Combined deep learning and molecular docking simulations approach identifies potentially effective FDA approved drugs for repurposing against SARS-CoV-2.
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.
Project description:With numerous infections and fatalities, COVID-19 has wreaked havoc around the globe. The main protease (Mpro), which cleaves the polyprotein to form non-structural proteins, thereby helping in the replication of SARS-CoV-2, appears as an attractive target for antiviral therapeutics. As FDA-approved drugs have shown effectiveness in targeting Mpro in previous SARS-CoV(s), molecular docking and virtual screening of existing antiviral, antimalarial, and protease inhibitor drugs were carried out against SARS-CoV-2 Mpro. Among 53 shortlisted drugs with binding energies lower than that of the crystal-bound inhibitor α-ketoamide 13 b (-6.7 kcal/mol), velpatasvir, glecaprevir, grazoprevir, baloxavir marboxil, danoprevir, nelfinavir, and indinavir (-9.1 to -7.5 kcal/mol) were the most significant on the list (hereafter referred to as the 53-list). Molecular dynamics (MD) simulations confirmed the stability of their Mpro complexes, with the MMPBSA binding free energy (ΔG<sub>bind</sub>) ranging between -124 kJ/mol (glecaprevir) and -28.2 kJ/mol (velpatasvir). Despite having the lowest initial binding energy, velpatasvir exhibited the highest ΔG<sub>bind</sub> value for escaping the catalytic site during the MD simulations, indicating its reduced efficacy, as observed experimentally. Available inhibition assay data adequately substantiated the computational forecast. Glecaprevir and nelfinavir (ΔG<sub>bind</sub> = -95.4 kJ/mol) appear to be the most effective antiviral drugs against Mpro. Furthermore, the remaining FDA drugs on the 53-list can be worth considering, since some have already demonstrated antiviral activity against SARS-CoV-2. Hence, theoretical pK<sub>i</sub> (K<sub>i</sub> = inhibitor constant) values for all 53 drugs were provided. Notably, ΔG<sub>bind</sub> directly correlates with the average distance of the drugs from the His41-Cys145 catalytic dyad of Mpro, providing a roadmap for rapid screening and improving the inhibitor design against SARS-CoV-2 Mpro.
Project description:Due to the lack of efficient therapeutic options and clinical trial limitations, the FDA-approved drugs can be a good choice to handle Coronavirus disease (COVID-19). Many reports have enough evidence for the use of FDA-approved drugs which have inhibitory potential against target proteins of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Here, we utilized a structure-based drug design approach to find possible drug candidates from the existing pool of FDA-approved drugs and checked their effectiveness against the SARS-CoV-2. We performed virtual screening of the FDA-approved drugs against the main protease (Mpro) of SARS-CoV-2, an essential enzyme, and a potential drug target. Using well-defined computational methods, we identified Glecaprevir and Maraviroc (MVC) as the best inhibitors of SARS-CoV-2 Mpro. Both drugs bind to the substrate-binding pocket of SARS-CoV-2 Mpro and form a significant number of non-covalent interactions. Glecaprevir and MVC bind to the conserved residues of substrate-binding pocket of SARS-CoV-2 Mpro. This work provides sufficient evidence for the use of Glecaprevir and MVC for the therapeutic management of COVID-19 after experimental validation and clinical manifestations.
