Suppr超能文献

基于定量构效关系的海洋藻类抗癌化合物作为蛋白激酶B(PKBβ)抑制剂的对接研究

QSAR based docking studies of marine algal anticancer compounds as inhibitors of protein kinase B (PKBβ).

作者信息

Davis G Dicky John, Vasanthi A Hannah Rachel

机构信息

Department of Bioinformatics, Sri Ramachandra University, Porur, Chennai 600116, India.

Department of Biotechnology, Pondicherry University, Kalapet, Puducherry 605014, India.

出版信息

Eur J Pharm Sci. 2015 Aug 30;76:110-8. doi: 10.1016/j.ejps.2015.04.026. Epub 2015 Apr 29.

Abstract

Marine algae are prolific source of bioactive secondary metabolites and are found to be active against different cancer cell lines. QSAR studies will explicate the significance of a particular class of descriptor in eliciting anticancer activity against a cancer type. Marine algal compounds showing anticancer activity against six different cancer cell lines namely MCF-7, A431, HeLa, HT-29, P388 and A549 taken from Seaweed metabolite database were subjected to comprehensive QSAR modeling studies. A hybrid-GA (genetic algorithm) optimization technique for descriptor space reduction and multiple linear regression analysis (MLR) approach was used as fitness functions. Cell lines HeLa and MCF-7 showed good statistical quality (R(2)∼0.75, Q(2)∼0.65) followed by A431, HT29 and P388 cell lines with reasonable statistical values (R(2)∼0.70, Q(2)∼0.60). The models developed were interpretable, with good statistical and predictive significance. Molecular descriptor analyses revealed that Baumann's alignment-independent topological descriptors had a major role in variation of activity along with other descriptors. Incidentally, earlier QSAR analysis on a variety of chemically diverse PKBα inhibitors revealed Baumann's alignment-independent topological descriptors that differentiated the molecules binding to Protein kinase B (PKBα) kinase or PH domain, hence a docking study of two crystal structures of PKBβ was performed for identification of novel ATP-competitive inhibitors of PKBβ. Five compounds had a good docking score and Callophycin A showed better ligand efficiency than other PKBβ inhibitors. Furthermore in silico pharmacokinetic and toxicity studies also showed that Callophycin A had a high drug score (0.85) compared to the other inhibitors. These results encourages discovering novel inhibitors for cancer therapeutic targets by screening metabolites from marine algae.

摘要

海洋藻类是生物活性次生代谢物的丰富来源,并且被发现对不同的癌细胞系具有活性。定量构效关系(QSAR)研究将阐明特定类别的描述符在引发针对某一癌症类型的抗癌活性方面的重要性。对取自海藻代谢物数据库的、对六种不同癌细胞系(即MCF-7、A431、HeLa、HT-29、P388和A549)显示抗癌活性的海洋藻类化合物进行了全面的QSAR建模研究。使用一种用于描述符空间缩减的混合遗传算法(GA)优化技术和多元线性回归分析(MLR)方法作为适应度函数。HeLa和MCF-7细胞系显示出良好的统计质量(R²约为0.75,Q²约为0.65),其次是A431、HT29和P388细胞系,具有合理的统计值(R²约为0.70,Q²约为0.60)。所开发的模型具有可解释性,具有良好的统计和预测意义。分子描述符分析表明,鲍曼的与排列无关的拓扑描述符与其他描述符一起在活性变化中起主要作用。顺便提一下,早期对多种化学性质不同的蛋白激酶Bα(PKBα)抑制剂的QSAR分析揭示了鲍曼的与排列无关的拓扑描述符,这些描述符区分了与蛋白激酶B(PKBα)激酶或PH结构域结合的分子,因此对PKBβ的两种晶体结构进行了对接研究,以鉴定新型的PKBβ的ATP竞争性抑制剂。五种化合物具有良好的对接分数,并且Callophycin A显示出比其他PKBβ抑制剂更好的配体效率。此外,计算机模拟的药代动力学和毒性研究还表明,与其他抑制剂相比,Callophycin A具有较高的药物分数(0.85)。这些结果鼓励通过筛选海洋藻类的代谢物来发现针对癌症治疗靶点的新型抑制剂。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验