Domain 2 of Hepatitis C Virus Protein NS5A Activates Glucokinase and Induces Lipogenesis in Hepatocytes

Hepatitis C virus (HCV) relies on cellular lipid metabolism for its replication, and actively modulates lipogenesis and lipid trafficking in infected hepatocytes. This translates into an intracellular accumulation of triglycerides leading to liver steatosis, cirrhosis and hepatocellular carcinoma, which are hallmarks of HCV pathogenesis. While the interaction of HCV with hepatocyte metabolic pathways is patent, how viral proteins are able to redirect central carbon metabolism towards lipogenesis is unclear. Here, we report that the HCV protein NS5A activates the glucokinase (GCK) isoenzyme of hexokinases through its D2 domain (NS5A-D2). GCK is the first rate-limiting enzyme of glycolysis in normal hepatocytes whose expression is replaced by the hexokinase 2 (HK2) isoenzyme in hepatocellular carcinoma cell lines. We took advantage of a unique cellular model specifically engineered to re-express GCK instead of HK2 in the Huh7 cell line to evaluate the consequences of NS5A-D2 expression on central carbon and lipid metabolism. NS5A-D2 increased glucose consumption but decreased glycogen storage. This was accompanied by an altered mitochondrial respiration, an accumulation of intracellular triglycerides and an increased production of very-low density lipoproteins. Altogether, our results show that NS5A-D2 can reprogram central carbon metabolism towards a more energetic and glycolytic phenotype compatible with HCV needs for replication.

The crystal structure of vaccinia virus protein E2 and perspectives on the prediction of novel viral protein folds

The morphogenesis of vaccinia virus (VACV, family Poxviridae), the smallpox vaccine, is a complex process involving multiple distinct cellular membranes and resulting in multiple different forms of infectious virion. Efficient release of enveloped virions, which promote systemic spread of infection within hosts, requires the VACV protein E2 but the molecular basis of E2 function remains unclear and E2 lacks sequence homology to any well-characterised family of proteins. We solved the crystal structure of VACV E2 to 2.3 Å resolution, revealing that it comprises two domains with novel folds: an N-terminal annular (ring) domain and a C-terminal globular (head) domain. The C-terminal head domain displays weak structural homology with cellular (pseudo)kinases but lacks conserved surface residues or kinase features, suggesting that it is not enzymatically active, and possesses a large surface basic patch that might interact with phosphoinositide lipid headgroups. Recent deep learning methods have revolutionised our ability to predict the three-dimensional structures of proteins from primary sequence alone. VACV E2 is an exemplar ‘difficult’ viral protein target for structure prediction, being comprised of multiple novel domains and lacking sequence homologues outside Poxviridae. AlphaFold2 nonetheless succeeds in predicting the structures of the head and ring domains with high and moderate accuracy, respectively, allowing accurate inference of multiple structural properties. The advent of highly accurate virus structure prediction marks a step-change in structural virology and beckons a new era of structurally-informed molecular virology.

The Future Is Bright for Polyoxometalates

Polyoxometalates (POMs) are clusters of units of oxoanions of transition metals, such as Mo, W, V and Nb, that can be formed upon acidification of neutral solutions. Once formed, some POMs have shown to persist in solution, even in the neutral and basic pH range. These inorganic clusters, amenable of a variety of structures, have been studied in environmental, chemical, and industrial fields, having applications in catalysis and macromolecular crystallography, as well as applications in biomedicine, such as cancer, bacterial and viral infections, among others. Herein, we connect recent POMs environmental applications in the decomposition of emergent pollutants with POMs’ biomedical activities and effects against cancer, bacteria, and viruses. With recent insights in POMs being pure, organic/inorganic hybrid materials, POM-based ionic liquid crystals and POM-ILs, and their applications in emergent pollutants degradation, including microplastics, are referred. It is perceived that the majority of the POMs studies against cancer, bacteria, and viruses were performed in the last ten years. POMs’ biological effects include apoptosis, cell cycle arrest, interference with the ions transport system, inhibition of mRNA synthesis, cell morphology changes, formation of reaction oxygen species, inhibition of virus binding to the host cell, and interaction with virus protein cages, among others. We additionally refer to POMs’ interactions with various proteins, including P-type ATPases, aquoporins, cinases, phosphatases, among others. Finally, POMs’ stability and speciation at physiological conditions are addressed.

Computational Study on Potential Novel Anti-Ebola Virus Protein VP35 Natural Compounds

Ebola virus (EBOV) is one of the most lethal pathogens that can infect humans. The Ebola viral protein VP35 (EBOV VP35) inhibits host IFN-α/β production by interfering with host immune responses to viral invasion and is thus considered as a plausible drug target. The aim of this study was to identify potential novel lead compounds against EBOV VP35 using computational techniques in drug discovery. The 3D structure of the EBOV VP35 with PDB ID: 3FKE was used for molecular docking studies. An integrated library of 7675 African natural product was pre-filtered using ADMET risk, with a threshold of 7 and, as a result, 1470 ligands were obtained for the downstream molecular docking using AutoDock Vina, after an energy minimization of the protein via GROMACS. Five known inhibitors, namely, amodiaquine, chloroquine, gossypetin, taxifolin and EGCG were used as standard control compounds for this study. The area under the curve (AUC) value, evaluating the docking protocol obtained from the receiver operating characteristic (ROC) curve, generated was 0.72, which was considered to be acceptable. The four identified potential lead compounds of NANPDB4048, NANPDB2412, ZINC000095486250 and NANPDB2476 had binding affinities of −8.2, −8.2, −8.1 and −8.0 kcal/mol, respectively, and were predicted to possess desirable antiviral activity including the inhibition of RNA synthesis and membrane permeability, with the probable activity (Pa) being greater than the probable inactivity (Pi) values. The predicted anti-EBOV inhibition efficiency values (IC50), found using a random forest classifier, ranged from 3.35 to 11.99 μM, while the Ki values ranged from 0.97 to 1.37 μM. The compounds NANPDB4048 and NANPDB2412 had the lowest binding energy of −8.2 kcal/mol, implying a higher binding affinity to EBOV VP35 which was greater than those of the known inhibitors. The compounds were predicted to possess a low toxicity risk and to possess reasonably good pharmacological profiles. Molecular dynamics (MD) simulations of the protein–ligand complexes, lasting 50 ns, and molecular mechanisms Poisson-Boltzmann surface area (MM-PBSA) calculations corroborated the binding affinities of the identified compounds and identified novel critical interacting residues. The antiviral potential of the molecules could be confirmed experimentally, while the scaffolds could be optimized for the design of future novel anti-EBOV chemotherapeutics.

