The present study attempts to identify the novel inhibitors of shikimate dehydrogenase (SD), the enzyme that catalyzes the fourth reaction in the shikimate pathway, through virtual screening and toxicity studies. Crystal structure of SD was obtained from Protein Data Bank (PDB ID 4P4G, 1.7 Å) and subjected to energy minimization and structure optimization. A total of 13,803 compounds retrieved from two public databases and used for the virtual screening based on physicochemical properties (Lipinski rule of five) and molecular docking analyses. A total of 26 compounds with good AutoDock binding energies values ranging between - 12.03 and - 8.33 kcal/mol was selected and further filtered for absorption distribution metabolism excretion and toxicity analyses (ADMET). In this, eight compounds were selected, which satisfied all the ADME and toxicity analysis properties. Three compounds with better AutoDock binding energies values (ZINC12135132, - 12.03 kcal/mol; ZINC08951370, - 10.04 kcal/mol; and ZINC14733847, 9.82 kcal/mol) were considered for molecular dynamic (MD) simulation and molecular generalized born surface area (MM-GBSA) analyses. The results of the analyses revealed that the two ligands (ZINC12135132 and ZINC08951370) had better inhibitory activities within their complexes, after the 50-ns MD simulation, which suggested that the complexes formed stable conformation. It is noteworthy that compounds identified by docking, MD simulation, and MM-GBSA methods could be a drug for tuberculosis which required further experimental validation.
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http://dx.doi.org/10.1007/s10123-018-0021-2 | DOI Listing |
Cardiooncology
January 2025
Dept of Oncology Tier 2 Canada Research Chair, University of Alberta, Alberta, Canada.
Background: With early detection and improvements in systemic and local therapies, millions of people are surviving cancer, but for some at a high cost. In some cancer types, cardiovascular disease now competes with recurrent cancer as the cause of death. Traditional care models, in which the cardiologist or oncologist assess patients individually, do not address complex cancer and cardiovascular needs.
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January 2025
First Affiliated Hospital of Kunming Medical University, No. 295, Xichang Road, Kunming, 650032, China.
Background: With the continuous development of educational methods, desktop virtual reality technology has gradually attracted widespread attention. Although current research has shown that this technology can promote learning among nursing students, the mechanism and intrinsic factors are not yet clear. This study aims to explore the mechanisms and factors of the application of desktop virtual reality technology in nursing students' education and discuss the possible outcomes.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Cardiology, University of Galway, University Road, Galway, H91 TK33, Ireland.
Diffuse coronary artery disease (CAD) impacts the immediate hemodynamic and clinical outcomes of percutaneous coronary intervention (PCI). We evaluated whether the diffuse pattern of CAD derived from angiographic Quantitative flow ratio (QFR) impacts the immediate hemodynamic outcome post-PCI and the medium term predicted vessel-oriented composite endpoint (VOCE). Paired pre-procedure QFRs were assessed in 503 patients and 1022 vessels in the Multivessel TALENT (MVT) trial.
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January 2025
Faculty of Computer and AI, Cairo University, Giza, Egypt.
Drug discovery and development is a challenging and time-consuming process. Laboratory experiments conducted on Vidarabine showed IC 6.97 µg∕mL, 25.
View Article and Find Full Text PDFNat Commun
January 2025
Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Accurate melanoma diagnosis is crucial for patient outcomes and reliability of AI diagnostic tools. We assess interrater variability among eight expert pathologists reviewing histopathological images and clinical metadata of 792 melanoma-suspicious lesions prospectively collected at eight German hospitals. Moreover, we provide access to the largest panel-validated dataset featuring dermoscopic and histopathological images with metadata.
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