Viral informatics: bioinformatics-based solution for managing viral infections.

Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.

Suppressing Mycobacterium tuberculosis virulence and drug resistance by targeting Eis protein through computational drug discovery.

Tuberculosis (TB) remains a critical health threat, particularly with the emergence of multidrug-resistant strains. This demands attention from scientific communities and healthcare professionals worldwide to develop effective treatments. The enhanced intracellular survival (Eis) protein is an acetyltransferase enzyme of Mycobacterium tuberculosis that functions by adding acetyl groups to aminoglycoside antibiotics, which interferes with their ability to bind to the bacterial ribosome, thereby preventing them from inhibiting protein synthesis and killing the bacterium. Therefore, targeting this protein accelerates the chance of restoring the aminoglycoside drug activity, thereby reducing the emergence of drug-resistant TB. For this, we have screened 406,747 natural compounds from the Coconut database against Eis protein. Based on MM/GBSA rescoring binding energy, the top 5 most prominent natural compounds, viz. CNP0187003 (- 96.14 kcal/mol), CNP0176690 (- 93.79 kcal/mol), CNP0136537 (- 92.31 kcal/mol), CNP0398701 (- 91.96 kcal/mol), and CNP0043390 (- 91.60 kcal/mol) were selected. These compounds exhibited the presence of a substantial number of hydrogen bonds and other significant interactions confirming their strong binding affinity with the Eis protein during the docking process. Subsequently, the MD simulation of these compounds exhibited that the Eis-CNP0043390 complex was the most stable, followed by Eis-CNP0187003 and Eis-CNP0176690 complex, further verified by binding free energy calculation, principal component analysis (PCA), and Free energy landscape analysis. These compounds demonstrated the most favourable results in all parameters utilised for this investigation and may have the potential to inhibit the Eis protein. There these findings will leverage computational techniques to identify and develop a natural compound inhibitor as an alternative for drug-resistant TB.

Repurposing of antibacterial compounds for suppression of Mycobacterium tuberculosis dormancy reactivation by targeting resuscitation-promoting factors B.

Tuberculosis infection has always been a global concern for public health, and the mortality rate has increased tremendously every year. The ability of the resuscitation Mycobacterium tuberculosis (Mtb) from the dormant state is one of the major reasons for the epidemic spread of tuberculosis infection, especially latent tuberculosis infection (LTBI). The element that encourages resuscitation, RpfB (resuscitation-promoting factors B), is mostly in charge of bringing Mtb out of slumber. This reason makes RpfB a promising target for developing tuberculosis drugs because of the effects of latent tuberculosis. Therefore, this work was executed using a computational three-level screening of the Selleckhem antibiotics database consisting of 462 antibiotics against the ligand binding region of the RpfB protein, followed by an estimation of binding free energy for ideal identification and confirmation of potential RpfB inhibitor. Subsequently, three antibiotic drug molecules, i.e., Amikacin hydrate (-66.87 kcal/mol), Isepamicin sulphate (-60.8 kcal/mol), and Bekanamycin (-46.89 kcal/mol), were selected on the basis of their binding free energy value for further computational studies in comparison to reference ligand, 4-benzoyl-2-nitrophenyl thiocyanate (NPT7). Based on the intermolecular interaction profiling, 200 ns molecular dynamic simulation (MD), post-simulation analysis and principal component analysis (PCA), the selected antibiotics showed substantial stability with the RpfB protein compared to the NPT7 inhibitor. Conclusively based on the computational results, the preferred drugs can be potent inhibitors of the RpfB protein, which can be further validated using in vivo research and in vitro enzyme inhibition to understand their therapeutic activity against tuberculosis infection.Communicated by Ramaswamy H. Sarma.

A multi-targeted computational drug discovery approach for repurposing tetracyclines against monkeypox virus.

Monkeypox viral infection is an emerging threat and a major concern for the human population. The lack of drug molecules to treat this disease may worsen the problem. Identifying potential drug targets can significantly improve the process of developing potent drug molecules for treating monkeypox. The proteins responsible for viral replication are attractive drug targets. Identifying potential inhibitors from known drug molecules that target these proteins can be key to finding a cure for monkeypox. In this work, two viral proteins, DNA-dependent RNA polymerase (DdRp) and viral core cysteine proteinase, were considered as potential drug targets. Sixteen antibiotic drugs from the tetracycline class were screened against both viral proteins through high-throughput virtual screening. These tetracycline class of antibiotic drugs have the ability to inhibit bacterial protein synthesis, which makes these antibiotics drugs a prominent candidate for drug repurposing. Based on the screening result obtained against DdRp, top two compounds, namely Tigecycline and Eravacycline with docking scores of - 8.88 and - 7.87 kcal/mol, respectively, were selected for further analysis. Omadacycline and minocycline, with docking scores of - 10.60 and - 7.51 kcal/mol, are the top two compounds obtained after screening proteinase with the drug library. These compounds, along with reference compounds GTP for DdRp and tecovirimat for proteinase, were used to form protein-ligand complexes, followed by their evaluation through a 300 ns molecular dynamic simulation. The MM/GBSA binding free energy calculation and principal components analysis of these selected complexes were also conducted for understanding the dynamic stability and binding affinity of these compounds with respective target proteins. Overall, this study demonstrates the repurposing of tetracycline-derived drugs as a therapeutic solution for monkeypox viral infection.

Computational Investigations on the Natural Small Molecule as an Inhibitor of Programmed Death Ligand 1 for Cancer Immunotherapy.

Several therapeutic monoclonal antibodies approved by the FDA are available against the PD-1/PD-L1 (programmed death 1/programmed death ligand 1) immune checkpoint axis, which has been an unprecedented success in cancer treatment. However, existing therapeutics against PD-L1, including small molecule inhibitors, have certain drawbacks such as high cost and drug resistance that challenge the currently available anti-PD-L1 therapy. Therefore, this study presents the screening of 32,552 compounds from the Natural Product Atlas database against PD-L1, including three steps of structure-based virtual screening followed by binding free energy to refine the ideal conformation of potent PD-L1 inhibitors. Subsequently, five natural compounds, i.e., Neoenactin B1, Actinofuranone I, Cosmosporin, Ganocapenoid A, and 3-[3-hydroxy-4-(3-methylbut-2-enyl)phenyl]-5-(4-hydroxybenzyl)-4-methyldihydrofuran-2(3H)-one, were collected based on the ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiling and binding free energy (>−60 kcal/mol) for further computational investigation in comparison to co-crystallized ligand, i.e., JQT inhibitor. Based on interaction mapping, explicit 100 ns molecular dynamics simulation, and end-point binding free energy calculations, the selected natural compounds were marked for substantial stability with PD-L1 via intermolecular interactions (hydrogen and hydrophobic) with essential residues in comparison to the JQT inhibitor. Collectively, the calculated results advocate the selected natural compounds as the putative potent inhibitors of PD-L1 and, therefore, can be considered for further development of PD-L1 immune checkpoint inhibitors in cancer immunotherapy.