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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Investigation of non-hydroxamate scaffolds against HDAC6 inhibition: A pharmacophore modeling, molecular docking, and molecular dynamics simulation approach
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Investigation of non-hydroxamate scaffolds against HDAC6 inhibition: A pharmacophore modeling, molecular docking, and molecular dynamics simulation approach

机译:对HDAC6抑制的非羟肟酸盐支架的调查:药物模型,分子对接和分子动力学模拟方法

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Proteins deacetylation by Histone deacetylase 6 (HDAC6) has been shown in various human chronic diseases like neurodegenerative diseases and cancer, and hence is an important therapeutic target. Since, the existing inhibitors have hydroxamate group, and are not HDAC6-selective, therefore, this study has designed to investigate non-hydroxamate HDAC6 inhibitors. Ligand-based pharmacophore was generated from 26 training set compounds of HDAC6 inhibitors. The statistical parameters of pharmacophore (Hypo1) included lowest total cost of 115.63, highest cost difference of 135.00, lowest RMSD of 0.70 and the highest correlation of 0.98. The pharmacophore was validated by Fischer's Randomization and Test Set validation, and used as screening tool for chemical databases. The screened compounds were filtered by fit value ( 10.00), estimated Inhibitory Concentration (IC50) (0.459), Lipinski's Rule of Five and Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Descriptors to identify drug-like compounds. Furthermore, the drug-like compounds were docked into the active site of HDAC6. The best docked compounds were selected having gold fitness score 66.46 and chemscore -28.31, and hydrogen bond interaction with catalytic active residues. Finally, three inhibitors having sulfamoyl group were selected by Molecular Dynamic (MD) simulation, which showed stable root mean square deviation (RMSD) (1.6-1.9 angstrom), lowest potential energy ( -6.3 x 10(5) kJ/mol), and hydrogen bonding with catalytic active residues of HDAC6.
机译:通过组蛋白脱乙酰酶6(HDAC6)的蛋白质脱乙酰化已被示出在各种人类慢性疾病,如神经变性疾病和癌症,因此是重要的治疗靶标。由于现有的抑制剂具有羟肟酸酯基团,并且不是HDAC6选择性,因此,该研究设计用于研究非羟胺HDAC6抑制剂。从HDAC6抑制剂的26次训练组化合物产生基于LigAnd的药效线。药物统计学(Hypo1)的统计参数包括115.63的最低总成本,最高成本差为135.00,最低RMSD为0.70,最高的相关性为0.98。 Pharmacore通过Fischer的随机化和测试设置验证验证,并用作化学数据库的筛选工具。通过配合值(& 10.00)过滤筛选的化合物,估计抑制浓度(IC 50)(& 0.459),Lipinski的五种和吸收,分布,代谢,排泄和毒性(呼气)描述符来识别药物状化合物。此外,将药物状化合物停靠在HDAC6的活性位点。选择最佳停靠的化合物,具有金色健身得分& 66.46和ChemScore& -28.31和催化活性残基的氢键相互作用。最后,通过分子动态(MD)模拟选择具有磺酰甘酰基的三种抑制剂,其显示出稳定的根部均方偏差(RMSD)(1.6-1.9埃),最低势能(<6-1.9埃)(& -6.3 x 10(5)kj / mol )和与HDAC6的催化活性残基的氢键。

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