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Exploration of Novel Inhibitors for Class I Histone Deacetylase Isoforms by QSAR Modeling and Molecular Dynamics Simulation Assays

机译:QSAR建模和分子动力学模拟方法探索新型I类组蛋白去乙酰化酶同工酶抑制剂

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摘要

Histone deacetylases (HDAC) are metal-dependent enzymes and considered as important targets for cell functioning. Particularly, higher expression of class I HDACs is common in the onset of multiple malignancies which results in deregulation of many target genes involved in cell growth, differentiation and survival. Although substantial attempts have been made to control the irregular functioning of HDACs by employing various inhibitors with high sensitivity towards transformed cells, limited success has been achieved in epigenetic cancer therapy. Here in this study, we used ligand-based pharmacophore and 2-dimensional quantitative structure activity relationship (QSAR) modeling approaches for targeting class I HDAC isoforms. Pharmacophore models were generated by taking into account the known IC50 values and experimental energy scores with extensive validations. The QSAR model having an external R2 value of 0.93 was employed for virtual screening of compound libraries. 10 potential lead compounds (C1-C10) were short-listed having strong binding affinities for HDACs, out of which 2 compounds (C8 and C9) were able to interact with all members of class I HDACs. The potential binding modes of HDAC2 and HDAC8 to C8 were explored through molecular dynamics simulations. Overall, bioactivity and ligand efficiency (binding energyon-hydrogen atoms) profiles suggested that proposed hits may be more effective inhibitors for cancer therapy.
机译:组蛋白脱乙酰基酶(HDAC)是金属依赖性酶,被认为是细胞功能的重要靶标。特别地,在多种恶性肿瘤的发作中,I类HDAC的较高表达是常见的,这导致许多参与细胞生长,分化和存活的靶基因的失调。尽管已通过采用对转化细胞高度敏感的各种抑制剂来控制HDAC的不规则功能的大量尝试,但在表观遗传学癌症治疗中取得了有限的成功。在本研究中,我们使用基于配体的药效团和二维定量结构活性关系(QSAR)建模方法来靶向I类HDAC同工型。药理学模型是通过考虑已知的IC50值和实验能量评分并进行广泛验证而生成的。使用外部R 2 值为0.93的QSAR模型对化合物库进行虚拟筛选。筛选出10种潜在的铅化合物(C1-C10),它们对HDAC具有很强的结合亲和力,其中2种化合物(C8和C9)能够与所有I类HDAC成员相互作用。通过分子动力学模拟探索了HDAC2和HDAC8与C8的潜在结合模式。总体而言,生物活性和配体效率(结合能/非氢原子)概况表明,拟议的命中可能是更有效的癌症治疗抑制剂。

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  • 年(卷),期 -1(10),10
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  • 页码 e0139588
  • 总页数 23
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