Perspectives for Potential Therapies for SARS-CoV-2

The current pandemic, novel corona virus 19 disease (COVID-19), has created havoc across the world. Now a third wave is possible, and we have already crossed two waves. The current review article presents recent reports about the COVID-19, the ways of treatments, and prevention. In view of the potential threats of a pandemic, various scientists have been trying to understand the pathophysiology of this disease to uncover possible treatment regimens and discover effective therapeutic agents and vaccines. To add further information to support the ongoing current research and development against SARS-CoV-2, the authors have provided the basics of pathophysiology, possible targets, and current treatment strategy for corona viruses. The current review highlights the antiviral strategies involving small molecules and different biological targets involved in corona virus infection and replication. The information included in this article provides a strong intellectual foundation for the ongoing development of therapeutic agents and vaccines.

In-Silico Analysis to Identify Potent Quinoline Analogues Against Multi-Targets of SARS-CoV-2

In the current outbreak of COVID-19, various studies have been conducted all over the world to develop effective drugs against the virus. Recent studies have shown that hydroxychloroquine, chloroquine (antimalarial drugs), isoflavones, flavonoids, etc. have potent antiviral properties, and few have been proven as effective drugs for the preventive treatment of COVID-19. But their exact action against SARS-CoV-2 is still unknown. The strategy of this study is the virtual screening of quinoline analogues, design new ligand molecules, perform molecular interaction analysis, their MD validation against multi targets (Spike-ACE2, TMPRSS2, and Spike Protein) of SARS-CoV-2, and to suggest the most promising and effective drug molecule. Hydroxychloroquine and chloroquine were considered as the reference molecules in this study. A ligand N-[4-(3-Benzylideneazetidine-1-carbonyl)phenyl]quinoline-8-sulfonamide interacting with TMPRSS2 shows better interaction among the list even after MD validation. Further in-vitro and in-vivo analysis of this study is needed for future validation.

Docking, Binding Free Energy Estimation, and MD Simulation of Newly Designed CQ and HCQ Analogues Against the Spike-ACE2 Complex of SARS-CoV-2

The coronavirus disease 2019 (COVID-19) virus has been spreading rapidly, and scientists are endeavouring to discover drugs for its efficacious treatment. Chloroquine phosphate, an old drug for treatment of malaria, has shown to have apparent efficacy and acceptable safety against COVID-19. As a part of Drug Discovery Hackathon-2020, in this study, the authors have tried making the derivatives of CQ and HCQ using MarvinSketch by ChemAxon. Molecular docking studies of these ligands were performed using Glide by Schrodinger, and ADME profiles were obtained by using QikProp. The obtained results after data analysis demonstrated that ligands HCQ_imidazoll, choloroquine_3c, HCQ_pyrrolC had good binding affinity and complied with all the ADME parameters. The molecular dynamic simulation of these ligands in complex with the 2019-nCoV RBD/ACE-2-B0AT1 complex PDB ID: 6M17 were carried out, and the parameters like RMSD, RMSF, and radius of gyration were observed to understand the fluctuations and protein-ligand interaction.

Homology Modeling and Evaluation of Sars-Cov-2 Spike Protein Mutant

Severe acute respiratory syndrome coronavirus 2 (SARS‐Cov-2), a global pandemic, affected the world, increasing every day. A mutated variant D614G, showing more virulence and transmission, was studied for forecasting the emergence of more virulent and pathogenic viral strains. This study focuses on structure modeling and validation. Characterization of proteins homologous to wild spike protein was done, and homology models of the mutated variant were modeled using these proteins. Validation of models was done using Ramachandran plot and ERRAT plot. Molecular dynamics simulation was used to validate the stability of the models, and binding affinity of these models were estimated by molecular docking with an approved antiviral drug. Docked complexes were studied and the best model was selected. Molecular dynamics simulation was used to estimate the stability of the docked complex. The model of 6VXX, a homologous of wild spike protein, was found to be stable with the interaction of the antiviral drug from this study.

Identification of Potential Scaffolds From the Shrub Justicia Adhatoda Against SARS-CoV-2 Main Protease Target

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious and pathogenic virus. To date, there is a lack of proper medication against this virus, which has triggered the scientific community to find therapeutics. Searching of SARS-CoV-2 main protease inhibitors from anti-viral natural products based on traditional knowledge may be an effective approach. In this work, structure-based virtual screening of the compounds of Justicia adhatoda was performed against SARS-CoV-2 Mpro, followed by ADME filtration, molecular dynamics, and MMGBSA-based binding free energy calculation. On the basis of docking score, crucial interacting amino acid residues, molecular dynamics, and binding energy profile, three novel phenolic compounds JA_38b, JA_38c, and JA_39 were selected as potential binders against SARS-CoV-2 Mpro. This information may be used to develop potential therapeutics countermeasures against SARS-CoV-2 infection after in vitro and detailed pharmacological study.

