Molecular docking and simulation studies on SARS-CoV-2 Mpro reveals Mitoxantrone, Leucovorin, Birinapant, and Dynasore as potent drugs against COVID-19

Mitoxantrone dihydrochloride, an FDA approved drug, binds with SARS-CoV-2 NSP1 C-terminal

One of the major virulence factors of SARS-CoV-2, NSP1, is a vital drug target due to its role in host immune evasion through multiple pathways. NSP1 protein is associated with inhibiting host mRNA translation by binding to the small subunit of ribosome through its C-terminal region. Previously, we have shown the structural dynamics of the NSP1 C-terminal region (NSP1-CTR) in different physiological environments. So, it would be very interesting to investigate the druggable compounds that could bind with NSP1-CTR. Here, in this article, we have performed different spectroscopic technique-based binding assays of an anticancer drug mitoxantrone dihydrochloride (MTX) against the NSP1-CTR. We have also performed molecular dynamics simulations of the docked complex with two different force fields up to one microsecond. Overall, our results have suggested good binding between NSP1-CTR and MTX and may have implications in developing therapeutic strategies targeting the NSP1 protein of SARS-CoV-2.

SARS-CoV-2 NSP1 is believed to be a potential drug target. Its C-terminal region which associates with ribosomal subunit has shown good binding with an anticancer drug, Mitoxantrone.

Whole genome analysis and homology modeling of SARS-CoV-2 Indian isolate reveals potent FDA approved drug choice for treating COVID-19

Coronaviruses have caused enough devastation in the last two decades. These viruses have some rare features while sharing some common features. Novel coronavirus disease (nCoV-19) caused an outbreak with a fatality rate of 5%. It emerged from China and spread into many countries. The present research focused on genome analysis of Indian nCoV-19 Isolate and its translational product subjected to homology modeling and its subsequent molecular simulations to find out potent FDA approved drug for treating COVID-19. Phylogenetic analysis of SARS-CoV-2 Indian isolate shows close resemblance with 17 countries SARS-CoV-2 isolates. Homology modeling of four non-structural proteins translational product of Indian SARS-CoV-2 genome shows high similarity and allowed regions with the existing PDB deposited SARS-CoV-2 target proteins. Finally, these four generated proteins show more affinity with cobicistat, remdesivir and indinavir out of 14 screened FDA approved drugs in molecular docking which is further proven by molecular dynamics simulation and MMGBSA analysis of target ligand complex with best simulation trajectories. Overall our present research findings is that three proposed drugs namely cobicistat, remdesivir and indinavir showed higher interaction with the model SARS-CoV-2 viral target proteins from the Indian nCoV-19 isolate. These compounds could be used as a starting point for the creation of active antiviral drugs to combat the deadly COVID-19 virus during global pandemic and its subsequent viral infection waves across the globe.Communicated by Ramaswamy H. Sarma.

Antiviral drug discovery by targeting the SARS-CoV-2 polyprotein processing by inhibition of the main protease

The spread of SARS-CoV-2, the causative agent for COVID-19, has led to a global and deadly pandemic. To date, few drugs have been approved for treating SARS-CoV-2 infections. In this study, a structure-based approach was adopted using the SARS-CoV-2 main protease (M^pro) and a carefully selected dataset of 37,060 compounds comprising M^pro and antiviral protein-specific libraries. The compounds passed two-step docking filtration, starting with standard precision (SP) followed by extra precision (XP) runs. Fourteen compounds with the highest XP docking scores were examined by 20 ns molecular dynamics simulations (MDs). Based on backbone route mean square deviations (RMSD) and molecular mechanics/generalized Born surface area (MM/GBSA) binding energy, four drugs were selected for comprehensive MDs analysis at 100 ns. Results indicated that birinapant, atazanavir, and ritonavir potently bound and stabilized SARS-CoV-2 M^pro structure. Binding energies higher than -102 kcal/mol, RMSD values <0.22 nm, formation of several hydrogen bonds with M^pro, favourable electrostatic contributions, and low radii of gyration were among the estimated factors contributing to the strength of the binding of these three compounds with M^pro. The top two compounds, atazanavir and birinapant, were tested for their ability to prevent SARS-CoV-2 plaque formation. At 10 µM of birinapant concentration, antiviral tests against SARS-CoV-2 demonstrated a 37% reduction of virus multiplication. Antiviral assays demonstrated that birinapant has high anti-SARS-CoV-2 activity in the low micromolar range, with an IC50 value of 18 ± 3.6 µM. Therefore, birinapant is a candidate for further investigation to determine whether it is a feasible therapy option.

