Suppr超能文献

天然化合物对结核分枝杆菌3-脱氢奎尼酸脱水酶的优先排序:一项计算机模拟与体外实验相结合的研究。

Prioritization of natural compounds against mycobacterium tuberculosis 3-dehydroquinate dehydratase: A combined in-silico and in-vitro study.

作者信息

Lone Mohsin Y, Athar Mohd, Gupta Vivek K, Jha Prakash C

机构信息

School of Chemical Sciences, Central University of Gujarat, Gandhinagar 382030, Gujarat, India.

Department of Biochemistry, National JALMA Institute for Leprosy and Other Mycobacterial Diseases (ICMR), Taj Ganj, Agra 282004, India.

出版信息

Biochem Biophys Res Commun. 2017 Sep 30;491(4):1105-1111. doi: 10.1016/j.bbrc.2017.08.020. Epub 2017 Aug 5.

Abstract

Enormous efforts have been endeavored to develop inhibitors against the potential therapeutic target, mycobacterium tuberculosis 3-dehydroquinate dehydratase (MtbDHQase) to combat resistance. Over a dozen of small molecules have been crystallized to characterize the structural basis of the inhibition. However, the studies accomplished so far, have not incorporated all the essential interactions of these complexes simultaneously, to identify the novel inhibitors. Therefore, an attempt was made to construct the pharmacophore models and identify the essential features that can be employed to prioritize the molecules against this target. Based on validation and expertise, we have identified such complimentary features from the natural compounds that can be used as initial hits. Subsequently, these hits were tested for their inhibitory roles in reducing the mycobacterium tuberculosis (Mtb) culture growth. Moreover, the docking simulations were performed to seek the possible interactions accountable for the activity of these candidates against MtbDHQase.

摘要

为了开发针对潜在治疗靶点——结核分枝杆菌3-脱氢奎尼酸脱水酶(MtbDHQase)的抑制剂以对抗耐药性,人们付出了巨大努力。已经有十几种小分子结晶以表征抑制作用的结构基础。然而,迄今为止完成的研究尚未同时纳入这些复合物的所有关键相互作用,以鉴定新型抑制剂。因此,人们尝试构建药效团模型并确定可用于对针对该靶点的分子进行优先级排序的关键特征。基于验证和专业知识,我们从天然化合物中确定了此类互补特征,可将其用作初始命中物。随后,测试了这些命中物在减少结核分枝杆菌(Mtb)培养物生长方面的抑制作用。此外,进行了对接模拟以寻找对这些候选物针对MtbDHQase的活性负责的可能相互作用。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验