Project description:<h4>Background</h4>Outbreak of COVID-19 has been recognized as a global health concern since it causes high rates of morbidity and mortality. No specific antiviral drugs are available for the treatment of COVID-19 till date. Drug repurposing strategy helps to find out the drugs for COVID-19 treatment from existing FDA approved antiviral drugs. In this study, FDA approved small molecule antiviral drugs were repurposed against the major viral proteins of SARS-CoV-2.<h4>Methods</h4>The 3D structures of FDA approved small molecule antiviral drugs were retrieved from PubChem. Virtual screening was performed to find out the lead antiviral drug molecules against main protease (Mpro) and RNA-dependent RNA polymerase (RdRp) using COVID-19 Docking Server. Furthermore, lead molecules were individually docked against protein targets using AutoDock 4.0.1 software and their drug-likeness and ADMET properties were evaluated.<h4>Results</h4>Out of 65 FDA approved small molecule antiviral drugs screened, Raltegravir showed highest interaction energy value of -9 kcal/mol against Mpro of SARS-CoV-2 and Indinavir, Tipranavir, and Pibrentasvir exhibited a binding energy value of ?-8 kcal/mol. Similarly Indinavir showed the highest binding energy of -11.5 kcal/mol against the target protein RdRp and Dolutegravir, Elbasvir, Tipranavir, Taltegravir, Grazoprevir, Daclatasvir, Glecaprevir, Ledipasvir, Pibrentasvir and Velpatasvir showed a binding energy value in range from -8 to -11.2 kcal/mol. The antiviral drugs Raltegravir, Indinavir, Tipranavir, Dolutegravir, and Etravirine also exhibited good bioavailability and drug-likeness properties.<h4>Conclusion</h4>This study suggests that the screened small molecule antiviral drugs Raltegravir, Indinavir, Tipranavir, Dolutegravir, and Etravirine could serve as potential drugs for the treatment of COVID-19 with further validation studies.
Project description:COVID-19 has emerged as a rapidly escalating serious global health issue, affecting every section of population in a detrimental way. Present situation invigorated researchers to look for potent targets, development as well as repurposing of conventional therapeutic drugs. NSP12, a RNA polymerase, is key player in viral RNA replication and, hence, viral multiplication. In our study, we have screened a battery of FDA-approved drugs against SARS-CoV-2 RNA polymerase using <i>in silico</i> molecular docking approach<b>.</b> Identification of potent inhibitors against SARS-CoV-2 NSP12 (RNA polymerase) were screeened from FDA approved drugs by virtual screening for therapeutic applications in treatment of COVID-19. In this study, virtual screening of 1749 antiviral drugs was executed using AutoDock Vina in PyRx software. Binding affinities between NSP12 and drug molecules were determined using Ligplot<sup>+</sup> and PyMOL was used for visualization of docking between interacting residues. Screening of 1749 compounds resulted in 14 compounds that rendered high binding affinity for NSP12 target molecule. Out of 14 compounds, 5 compounds which include 3a (Paritaprevir), 3d (Glecaprevir), 3h (Velpatasvir), 3j (Remdesivir) and 3l (Ribavirin) had a binding affinity of - 10.2 kcal/mol, -9.6 kcal/mol, - 8.5 kcal/mol, - 8.0 kcal/mol and - 6.8 kcal/mol, respectively. Moreover, a number of hydrophobic interactions and hydrogen bonding between these 5 compounds and NSP12 active site were observed. Further, 3l (Ribavirin) was docked with 6M71 and molecular dynamic simulation of the complex was also performed to check the stability of the conformation. <i>In silico</i> analysis postulated the potential of conventional antiviral drugs in treatment of COVID-19. However, these finding may be further supported by experimental data for its possible clinical application in present scenario.
Project description:The rapid spread of novel coronavirus pneumonia (COVID-19) has led to a dramatically increased mortality rate worldwide. Despite many efforts, the rapid development of an effective vaccine for this novel virus will take considerable time and relies on the identification of drug-target (DT) interactions utilizing commercially available medication to identify potential inhibitors. Motivated by this, we propose a new framework, called DeepH-DTA, for predicting DT binding affinities for heterogeneous drugs. We propose a heterogeneous graph attention (HGAT) model to learn topological information of compound molecules and bidirectional ConvLSTM layers for modeling spatio-sequential information in simplified molecular-input line-entry system (SMILES) sequences of drug data. For protein sequences, we propose a squeezed-excited dense convolutional network for learning hidden representations within amino acid sequences; while utilizing advanced embedding techniques for encoding both kinds of input sequences. The performance of DeepH-DTA is evaluated through extensive experiments against cutting-edge approaches utilising two public datasets (Davis, and KIBA) which comprise eclectic samples of the kinase protein family and the pertinent inhibitors. DeepH-DTA attains the highest Concordance Index (CI) of 0.924 and 0.927 and also achieved a mean square error (MSE) of 0.195 and 0.111 on the Davis and KIBA datasets respectively. Moreover, a study using FDA-approved drugs from the Drug Bank database is performed using DeepH-DTA to predict the affinity scores of drugs against SARS-CoV-2 amino acid sequences, and the results show that that the model can predict some of the SARS-Cov-2 inhibitors that have been recently approved in many clinical studies.
