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通过定量构效关系建模和分子动力学模拟试验探索I类组蛋白去乙酰化酶亚型的新型抑制剂

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

作者信息

Noor Zainab, Afzal Noreen, Rashid Sajid

机构信息

National Center for Bioinformatics, Quaid I Azam University, Islamabad, Pakistan.

出版信息

PLoS One. 2015 Oct 2;10(10):e0139588. doi: 10.1371/journal.pone.0139588. eCollection 2015.

Abstract

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 energy/non-hydrogen atoms) profiles suggested that proposed hits may be more effective inhibitors for cancer therapy.

摘要

组蛋白去乙酰化酶(HDAC)是金属依赖性酶,被认为是细胞功能的重要靶点。特别是,I类HDACs的高表达在多种恶性肿瘤的发生中很常见,这导致许多参与细胞生长、分化和存活的靶基因失调。尽管已经进行了大量尝试,通过使用对转化细胞具有高敏感性的各种抑制剂来控制HDACs的异常功能,但在表观遗传癌症治疗中取得的成功有限。在本研究中,我们使用基于配体的药效团和二维定量构效关系(QSAR)建模方法来靶向I类HDAC亚型。通过考虑已知的IC50值和经过广泛验证的实验能量得分来生成药效团模型。具有0.93的外部R2值的QSAR模型用于化合物库的虚拟筛选。筛选出了10种对HDACs具有强结合亲和力的潜在先导化合物(C1 - C10),其中2种化合物(C8和C9)能够与I类HDACs的所有成员相互作用。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,所提出的命中化合物可能是更有效的癌症治疗抑制剂。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/042e/4592208/f366f2ecbcce/pone.0139588.g001.jpg

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