interactions with the binding pocket amino acids.
- FigureS5.jpg
(A) 2D representation of nsp10-nsp16 (6W4H) ligand-binding pocket showing hydrogen bond interactions between SAM and amino acids. (B) 3D representation of ligand SAM (pink colour) interactions with amino acids. (C) Ligand inside the binding pocket with surface representation. (D) Superposition of 6W4H and 7BQ7 showing the structural similarity between them (Red colour- 7BQ7, Dark green colour- 6W4H).
- FigureS6.jpg
(A) First Pharmacophore features on SAM ligand (pink colour) of 6W4H protein structure. (B) Structures of select hits (ATN-161, adaptavir and dihydrostreptomycin) obtained after VS.
- FigureS7.jpg
(A) Second Pharmacophore features on SAM ligand (pink colour) of 6W4H protein structure. (B) Structures of hits (TMC-353121 and paromomycin) obtained after VS.
- FigureS8.jpg
(A) First pharmacophore features around the hit molecule ATN-161 (brown colour) and interactions with the amino acids of the binding pocket. (B) Second pharmacophore features around the hit molecule TMC-35121 (green colour) and interactions with the amino acid of the binding pocket.
- FigureS9.jpg
(A) 2D representation of the nsp15 ligand-binding pocket showing hydrogen bond interactions between tipiracil and amino acid. (B) 3D representation of ligand tipiracil (pink colour) interactions with amino acids. (C) Ligand inside the binding pocket with surface representation. (D) Superposition of 6WLC and 6WXC showing the structural similarity between them (Purple colour- 6WXC, Orange colour- 6WLC).
- FigureS10.jpg
2D representation of nsp15 ligand-binding pocket showing hydrogen bond interactions between U5P and amino acids. (B) 3D representation of ligand U5P (blue colour) interactions with amino acid. (C) Ligand inside the binding pocket with surface representation.
- FigureS11.jpg
(A) First Pharmacophore features on tipiracil ligand (pink colour) from 6WXC protein structure. (B) Structures of select hits (acadesine, olomoucine, sapropterin and tetrahydrofolic acid) obtained after VS.
- FigureS12.jpg
(A) Second Pharmacophore features on U5P ligand (blue colour) from 6WLC protein structure. (B) Structures of hit molecule (INS316) obtained after VS.
- FigureS13.jpg
(A) First pharmacophore features around the hit molecule sapropterin (green colour) and its interactions with the amino acids of the binding pocket. (B) Second pharmacophore features around the hit molecule INS316 (brown colour) and interactions with the amino acids of the binding pocket.
- FigureS14.jpg
(A) Superposition of 6WLC, 6WXC and 6X4I showing the structural similarity between them. (B) Three different ligands in the binding site of nsp15 making different interactions (6WLC- yellow U5P, 6WXC- blue tipiracil, 6W4I-green U3P).
- FigureS15.jpg
(A) 2D representation of nucleocapsid ligand-binding pocket showing hydrogen bond interactions between MES and amino acids. (B) 3D representation of ligand MES (pink colour) interactions with amino acids. (C) Ligand inside the binding pocket with surface representation. (D) Superposition of 6WKP and 6M3M showing the structural similarity between them (Blue colour- 6WKP, Pink colour- 6M3M).
- FigureS16.jpg
(A) First Pharmacophore features on MES ligand D chain (pink colour) of 6WKP protein structu
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This is a preprint. It has not completed peer review.
Research Article
Darren Fayne
Trinity College Dublin
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2417-5406
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 06 Aug, 2020
Community comments: 4
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Declarations:
Declarations
- The author has confirmed that a statement listing potential conflicts of interest or lack thereof is included in the text.
The COVID-19 pandemic has negatively affected human life globally. It has led to economic crises and health emergencies across the world, spreading rapidly among the human population and has caused many deaths. Currently, there are no treatments available for COVID-19 so there is an urgent need to develop therapeutic interventions that could be used against the novel coronavirus infection. In this research, we used computational drug design technologies to repurpose existing drugs as inhibitors of SARS-CoV-2 viral proteins. The Broad Institute’s Drug Repurposing Hub consists of in-development/approved drugs and was computationally screened to identify potential hits which could inhibit protein targets encoded by the SARS-CoV-2 genome. By virtually screening the Broad collection, using rationally designed pharmacophore features, we identified molecules which may be repurposed against viral nucleocapsid and non-structural proteins. The pharmacophore features were generated after careful visualisation of the interactions between co-crystalised ligands and the protein binding site. The ChEMBL database was used to determine the compound’s level of inhibition of SARS-CoV-2 and correlate the predicted viral protein target with whole virus in vitro data. The results from this study may help to accelerate drug development against COVID-19 and the hit compounds should be progressed through further in vitro and in vivo studies on SARS-CoV-2.
Figure 1
Figure 2
Figure 3
Figure 4
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