Project description:Coronavirus disease 2019 (COVID-19) is a major threat worldwide due to its fast spreading. As yet, there are no established drugs available. Speeding up drug discovery is urgently required. We applied a workflow of combined in silico methods (virtual drug screening, molecular docking and supervised machine learning algorithms) to identify novel drug candidates against COVID-19. We constructed chemical libraries consisting of FDA-approved drugs for drug repositioning and of natural compound datasets from literature mining and the ZINC database to select compounds interacting with SARS-CoV-2 target proteins (spike protein, nucleocapsid protein, and 2'-o-ribose methyltransferase). Supported by the supercomputer MOGON, candidate compounds were predicted as presumable SARS-CoV-2 inhibitors. Interestingly, several approved drugs against hepatitis C virus (HCV), another enveloped (-) ssRNA virus (paritaprevir, simeprevir and velpatasvir) as well as drugs against transmissible diseases, against cancer, or other diseases were identified as candidates against SARS-CoV-2. This result is supported by reports that anti-HCV compounds are also active against Middle East Respiratory Virus Syndrome (MERS) coronavirus. The candidate compounds identified by us may help to speed up the drug development against SARS-CoV-2.
Project description:<h4>Aims</h4>Cell surface binding immunoglobin protein (csBiP) is predicted to be susceptible to SARS-CoV-2 binding. With a substrate-binding domain (SBD) that binds to polypeptides and a nucleotide-binding domain (NBD) that can initiate extrinsic caspase-dependent apoptosis, csBiP may be a promising therapeutic target for COVID-19. This study aims to identify FDA-approved drugs that can neutralize viral binding and prevent viral replication by targeting the functional domains of csBiP.<h4>Methods</h4>In silico screening of 1999 FDA-approved drugs against the functional domains of BiP were performed using three molecular docking programs to avoid bias from individual docking programs. Top ligands were selected by averaging the ligand rankings from three programs. Interactions between top ligands and functional domains of BiP were analyzed.<h4>Key findings</h4>The top 10 SBD-binding candidates are velpatasvir, irinotecan, netupitant, lapatinib, doramectin, conivaptan, fenoverine, duvelisib, irbesartan, and pazopanib. The top 10 NBD-binding candidates are nilotinib, eltrombopag, grapiprant, topotecan, acetohexamide, vemurafenib, paritaprevir, pixantrone, azosemide, and piperaquine-phosphate. Among them, Velpatasvir and paritaprevir are antiviral agents that target the protease of hepatitis C virus. Netupitant is an anti-inflammatory drug that inhibits neurokinin-1 receptor, which contributes to acute inflammation. Grapiprant is an anti-inflammatory drug that inhibits the prostaglandin E<sub>2</sub> receptor protein subtype 4, which is expressed on immune cells and triggers inflammation. These predicted SBD-binding drugs could disrupt SARS-CoV-2 binding to csBiP, and NBD-binding drugs may falter viral attachment and replication by locking the SBD in closed conformation and triggering apoptosis in infected cells.<h4>Significance</h4>csBiP appears to be a novel therapeutic target against COVID-19 by preventing viral attachment and replication. These identified drugs could be repurposed to treat COVID-19 patients.
