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Oligosaccharides

Oligosaccharides are short chains of monosaccharides (simple sugars) linked together.
They play crucial roles in various biological processes, including cell signaling, immune function, and pathogen recognition.
Oligsaccharides can be found in a variety of natural sources, such as plants, animals, and microorganisms.
Understanding the structure and function of oligosacharides is essential for developing new therapeptic approaches and improving diagnostic tools.
PubCompare.ai, an AI-driven platform, revolutionizes oligsaccharide research by helping researchers easily locate the best protocols from literature, pre-prints, and patents using advanced AI comparisons.
This one-stop solution enhances reproducibility and accuracy in oligsaccharide studies, making it an invaluable tool for scientists working in this dynamic field.

Most cited protocols related to «Oligosaccharides»

1
Expanded Secondary Metabolite Detection in antiSMASH 2.0
Cited 25 times
In addition to the secondary metabolite cluster types supported in the original release of antiSMASH (type I, II and III polyketides, non-ribosomal peptides, terpenes, lantipeptides, bacteriocins, aminoglycosides/aminocyclitols, β-lactams, aminocoumarins, indoles, butyrolactones, ectoines, siderophores, phosphoglycolipids, melanins and a generic class of clusters encoding unusual secondary metabolite biosynthesis genes), version 2.0 adds support for oligosaccharide antibiotics, phenazines, thiopeptides, homoserine lactones, phosphonates and furans. The cluster detection uses the same pHMM rule-based approach as the initial release (17 (link)): in short, the pHMMs are used to detect signature proteins or protein domains that are characteristic for the respective secondary metabolite biosynthetic pathway. Some pHMMs were obtained from PFAM or TIGRFAM. If no suitable pHMMs were available from these databases, custom pHMMs were constructed based on manually curated seed alignments (Supplementary Table S1). These are composed of protein sequences of experimentally characterized biosynthetic enzymes described in literature, as well as their close homologs found in gene clusters from the same type. The models were curated by manually inspecting the output of searches against the non-redundant (nr) database of protein sequences. The seed alignments are available online at http://antismash.secondarymetabolites.org/download.html#extras. After scanning the genome with the pHMM library, antiSMASH evaluates all hits using a set of rules (Supplementary Table S2) that describe the different cluster types. Unlike the hard-coded rules in the initial release of antiSMASH, the detection rules and profile lists are now located in editable TXT files, making it easy for users to add and modify cluster rules in the stand-alone version, e.g. to accommodate newly discovered or proprietary compound classes without code changes. The results of gene cluster predictions by antiSMASH are continuously checked on new data arising from research performed throughout the natural products community, and pHMMs and their cut-offs are regularly updated when either false positives or false negatives become apparent.
The profile-based detection of secondary metabolite clusters has now been augmented by a tighter integration of the generalized PFAM (22 (link)) domain-based ClusterFinder algorithm (Cimermancic et al., in preparation) already included in version 1.0 of antiSMASH. This algorithm performs probabilistic inference of gene clusters by identifying genomic regions with unusually high frequencies of secondary metabolism-associated PFAM domains, and it was designed to detect ‘classical’ as well as less typical and even novel classes of secondary metabolite gene clusters. While antiSMASH 1.0 only generated the output of this algorithm in a static image, version 2.0 displays these additional putative gene clusters along with the other gene clusters in the HTML output. A key advantage of this is that these putative gene clusters will now also be included in the subsequent (Sub)ClusterBlast analyses.
Blin K., Medema M.H., Kazempour D., Fischbach M.A., Breitling R., Takano E, & Weber T. (2013). antiSMASH 2.0—a versatile platform for genome mining of secondary metabolite producers. Nucleic Acids Research, 41(Web Server issue), W204-W212.
