Host antiviral factors hijack furin to block SARS-CoV-2, ebola virus, and HIV-1†glycoproteins cleavage.

Viral envelope glycoproteins are crucial for viral infections. In the process of enveloped viruses budding and release from the producer cells, viral envelope glycoproteins are presented on the viral membrane surface as spikes, promoting the virus's next-round infection of target cells. However, the host cells evolve counteracting mechanisms in the long-term virus-host co-evolutionary processes. For instance, the host cell antiviral factors could potently suppress viral replication by targeting their envelope glycoproteins through multiple channels, including their intracellular synthesis, glycosylation modification, assembly into virions, and binding to target cell receptors. Recently, a group of studies discovered that some host antiviral proteins specifically recognized host proprotein convertase (PC) furin and blocked its cleavage of viral envelope glycoproteins, thus impairing viral infectivity. Here, in this review, we briefly summarize several such host antiviral factors and analyze their roles in reducing furin cleavage of viral envelope glycoproteins, aiming at providing insights for future antiviral studies.

DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis.

BACKGROUND: Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy. METHODS: We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning. RESULTS: To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF. CONCLUSIONS: All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.

A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks.

Drug-drug interactions (DDIs) prediction is a challenging task in drug development and clinical application. Due to the extremely large complete set of all possible DDIs, computer-aided DDIs prediction methods are getting lots of attention in the pharmaceutical industry and academia. However, most existing computational methods only use single perspective information and few of them conduct the task based on the biomedical knowledge graph (BKG), which can provide more detailed and comprehensive drug lateral side information flow. To this end, a deep learning framework, namely DeepLGF, is proposed to fully exploit BKG fusing local-global information to improve the performance of DDIs prediction. More specifically, DeepLGF first obtains chemical local information on drug sequence semantics through a natural language processing algorithm. Then a model of BFGNN based on graph neural network is proposed to extract biological local information on drug through learning embedding vector from different biological functional spaces. The global feature information is extracted from the BKG by our knowledge graph embedding method. In DeepLGF, for fusing local-global features well, we designed four aggregating methods to explore the most suitable ones. Finally, the advanced fusing feature vectors are fed into deep neural network to train and predict. To evaluate the prediction performance of DeepLGF, we tested our method in three prediction tasks and compared it with state-of-the-art models. In addition, case studies of three cancer-related and COVID-19-related drugs further demonstrated DeepLGF's superior ability for potential DDIs prediction. The webserver of the DeepLGF predictor is freely available at http://120.77.11.78/DeepLGF/.

MARCH8 Inhibits Ebola Virus Glycoprotein, Human Immunodeficiency Virus Type 1 Envelope Glycoprotein, and Avian Influenza Virus H5N1 Hemagglutinin Maturation.

Membrane-associated RING-CH-type 8 (MARCH8) strongly blocks human immunodeficiency virus type 1 (HIV-1) envelope glycoprotein (Env) incorporation into virions by downregulating its cell surface expression, but the mechanism is still unclear. We now report that MARCH8 also blocks the Ebola virus (EBOV) glycoprotein (GP) incorporation via surface downregulation. To understand how these viral fusion proteins are downregulated, we investigated the effects of MARCH8 on EBOV GP maturation and externalization via the conventional secretion pathway. MARCH8 interacted with EBOV GP and furin when detected by immunoprecipitation and retained the GP/furin complex in the Golgi when their location was tracked by a bimolecular fluorescence complementation (BiFC) assay. MARCH8 did not reduce the GP expression or affect the GP modification by high-mannose N-glycans in the endoplasmic reticulum (ER), but it inhibited the formation of complex N-glycans on the GP in the Golgi. Additionally, the GP O-glycosylation and furin-mediated proteolytic cleavage were also inhibited. Moreover, we identified a novel furin cleavage site on EBOV GP and found that only those fully glycosylated GPs were processed by furin and incorporated into virions. Furthermore, the GP shedding and secretion were all blocked by MARCH8. MARCH8 also blocked the furin-mediated cleavage of HIV-1 Env (gp160) and the highly pathogenic avian influenza virus H5N1 hemagglutinin (HA). We conclude that MARCH8 has a very broad antiviral activity by prohibiting different viral fusion proteins from glycosylation and proteolytic cleavage in the Golgi, which inhibits their transport from the Golgi to the plasma membrane and incorporation into virions.IMPORTANCE Enveloped viruses express three classes of fusion proteins that are required for their entry into host cells via mediating virus and cell membrane fusion. Class I fusion proteins are produced from influenza viruses, retroviruses, Ebola viruses, and coronaviruses. They are first synthesized as a type I transmembrane polypeptide precursor that is subsequently glycosylated and oligomerized. Most of these precursors are cleaved en route to the plasma membrane by a cellular protease furin in the late secretory pathway, generating the trimeric N-terminal receptor-binding and C-terminal fusion subunits. Here, we show that a cellular protein, MARCH8, specifically inhibits the furin-mediated cleavage of EBOV GP, HIV-1 Env, and H5N1 HA. Further analyses uncovered that MARCH8 blocked the EBOV GP glycosylation in the Golgi and inhibited its transport from the Golgi to the plasma membrane. Thus, MARCH8 has a very broad antiviral activity by specifically inactivating different viral fusion proteins.