A Computer-Aided Approach for the Discovery of D-Peptides as Inhibitors of SARS-CoV-2 Main Protease

The SARS-CoV-2 main protease, also known as 3-chymotrypsin-like protease (3CLpro), is a cysteine protease responsible for the cleavage of viral polyproteins pp1a and pp1ab, at least, at eleven conserved sites, which leads to the formation of mature nonstructural proteins essential for the replication of the virus. Due to its essential role, numerous studies have been conducted so far, which have confirmed 3CLpro as an attractive drug target to combat Covid-19 and have reported a vast number of inhibitors and their co-crystal structures. Despite all the ongoing efforts, D-peptides, which possess key advantages over L-peptides as therapeutic agents, have not been explored as potential drug candidates against 3CLpro. The current work fills this gap by reporting an in silico approach for the discovery of D-peptides capable of inhibiting 3CLpro that involves structure-based virtual screening (SBVS) of an in-house library of D-tripeptides and D-tetrapeptides into the protease active site and subsequent rescoring steps, including Molecular Mechanics Generalized-Born Surface Area (MM-GBSA) free energy calculations and molecular dynamics (MD) simulations. In vitro enzymatic assays conducted for the four top-scoring D-tetrapeptides at 20 μM showed that all of them caused 55–85% inhibition of 3CLpro activity, thus highlighting the suitability of the devised approach. Overall, our results present a promising computational strategy to identify D-peptides capable of inhibiting 3CLpro, with broader application in problems involving protein inhibition.

Drug repurposing for coronavirus (SARS-CoV-2) based on gene co-expression network analysis

Severe acute respiratory syndrome (SARS) is a highly contagious viral respiratory illness. This illness is spurred on by a coronavirus known as SARS-associated coronavirus (SARS-CoV). SARS was first detected in Asia in late February 2003. The genome of this virus is very similar to the SARS-CoV-2. Therefore, the study of SARS-CoV disease and the identification of effective drugs to treat this disease can be new clues for the treatment of SARS-Cov-2. This study aimed to discover novel potential drugs for SARS-CoV disease in order to treating SARS-Cov-2 disease based on a novel systems biology approach. To this end, gene co-expression network analysis was applied. First, the gene co-expression network was reconstructed for 1441 genes, and then two gene modules were discovered as significant modules. Next, a list of miRNAs and transcription factors that target gene co-expression modules' genes were gathered from the valid databases, and two sub-networks formed of transcription factors and miRNAs were established. Afterward, the list of the drugs targeting obtained sub-networks' genes was retrieved from the DGIDb database, and two drug-gene and drug-TF interaction networks were reconstructed. Finally, after conducting different network analyses, we proposed five drugs, including FLUOROURACIL, CISPLATIN, SIROLIMUS, CYCLOPHOSPHAMIDE, and METHYLDOPA, as candidate drugs for SARS-CoV-2 coronavirus treatment. Moreover, ten miRNAs including miR-193b, miR-192, miR-215, miR-34a, miR-16, miR-16, miR-92a, miR-30a, miR-7, and miR-26b were found to be significant miRNAs in treating SARS-CoV-2 coronavirus.