Predicting the hosts of prokaryotic viruses using GCN-based semi-supervised learning

Background Prokaryotic viruses, which infect bacteria and archaea, are the most abundant and diverse biological entities in the biosphere. To understand their regulatory roles in various ecosystems and to harness the potential of bacteriophages for use in therapy, more knowledge of viral-host relationships is required. High-throughput sequencing and its application to the microbiome have offered new opportunities for computational approaches for predicting which hosts particular viruses can infect. However, there are two main challenges for computational host prediction. First, the empirically known virus-host relationships are very limited. Second, although sequence similarity between viruses and their prokaryote hosts have been used as a major feature for host prediction, the alignment is either missing or ambiguous in many cases. Thus, there is still a need to improve the accuracy of host prediction. Results In this work, we present a semi-supervised learning model, named HostG, to conduct host prediction for novel viruses. We construct a knowledge graph by utilizing both virus-virus protein similarity and virus-host DNA sequence similarity. Then graph convolutional network (GCN) is adopted to exploit viruses with or without known hosts in training to enhance the learning ability. During the GCN training, we minimize the expected calibrated error (ECE) to ensure the confidence of the predictions. We tested HostG on both simulated and real sequencing data and compared its performance with other state-of-the-art methods specifically designed for virus host classification (VHM-net, WIsH, PHP, HoPhage, RaFAH, vHULK, and VPF-Class). Conclusion HostG outperforms other popular methods, demonstrating the efficacy of using a GCN-based semi-supervised learning approach. A particular advantage of HostG is its ability to predict hosts from new taxa.

Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2

Back and forth transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between humans and animals will establish wild reservoirs of virus that endanger long-term efforts to control COVID-19 in people and to protect vulnerable animal populations. Better targeting surveillance and laboratory experiments to validate zoonotic potential requires predicting high-risk host species. A major bottleneck to this effort is the few species with available sequences for angiotensin-converting enzyme 2 receptor, a key receptor required for viral cell entry. We overcome this bottleneck by combining species' ecological and biological traits with three-dimensional modelling of host-virus protein–protein interactions using machine learning. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for greater than 5000 mammals—an order of magnitude more species than previously possible. Our predictions are strongly corroborated by in vivo studies. The predicted zoonotic capacity and proximity to humans suggest enhanced transmission risk from several common mammals, and priority areas of geographic overlap between these species and global COVID-19 hotspots. With molecular data available for only a small fraction of potential animal hosts, linking data across biological scales offers a conceptual advance that may expand our predictive modelling capacity for zoonotic viruses with similarly unknown host ranges.

Ultrasensitive Detection of COVID-19 Causative Virus (SARS-CoV-2) Spike Protein Using Laser Induced Graphene Field-Effect Transistor

COVID-19 is a highly contagious human infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the war with the virus is still underway. Since no specific drugs have been made available yet and there is an imbalance between supply and demand for vaccines, early diagnosis and isolation are essential to control the outbreak. Current nucleic acid testing methods require high sample quality and laboratory conditions, which cannot meet flexible applications. Here, we report a laser-induced graphene field-effect transistor (LIG-FET) for detecting SARS-CoV-2. The FET was manufactured by different reduction degree LIG, with an oyster reef-like porous graphene channel to enrich the binding point between the virus protein and sensing area. After immobilizing specific antibodies in the channel, the FET can detect the SARS-CoV-2 spike protein in 15 min at a concentration of 1 pg/mL in phosphate-buffered saline (PBS) and 1 ng/mL in human serum. In addition, the sensor shows great specificity to the spike protein of SARS-CoV-2. Our sensors can realize fast production for COVID-19 rapid testing, as each LIG-FET can be fabricated by a laser platform in seconds. It is the first time that LIG has realized a virus sensing FET without any sample pretreatment or labeling, which paves the way for low-cost and rapid detection of COVID-19.

Targeting Extracellular Vesicles to Dendritic Cells and Macrophages

Targeting protein therapeutics to specific cells and tissues is a major challenge in modern medicine. Improving the specificity of protein therapeutic delivery will significantly enhance efficiency in drug development. One of the promising tools for protein delivery is extracellular vesicles (EVs) that are enveloped by a complex lipid bilayer. EVs are secreted by almost all cell types and possess significant advantages: biocompatibility, stability, and the ability to penetrate the bloodbrain barrier. Overexpression of the vesicular stomatitis virus protein G (VSV-G) was shown to promote EV formation by the producer cell. We have developed an EV-based system for targeted delivery of protein cargoes to antigen-presenting cells (APCs). In this study, we show that attachment of a recombinant llama nanobody -CD206 to the N-terminus of a truncated VSV-G increases the selectivity of EV cargo delivery mainly to APCs. These results highlight the outstanding technological and biomedical potential of EV-based delivery systems for correcting the immune response in patients with autoimmune, viral, and oncological diseases.