Protein phosphorylation is a post-translational modification that enables various cellular activities and plays essential roles in protein interactions. Phosphorylation is an important process for the replication of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). To shed more light on the effects of phosphorylation, we used an ensemble of neural networks to predict potential kinases that might phosphorylate SARS-CoV-2 nonstructural proteins (nsps) and molecular dynamics (MD) simulations to investigate the effects of phosphorylation on nsps structure, which could be a potential inhibitory target to attenuate viral replication. Eight target candidate sites were found as top-ranked phosphorylation sites of SARS-CoV-2. During the process of molecular dynamics (MD) simulation, the root-mean-square deviation (RMSD) analysis was used to measure conformational changes in each nsps. Root-mean-square fluctuation (RMSF) was employed to measure the fluctuation in each residue of 36 systems considered, allowing us to evaluate the most flexible regions. These analysis shows that there are significant structural deviations in the residues namely nsp1 THR 72, nsp2 THR 73, nsp3 SER 64, nsp4 SER 81, nsp4 SER 455, nsp5 SER284, nsp6 THR 238, and nsp16 SER 132. The identified list of residues suggests how phosphorylation affects SARS-CoV-2 nsps function and stability. This research also suggests that kinase inhibitors could be a possible component for evaluating drug binding studies, which are crucial in therapeutic discovery research.

Dengue virus infection is a global health problem for which there have been challenges to obtaining a cure. Current vaccines and anti-viral drugs can only be narrowly applied in ongoing clinical trials. We employed computational methods based on structure-function relationships between human host kinases and viral nonstructural protein 3 (NS3) to understand viral replication inhibitors’ therapeutic effect. Phosphorylation at each of the two most evolutionarily conserved sites of NS3, serine 137 and threonine 189, compared to the unphosphorylated state were studied with molecular dynamics and docking simulations. The simulations suggested that phosphorylation at serine 137 caused a more remarkable structural change than phosphorylation at threonine 189, specifically located at amino acid residues 49–95. Docking studies supported the idea that phosphorylation at serine 137 increased the binding affinity between NS3 and nonstructural Protein 5 (NS5), whereas phosphorylation at threonine 189 decreased it. The interaction between NS3 and NS5 is essential for viral replication. Docking studies with the antiviral plant flavonoid Quercetin with NS3 indicated that Quercetin physically occluded the serine 137 phosphorylation site. Taken together, these findings suggested a specific site and mechanism by which Quercetin inhibits dengue and possible other flaviviruses.

Recent advances in peptide research revolutionized therapeutic discoveries for various infectious diseases. In view of the ongoing threat of the COVID-19 pandemic, there is an urgent need to develop potential therapeutic options. Intense and accomplishing research is being carried out to develop broad-spectrum vaccines and treatment options for corona viruses, due to the risk of recurrent infection by the existing strains or pandemic outbreaks by new mutant strains. Developing a novel medicine is costly and time consuming, which increases the value of repurposing existing therapies. Since, SARS-CoV-2 shares significant genomic homology with SARS-CoV, we have summarized various peptides identified against SARS-CoV using in silico and molecular studies and also the peptides effective against SARS-CoV-2. Dissecting the molecular mechanisms underlying viral infection could yield fundamental insights in the discovery of new antiviral agents, targeting viral proteins or host factors. We postulate that these peptides can serve as effective components for therapeutic options against SARS-CoV-2, supporting clinical scientists globally in selectively identifying and testing the therapeutic and prophylactic agents for COVID-19 treatment. In addition, we also summarized the latest updates on peptide therapeutics against SARS-CoV-2.

B-lineage acute lymphocytic leukemia (B-ALL) is characterized by different genetic aberrations at a chromosomal and gene level which are very crucial for diagnosis, prognosis and risk assessment of the disease. However, there is still controversial arguments in regard to disease outcomes in specific genetic abnormalities, e.g., 9p-deletion. Moreover, in absence of cytogenetic abnormalities it is difficult to predict B-ALL progression. Here, we use the advantage of Next-generation sequencing (NGS) technology to study the mutation landscape of 12 patients with B-ALL using Comprehensive Cancer Panel (CCP) which covers the most common mutated cancer genes. Our results describe new mutations in CSF3R gene including S661N, S557G, and Q170X which might be associated with disease progression.

SARS-CoV-2, the virus that caused the widespread COVID-19 pandemic, is homologous to SARS-CoV. It would be ideal to develop antivirals effective against SARS-CoV-2. In this study, we chose one therapeutic target known as the main protease (M^pro) of SARS-CoV-2. A crystal structure (Id: 6LU7) from the protein data bank (PDB) was used to accomplish the screening and docking studies. A set of phytocompounds was used for the docking investigation. The nature of the interaction and the interacting residues indicated the molecular properties that are essential for significant affinity. Six compounds were selected, based on the docking as well as the MM-GBSA score. Pentagalloylglucose, Shephagenin, Isoacteoside, Isoquercitrin, Kappa-Carrageenan, and Dolabellin are the six compounds with the lowest binding energies (−12 to −8 kcal/mol) and show significant interactions with the target M^pro protein. The MMGBSA scores of these compounds are highly promising, and they should be investigated to determine their potential as M^pro inhibitors, beneficial for COVID-19 treatment. In this study, we highlight the crucial role of in silico technologies in the search for novel therapeutic components. Computational biology, combined with structural biology, makes drug discovery studies more rigorous and reliable, and it creates a scenario where researchers can use existing drug components to discover new roles as modulators or inhibitors for various therapeutic targets. This study demonstrated that computational analyses can yield promising findings in the search for potential drug components. This work demonstrated the significance of increasing in silico and wetlab research to generate improved structure-based medicines.