Sever acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA (ssRNA) virus, responsible for severe acute respiratory disease (COVID-19). A large number of natural compounds are under trial for screening compounds, possessing potential inhibitory effect against the viral infection. Keeping in view the importance of marine compounds in antiviral activity, we investigated the potency of some marine natural products to target SARS-CoV-2 main protease (M^pro) (PDB ID 6MO3). The crystallographic structure of M^pro in an apo form was retrieved from Protein Data Bank and marine compounds from PubChem. These structures were prepared for docking and the complex with good docking score was subjected to molecular dynamic (MD) simulations for a period of 100ns. To measure the stability, flexibility, and average distance between the target and compounds, root means square deviations (RMSD), root means square fluctuation (RMSF), and the distance matrix were calculated. Among five marine compounds, C-1 (PubChem CID 11170714) exhibited good activity, interacting with the active site and surrounding residues, forming many hydrogen and hydrophobic interactions. The C-1 also attained a stable dynamic behavior, and the average distance between compound and target remains constant. In conclusion, marine natural compounds may be used as a potential inhibitor against SARS-CoV-2 for better management of COVID-19.

The emergence of recent SARS-CoV-2 has become a global health issue. This single-stranded positive-sense RNA virus is continuously spreading with increasing morbidities and mortalities. The proteome of this virus contains four structural and sixteen nonstructural proteins that ensure the replication of the virus in the host cell. However, the role of phosphoprotein (N) in RNA recognition, replicating, transcribing the viral genome, and modulating the host immune response is indispensable. Recently, the NMR structure of the N-terminal domain of the Nucleocapsid Phosphoprotein has been reported, but its precise structural mechanism of how the ssRNA interacts with it is not reported yet. Therefore, here, we have used an integrated computational pipeline to identify the key residues, which play an essential role in RNA recognition. We generated multiple variants by using an alanine scanning strategy and performed an extensive simulation for each system to signify the role of each interfacial residue. Our analyses suggest that residues T57A, H59A, S105A, R107A, F171A, and Y172A significantly affected the dynamics and binding of RNA. Furthermore, per-residue energy decomposition analysis suggests that residues T57, H59, S105 and R107 are the key hotspots for drug discovery. Thus, these residues may be useful as potential pharmacophores in drug designing.

The current coronavirus (SARS-COV-2) pandemic and phenomenal spread to every nook and cranny of the world has raised major apprehensions about the modern public health care system. So far as a result of this epidemic, 4,434,653 confirmed cases and 302,169 deaths are reported. The growing infection rate and death toll demand the use of all possible approaches to design novel drugs and vaccines to curb this disease. In this study, we combined drugs repurposing and virtual drug screening strategies to target 3CLpro, which has an essential role in viral maturation and replication. A total of 31 FDA approved anti-HIV drugs, and Traditional Chinese medicines (TCM) database were screened to find potential inhibitors. As a result, Saquinavir, and five drugs (TCM5280805, TCM5280445, TCM5280343, TCM5280863, and TCM5458190) from the TCM database were found as promising hits. Furthermore, results from molecular dynamics simulation and total binding free energy revealed that Saquinavir and TCM5280805 target the catalytic dyad (His41 and Cys145) and possess stable dynamics behavior. Thus, we suggest that these compounds should be tested experimentally against the SARS-COV-2 as Saquinavir has been reported to inhibit HIV protease experimentally. Considering the intensity of coronavirus dissemination, the present research is in line with the idea of discovering the latest inhibitors against the coronavirus essential pathways to accelerate the drug development cycle. Communicated by Ramaswamy H. Sarma.

Background: The global burden of tuberculosis (TB) and antibiotic resistance is attracting the attention of researchers to develop some novel and rapid diagnostic tools. Although, the conventional methods like culture are considered as the gold standard, they are time consuming and offer more time in the transmission of disease. Further, the Xpert MTB/RIF assay offers the fast diagnostic facility within two hours, but due to low the sensitivity in some sample types may lead to more serious state of the disease. The role of computer technologies is now increasing in the diagnostic procedures. Here, in the current study we have applied the artificial neutral network (ANN) that predicted the TB disease based on the TB suspect data. Methods: We developed an approach for prediction of TB, based on artificial neural network (ANN). The data was collected from the TB suspects, guardians or care takers along with sample, referred by TB units and health centers. All the samples were processed and cultured. Data was trained on 12636 records of TB patients, collected during the years, 2016 and 2017 from provincial tuberculosis reference laboratory, Khyber Pakhtunkhwa, Pakistan. The training and test set of the suspect data were kept as 70% and 30% respectively followed by validation and normalization. The ANN take the TB suspects information’s like gender, age, HIV-status, previous TB history, sample type, sign and symptoms for TB prediction. Results: Based on TB patient’s data, ANN accurately predicted the MTB positive or negative with overall accuracy of >94%. Further, the test and validation accuracies were found >93%. This increased accuracy of ANN in detection of TB suspected patients might be useful for early management of disease to adopt some control measure in further transmission and reduce the drug resistance burden. Conclusion: ANNs algorithms may play effective role in early diagnosis of TB disease that might be applied as a supportive tool. Modern computer technologies should be trained in the diagnostics for a rapid management of disease. Delays in TB diagnosis and initiation treatment may allow the emergence of new cases by transmission, causing high drug resistance in TB high burden countries.

Resistance to key first-line drugs is a major hurdle to achieve the global end tuberculosis (TB) targets. A prodrug, pyrazinamide (PZA) is the only drug, effective in latent TB, recommended in drug resistance and susceptible Mycobacterium tuberculosis (MTB) isolates. The prodrug conversion into active form, pyrazinoic acid (POA), required the activity of pncA gene encoded pyrazinamidase (PZase). Although pncA mutations have been commonly associated with PZA resistance but a small number of resistance cases have been associated with mutationss in RpsA protein. Here in this study a total of 69 PZA resistance isolates have been sequenced for pncA mutations. However, samples that were found PZA resistant but pncA wild type (pncA^WT), have been sequenced for rpsA and panD genes mutation. We repeated a drug susceptibility testing according to the WHO guidelines on 18 pncA^WT MTB isolates. The rpsA and panD genes were sequenced. Out of total 69 PZA resistant isolates, 51 harbored 36 mutations in pncA gene (GeneBank Accession No. MH46111) while, fifteen different mutations including seven novel, were detected in the fourth S1 domain of RpsA known as C-terminal (MtRpsA^CTD) end. We did not detect any mutations in panD gene. Among the rpsA mutations, we investigated the molecular mechanism of resistance behind mutations, D342N, D343N, A344P, and I351F, present in the MtRpsA^CTD through molecular dynamic simulations (MD). WT showed a good drug binding affinity as compared to mutants (MTs), D342N, D343N, A344P, and I351F. Binding pocket volume, stability, and fluctuations have been altered whereas the total energy, protein folding, and geometric shape analysis further explored a significant variation between WT and MTs. In conclusion, mutations in MtRpsA^CTD might be involved to alter the RpsA activity, resulting in drug resistance. Such molecular mechanism behind resistance may provide a better insight into the resistance mechanism to achieve the global TB control targets.