CORona Drug InTEractions database
Drug repurposing for identification of potential inhibitors against SARS-CoV-2 spike receptor-binding domain: An in silico approach
Santosh Kumar Behera, Namita Mahapatra, Chandra Sekhar Tripathy, Sanghamitra Pati
Abstract
Background & objectives: The world is currently under the threat of coronavirus disease 2019 (COVID-19) infection, caused by SARS-CoV-2. The objective of the present investigation was to repurpose the drugs with potential antiviral activity against receptor-binding domain (RBD) of SARS-CoV-2 spike (S) protein among 56 commercially available drugs. Therefore, an integrative computational approach, using molecular docking, quantum chemical calculation and molecular dynamics, was performed to unzip the effective drug-target interactions between RBD and 56 commercially available drugs. Methods: The present in silico approach was based on information of drugs and experimentally derived crystal structure of RBD of SARS-CoV-2 S protein. Molecular docking analysis was performed for RBD against all 56 reported drugs using AutoDock 4.2 tool to screen the drugs with better potential antiviral activity which were further analysed by other computational tools for repurposing potential drug or drugs for COVID-19 therapeutics. Results: Drugs such as chalcone, grazoprevir, enzaplatovir, dolutegravir, daclatasvir, tideglusib, presatovir, remdesivir and simeprevir were predicted to be potentially effective antiviral drugs against RBD and could have good COVID-19 therapeutic efficacy. Simeprevir displayed the highest binding affinity and reactivity against RBD with the values of -8.52 kcal/mol (binding energy) and 9.254 kcal/mol (band energy gap) among all the 56 drugs under investigation. Interpretation & conclusions: In the current investigation, simeprevir was identified as the potential antiviral drug based on the in silico findings in comparison to remdesivir, favipiravir and other 53 drugs. Further, laboratory and clinical investigations are needed to be carried out which will aid in the development of quick therapeutics designed for COVID-19.
Source: PubMed
Related molecules
Related interactions
| Target | Target affiliation | Drug | Type | Result |
|---|---|---|---|---|
| Target | Target affiliation | Drug | Type | Result |
| Name | Synonyms | Genes | Origin |
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| Name | Synonyms | Genes | Origin |
| Name | Synonyms | PubChem | DrugBank | RCSB PDB | ATC |
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| Name | Synonyms | PubChem | DrugBank | RCSB PDB | ATC |
| Title | Authors | DOI | Source | Article type | Date |
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| Title | Authors | DOI | Source | Article type | Date |
| Title | Status | Phases | Start Date | Prim. Comp. Date | Comp. Date | First Post. Date |
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| Title | Status | Phases | Start Date | Prim. Comp. Date | Comp. Date | First Post. Date |
CORDITE (CORona Drug InTEractions database) collects and aggregates data from PubMed, MedRxiv, BioRxiv, ChemRxiv and PMC for SARS-CoV-2. Its main focus is set on drug interactions either addressing viral proteins or human proteins that could be used to treat COVID. It collects and provides up-to-date information on computational predictions, in vitro, as well as in vivo study data.
The information provided is for research only and we cannot guarantee the correctness of the data.
Please contact dominik.heider@uni-muenster.de for further information.
Programmable access
There is an open API for access programmatically to the database. The API will print a JSON output:
- Interactions
https://cordite-api.uni-muenster.de/api.php?action=list&table=interaction
- Targets
https://cordite-api.uni-muenster.de/api.php?action=list&table=target
- Drugs
https://cordite-api.uni-muenster.de/api.php?action=list&table=drug
- Publications
https://cordite-api.uni-muenster.de/api.php?action=list&table=publication
- Clinical trials
https://cordite-api.uni-muenster.de/api.php?action=list&table=clinical_trial