费斯特共振能量转移
化学
荧光团
变构调节
三元络合物
荧光
生物物理学
色氨酸
氨基酸
蛋白质-蛋白质相互作用
核酸
生物化学
受体
酶
生物
物理
量子力学
作者
Shen-Ming Huang,Fan Yang,Bai-Yang Cai,Qing‐tao He,Qi Liu,Changxiu Qu,Mingjie Han,Wei Kong,Yingli Jia,Fahui Li,Xiao Yu,Jin‐Peng Sun,Jiangyun Wang
出处
期刊:Analytical Chemistry
[American Chemical Society]
日期:2019-10-31
卷期号:91 (23): 14936-14942
被引量:17
标识
DOI:10.1021/acs.analchem.9b03305
摘要
Förster resonance energy transfer (FRET) is a well-established method for studying macromolecular interactions and conformational changes within proteins. Such a method normally uses fluorescent proteins or chemical-labeling methods which are often only accessible to surface-exposed residues and risk-disturbing target protein structures. Here, we demonstrate that the genetic incorporation of a synthetic fluorescent amino acid, L-(7-hydroxycoumarin-4-yl) ethylglycine (Cou) and natural endogenous fluorophore Tryptophan (Trp) residues of a protein could serve as an efficient FRET pair to monitor protein interactions, using the signaling transducer β-arrestin-1 as a model system. We used this technology to record the dynamic spectra in both binding and competition experiments of β-arrestin-1, the contribution of each specific phosphate in ternary complex formation, in a rapid and efficient manner. The determined Kd value for the association between the active arrestin and Fab30 is 0.68 μM in the three-component interaction system. Moreover, we were able to determine the contributions of the site 3 phospho-site and the site 6 phospho-site binding, each contributing to the high affinity ternary complex assembly as 2.7 fold and 15.5 fold, respectively, which were never determined before. These results thus highlighted the potential usage of this new method in measurement of the allosteric-induced enhanced affinity with small amount proteins and in a fast manner and in a complex system. Collectively, our newly developed Trp:Cou FRET system based on genetic expansion technology has extended the molecular toolboxes available for biochemical and structural biology studies.
科研通智能强力驱动
Strongly Powered by AbleSci AI