Drug repositioning by merging active subnetworks validated in cancer and COVID-19

Computational drug repositioning aims at ranking and selecting existing drugs for novel diseases or novel use in old diseases. In silico drug screening has the potential for speeding up considerably the shortlisting of promising candidates in response to outbreaks of diseases such as COVID-19 for which no satisfactory cure has yet been found. We describe DrugMerge as a methodology for preclinical computational drug repositioning based on merging multiple drug rankings obtained with an ensemble of disease active subnetworks. DrugMerge uses differential transcriptomic data on drugs and diseases in the context of a large gene co-expression network. Experiments with four benchmark diseases demonstrate that our method detects in first position drugs in clinical use for the specified disease, in all four cases. Application of DrugMerge to COVID-19 found rankings with many drugs currently in clinical trials for COVID-19 in top positions, thus showing that DrugMerge can mimic human expert judgment.

Network-Based Identification and Experimental Validation of Drug Candidates Toward SARS-CoV-2 via Targeting Virus-Host Interactome

Despite that several therapeutic agents have exhibited promising prevention or treatment on Coronavirus disease-2019 (COVID-19), there is no specific drug discovered for this pandemic. Targeting virus-host interactome provides a more effective strategy for antivirus drug discovery compared with targeting virus proteins. In this study, we developed a network-based infrastructure to prioritize promising drug candidates from natural products and approved drugs via targeting host proteins of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). We firstly measured the network distances between drug targets and COVID-19 disease module utilizing the network proximity approach, and identified 229 approved drugs as well as 432 natural products had significant associations with SARS-CoV-2. After searching for previous literature evidence, we found that 60.7% (139/229) of approved drugs and 39.6% (171/432) of natural products were confirmed with antivirus or anti-inflammation. We further integrated our network-based predictions and validated anti-SARS-CoV-2 activities of some compounds. Four drug candidates, including hesperidin, isorhapontigenin, salmeterol, and gallocatechin-7-gallate, have exhibited activity on SARS-COV-2 virus-infected Vero cells. Finally, we showcased the mechanism of actions of isorhapontigenin and salmeterol via network analysis. Overall, this study offers forceful approaches for in silico identification of drug candidates on COVID-19, which may facilitate the discovery of antiviral drug therapies.

Comprehensive investigation of RNA-sequencing dataset reveals the hub genes and molecular mechanisms of coronavirus disease 2019 acute respiratory distress syndrome

The goal of this study is to reveal the hub genes and molecular mechanisms of the coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS) based on the genome-wide RNA sequencing dataset. The RNA sequencing dataset of COVID-19 ARDS was obtained from GSE163426. A total of 270 differentially expressed genes (DEGs) were identified between COVID-19 ARDS and control group patients. Functional enrichment analysis of DEGs suggests that these DEGs may be involved in the following biological processes: response to cytokine, G-protein coupled receptor activity, ionotropic glutamate receptor signalling pathway and G-protein coupled receptor signalling pathway. By using the weighted correlation network analysis approach to analyse these DEGs, 10 hub DEGs that may play an important role in COVID-19 ARDS were identified. A total of 67 potential COVID-19 ARDS targetted drugs were identified by a complement map analysis. Immune cell infiltration analysis revealed that the levels of T cells CD4 naive, T cells follicular helper, macrophages M1 and eosinophils in COVID-19 ARDS patients were significantly different from those in control group patients. In conclusion, this study identified 10 COVID-19 ARDS-related hub DEGs and numerous potential molecular mechanisms through a comprehensive analysis of the RNA sequencing dataset and also revealed the difference in immune cell infiltration of COVID-19 ARDS.

