Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C1175175 (SARS)
 19,188 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

To contribute to an understanding of biological recognition and interaction, an easy-to-use procedure was developed to generate and display molecular surfaces and selected electron density based surface properties. To overcome the present limitations to derive electron densities of macromolecules, the considered systems were reduced to appropriate substructures around the active centers. The combination of experimental X-ray structural information and aspherical atomic electron density data from theoretical calculations resulted in properties like the electrostatic potential and the Hirshfeld surface which allowed a study of electronic complementarity and the identification of sites and strengths of drug-receptor interactions. Applications were examined for three examples. The anilinoquinazoline gefitinib (Iressa(R)) belongs to a new class of anticancer drugs that inhibit the tyrosine kinase activity of the epidermal growth factor receptor (EGFR). In the second example, the interaction of epoxide inhibitors with the main protease of the SARS coronavirus was investigated. Furthermore, the progesterone receptor complex was examined. The quantitative analysis of hydrogen bonding in the chosen substructure systems follows a progression elaborated earlier on the basis of accurate small molecule crystal structures. This finding and results from modified substructures suggest that also the surface properties seem robust enough to provide stable information about the recognition of interacting biomolecular species although they are obtained from medium molecular weighted subfragments of macromolecular complexes, which consist of no more than approximately 40 residues.
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PMID:A simple procedure for the derivation of electron density based surfaces of drug-receptor complexes from a combination of X-ray data and theoretical calculations. 2063 77

Establishing consensus around the transcriptional interface between coronavirus (CoV) infection and human cellular signaling pathways can catalyze the development of novel anti-CoV therapeutics. Here, we used publicly archived transcriptomic datasets to compute consensus regulatory signatures, or consensomes, that rank human genes based on their rates of differential expression in MERS-CoV (MERS), SARS-CoV-1 (SARS1) and SARS-CoV-2 (SARS2)-infected cells. Validating the CoV consensomes, we show that high confidence transcriptional targets (HCTs) of MERS, SARS1 and SARS2 infection intersect with HCTs of signaling pathway nodes with known roles in CoV infection. Among a series of novel use cases, we gather evidence for hypotheses that SARS2 infection efficiently represses E2F family HCTs encoding key drivers of DNA replication and the cell cycle; that progesterone receptor signaling antagonizes SARS2-induced inflammatory signaling in the airway epithelium; and that SARS2 HCTs are enriched for genes involved in epithelial to mesenchymal transition. The CoV infection consensomes and HCT intersection analyses are freely accessible through the Signaling Pathways Project knowledgebase, and as Cytoscape-style networks in the Network Data Exchange repository.
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PMID:Consensus transcriptional regulatory networks of coronavirus-infected human cells. 3251 79