Combined deep learning and molecular docking simulations approach identifies potentially effective FDA approved drugs for repurposing against SARS-CoV-2.

Anwaar, Muhammad U.Adnan, FarjadAbro, AsmaKhan, Rayyan A.Rehman, Asad U.Osama, MuhammadRainville, ChristopherKumar, SureshSterner, DavidJaved, SaadJamal, Syed B.Baig, AhmadullahShabbir, Muhammad R.Ahsan, WasehButt, Tauseef R.Assir, Muhammad Z.

10.1016/j.compbiomed.2021.105049

Research curated

Abstract

The ongoing pandemic of Coronavirus Disease 2019 (COVID-19) has posed a serious threat to global public health. Drug repurposing is a time-efficient approach to finding effective drugs against SARS-CoV-2 in this emergency. Here, we present a robust experimental design combining deep learning with molecular docking experiments to identify the most promising candidates from the list of FDA-approved drugs that can be repurposed to treat COVID-19. We have employed a deep learning-based Drug Target Interaction (DTI) model, called DeepDTA, with few improvements to predict drug-protein binding affinities, represented as KIBA scores, for 2440 FDA-approved and 8168 investigational drugs against 24 SARS-CoV-2 viral proteins. FDA-approved drugs with the highest KIBA scores were selected for molecular docking simulations. We ran around 50,000 docking simulations for 168 selected drugs against 285 total predicted and/or experimentally proven active sites of all 24 SARS-CoV-2 viral proteins. A list of 49 most promising FDA-approved drugs with the best consensus KIBA scores and binding affinity values against selected SARS-CoV-2 viral proteins was generated. Most importantly, 16 drugs including anidulafungin, velpatasvir, glecaprevir, rifapentine, flavin adenine dinucleotide (FAD), terlipressin, and selinexor demonstrated the highest predicted inhibitory potential against key SARS-CoV-2 viral proteins. We further measured the inhibitory activity of 5 compounds (rifapentine, velpatasvir, glecaprevir, anidulafungin, and FAD disodium) on SARS-CoV-2 PLpro using Ubiquitin-Rhodamine 110 Gly fluorescent intensity assay. The highest inhibition of PLpro activity was seen with rifapentine (IC50: 15.18 μM) and FAD disodium (IC50: 12.39 μM), the drugs with high predicted KIBA scores and binding affinities.

Source: PMC

Related molecules

Name	Synonyms	Genes
Velpatasvir
Glecaprevir	Glecaprevir , UNII-K6BUU8J72P , ABT-493 , 1365970-03-1 , K6BUU8J72P
Rifabutin
Rifapentine
Procaine penicillin G
Tadalafil
Riboflavin 5'-monophosphate	Flavin mononucleotide
Flavin adenine dinucleotide
Terlipressin
Desmopressin
Elbasvir
Oxatomide
Enasidenib
Edoxaban
Selinexor
Papain-like proteinases 1/2	Non-structural protein 3, papain like viral protease, nsp3, PL1-PRO/PL2-PRO, PLP1/PLP2, p195, Plpro	rep, ORF1a-1b
Anidulafungin

Related interactions

Target	Drug	Type	Result
Papain-like proteinases 1/2	Anidulafungin	Computational	positive
Papain-like proteinases 1/2	Velpatasvir	Computational	positive
Papain-like proteinases 1/2	Glecaprevir	Computational	positive
Papain-like proteinases 1/2	Rifabutin	Computational	positive
Papain-like proteinases 1/2	Rifapentine	Computational	positive
Papain-like proteinases 1/2	Procaine penicillin G	Computational	positive
Papain-like proteinases 1/2	Tadalafil	Computational	positive
Papain-like proteinases 1/2	Riboflavin 5'-monophosphate	Computational	positive
Papain-like proteinases 1/2	Riboflavin 5'-monophosphate	Computational	positive
Papain-like proteinases 1/2	Terlipressin	Computational	positive
Papain-like proteinases 1/2	Desmopressin	Computational	positive
Papain-like proteinases 1/2	Elbasvir	Computational	positive
Papain-like proteinases 1/2	Oxatomide	Computational	positive
Papain-like proteinases 1/2	Enasidenib	Computational	positive
Papain-like proteinases 1/2	Edoxaban	Computational	positive
Papain-like proteinases 1/2	Selinexor	Computational	positive