Transmembrane serine protease 2 (TMPRSS2) has been established as one of the host proteins that facilitate entry of coronaviruses into host cells. One of the approaches often employed towards preventing the entry and proliferation of viruses is computer-aided inhibition studies to identify potent compounds that can inhibit activity of viral targets in the host through binding at the active site. In this study, we developed a pharmacophore model of reportedly potent drugs against severe acute respiratory syndrome coronaviruses 1 and 2 (SARS-CoV-1 and -2). The model was used to screen the ZINC database for commercially available compounds having similar features with the experimentally tested drugs. The top 3000 compounds retrieved were docked into the active sites of a homology-modelled TMPRSS2. Docking scores of the top binders were validated and the top-ranked compounds were subjected to ADME, Lipinski’s and medicinal Chemistry property predictions for druglikeness analyses. Two lead compounds, ZINC64606047 and ZINC05296775, were identified having binding affinities higher than those of the reference inhibitors, favorable interactions with TMPRSS2 active site residues and good ADME and medicinal chemistry properties. Molecular dynamics simulation was used to assess the stability and dynamics of the interactions of these compounds with TMPRSS2. Binding free energy and contribution energy evaluations were determined using MMPBSA method. Analyses of the trajectory dynamics collectively established further that the lead compounds bound and interacted stably with active site residues of TMPRSS2. Nonetheless, experimental studies are needed to further assess the potentials of these compounds as possible therapeutics against coronaviruses. Communicated by Ramaswamy H. Sarma.

Progression of hepatocellular carcinoma involves multiple genetic and epigenetic alterations that promote cancer invasion and metastasis. Our recent study revealed that hyperphosphorylation of ezrin promotes intrahepatic metastasis in vivo and cell migration in vitro. Celastrol is a natural product from traditional Chinese medicine which has been used in treating liver cancer. However, the mechanism of action underlying celastrol treatment was less clear. Here we show that ROCK2 is a novel target of celastrol and inhibition of ROCK2 suppresses elicited ezrin activation and liver cancer cell migration. Using cell monolayer wound healing, we carried out a phenotype-based screen of natural products and discovered the efficacy of celastrol in inhibiting cell migration. The molecular target of celastrol was identified as ROCK2 using celastrol affinity pull-down assay. Our molecular docking analyses indicated celastrol binds to the active site of ROCK2 kinase. Mechanistically, celastrol inhibits the ROCK2-mediated phosphorylation of ezrin at Thr567 which harnesses liver cancer cell migration. Our findings suggest that targeting ROCK2-ezrin signaling is a potential therapeutic niche for celastrol-based intervention of cancer progression in hepatocellular carcinoma.

The SARS-CoV-2 has infected many people globally with the ravaging COVID-19; a disease, which has become challenging for every aspect of modern healthcare. The saliva and oral mucosa are sites of high risk for increased viral loads, and aside from the usual epithelial functions like lining and protection, the oral mucosa is also specialized for crucial functions, such as secretion, mastication, sensory perception, and taste perception. The human ACE2 receptor has been extensively studied for its essential role in the regulation of blood pressure homeostasis. However, scRNA-Seq studies have revealed high expression levels of the protein in keratinized epithelial surfaces of the oral cavity. The SARS-CoV-2 have access to the host’s body by binding to the ACE2 receptor, leading to the cleavage and major conformational changes in the viral spike glycoprotein for the release of its nucleocapsid into the cellular cytoplasm. This proteolytic cleavage is carried out by the TMPRSS2 and cathepsin L. In this study, we harnessed the information from the binding interface of TMPRSS2 and PAI-1 (a protease inhibitor known to inhibit the TMPRSS2 and several other proteases) to design a potential therapeutic peptide for the inhibition of the TMPRSS2, while also emphasizing the need for preventive masking.

Background: Resistance to antifungal drugs for treating Candida infections remains a major concern globally despite the range of medications available. Most of these drugs target key proteins essential to the life cycle of the organism. An enzyme essential for fungal cell membrane integrity, lanosterol 14–α demethylase (CYP51), is encoded by the ERG11 gene in Candida species. This enzyme is the target of azole–based drugs. The organism has, however, devised molecular adaptations to evade the activity of these drugs. Materials and Methods: Classical methods were employed to characterize clinical isolates sampled from women and dogs of reproductive age. For fluconazole efficacy studies, CLSI guidelines on drug susceptibility testing were used. To understand the susceptibility pattern, various molecular and structural analytic approaches, including sequencing, in silico site-directed mutagenesis, and protein-ligand profiling, were applied to the ERG11 gene and CYP51 protein sequences. Several platforms, comprising Clustal Omega, Pymol plugin manager, Pymol molecular visualizer, Chimera–curated Dynameomics rotamer library, protein–ligand interaction profiler, Charmm36 force field, GROMACS, Geneious, and Mega7, were employed for this analysis. Results: The following Candida species distribution was obtained: 37.84% C. albicans, 8.12% C. glabrata, 10.81% C. krusei, 5.41% C. tropicalis, and 37.84% of other unidentified Candida species. Two codons in the nucleotide sequence of the wild-type (CTC and CCA) coding for LEU– 370 and PRO– 375, respectively, were mutated to L370S and P375H in the resistant strain. The mutation stabilized the protein at the expense of the heme moiety. We found that the susceptible isolate from dogs (Can–iso– 029/dog) is closely related to the most resistant isolate from humans. Conclusion: Taken together, our results showed new mutations in the heme-binding pocket of caCYP51 that explain the resistance to fluconazole exhibited by the Candida isolates. So far, the L370S and P375H resistance-linked mutations have not been previously reported.

Breakthrough achievements in protein structure prediction have occurred recently, mostly due to the advent of sophisticated machine learning methods and significant advancements in algorithmic approaches. The most recent version of the AlphaFold model, known as “AlphaFold-latest,” which expands the functionalities of the groundbreaking AlphaFold2, is the subject of this article. The goal of this novel model is to predict the three-dimensional structures of various biomolecules, such as ions, proteins, nucleic acids, small molecules, and non-standard residues. We demonstrate notable gains in precision, surpassing specialized tools across multiple domains, including protein–ligand interactions, protein–nucleic acid interactions, and antibody–antigen predictions. In conclusion, this AlphaFold framework has the ability to yield atomically-accurate structural predictions for a variety of biomolecular interactions, hence facilitating advancements in drug discovery.