Publication 2013
Amino Acid Sequence Aminocoumarins Aminoglycosides Anabolism Antibiotics Bacteriocins Biosynthetic Pathways Childbirth Classes Enzymes Furans Gene Clusters Generic Drugs Genes Genome Genomic Library homoserine lactone Indoles Lactams Melanins Natural Products Oligosaccharides Peptides Phenazines Phosphonates Polyketides Prognosis Protein Domain Proteins Ribosomes Secondary Metabolism Siderophores Terpenes
2
Martini Modeling of Lipopolysaccharides in CHARMM-GUI
Cited 16 times
Martini models of two different LPS, Ra LPS (RAMP) and Re LPS (REMP), were added in CHARMM-GUI Martini Maker (Figure 1). The LPS models follow a 4-to-1 mapping scheme of the Martini force field and the parameters were optimized based on united-atom LPS simulations to improve accuracy.35 (link),36 (link)The overall building procedures of all LPS-containing Martini Maker modules (Bilayer/Nanodisc/Vesicle/Micelle/Random Builders) are identical from the original implementation.34 (link) Briefly, in STEP 1, a user-specified protein structure is read-in through PDB Reader. In STEP 2, the protein orientation is changed based on the user-specific input; by definition, the Z axis is the membrane normal and Z = 0 is the membrane center. In STEP 3, the system size is determined, and the pseudo spheres are placed for assigning lipid head group positions. Note that this is the first step when the membrane-only generation option is selected. In STEP 4, the system components (lipids, water, and ions) are generated. Finally, all the components are assembled in STEP 5. During STEP 5, the CHARMM structure (PSF) and coordinate (CRD/PDB) files of each component generated in STEP 4 are merged into single PSF and CRD/PDB files, and water beads in close proximity to the solutes are removed.
Some LPS-specific changes were introduced in the system size calculation and ion placement steps. As described above, the system size was previously determined in STEP 3. However, as the LPS molecule has a long carbohydrate chain, a portion of the LPS molecule can be stretched out beyond the system box determined in STEP 3 based on phospholipids. To resolve this issue, if the system contains LPS, the system size is recalculated by taking the LPS height into account in STEP 4, and the updated system size information is used for further steps (building water box and placing ions).
As divalent cations play an important role in stabilizing the bacterial OM by interacting with the LPS,22 (link),37 (link)–40 (link) the ion placement procedure in STEP 4 was modified to use Ca2+ as the counterions for LPS lipid A and core oligosaccharides. By default, CHARMM-GUI adds Ca2+ ions to neutralize lipid A, but for the LPS core, CHARMM-GUI provides an option to select an ion type (Na+ or Ca2+).
Hsu P.C., Bruininks B.M., Jefferies D., de Souza P.C., Lee J., Patel D.S., Marrink S.J., Qi Y., Khalid S, & Im W. (2017). CHARMM-GUI Martini Maker for Modeling and Simulation of Complex Bacterial Membranes with Lipopolysaccharides. Journal of computational chemistry, 38(27), 2354-2363.
Publication 2017
Bacteria Carbohydrates Cations, Divalent Epistropheus Head Lipid A Lipids Micelles Oligosaccharides Phospholipids Proteins Re lipopolysaccharide Tissue, Membrane
3
Ligand Docking Accuracy Comparison
Cited 15 times
The results of each ADV docking experiment are variable due to the random seed implemented within the genetic algorithm. In order to account for this variation, the results from multiple independent docking experiments were averaged for each system examined. Unless otherwise stated, each root-mean-squared-deviation (RMSD) represents the average result of 10 docking runs. This method of analysis aims to eliminate spurious results, enabling a more accurate comparison between ADV and VC. To increase comparability, the same 10 random seeds generated for each of the 10 ADV docking experiments were employed for the 10 corresponding VC docking runs.
Docking accuracy is determined through two types of RMSD values, namely, those for the ligand pose and the ligand shape; all RMSDs were calculated with respect to the six atoms that define the pyranose ring (typically C1, C2, C3, C4, C5, and O5). The pose RMSD (PRMSD) quantifies the deviation of the docked model from the location of the reference structure relative to the protein surface. In this manner, the PRMSD defines the accuracy of docking the ligand to the receptor. The shape RMSD (SRMSD) compares the docked oligosaccharide conformation to that of the reference structure independent of their locations in space. PRMSDmin(5) and PRMSDmin(20) represent the minimum PRMSD from the top 5 and top 20 ranked models, respectively, averaged across 10 docking runs. The average SRMSD (SRMSDavg) was calculated for each of the 20 models from the 10 docking experiments.