Project description:Coronaviruses are enveloped positive-strand RNA viruses belonging to family <i>Coronaviridae</i> and order <i>Nidovirales</i> which cause infections in birds and mammals. Among the human coronaviruses, highly pathogenic ones are Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) and the Middle East Respiratory Syndrome coronavirus (MERS-CoV) which have been implicated in severe respiratory syndrome in humans. There are no approved antiviral drugs or vaccines for the treatment of human CoV infection to date. The recent outbreak of new coronavirus pandemic, coronavirus disease 2019 (COVID-19) has caused a high mortality rate and infections around the world which necessitates the need for the discovery of novel anti-coronaviral drugs. Among the coronaviruses proteins, 3C-like protease (3CL<sup>pro</sup>) is an important drug target against coronaviral infection as the auto-cleavage process catalysed by the enzyme is crucial for viral maturation and replication. The present work is aimed at the identification of suitable lead molecules for the inhibition of 3CL<sup>pro</sup> enzyme via a computational screening of the Food and Drug Administration (FDA) approved antiviral drugs and phytochemicals. Based on binding energies and molecular interaction studies, we shortlisted five lead molecules (both FDA approved drugs and phytochemicals) for each enzyme targets (SARS-CoV-2 3CL<sup>pro</sup>, SARS-CoV 3CL<sup>pro</sup> and MERS-CoV 3CL<sup>pro</sup>). The lead molecules showed higher binding affinity compared to the standard inhibitors and exhibited favourable hydrophobic interactions and a good number of hydrogen bonds with their respective targets. A few promising leads with dual inhibition potential were identified among FDA approved antiviral drugs which include DB13879 (Glecaprevir), DB09102 (Daclatasvir), molecule DB09297 (Paritaprevir) and DB01072 (Atazanavir). Among the phytochemicals, 11,646,359 (Vincapusine), 120,716 (Alloyohimbine) and 10,308,017 (Gummadiol) showed triple inhibition potential against all the three targets and 102,004,710 (18-Hydroxy-3-epi-alpha-yohimbine) exhibited dual inhibition potential. Hence, the proposed lead molecules from our findings can be further investigated through <i>in vitro</i> and <i>in vivo</i> studies to develop into potential drug candidates against human coronaviral infections.
Project description:SARS-CoV-2, a member of the coronavirus family, has caused a global public health emergency. Based on our analysis of hepatitis C virus and coronavirus replication, and the molecular structures and activities of viral inhibitors, we previously reasoned that the FDA-approved hepatitis C drug EPCLUSA (Sofosbuvir/Velpatasvir) should inhibit coronaviruses, including SARS-CoV-2. Here, using model polymerase extension experiments, we demonstrate that the active triphosphate form of Sofosbuvir is incorporated by low-fidelity polymerases and SARS-CoV RNA-dependent RNA polymerase (RdRp), and blocks further incorporation by these polymerases; the active triphosphate form of Sofosbuvir is not incorporated by a host-like high-fidelity DNA polymerase. Using the same molecular insight, we selected 3'-fluoro-3'-deoxythymidine triphosphate and 3'-azido-3'-deoxythymidine triphosphate, which are the active forms of two other anti-viral agents, Alovudine and AZT (an FDA-approved HIV/AIDS drug) for evaluation as inhibitors of SARS-CoV RdRp. We demonstrate the ability of two of these HIV reverse transcriptase inhibitors to be incorporated by SARS-CoV RdRp where they also terminate further polymerase extension. Given the 98% amino acid similarity of the SARS-CoV and SARS-CoV-2 RdRps, we expect these nucleotide analogues would also inhibit the SARS-CoV-2 polymerase. These results offer guidance to further modify these nucleotide analogues to generate more potent broad-spectrum anti-coronavirus agents.
Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for coronavirus disease 2019 (COVID-19). Since its emergence, the COVID-19 pandemic has not only distressed medical services but also caused economic upheavals, marking urgent the need for effective therapeutics. The experience of combating SARS-CoV and MERS-CoV has shown that inhibiting the 3-chymotrypsin-like protease (3CLpro) blocks the replication of the virus. Given the well-studied properties of FDA-approved drugs, identification of SARS-CoV-2 3CLpro inhibitors in an FDA-approved drug library would be of great therapeutic value. Here, we screened a library consisting of 774 FDA-approved drugs for potent SARS-CoV-2 3CLpro inhibitors, using an intramolecularly quenched fluorescence (IQF) peptide substrate. Ethacrynic acid, naproxen, allopurinol, butenafine hydrochloride, raloxifene hydrochloride, tranylcypromine hydrochloride, and saquinavir mesylate have been found to block the proteolytic activity of SARS-CoV-2 3CLpro. The inhibitory activity of these repurposing drugs against SARS-CoV-2 3CLpro highlights their therapeutic potential for treating COVID-19 and other Betacoronavirus infections.