U7752
尿素酶 来源于洋刀豆 （刀豆）
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关于此项目
化学文摘社编号:
UNSPSC Code:
12352204
eCl@ss:
32160410
EC Number:
232-656-0
NACRES:
NA.54
MDL number:
Biological source:
Canavalia ensiformis
General description
亚基分子量：约 90,770
由六个亚基组成，总分子量：约 544620
由六个亚基组成，总分子量：约 544620
尿素酶（脲酶）是一种多亚基的镍依赖型金属酶。大量存在于真菌、酵母、植物和细菌中。属于酰胺水解酶和磷酸三酯酶超家族。尿素酶也存在于豆科植物种子中。
Application
洋刀豆尿素酶已用于：
- 作为电泳的分子量标准品组分
- 作为体积排阻色谱的标准品组分
- 作为天然聚丙烯酰胺凝胶电泳(PAGE)的参比物质
Biochem/physiol Actions
尿素酶是氮循环的必需酶。它催化尿素分解成为氨和甲酸。尿素酶为各种生物提供生长所需氮源。在种子发芽和种子化学防御中同样发挥重要作用。
Other Notes
包含约1 mg蛋白质
signalword
Danger
hcodes
Hazard Classifications
Eye Irrit. 2 - Resp. Sens. 1 - Skin Irrit. 2 - STOT SE 3
target_organs
Respiratory system
存储类别
11 - Combustible Solids
wgk
WGK 1
法规信息
低风险生物材料
此项目有
Anuradha Balasubramanian et al.
Journal of molecular biology, 400(3), 274-283 (2010-05-18)
Urease, a nickel-dependent metalloenzyme, is synthesized by plants, some bacteria, and fungi. It catalyzes the hydrolysis of urea into ammonia and carbon dioxide. Although the amino acid sequences of plant and bacterial ureases are closely related, some biological activities differ
Muhammad Ajmal Rana et al.
PloS one, 16(10), e0258568-e0258568 (2021-10-15)
Urea is the most popular and widely used nitrogenous fertilizer. High soil urease activity rapidly hydrolyses applied urea to ammonia which contributes to soil nitrogen (N) losses and reduces N use efficiency of crop plants. The ammonia losses can be
Karine Kappaun et al.
Journal of advanced research, 13, 3-17 (2018-08-11)
Urease (urea amidohydrolase, EC 3.5.1.5) is a nickel-containing enzyme produced by plants, fungi, and bacteria that catalyzes the hydrolysis of urea into ammonia and carbamate. Urease is of historical importance in Biochemistry as it was the first enzyme ever to
Enhanced catalytic properties of novel (alphagamma) 2 heterohexameric Rhodobacter capsulatus xanthine dehydrogenase by separate expression of the redox domains in Escherichia coli
Wang C, et al.
Biochemical Engineering Journal, 119, 1-8 (2017)
Jonathan Parkinson et al.
Nature communications, 14(1), 454-454 (2023-01-29)
High-affinity antibodies are often identified through directed evolution, which may require many iterations of mutagenesis and selection to find an optimal candidate. Deep learning techniques hold the potential to accelerate this process but the existing methods cannot provide the confidence
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