Images of the molecules were prepared using the Visual Molecular Dynamics (VMD) program.16 Unless otherwise noted, the ligands are colored according to the source of the structure; crystal structures are blue, whereas output from ADV or VC are yellow and green, respectively. Additionally, each carbohydrate ring is colored according to whether the CHI-energy penalty is applied to that monosaccharide. CHI-energies are applied to monosaccharides in the 1C4 and 4C1 chair conformations, and these are colored green. Monosaccharides in any other conformations, which would be skipped by VC, are colored red.
Nivedha A.K., Thieker D.F., Makeneni S., Hu H, & Woods R.J. (2016). Vina-Carb: Improving Glycosidic Angles during Carbohydrate Docking. Journal of chemical theory and computation, 12(2), 892-901.
Publication 2016
Carbohydrates Ligands Membrane Proteins Molecular Dynamics Monosaccharides Oligosaccharides Plant Embryos Plant Roots Reproduction
4
Heparan Sulfate Oligosaccharide Analysis by LC/MS
Cited 15 times
LC/MS data were acquired on bovine organ HS samples using an Agilent Technologies 6520 QTOF mass spectrometer using a chip interface as described [9] (link), [10] (link). Briefly, HS samples were digested exhaustively using heparin lyase III. The oligosaccharides were analyzed using a chromatography chip (Agilent Technologies, Santa Clara, CA) packed with amide-silica hydrophilic interaction chromatography (HILIC) stationary phase [10] (link). The HS oligosaccharides were analyzed using negative polarity MS detection. All LC/MS data were processed using the DeconTools [20] version of the Decon2LS program [4] (link). The averagine formula was set to C6 H11.375 N1.125 O9.5S1.5. The DeconTools parameters, output files, the GlyReSoft compiled software, source code, and user instructions have been publicly archived (http://code.google.com/p/glycresoft/downloads/list).
GlycReSoft is in principle applicable to any compound class from LC/MS data deconvoluted using DeconTools. Users interested in glycan classes other than heparan sulfate are advised to estimate the average monosaccharide elemental composition and use this as the averagine formula with DeconTools.
Maxwell E., Tan Y., Tan Y., Hu H., Benson G., Aizikov K., Conley S., Staples G.O., Slysz G.W., Smith R.D, & Zaia J. (2012). GlycReSoft: A Software Package for Automated Recognition of Glycans from LC/MS Data. PLoS ONE, 7(9), e45474.
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Publication 2012
Amides Cattle Chromatography DNA Chips heparinase III Hydrophilic Interactions Monosaccharides Oligosaccharides Polysaccharides Silicon Dioxide Sulfate, Heparan
5
Comprehensive Microbial Secondary Metabolite Prediction
Cited 13 times
We used PRISM 4 and antiSMASH 5 to predict the chemical structures of secondary metabolites encoded within 3759 complete bacterial genomes and 6362 metagenome-assembled genomes (MAGs). All bacterial genomes with an assembly level of ‘Complete’ were downloaded from NCBI Genome, and a set of dereplicated genomes as determined by the Genome Taxonomy Database15 (link) were retained to mitigate the impact of highly similar genomes on our analysis. Similarly, a set of 7902 MAGs23 (link) was obtained from NCBI BioProject (accession PRJNA348753) and the subset of dereplicated genomes was retained. Detected BGCs were matched between PRISM and antiSMASH if their nucleotide sequence overlapped over any range. A small number of PRISM BGC types were mapped to more than one antiSMASH BGC type, including aminoglycosides (‘amglyccycl’ and ‘oligosaccharide’), type I polyketides (‘t1pks’ and ‘transatpks’), and RiPPs (‘bottromycin’, ‘cyanobactin’, ‘glycocin’, ‘head_to_tail’, ‘LAP’, ‘lantipeptide’, ‘lassopeptide’, ‘linaridin’, ‘microviridin’, ‘proteusin’, ‘sactipeptide’, and ‘thiopeptide’). The “hybrid” category encompassed all BGCs assigned any combination of two or more cluster types, i.e., it was not limited to hybrid NRPS-PKS BGCs. The “other” category encompassed aryl polyenes, bacteriocins, butyrolactones, ectoines, furans, homoserine lactones, ladderanes, melanins, N-acyl amino acids, NRPS-independent siderophores, phenazines, phosphoglycolipids, resorcinols, stilbenes, terpenes, and type III polyketides. Producing organism taxonomy was based on genome phylogeny and retrieved from the Genome Taxonomy Database15 (link).
