Several highly effective Covid-19 vaccines are in emergency use, although more-infectious coronavirus strains, could delay the end of the pandemic even further. Because of this, it is highly desirable to develop fast antiviral drug treatments to accelerate the lasting immunity against the virus. From a theoretical perspective, computational approaches are useful tools for antiviral drug development based on the data analysis of gene expression, chemical structure, molecular pathway, and protein interaction mapping. This work studies the structural stability of virus-host interactome networks based on the graphical representation of virus-host protein interactions as vertices or nodes connected by commonly shared proteins. These graphical network visualization methods are analogous to those use in the design of artificial neural networks in neuromorphic computing. In standard protein-node-based network representation, virus-host interaction merges with virus-protein and host-protein networks, introducing redundant links associated with the internal virus and host networks. On the contrary, our approach provides a direct geometrical representation of viral infection structure and allows the effective and fast detection of the structural robustness of the virus-host network through proteins removal. This method was validated by applying it to H1N1 and HIV viruses, in which we were able to pinpoint the changes in the Interactome Network produced by known vaccines. The application of this method to the SARS-CoV-2 virus-host protein interactome implies that nonstructural proteins nsp4, nsp12, nsp16, the nuclear pore membrane glycoprotein NUP210, and ubiquitin specific peptidase USP54 play a crucial role in the viral infection, and their removal may provide an efficient therapy. This method may be extended to any new mutations or other viruses for which the Interactome Network is experimentally determined. Since time is of the essence, because of the impact of more-infectious strains on controlling the spread of the virus, this method may be a useful tool for novel antiviral therapies.
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http://dx.doi.org/10.1038/s41598-022-13373-0 | DOI Listing |
J Ovarian Res
January 2025
Department of Urology, Zigong Fourth People's Hospital, Zigong, Sichuan, China.
Background: Granulosa cell proliferation and survival are essential for normal ovarian function and follicular development. Long non-coding RNAs (lncRNAs) have emerged as important regulators of cell proliferation and differentiation. Nuclear paraspeckle assembly transcript 1 (NEAT1) has been implicated in various cellular processes, but its role in granulosa cell function remains unclear.
View Article and Find Full Text PDFRespir Res
January 2025
Department of Pulmonary and Critical Care Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China.
Background: The emergence of new molecular targeted drugs marks a breakthrough in asthma treatment, particularly for severe cases. Yet, options for moderate-to-severe asthma treatment remain limited, highlighting the urgent need for novel therapeutic drug targets. In this study, we aimed to identify new treatment targets for asthma using the Mendelian randomization method and large-scale genome-wide association data (GWAS).
View Article and Find Full Text PDFJ Transl Med
January 2025
Department of Hematology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
Background: Targeting exportin1 (XPO1) with Selinexor (SEL) is a promising therapeutic strategy for patients with multiple myeloma (MM). However, intrinsic and acquired drug resistance constitute great challenges. SEL has been reported to promote the degradation of XPO1 protein in tumor cells.
View Article and Find Full Text PDFJ Transl Med
January 2025
School of Clinical Laboratory Science, Guizhou Medical University, Guiyang, Guizhou, 550000, China.
Background: Human kinesin family member 11 (KIF11) plays a vital role in regulating the cell cycle and is implicated in the tumorigenesis and progression of various cancers, but its role in endometrial cancer (EC) is still unclear. Our current research explored the prognostic value, biological function and targeting strategy of KIF11 in EC through approaches including bioinformatics, machine learning and experimental studies.
Methods: The GSE17025 dataset from the GEO database was analyzed via the limma package to identify differentially expressed genes (DEGs) in EC.
BMC Med Genomics
January 2025
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China.
Background: Drug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development. In order to shorten the drug development cycle and reduce costs, machine learning methods are gradually playing an important role in the field of drug-target interactions.
Results: Compared with other methods, regression-based drug target affinity is more representative of the binding ability.
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