The transmembrane glycoprotein CD93 has been identified as a potential new target to inhibit tumor angiogenesis. Recently, Multimerin-2 (MMRN2), a pan-endothelial extracellular matrix protein, has been identified as a ligand for CD93, but the interaction mechanism between these two proteins is yet to be studied. In this article, we aim to investigate the structural and functional effects of induced mutations on the binding domain of CD93 to MMRN2. Starting from experimental data, we assessed how specific mutations in the C-type lectin-like domain (CTLD) affect the binding interaction profile. We described a four-step workflow in order to predict the effects of variations on the inter-residue interaction network at the PPI, based on evolutionary information, complex network metrics, and energetic affinity. We showed that the application of computational approaches, combined with experimental data, allowed us to gain more in-depth molecular insights into the CD93–MMRN2 interaction, offering a platform for developing innovative therapeutics able to target these molecules and block their interaction. This comprehensive molecular insight might prove useful in drug design in cancer therapy.
The role of computational tools in the drug discovery and development process is becoming central, thanks to the possibility to analyze large amounts of data. The high throughput and affordability of current omics technologies, allowing quantitative measurements of many putative targets, has exponentially increased the volume of scientific data available. The quality of the data and the speed with which in silico predictions can be validated in vitro is instrumental in accelerating clinical laboratory medicine, significantly and substantially impacting Precision Medicine (PM). PM affords the basis to develop new drugs by providing a wide knowledge of the patient as an essential step towards individualized medicine. It is, therefore, essential to collect as much information and data as possible on each patient to identify the causes of the different responses to drugs from a pharmacogenomics perspective and to identify biological biomarkers capable of accurately describing the risk signals to develop specific diseases. Furthermore, the role of biomarkers in early drug discovery is increasing, as they can significantly reduce the time it takes to develop new drugs. This review article will discuss how Artificial Intelligence fits in the drug discovery pipeline, covering the benefits of an automated, integrated laboratory framework where the application of Machine Learning methodologies to interpret omics-based data can avail the future perspective of Translational Precision Medicine.
The novel pathogen SARS-CoV-2 has caused the global pandemic of Covid-19. The hypothesis of this study is that Nicotinic Acetylcholine Receptors (nAChRs) are involved in SARS-CoV-2 infection, explaining the hyper-inflammatory characteristics observed in a subset of Covid-19 patients. nAChRs represent specific receptors for a wide variety of toxins and have been proposed to serve as receptors for Rabies Virus (RABV) and Human Immunodeficiency Virus (HIV) as well, based on sequence homology with the so called “toxic loop” of α-bungarotoxin. Sequence similarities between a motif found in SARS-CoV-2 S protein and in snake neurotoxins, as well as RABV neurotoxin-like region, HIV-1 gp120 and α-conotoxin from Conus geographus, highlights the existence of a correlation between these proteins’ functional sites. In this study, in silico procedures were used to determine SARS-CoV-2 S protein structure-function relationships, revealing the presence of features characteristic of the “toxic loop” known to bind nAChRs and their involvement in the S protein-nAChR interaction. Our results suggest that a polybasic sequence-carrying motif found in SARS-CoV-2 S protein could be involved in the binding, in particular underling the role of Arg685 in the interaction with the receptor.
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