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A highly critical event in a virus's life cycle is successfully entering a given host. This process begins when a viral glycoprotein interacts with a target cell receptor, which provides the molecular basis for target virus–host cell interactions for novel drug discovery. Over the years, extensive research has been carried out in the field of virus–host cell interaction, generating a massive number of genetic and molecular data sources. These datasets are an asset for predicting virus–host interactions at the molecular level using machine learning (ML), a subset of artificial intelligence (AI). In this direction, ML tools are now being applied to recognize patterns in these massive datasets to predict critical interactions between virus and host cells at the protein–protein and protein–sugar levels, as well as to perform transcriptional and translational analysis. On the other end, deep learning (DL) algorithms—a subfield of ML—can extract high-level features from very large datasets to recognize the hidden patterns within genomic sequences and images to develop models for rapid drug discovery predictions that address pathogenic viruses displaying heightened affinity for receptor docking and enhanced cell entry. ML and DL are pivotal forces, driving innovation with their ability to perform analysis of enormous datasets in a highly efficient, cost-effective, accurate, and high-throughput manner. This review focuses on the complexity of virus–host cell interactions at the molecular level in light of the current advances of ML and AI in viral pathogenesis to improve new treatments and prevention strategies. [ABSTRACT FROM AUTHOR]

The entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into human embryonic kidney (HEK293T) cells has been shown to be a cholesterol-rich, lipid raft-dependent process. In this study, we investigated if the presence of a cholesterol uptake receptor Niemann-pick type c1-like1 (NPC1L1) impacts SARS-CoV-2 cell entry. Initially, we utilized reporter-based pseudovirus cell entry assays and a spike (S) glycoprotein-mediated cell-to-cell fusion assay. Using Chinese hamster ovary (CHO-K1) cells, which lack endogenous receptors for SARS-CoV-2 entry, our data showed that the co-expression of NPC1L1 together with the ACE2 receptor synergistically increased SARS-CoV-2 pseudovirus entry even more than the cells expressing ACE-2 receptor alone. Similar results were also found with the HEK293T cells endogenously expressing the ACE2 receptor. Co-cultures of effector cells expressing S glycoprotein together with target cells co-expressing ACE-2 receptor with NPC1L1 significantly promoted quantitative cell-to-cell fusion, including syncytia formation. Finally, we substantiated that an elevated expression of NPC1L1 enhanced entry, whereas the depletion of NPC1L1 resulted in a diminished SARS-CoV-2 entry in HEK293T-ACE2 cells using authentic SARS-CoV-2 virus in contrast to their respective control cells. Collectively, these findings underscore the pivotal role of NPC1L1 in facilitating the cellular entry of SARS-CoV-2. Importance: Niemann-Pick type C1-like1 (NPC1L1) is an endosomal membrane protein that regulates intracellular cholesterol trafficking. This protein has been demonstrated to play a crucial role in the life cycle of several clinically important viruses. Although SARS-CoV-2 exploits cholesterol-rich lipid rafts as part of its viral entry process, the role of NPC1L1 in SARS-CoV-2 entry remains unclear. Our research represents the first-ever demonstration of NPC1L1's involvement in facilitating SARS-CoV-2 entry. The observed role of NPC1L1 in human kidney cells is not only highly intriguing but also quite relevant. This relevance stems from the fact that NPC1L1 exhibits high expression levels in several organs, including the kidneys, and the fact that kidney damages are reported during severe cases of SARS-CoV-2. These findings may help us understand the new functions and mechanisms of NPC1L1 and could contribute to the identification of new antiviral targets. [ABSTRACT FROM AUTHOR]

The coronavirus SARS-CoV-2 has caused a pandemic with > 550 millions of cases and > 6 millions of deaths worldwide. Medical management of COVID-19 relies on supportive care as no specific targeted therapies are available yet. Given its devastating effects on the economy and mental health, it is imperative to develop novel antivirals. An ideal candidate will be an agent that blocks the early events of viral attachment and cell entry, thereby preventing viral infection and spread. This work reports functionalized titanium dioxide (TiO2)-based nanoparticles adsorbed with flavonoids that block SARS-CoV-2 entry and fusion. Using molecular docking analysis, two flavonoids were chosen for their specific binding to critical regions of the SARS-CoV-2 spike glycoprotein that interacts with the host cell angiotensin-converting enzyme-2 (ACE-2) receptor. These flavonoids were adsorbed onto TiO2 functionalized nanoparticles (FTNP). This new nanoparticulate compound was assayed in vitro against two different coronaviruses; HCoV 229E and SARS-CoV-2, in both cases a clear antiviral effect was observed. Furthermore, using a reporter-based cell culture model, a potent antiviral activity is demonstrated. The adsorption of flavonoids to functionalized TiO2 nanoparticles induces a ~ threefold increase of that activity. These studies also indicate that FTNP interferes with the SARS-CoV-2 spike, impairing the cell fusion mechanism. Key points/Highlights: • Unique TiO2nanoparticles displaying flavonoid showed potent anti-SARS-CoV-2 activity. • The nanoparticles precisely targeting SARS-CoV-2 were quantitatively verified by cell infectivity in vitro. • Flavonoids on nanoparticles impair the interactions between the spike glycoprotein and ACE-2 receptor. [ABSTRACT FROM AUTHOR]