In Silico Exploration of Phytoconstituents From Phyllanthus emblica and Aegle marmelos as Potential Therapeutics Against SARS-CoV-2 RdRp

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) worldwide has increased the importance of computational tools to design a drug or vaccine in reduced time with minimum risk. Earlier studies have emphasized the important role of RNA-dependent RNA polymerase (RdRp) in SARS-CoV-2 replication as a potential drug target. In our study, comprehensive computational approaches were applied to identify potential compounds targeting RdRp of SARS-CoV-2. To study the binding affinity and stability of the phytocompounds from Phyllanthus emblica and Aegel marmelos within the defined binding site of SARS-CoV-2 RdRp, they were subjected to molecular docking, 100 ns molecular dynamics (MD) simulation followed by post-simulation analysis. Furthermore, to assess the importance of features involved in the strong binding affinity, molecular field-based similarity analysis was performed. Based on comparative molecular docking and simulation studies of the selected phytocompounds with SARS-CoV-2 RdRp revealed that EBDGp possesses a stronger binding affinity (-23.32 kcal/mol) and stability than other phytocompounds and reference compound, Remdesivir (-19.36 kcal/mol). Molecular field-based similarity profiling has supported our study in the validation of the importance of the presence of hydroxyl groups in EBDGp, involved in increasing its binding affinity toward SARS-CoV-2 RdRp. Molecular docking and dynamic simulation results confirmed that EBDGp has better inhibitory potential than Remdesivir and can be an effective novel drug for SARS-CoV-2 RdRp. Furthermore, binding free energy calculations confirmed the higher stability of the SARS-CoV-2 RdRp-EBDGp complex. These results suggest that the EBDGp compound may emerge as a promising drug against SARS-CoV-2 and hence requires further experimental validation.

A Multidisciplinary Approach to Coronavirus Disease (COVID-19)

Since December 2019, humanity has faced an important global threat. Many studies have been published on the origin, structure, and mechanism of action of the SARS-CoV-2 virus and the treatment of its disease. The priority of scientists all over the world has been to direct their time to research this subject. In this review, we highlight chemical studies and therapeutic approaches to overcome COVID-19 with seven different sections. These sections are the structure and mechanism of action of SARS-CoV-2, immunotherapy and vaccine, computer-aided drug design, repurposing therapeutics for COVID-19, synthesis of new molecular structures against COVID-19, food safety/security and functional food components, and potential natural products against COVID-19. In this work, we aimed to screen all the newly synthesized compounds, repurposing chemicals covering antiviral, anti-inflammatory, antibacterial, antiparasitic, anticancer, antipsychotic, and antihistamine compounds against COVID-19. We also highlight computer-aided approaches to develop an anti-COVID-19 molecule. We explain that some phytochemicals and dietary supplements have been identified as antiviral bioproducts, which have almost been successfully tested against COVID-19. In addition, we present immunotherapy types, targets, immunotherapy and inflammation/mutations of the virus, immune response, and vaccine issues.

Screening of world approved drugs against highly dynamical spike glycoprotein of SARS-CoV-2 using CaverDock and machine learning

The new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes pathological pulmonary symptoms. Most efforts to develop vaccines and drugs against this virus target the spike glycoprotein, particularly its S1 subunit, which is recognised by angiotensin-converting enzyme 2. Here we use the in-house developed tool CaverDock to perform virtual screening against spike glycoprotein using a cryogenic electron microscopy structure (PDB-ID: 6VXX) and the representative structures of five most populated clusters from a previously published molecular dynamics simulation. The dataset of ligands was obtained from the ZINC database and consists of drugs approved for clinical use worldwide. Trajectories for the passage of individual drugs through the tunnel of the spike glycoprotein homotrimer, their binding energies within the tunnel, and the duration of their contacts with the trimer's three subunits were computed for the full dataset. Multivariate statistical methods were then used to establish structure-activity relationships and select top candidate for movement inhibition. This new protocol for the rapid screening of globally approved drugs (4359 ligands) in a multi-state protein structure (6 states) showed high robustness in the rate of finished calculations. The protocol is universal and can be applied to any target protein with an experimental tertiary structure containing protein tunnels or channels. The protocol will be implemented in the next version of CaverWeb (https://loschmidt.chemi.muni.cz/caverweb/) to make it accessible to the wider scientific community.