Cheminformatic metrics, including molecular weight, number of hydrogen bond donors and acceptors, octanol-water partition coefficients, and Bertz topological complexity, were calculated in RDKit. Both platforms occasionally generated very small, non-specific structure predictions (for example, a single unspecified amino acid or a single malonyl unit) that did not provide actionable information about the chemical structure of the encoded product; to remove these from consideration, we applied a molecular weight filter to remove structures under 100 Da output by either platform. To evaluate the internal structural diversity of each set of predicted structures, we computed the distribution of pairwise Tcs for each set45 , taking the median pairwise Tc instead of the mean as a summary statistic to ensure robustness against outliers. Structural similarity to known natural products was assessed using the RDKit implementation of the ‘natural product-likeness’ score22 (link), and by the median Tc between predicted structures and the known secondary metabolite structures deposited in the NP Atlas database46 (link).
Skinnider M.A., Johnston C.W., Gunabalasingam M., Merwin N.J., Kieliszek A.M., MacLellan R.J., Li H., Ranieri M.R., Webster A.L., Cao M.P., Pfeifle A., Spencer N., To Q.H., Wallace D.P., Dejong C.A, & Magarvey N.A. (2020). Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences. Nature Communications, 11, 6058.
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Publication 2020
Amino Acids Aminoglycosides Bacteriocins Base Sequence bottromycin cyanobactins Donors Furans Genome Genome, Bacterial Head homoserine lactone Hybrids Hydrogen Bonds Melanins Metagenome Natural Products Octanols Oligosaccharides Phenazines Polyenes Polyketides prisma Prokaryotic Cells Resorcinols Secondary Metabolism Siderophores Stilbenes Tail Terpenes

Most recents protocols related to «Oligosaccharides»

1
Rehmannia glutinosa Oligosaccharide Extraction
Fresh Rhizomes of Rehmannia glutinosa were cut into small pieces of 5–10 mm after being washed, added four times the amount of water, and extracted twice at 90°C, at 1 h duration. The two extracts were combined, adding activated carbon (2 g/100 ml) and activated clay (2 g/100 ml) to the extract. It was stirred and decolored at 80°C for 30 min, then centrifuged. The supernatant was passed through 001 × 7 cation exchange resin column (diameter: high = 6:1), D201 type anion exchange resin column (diameter: high = 6:1), D101 macroporous adsorption resin column (diameter: high = 10:1) one by one, sample volume (mL): resin column volume = 1:1.5, flow rate was 500 mL/h. Finally, the macroporous adsorption resin effluent was collected and concentrated and dried at 60°C to get white powder, that is RGO.
