Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds.
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ABSTRACT: The recently emerged 2019 Novel Coronavirus (SARS-CoV-2) and associated COVID-19 disease cause serious or even fatal respiratory tract infection and yet no approved therapeutics or effective treatment is currently available to effectively combat the outbreak. This urgent situation is pressing the world to respond with the development of novel vaccine or a small molecule therapeutics for SARS-CoV-2. Along these efforts, the structure of SARS-CoV-2 main protease (Mpro) has been rapidly resolved and made publicly available to facilitate global efforts to develop novel drug candidates. Recently, our group has developed a novel deep learning platform - Deep Docking (DD) which provides fast prediction of docking scores of Glide (or any other docking program) and, hence, enables structure-based virtual screening of billions of purchasable molecules in a short time. In the current study we applied DD to all 1.3 billion compounds from ZINC15 library to identify top 1,000 potential ligands for SARS-CoV-2 Mpro protein. The compounds are made publicly available for further characterization and development by scientific community.
Project description:Recent explosive growth of 'make-on-demand' chemical libraries brought unprecedented opportunities but also significant challenges to the field of computer-aided drug discovery. To address this expansion of the accessible chemical universe, molecular docking needs to accurately rank billions of chemical structures, calling for the development of automated hit-selecting protocols to minimize human intervention and error. Herein, we report the development of an artificial intelligence-driven virtual screening pipeline that utilizes Deep Docking with Autodock GPU, Glide SP, FRED, ICM and QuickVina2 programs to screen 40 billion molecules against SARS-CoV-2 main protease (Mpro). This campaign returned a significant number of experimentally confirmed inhibitors of Mpro enzyme, and also enabled to benchmark the performance of twenty-eight hit-selecting strategies of various degrees of stringency and automation. These findings provide new starting scaffolds for hit-to-lead optimization campaigns against Mpro and encourage the development of fully automated end-to-end drug discovery protocols integrating machine learning and human expertise.
Project description:Originating for the first time in Wuhan, China, the outbreak of SARS-CoV-2 has caused a serious global health issue. An effective treatment for SARS-CoV-2 is still unavailable. Therefore, in this study, we have tried to predict a list of potential inhibitors for SARS-CoV-2 main protease (Mpro) using a combination of molecular docking and fast pulling of ligand (FPL) simulations. The approaches were initially validated over a set of eleven available inhibitors. Both Autodock Vina and FPL calculations produced consistent results with the experiments with correlation coefficients of R Dock = 0.72 ± 0.14 and R W = -0.76 ± 0.10, respectively. The combined approaches were then utilized to predict possible inhibitors that were selected from a ZINC15 sub-database for SARS-CoV-2 Mpro. Twenty compounds were suggested to be able to bind well to SARS-CoV-2 Mpro. Among them, five top-leads are periandrin V, penimocycline, cis-p-Coumaroylcorosolic acid, glycyrrhizin, and uralsaponin B. The obtained results could probably lead to enhance the COVID-19 therapy.
Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has adversely affected global health since its emergence in 2019. The lack of effective treatments prompted worldwide efforts to immediately develop therapeutic strategies against COVID-19. The main protease (Mpro) of SARS-CoV-2 plays a crucial role in viral replication, and therefore it serves as an attractive target for COVID-19-specific drug development. Due to the richness and diversity of insect protease inhibitors, we docked SARS-CoV-2 Mpro onto 25 publicly accessible insect-derived protease inhibitors using the ClusPro server, and the regions with high inhibitory potentials against Mpro were used to design peptides. Interactions of these inhibitory peptides with Mpro were further assessed by two directed docking programs, AutoDock and Haddock. AutoDock analysis predicted the highest binding energy (-9.39 kcal/mol) and the lowest inhibition constant (130 nM) for the peptide 1KJ0-7 derived from SGCI (Schistocerca gregaria chymotrypsin inhibitor). On the other hand, Haddock analysis resulted in the discovery of a different peptide designated 2ERW-9 from infestin, a serine protease inhibitor of Triatoma infestans, with the best docking score (-131), binding energy (-11.7 kcal/mol), and dissociation constant (2.6E-09 M) for Mpro. Furthermore, using molecular dynamic simulations, 1KJ0-7 and 2ERW-9 were demonstrated to form stable complexes with Mpro. The peptides also showed suitable drug-likeness properties compared to commercially available drugs based on Lipinski's rule. Our findings present two peptides with possible protease inhibitor activities against Mpro and further demonstrate the potential of insect-derived peptides and computer-aided methods for drug discovery.
Project description:SARS-CoV-2 has rapidly emerged as a global pandemic with high infection rate. At present, there is no drug available for this deadly disease. Recently, Mpro (Main Protease) enzyme has been identified as essential proteins for the survival of this virus. In the present work, Lipinski's rules and molecular docking have been performed to identify plausible inhibitors of Mpro using food compounds. For virtual screening, a database of food compounds was downloaded and then filtered using Lipinski's rule of five. Then, molecular docking was accomplished to identify hits using Mpro protein as the target enzyme. This led to identification of a Spermidine derivative as a hit. In the next step, Spermidine derivatives were collected from PubMed and screened for their binding with Mpro protein. In addition, molecular dynamic simulations (200 ns) were executed to get additional information. Some of the compounds are found to have strong affinity for Mpro, therefore these hits could be used to develop a therapeutic agent for SARS-CoV-2.
