Suppr超能文献

证明AutoDock作为药物发现教育工具的作用。

Demonstration of AutoDock as an Educational Tool for Drug Discovery.

作者信息

Helgren Travis R, Hagen Timothy J

机构信息

Department of Chemistry and Biochemistry, Northern Illinois University, 1425 West Lincoln Highway, DeKalb, Illinois 60115, United States.

出版信息

J Chem Educ. 2017 Mar 14;94(3):345-349. doi: 10.1021/acs.jchemed.6b00555. Epub 2017 Feb 13.

Abstract

Drug design and discovery remains a popular topic of study to many students interested in visible, real-world applications of the chemical sciences. It is important that laboratory experiments detailing the early stages of drug discovery incorporate both compound design and an exploration of ligand/receptor interactions. Molecular modeling is widely employed in research endeavors seeking to predict the activity of potential compounds prior to synthesis and can therefore be used to illustrate these concepts. The following activity therefore details the use of AutoDock to predict the binding affinity and docked pose of a series of CDK2 inhibitors. Students can then compare their docking output to experimentally determined inhibitory activities and crystal structures. Finally, the AutoDock workflow detailed in this activity can be used in research settings, provided the receptor crystal structure is known.

摘要

药物设计与发现对于许多对化学科学的可见实际应用感兴趣的学生来说,仍然是一个热门的研究课题。重要的是,详细介绍药物发现早期阶段的实验室实验应包括化合物设计以及对配体/受体相互作用的探索。分子建模广泛应用于在合成前预测潜在化合物活性的研究工作中,因此可用于阐释这些概念。因此,以下活动详细介绍了使用AutoDock预测一系列CDK2抑制剂的结合亲和力和对接构象。然后,学生可以将他们的对接输出与实验确定的抑制活性和晶体结构进行比较。最后,如果已知受体晶体结构,本活动中详细介绍的AutoDock工作流程可用于研究环境。

相似文献

1
Demonstration of AutoDock as an Educational Tool for Drug Discovery.证明AutoDock作为药物发现教育工具的作用。
J Chem Educ. 2017 Mar 14;94(3):345-349. doi: 10.1021/acs.jchemed.6b00555. Epub 2017 Feb 13.
3
Docking studies on DNA intercalators.DNA 嵌入剂的对接研究。
J Chem Inf Model. 2014 Jan 27;54(1):96-107. doi: 10.1021/ci400352t. Epub 2013 Dec 13.
5
Virtual Screening of Novel Glucosamine-6-Phosphate Synthase Inhibitors.新型6-磷酸葡萄糖胺合酶抑制剂的虚拟筛选
Comb Chem High Throughput Screen. 2018;21(3):182-193. doi: 10.2174/1386207321666180330114457.
6
Design of a drug discovery course for non-science majors.面向非理科专业学生的药物发现课程设计。
Biochem Mol Biol Educ. 2018 Jul;46(4):327-335. doi: 10.1002/bmb.21121. Epub 2018 Mar 12.
10
Towards predictive docking at aminergic G-protein coupled receptors.迈向胺能G蛋白偶联受体的预测性对接
J Mol Model. 2015 Nov;21(11):284. doi: 10.1007/s00894-015-2824-9. Epub 2015 Oct 9.

引用本文的文献

本文引用的文献

2
Role of computer-aided drug design in modern drug discovery.计算机辅助药物设计在现代药物发现中的作用。
Arch Pharm Res. 2015 Sep;38(9):1686-701. doi: 10.1007/s12272-015-0640-5. Epub 2015 Jul 25.
4
Fragment-based lead discovery and design.基于片段的先导化合物发现与设计
J Chem Inf Model. 2014 Mar 24;54(3):693-704. doi: 10.1021/ci400731w. Epub 2014 Feb 19.
6
A docking interaction study of the effect of critical mutations in ribonuclease a on protein-ligand binding.
Biochem Mol Biol Educ. 2005 Sep;33(5):335-43. doi: 10.1002/bmb.2005.49403305335.

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