The type and content of oligosaccharides in RGO were detected using high-performance liquid chromatography (HPLC) (Agilent1260), configured using a Refractive Index Detector (RID) (13 (link)). The standard reference substances of sucrose, stachyose, raffinose, and mulberry sugar were weighed precisely and prepared with 70% acetonitrile aqueous solution into the standard reference solution with a concentration of 0.5 mg/mL, respectively. The RGO powder was also weighed precisely, and prepared with 70% acetonitrile aqueous solution into the sample solution with a concentration of 1 mg/mL. The chromatographic column was Agilent ZORBOX NH2 (4.6 mm × 250 mm, 5 μm); the mobile phase was acetonitrile: water (7:3); the injection volume was 10 μL, the flow rate was 1.0 mL/min, and the temperature of column incubator was 40°C. The temperature of the detection was 50°C with RID. The types of oligosaccharides in RGO were determined by comparing the HPLC peaks of reference substance with those in RGO, and the content of oligosaccharides was calculated by external standard method.
Li X., Gui R., Wang X., Ning E., Zhang L., Fan Y., Chen L., Yu L., Zhu J., Li Z., Wei L., Wang W., Li Z., Wei Y, & Wang X. (2023). Oligosaccharides isolated from Rehmannia glutinosa protect LPS-induced intestinal inflammation and barrier injury in mice. Frontiers in Nutrition, 10, 1139006.
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Publication 2023
acetonitrile Adsorption Anion Exchange Resins Carbohydrates Cation Exchange Resins Charcoal, Activated Chromatography Clay High-Performance Liquid Chromatographies Morus Oligosaccharides Powder Raffinose Rehmannia glutinosa Resins, Plant Rhizome stachyose Sucrose
2
Probiotic-Prebiotic-Vitamin Supplement
Each kg contains Enterococcus faecium (3.3X1012 CFU), Galacto-oligosaccharides (136,000 mg), Vitamin D3 (200,000 IU), and Vitamin C (200,000 mg).
Suliman G.M., Hussein E.O., Alsagan A., Al-Owaimer A.N., Alhotan R., Al-Baadani H.H., Ba-Awadh H.A., Qaid M.M, & Swelum A.A. (2023). Effects of adding nano-emulsified plant oil and probiotics to drinking water during different periods besides sex on processing characteristics, physicochemical properties, and meat quality traits of broiler chickens. Frontiers in Veterinary Science, 10, 1133605.
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Publication 2023
Ascorbic Acid Cholecalciferol Enterococcus faecium Oligosaccharides
3
Enzymatic Assay for β-Mannanase Activity
The determination of ß-mannanase activity was performed by the 3,5-dinitrosalicylic acid (DNS) method. Briefly, the purified enzyme solution and LBG were diluted and dissolved with 0.1 M acetic acid-sodium acetate buffer (pH 3.0, Buffer C), and the reaction system consisted of 80 μL of pure enzyme and 160 μL of LBG (0.3 mg/mL), incubated at 37°C for 30 min and then terminated by adding 200 μL of DNS. After boiling for 5 min, 560 μL of water was added to the reaction system, and the absorbance of the reaction solution at 540 nm was measured using a microplate reader. Under the same conditions, the absorbance of mannan oligosaccharides was used as the standard curve. One unit of ß-mannanase activity was defined as the amount of enzyme that releases 1 μmol of reducing sugar per minute under the above conditions. All experiments were performed in three parallel experiments.
Tan S., Tao X., Zheng P., Chen P., Yu X., Li N., Gao T, & Wu D. (2023). Thermostability modification of β-mannanase from Aspergillus niger via flexibility modification engineering. Frontiers in Microbiology, 14, 1119232.
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Publication 2023
Acetic Acid Acids beta Mannosidase Buffers Carbohydrates Enzymes Mannans Oligosaccharides Patient Discharge Sodium Acetate
4
Heparin Oligosaccharide Characterization Protocols
All heparin oligosaccharides (HOs) used in this study were sourced from Iduron, Alderley Edge, UK. dp8: cat #HO08; dp10: cat. # HO10; dp12: cat. # HO12; dp20: cat. # HO20.
Meier M., Gupta M., Akgül S., McDougall M., Imhof T., Nikodemus D., Reuten R., Moya-Torres A., To V., Ferens F., Heide F., Padilla-Meier G.P., Kukura P., Huang W., Gerisch B., Mörgelin M., Poole K., Antebi A., Koch M, & Stetefeld J. (2023). The dynamic nature of netrin-1 and the structural basis for glycosaminoglycan fragment-induced filament formation. Nature Communications, 14, 1226.