Binding ability of arginine, citrulline, N-acetyl citrulline and thiocitrulline with SARS COV-2 main protease using molecular docking studies

In this article, the binding abilities of arginine, citrulline, N-acetyl citrulline and thiocitrulline on the active sites of SARS-COV-2 protease have been investigated using in-silico studies. All the above ligands bind selectively and preferentially to Cys-145 active site and also to other amino acids surrounding to it in the main protease. Of which arginine forms less number of weaker bonds compared to the other ligands, it by itself is a precursor for the formation of citrulline analogues with in the cell. Major advantage of using the above ligands is that in addition to its preferential binding, they have the ability to increase the immunity by assisting NO generation. Our results show that N-acetyl citrulline, citrulline, thiocitrulline and arginine may be used as a supplement during the treatment of SARS-COV-2.

SARS-CoV-2 3D database: understanding the coronavirus proteome and evaluating possible drug targets

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a rapidly growing infectious disease, widely spread with high mortality rates. Since the release of the SARS-CoV-2 genome sequence in March 2020, there has been an international focus on developing target-based drug discovery, which also requires knowledge of the 3D structure of the proteome. Where there are no experimentally solved structures, our group has created 3D models with coverage of 97.5% and characterized them using state-of-the-art computational approaches. Models of protomers and oligomers, together with predictions of substrate and allosteric binding sites, protein-ligand docking, SARS-CoV-2 protein interactions with human proteins, impacts of mutations, and mapped solved experimental structures are freely available for download. These are implemented in SARS CoV-2 3D, a comprehensive and user-friendly database, available at https://sars3d.com/. This provides essential information for drug discovery, both to evaluate targets and design new potential therapeutics.

Computationally approached inhibition potential of Tinospora cordifolia towards COVID-19 targets

The recent emergence of novel coronavirus (SARS-CoV-2) has been a major threat to human society, as the challenge of finding suitable drug or vaccine is not met till date. With increasing morbidity and mortality, the need for novel drug candidates is under great demand. The investigations are progressing towards COVID-19 therapeutics. Among the various strategies employed, the use of repurposed drugs is competing along with novel drug inventions. Based on the therapeutic significance, the chemical constituents from the extract of Tinospora cordifolia belonging to various classes like alkaloids, lignans, steroids and terpenoids are investigated as potential drug candidates for COVID-19. The inhibition potential of the proposed compounds against viral spike protein and human receptor ACE2 were evaluated by computational molecular modeling (Auto dock), along with their ADME/T properties. Prior to docking, the initial geometry of the compounds were optimized by Density functional theory (DFT) method employing B3LYP hybrid functional and 6-311 +  + G (d,p) basis set. The results of molecular docking and ADME/T studies have revealed 6 constituents as potential drug candidates that can inhibit the binding of SARS-CoV-2 spike protein with the human receptor ACE2 protein. The narrowed down list of constituents from Tinospora cordifolia paved way for further tuning their ability to inhibit COVID-19 by modifying the chemical structures and by employing computational geometry optimization and docking methods.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13337-021-00666-7.