Biological Activity, Physical Properties, and Toxicity

In the current pandemic-stricken world, quantitative structure-activity relationship (QSAR) analysis has become a necessity in the domain of molecular biology and drug design, realizing that it helps estimate properties and activities of a compound, without actually having to spend time and resources to synthesize it in the laboratory. Correlating the molecular structure of a compound with its activity depends on the choice of the descriptors, which becomes a difficult and confusing task when we have so many to choose from. In this mini-review, the authors delineate the importance of very simple and easy to compute descriptors in estimating various molecular properties/toxicity.

Preliminary Screening of COVID-19 Infection Employing Machine Learning Techniques From Simple Blood Profile

Frequent testing of the entire population would help to identify individuals with active COVID-19 and allow us to identify concealed carriers. Molecular tests, antigen tests, and antibody tests are being widely used to confirm COVID-19 in the population. Molecular tests such as the real-time reverse transcription-polymerase chain reaction (rRT-PCR) test will take a minimum of 3 hours to a maximum of 4 days for the results. The authors suggest using machine learning and data mining tools to filter large populations at a preliminary level to overcome this issue. The ML tools could reduce the testing population size by 20 to 30%. In this study, they have used a subset of features from full blood profile which are drawn from patients at Israelita Albert Einstein hospital located in Brazil. They used classification models, namely KNN, logistic regression, XGBooting, naive Bayes, decision tree, random forest, support vector machine, and multilayer perceptron with k-fold cross-validation, to validate the models. Naïve bayes, KNN, and random forest stand out as the most predictive ones with 88% accuracy each.

In-Silico Analysis to Identify Potential Inhibitors Against the Protein NSP12 of SARS-CoV-2

In this pandemic condition, a drug candidate which is effective against COVID-19 is very much desired. This study initiates an in silico analysis to screen small molecules such as phytochemicals, drug metabolites, and natural metabolites against Nsp12 (a catalytic unit for RNA transcription and replication). Molecular interaction analysis of 6M71 was carried out against 2,860 ligands using Schrodinger Glide software. After docking analysis, the top 10 molecules (Glide score) were subjected to MD simulation for validating the stability. It resulted in top 10 compounds with high binding affinities with the target molecule NSP 12. Out of these, top 3 compounds including PSID_08_LIG3 (HMDB0133544), PSID_08_LIG4 (HMDB0132898), and PSID_08_LIG9 (HMDB0128199) show better Glide scores, better H-bond interaction, better MMGBSA value and stability on dynamic simulation after analysis of the results. The suggested ligands can be postulated as effective antiviral drugs against COVID-19. Further in vivo analysis is needed for validating the drug efficacy.

Virtual Screening of Phyto Chemicals Against SARS-CoV-2 Targets

The COVID-19 pandemic wave has recommenced and is spreading like wildfire across the globe. The well-reported antiviral potency of phyto compounds could offer potential drug molecules for the current predicament. The present study analyses the molecular interaction of selected phyto compounds and SARS-CoV-2 molecular target proteins, namely spike protein, RNA-dependent RNA polymerase, 3C-like proteases, and papain-like protease. Ten newly modeled ligands were also considered for the study. Molecular docking analysis was carried out independently using MOE, AutoDock Vina, Schrodinger-Glide, and the stability of protein-ligand interaction was validated through molecular dynamics simulation. Petunidin interacts with spike protein resulting in a good Gscore, binding energy, and H-bond interaction. Also, alions, letestuianin-A, (+)-pinitol show better interaction with RdRp, 3CL-protease, and papain-like protease, respectively. The presented work screens through 2314 ligands to yield top-ranked molecules which could be taken up to develop potential lead molecules via in-vivo analysis.

Intelligent Consensus Predictions of Biodegradation Half-Life of Petroleum Hydrocarbons (PHCs)

The present study explores the important chemical features of diverse petroleum hydrocarbons (PHCs) responsible for their biodegradation by developing partial least squares (PLS) regression-based quantitative structure-property relationship (QSPR) models. The biodegradability is estimated in terms of biodegradation half-life (Logt1/2). All the PLS models were extensively validated by different internationally acceptable internal (R2= 0.849–0.861; Q2 = 0.833–0.849; R2adj = 0.845–0.858) and external (Q2F1= 0.825-0.848; Q2F2 = 0.822–0.845) validation parameters. The consensus predictions were also performed by using the “intelligent consensus predictor” (ICP) tool, which improves the predictive ability of individual models based on mean absolute error (MAE)-based criteria. The models suggested that the biodegradation of PHCs is dependent on the presence of substituents on the aromatic ring, 12 atom containing ring system, thiophene moiety, electron rich chemicals, large molecular size, degree of unsaturation, degree of branching, cyclization, and hydrophobicity.