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Publication 2023
Heparin Oligosaccharides
5
Calculating Hydrodynamic and Geometric Properties of DENSS Electron Density Models
To calculate the hydrodynamic and geometric properties of the DENSS electron density models, we first cut the electron density volume to the support volume reported by DENSS by setting the electron density outside the support volume to 0. The support volume marks the upper limit of the particle volume. The support volume was then filled with the expected numbers of electrons (26376 for monomeric NET1ΔC, 52752 for dimeric NET1ΔC, 131800 for the NET1ΔC associated with heparin dp8/dp10 oligosaccharides). To estimate the real particle volume, we used the ATSAS suite to build bead models97 (link). We first generated two unique cores (damstart.pdb), each averaged98 (link) from a set of 20 different (random seed) DAMMMIF models99 (link). The shape setting parameter suggested by DAMMIF was used for the 1st core and the “unknown” shape setting parameter for the 2nd core. From the 2 cores we calculated 4 DAMMIN models using different random seeds100 (link). The average volume of the 4 DAMMIN models was used as target volume for the hydrodynamic calculations. If the DAMMIN volume was larger than the average DENSS support volume, we used the latter, instead. We then proceeded with the programme HYDROMIC to calculate the hydrodynamic and geometric properties of the DENSS electron density models101 (link). For each dataset we had generated a set of 25 separate DENSS models (see SEC-SAXS section). For each set we provided a common electron density cut-off level to HYDROMIC, such that the resulting average volume of the entire set matched the target volume. We also calculated these properties for the averaged electron density map. The values for the averaged map and spread of the values for the refined models in brackets are given in Supplementary Table 5. We also included the properties of the DAMMIN bead models that were calculated using the programme HYDROPRO using the procedure we published earlier87 (link),102 (link). To accomplish the electron density volume calculations and manipulations we wrote Python scripts that were based on the saxstats module in DENSS30 (link) and the volumeInfo and volumeViewer module from UCSF Chimera68 (link). The scripts are available from the authors on request.
Meier M., Gupta M., Akgül S., McDougall M., Imhof T., Nikodemus D., Reuten R., Moya-Torres A., To V., Ferens F., Heide F., Padilla-Meier G.P., Kukura P., Huang W., Gerisch B., Mörgelin M., Poole K., Antebi A., Koch M, & Stetefeld J. (2023). The dynamic nature of netrin-1 and the structural basis for glycosaminoglycan fragment-induced filament formation. Nature Communications, 14, 1226.
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Publication 2023
Electrons Heparin Hydrodynamics Oligosaccharides Python

Top products related to «Oligosaccharides»

1
Pngase f by New England Biolabs
Sourced in United States, United Kingdom, Germany, Japan, Canada, France, China, Morocco, Italy
PNGase F is an enzyme that cleaves the bond between the asparagine residue and the N-acetylglucosamine residue in N-linked glycoproteins. It is commonly used in the analysis and characterization of glycoproteins.
2
Carbopac pa1 column by Thermo Fisher Scientific
Sourced in United States, Germany
The CarboPac PA1 column is a high-performance anion-exchange chromatography column designed for the analysis of carbohydrates. It features a polymer-based packing material that provides excellent resolution and peak shape for a wide range of carbohydrate species.
3
Ics 5000 by Thermo Fisher Scientific
Sourced in United States, Germany, France
The ICS-5000 is a high-performance ion chromatography system designed for the analysis of ionic compounds. It features a modular design, allowing for customization based on specific analytical needs. The ICS-5000 provides accurate and reliable ion detection and quantification.
4
Chitosan oligosaccharide lactate by Merck Group
Sourced in United States, Germany
Chitosan oligosaccharide lactate is a water-soluble derivative of chitosan, a natural polysaccharide. It is commonly used as a component in various laboratory applications.
