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32-β-D-Cellobiosyl-cellobiose +
33-β-D-Glucosyl-cellotriose

32-beta-D-Cellobiosyl-cellobiose + 33-beta-D-Glucosyl-cellotriose O-BGTETC
Product code: O-BGTETC
€161.00

30 mg

Prices exclude VAT

Available for shipping

Content: 30 mg
Shipping Temperature: Ambient
Storage Temperature: Below -10oC
Physical Form: Powder
Stability: > 10 years under recommended storage conditions
CAS Number: 103762-93-2,
58484-04-1
Synonyms: Cellobiosyl-(1→3)-β-D-Cellobiose + Glucosyl-(1→3)-β-D-Cellotriose, 1,3:1,4-β-D-Glucotetraose C + 1,3:1,4-β-D-Glucotetraose A
Molecular Formula: C24H42O21
Molecular Weight: 666.6
Purity: > 95%
Substrate For (Enzyme): β-Glucanase/Lichenase

High purity 32-β-D-Cellobiosyl-cellobiose + 33-β-D-Glucosyl-cellotriose mixture for use in research, biochemical enzyme assays and in vitro diagnostic analysis.

These tetrasaccharides are produced on hydrolysis of 1:3,1:4-β-D-glucan by cellulase.

Documents
Certificate of Analysis
Safety Data Sheet
Booklet
Publications
Publication
Versatile high resolution oligosaccharide microarrays for plant glycobiology and cell wall research.

Pedersen, H. L., Fangel, J. U., McCleary, B., Ruzanski, C., Rydahl, M. G., Ralet, M. C., Farkas, V., Von Schantz, L., Marcus, S. E., Andersen, M.C. F., Field, R., Ohlin, M., Knox, J. P., Clausen, M. H. & Willats, W. G. T. (2012). Journal of Biological Chemistry, 287(47), 39429-39438.

Microarrays are powerful tools for high throughput analysis, and hundreds or thousands of molecular interactions can be assessed simultaneously using very small amounts of analytes. Nucleotide microarrays are well established in plant research, but carbohydrate microarrays are much less established, and one reason for this is a lack of suitable glycans with which to populate arrays. Polysaccharide microarrays are relatively easy to produce because of the ease of immobilizing large polymers noncovalently onto a variety of microarray surfaces, but they lack analytical resolution because polysaccharides often contain multiple distinct carbohydrate substructures. Microarrays of defined oligosaccharides potentially overcome this problem but are harder to produce because oligosaccharides usually require coupling prior to immobilization. We have assembled a library of well characterized plant oligosaccharides produced either by partial hydrolysis from polysaccharides or by de novo chemical synthesis. Once coupled to protein, these neoglycoconjugates are versatile reagents that can be printed as microarrays onto a variety of slide types and membranes. We show that these microarrays are suitable for the high throughput characterization of the recognition capabilities of monoclonal antibodies, carbohydrate-binding modules, and other oligosaccharide-binding proteins of biological significance and also that they have potential for the characterization of carbohydrate-active enzymes.

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Publication
HPAEC-PAD for oligosaccharide analysis—novel insights into analyte sensitivity and response stability.

Mechelke, M., Herlet, J., Benz, J. P., Schwarz, W. H., Zverlov, V. V., Liebl, W. & Kornberger, P. (2017). Analytical and Bioanalytical Chemistry, 1-13.

The rising importance of accurately detecting oligosaccharides in biomass hydrolyzates or as ingredients in food, such as in beverages and infant milk products, demands for the availability of tools to sensitively analyze the broad range of available oligosaccharides. Over the last decades, HPAEC-PAD has been developed into one of the major technologies for this task and represents a popular alternative to state-of-the-art LC-MS oligosaccharide analysis. This work presents the first comprehensive study which gives an overview of the separation of 38 analytes as well as enzymatic hydrolyzates of six different polysaccharides focusing on oligosaccharides. The high sensitivity of the PAD comes at cost of its stability due to recession of the gold electrode. By an in-depth analysis of the sensitivity drop over time for 35 analytes, including xylo- (XOS), arabinoxylo- (AXOS), laminari- (LOS), manno- (MOS), glucomanno- (GMOS), and cellooligosaccharides (COS), we developed an analyte-specific one-phase decay model for this effect over time. Using this model resulted in significantly improved data normalization when using an internal standard. Our results thereby allow a quantification approach which takes the inevitable and analyte-specific PAD response drop into account.

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Precautionary Statements : Not Applicable
Safety Data Sheet
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