Molecular dynamics has been demonstrated to be crucial for unveiling otherwise hidden binding sites especially for the undruggable target challenge.

To date, SARS-CoV-2 infectious disease, named COVID-19 by the World Health Organization (WHO) in February 2020, has caused millions of infections and hundreds of thousands of deaths. Despite the scientific community efforts, there are currently no approved therapies for treating this coronavirus infection. The process of new drug development is expensive and time-consuming, so that drug repurposing may be the ideal solution to fight the pandemic. In this paper, we selected the proteins encoded by SARS-CoV-2 and using homology modeling we identified the high-quality model of proteins. A structure-based pharmacophore modeling study was performed to identify the pharmacophore features for each target. The pharmacophore models were then used to perform a virtual screening against the DrugBank library (investigational, approved and experimental drugs). Potential inhibitors were identified for each target using XP docking and induced fit docking. MM-GBSA was also performed to better prioritize potential inhibitors. This study will provide new important comprehension of the crucial binding hot spots usable for further studies on COVID-19. Our results can be used to guide supervised virtual screening of large commercially available libraries.

NLRP3 (NOD-like receptor family, pyrin domain-containing protein 3) activation has been linked to several chronic pathologies, including atherosclerosis, type-II diabetes, fibrosis, rheumatoid arthritis, and Alzheimer’s disease. Therefore, NLRP3 represents an appealing target for the development of innovative therapeutic approaches. A few companies are currently working on the discovery of selective modulators of NLRP3 inflammasome. Unfortunately, limited structural data are available for this target. To date, MCC950 represents one of the most promising noncovalent NLRP3 inhibitors. Recently, a possible region for the binding of MCC950 to the NLRP3 protein was described but no details were disclosed regarding the key interactions. In this communication, we present an in silico multiple approach as an insight useful for the design of novel NLRP3 inhibitors. In detail, combining different computational techniques, we propose consensus-retrieved protein residues that seem to be essential for the binding process and for the stabilization of the protein–ligand complex.

The family of protein kinases comprises more than 500 genes involved in numerous functions. Hence, their physiological dysfunction has paved the way toward drug discovery for cancer, cardiovascular, and inflammatory diseases. As a matter of fact, Kinase binding sites high similarity has a double role. On the one hand it is a critical issue for selectivity, on the other hand, according to poly-pharmacology, a synergistic controlled effect on more than one target could be of great pharmacological interest. Another important aspect of binding similarity is the possibility of exploit it for repositioning of drugs on targets of the same family. In this study, we propose our approach called Kinase drUgs mAchine Learning frAmework (KUALA) to automatically identify kinase active ligands by using specific sets of molecular descriptors and provide a multi-target priority score and a repurposing threshold to suggest the best repurposable and non-repurposable molecules. The comprehensive list of all kinase-ligand pairs and their scores can be found at https://github.com/molinfrimed/multi-kinases.

Natural killer (NK) cells mount an immune response against hepatitis C virus (HCV) infection and can be activated by several cytokines, including interleukin-2 (IL-2), IL-15, and interferon-alpha (IFN-α). By exploiting the Huh7.5 hepatoma cell line infected with the HCV JFH1 genome, we provide novel insights into the antiviral effector functions of human primary NK cells after cytokine stimulation. NK cells activated with IFN-α (IFNα-NKs) had enhanced contact-dependent and -independent responses as compared with NK cells activated with IL-2/IL-15 (IL2/IL15-NKs) and could inhibit HCV replication both in vitro and in vivo. Importantly, IFN-α, but not IL-2/IL-15, protected NK cells from the functional inhibition exerted by HCV. By performing flow cytometry, multiplex cytokine profiling, and mass-spectrometry-based proteomics, we discovered that IFNα-NKs secreted high levels of galectin-9 and interferon-gamma (IFN-γ), and by conducting neutralization assays, we confirmed the major role of these molecules in HCV suppression. We speculated that galectin-9 might act extracellularly to inhibit HCV binding to host cells and downstream infection. In silico approaches predicted the binding of HCV envelope protein E2 to galectin-9 carbohydrate-recognition domains, and co-immunoprecipitation assays confirmed physical interaction. IFN-γ, on the other hand, triggered the intracellular expressions of two antiviral gate-keepers in target cells, namely, myxovirus-1 (MX1) and interferon-induced protein with tetratricopeptide repeats 1 (IFIT1). Collectively, our data add more complexity to the antiviral innate response mediated by NK cells and highlight galectin-9 as a key molecule that might be exploited to neutralize productive viral infection.