5
Carbopac pa 1 guard column by Thermo Fisher Scientific
Sourced in United States
The CarboPac PA-1 guard column is a component used in ion chromatography systems. It is designed to protect the main analytical column from contaminants, thereby extending the column's lifespan and performance. The guard column is placed in front of the analytical column and serves to filter out impurities, ensuring the sample reaches the analytical column in optimal condition.
6
Hpaec pad by Thermo Fisher Scientific
Sourced in United States, United Kingdom
The HPAEC-PAD (High-Performance Anion-Exchange Chromatography with Pulsed Amperometric Detection) is an analytical technique used for the separation, identification, and quantification of carbohydrates and other ionic compounds. It utilizes a combination of high-performance liquid chromatography and electrochemical detection to provide sensitive and precise analysis of these analytes.
7
Carbopac pa200 column by Thermo Fisher Scientific
Sourced in United States, United Kingdom
The CarboPac PA200 column is a high-performance anion-exchange chromatography column designed for the separation and analysis of carbohydrates. It features a pellicular anion-exchange resin with a polystyrene-divinylbenzene substrate and a quaternary ammonium functional group. The column is suitable for the analysis of a wide range of mono-, oligo-, and polysaccharides.
8
Bio gel p 2 by Bio-Rad
Sourced in United States
Bio-Gel P-2 is a gel filtration chromatography media. It is used for the separation and purification of macromolecules, such as proteins and peptides, based on their size and molecular weight. The porous structure of the gel beads allows smaller molecules to penetrate the pores, while larger molecules are excluded, resulting in their separation.
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Thermomixer by Eppendorf
Sourced in Germany, United States, France, Switzerland, United Kingdom, Italy, Norway, Poland
The Eppendorf Thermomixer is a compact benchtop instrument designed for temperature-controlled mixing of samples. It can incubate and mix a wide range of sample tubes, microplates, and other vessels with precise temperature control and mixing speeds.
10
Tskgel amide 80 column by Tosoh
Sourced in Japan, United States
The TSKgel Amide-80 column is a high-performance liquid chromatography (HPLC) column designed for the separation and analysis of peptides, proteins, and other biomolecules. The column features a hydrophilic amide-based stationary phase that provides superior performance in the separation of these compounds.

More about "Oligosaccharides"

Oligosaccharides are a class of carbohydrates composed of short chains of monosaccharides (simple sugars) linked together.
These complex molecules play crucial roles in various biological processes, including cell signaling, immune function, and pathogen recognition.
Understanding the structure and function of oligosaccharides is essential for developing new therapeutic approaches and improving diagnostic tools.
Oligosaccharides can be found in a variety of natural sources, such as plants, animals, and microorganisms.
They are often analyzed using techniques like high-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD), which can separate and detect these molecules with high sensitivity and specificity.
Researchers may also utilize enzymes like PNGase F to release oligosaccharides from glycoproteins, or columns like CarboPac PA1, CarboPac PA-1 guard, CarboPac PA200, and TSKgel Amide-80 to purify and analyze these compounds.
The emerging field of oligosaccharide research is being revolutionized by AI-driven platforms like PubCompare.ai, which help researchers easily locate the best protocols from literature, pre-prints, and patents.
This one-stop solution enhances reproducibility and accuracy in oligosaccharide studies, making it an invaluable tool for scientists working in this dynamic field.
By incorporating synonyms, related terms, abbreviations, and key subtopics, researchers can optimize their oligosaccharide research using techniques like Bio-Gel P-2 and Thermomixer to improve their understanding of these complex biomolecules and their role in biological systems.
One common typo that may be encountered in this field is the occasional misspelling of 'oligosaccharide' as 'oligsaccharide'.
Nonetheless, the importance of oligosaccharides in areas such as Chitosan oligosaccharide lactate cannot be overstated, and the continued advancement of AI-powered tools like PubCompare.ai will undoubtedly play a crucial role in accelerating progress in this rapidly evolving domain of scientific inquiry.