Molecular descriptors and in silico studies of 4-((5-(decylthio)-4-methyl-4n-1,2,4-triazol-3-yl)methyl)morpholine as a potential drug for the treatment of fungal pathologies

IF 2.6 4区 生物学 Q2 BIOLOGY
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Abstract

The article explores the polypharmacological profiling of 4-((5-(decylthio)-4-methyl-4H-1,2,4-triazole-3-yl)methyl)morpholine as a potential antimicrobial agent. The study utilized 15148 electronic pharmacophore models of organisms, ranked by the Tversky index. Detailed analysis revealed classical bonding patterns with selected enzymes, identifying key amino acid residues involved in complex formation. Protein target prediction was conducted through various stages using the Galaxy web service, including ligand structure creation, pharmacophore alignment, and target ranking. The activities of the molecules against 1G6C, 2W6O, 3G7F, 3OWU, 4IVR, and 4TZT proteins were compared. Docking studies with PyMOL and Discovery Studio Visualizer revealed binding to thymidine kinase, thiamine phosphate synthase, and biotin carboxylase with promising binding affinities. These interactions suggest potential antibacterial and antiviral effects, warranting further virtual screening and in-depth studies for the development of effective antimicrobial drugs. Calculations of the molecules were made with the gaussian package program. Calculations were made on the 6-31++g** basis set at B3LYP, HF, and M062X levels with Gaussian software. Afterwards, the 0–100 ns interaction of the molecule with the highest activity was examined.

4-((5-(癸硫基)-4-甲基-4n-1,2,4-三唑-3-基)甲基)吗啉作为治疗真菌病症的潜在药物的分子描述符和硅学研究
文章探讨了作为潜在抗菌剂的 4-[(5-(癸硫基)-4-甲基-4H-1,2,4-三唑-3-基)甲基]吗啉的多药理学特征。这项研究利用了 15148 个生物电子药理模型,并按照 Tversky 指数进行了排序。详细分析揭示了与选定酶的经典结合模式,确定了参与复合物形成的关键氨基酸残基。蛋白质靶标预测是通过银河网络服务的各个阶段进行的,包括配体结构创建、药源比对和靶标排序。比较了分子对 1G6C、2W6O、3G7F、3OWU、4IVR 和 4TZT 蛋白的活性。利用 PyMOL 和 Discovery Studio Visualizer 进行的对接研究显示,这些分子与胸苷激酶、磷酸硫胺素合成酶和生物素羧化酶的结合亲和力良好。这些相互作用表明它们具有潜在的抗菌和抗病毒作用,值得进一步进行虚拟筛选和深入研究,以开发有效的抗菌药物。分子的计算采用高斯软件包程序。计算是在 B3LYP、HF 和 M062X 水平的 6-31++g** 基础集上用高斯软件进行的。随后,研究了活性最高的分子的 0-100 ns 相互作用。
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来源期刊
Computational Biology and Chemistry
Computational Biology and Chemistry 生物-计算机:跨学科应用
CiteScore
6.10
自引率
3.20%
发文量
142
审稿时长
24 days
期刊介绍: Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.
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