De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning

The global pandemic of coronavirus disease (COVID-19) caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) created a rush to discover drug candidates. Despite the efforts, so far no vaccine or drug has been approved for treatment. Artificial intelligence offers solutions that could accelerate the discovery and optimization of new antivirals, especially in the current scenario dominated by the scarcity of compounds active against SARS-CoV-2. The main protease (Mpro) of SARS-CoV-2 is an attractive target for drug discovery due to the absence in humans and the essential role in viral replication. In this work, we developed a deep learning platform for de novo design of putative inhibitors of SARS-CoV-2 main protease (Mpro). Our methodology consists of 3 main steps: (1) training and validation of general chemistry-based generative model; (2) fine-tuning of the generative model for the chemical space of SARS-CoV- Mpro inhibitors and (3) training of a classifier for bioactivity prediction using transfer learning. The fine-tuned chemical model generated > 90% valid, diverse and novel (not present on the training set) structures. The generated molecules showed a good overlap with Mpro chemical space, displaying similar physicochemical properties and chemical structures. In addition, novel scaffolds were also generated, showing the potential to explore new chemical series. The classification model outperformed the baseline area under the precision-recall curve, showing it can be used for prediction. In addition, the model also outperformed the freely available model Chemprop on an external test set of fragments screened against SARS-CoV-2 Mpro, showing its potential to identify putative antivirals to tackle the COVID-19 pandemic. Finally, among the top-20 predicted hits, we identified nine hits via molecular docking displaying binding poses and interactions similar to experimentally validated inhibitors.

Cheminformatics-Based Identification of Potential Novel Anti-SARS-CoV-2 Natural Compounds of African Origin

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome virus 2 (SARS-CoV-2) has impacted negatively on public health and socioeconomic status, globally. Although, there are currently no specific drugs approved, several existing drugs are being repurposed, but their successful outcomes are not guaranteed. Therefore, the search for novel therapeutics remains a priority. We screened for inhibitors of the SARS-CoV-2 main protease and the receptor-binding domain of the spike protein from an integrated library of African natural products, compounds generated from machine learning studies and antiviral drugs using AutoDock Vina. The binding mechanisms between the compounds and the proteins were characterized using LigPlot+ and molecular dynamics simulations techniques. The biological activities of the hit compounds were also predicted using a Bayesian-based approach. Six potential bioactive molecules NANPDB2245, NANPDB2403, fusidic acid, ZINC000095486008, ZINC0000556656943 and ZINC001645993538 were identified, all of which had plausible binding mechanisms with both viral receptors. Molecular dynamics simulations, including molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) computations revealed stable protein-ligand complexes with all the compounds having acceptable free binding energies <-15 kJ/mol with each receptor. NANPDB2245, NANPDB2403 and ZINC000095486008 were predicted as antivirals; ZINC000095486008 as a membrane permeability inhibitor; NANPDB2403 as a cell adhesion inhibitor and RNA-directed RNA polymerase inhibitor; and NANPDB2245 as a membrane integrity antagonist. Therefore, they have the potential to inhibit viral entry and replication. These drug-like molecules were predicted to possess attractive pharmacological profiles with negligible toxicity. Novel critical residues identified for both targets could aid in a better understanding of the binding mechanisms and design of fragment-based de novo inhibitors. The compounds are proposed as worthy of further in vitro assaying and as scaffolds for the development of novel SARS-CoV-2 therapeutic molecules.

Novel Small-Molecule Scaffolds as Candidates against the SARS Coronavirus 2 Main Protease: A Fragment-Guided in Silico Approach

The ongoing pandemic caused by the novel coronavirus has been the greatest global health crisis since the Spanish flu pandemic of 1918. Thus far, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in over 1 million deaths, and there is no cure or vaccine to date. The recently solved crystal structure of the SARS-CoV-2 main protease has been a major focus for drug-discovery efforts. Here, we present a fragment-guided approach using ZINCPharmer, where 17 active fragments known to bind to the catalytic centre of the SARS-CoV-2 main protease (SARS-CoV-2 M^pro) were used as pharmacophore queries to search the ZINC databases of natural compounds and natural derivatives. This search yielded 134 hits that were then subjected to multiple rounds of in silico analyses, including blind and focused docking against the 3D structure of the main protease. We scrutinised the poses, scores, and protein-ligand interactions of 15 hits and selected 7. The scaffolds of the seven hits were structurally distinct from known inhibitor scaffolds, thus indicating scaffold novelty. Our work presents several novel scaffolds as potential candidates for experimental validation against SARS-CoV-2 M^pro.