Project description:Coronavirus disease 2019 (COVID-19) has emerged from China and globally affected the entire population through the human-to-human transmission of a newly emerged virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The genome of SARS-CoV-2 encodes several proteins that are essential for multiplication and pathogenesis. The main protease (Mpro or 3CLpro) of SARS-CoV-2 plays a central role in its pathogenesis and thus is considered as an attractive drug target for the drug design and development of small-molecule inhibitors. We have employed an extensive structure-based high-throughput virtual screening to discover potential natural compounds from the ZINC database which could inhibit the Mpro of SARS-CoV-2. Initially, the hits were selected on the basis of their physicochemical and drug-like properties. Subsequently, the PAINS filter, estimation of binding affinities using molecular docking, and interaction analyses were performed to find safe and potential inhibitors of SARS-CoV-2 Mpro. We have identified ZINC02123811 (1-(3-(2,5,9-trimethyl-7-oxo-3-phenyl-7H-furo[3,2-g]chromen-6-yl)propanoyl)piperidine-4-carboxamide), a natural compound bearing appreciable affinity, efficiency, and specificity towards the binding pocket of SARS-CoV-2 Mpro. The identified compound showed a set of drug-like properties and preferentially binds to the active site of SARS-CoV-2 Mpro. All-atom molecular dynamics (MD) simulations were performed to evaluate the conformational dynamics, stability and interaction mechanism of Mpro with ZINC02123811. MD simulation results indicated that Mpro with ZINC02123811 forms a stable complex throughout the trajectory of 100 ns. These findings suggest that ZINC02123811 may be further exploited as a promising scaffold for the development of potential inhibitors of SARS-CoV-2 Mpro to address COVID-19.
Project description:SARS-CoV-2 is the pathogen agent of the new corona virus disease that appeared at the end of 2019 in China. There is, currently, no effective treatment against COVID-19. We report in this study a molecular docking study of ten Aloe vera molecules with the main protease (3CLpro) responsible for the replication of coronaviruses. The outcome of their molecular simulation and ADMET properties reveal three potential inhibitors of the enzyme (ligands 6, 1 and 8) with a clear preference of ligand 6 that has the highest binding energy (-7.9 kcal/mol) and fully obeys the Lipinski's rule of five.
Project description:In this paper we investigate 10 different HIV protease inhibitors (HPIs) as possible repurposed-drugs candidates against SARS-CoV-2. To this end, we execute molecular docking and molecular dynamics simulations. The in silico data demonstrated that, despite their molecular differences, all HPIs presented a similar behavior for the parameters analyzed, with the exception of Nelfinavir that showed better results for most of the molecular dynamics parameters in comparison with the N3 inhibitor.
Project description:The COVID-19 outbreak has thrown the world into an unprecedented crisis. It has posed a challenge to scientists around the globe who are working tirelessly to combat this pandemic. We herein report a set of molecules that may serve as possible inhibitors of the SARS-CoV-2 main protease. To identify these molecules, we followed a combinatorial structure-based strategy, which includes high-throughput virtual screening, molecular docking and WaterMap calculations. The study was carried out using Protein Data Bank structures 5R82 and 6Y2G. DrugBank, Enamine, Natural product and Specs databases, along with a few known antiviral drugs, were used for the screening. WaterMap analysis aided in the recognition of high-potential molecules that can efficiently displace binding-site waters. This study may help the discovery and development of antiviral drugs against SARS-CoV-2.
Project description:In this study, chemical investigation of methanol extract of the air-dried fruits of Luffa cylindrica led to the identification of a new δ-valerolactone (1), along with sixteen known compounds (2-17). Their chemical structures including the absolute configuration were elucidated by extensive spectroscopic analysis and electronic circular dichroism analysis, as well as by comparison with those reported in the literature. For the first time in literature, we have examined the binding potential of the isolated compounds to highly conserved protein, Mpro of SARS-CoV-2 using the molecular docking technique. We found that the isolated saponins (14-17) bind to the substrate-binding pocket of SARS-CoV-2 Mpro with docking energy scores of -7.13, -7.29, -7.47, and -7.54 kcal.mol-1, respectively, along with binding abilities equivalent to an already claimed N3 protease inhibitor (-7.51 kcal.mol-1).
Project description:During almost all 2020, coronavirus disease 2019 (COVID-19) pandemic has constituted the major risk for the worldwide health and economy, propelling unprecedented efforts to discover drugs for its prevention and cure. At the end of the year, these efforts have culminated with the approval of vaccines by the American Food and Drug Administration (FDA) and the European Medicines Agency (EMA) giving new hope for the future. On the other hand, clinical data underscore the urgent need for effective drugs to treat COVID-19 patients. In this work, we embarked on a virtual screening campaign against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Mpro chymotrypsin-like cysteine protease employing our in-house database of peptide and non-peptide ligands characterized by different types of warheads acting as Michael acceptors. To this end, we employed the AutoDock4 docking software customized to predict the formation of a covalent adduct with the target protein. In vitro verification of the inhibition properties of the most promising candidates allowed us to identify two new lead inhibitors that will deserve further optimization. From the computational point of view, this work demonstrates the predictive power of AutoDock4 and suggests its application for the in silico screening of large chemical libraries of potential covalent binders against the SARS-CoV-2 Mpro enzyme.