Unser 10. Leibniz Plant Biochemistry Symposium am 7. und 8. Mai war ein großer Erfolg. Thematisch ging es in diesem Jahr um neue Methoden und Forschungsansätze der Naturstoffchemie. Die exzellenten Vorträge über Wirkstoffe…
Omanische Heilpflanze im Fokus der Phytochemie IPB-Wissenschaftler und Partner aus Dhofar haben jüngst die omanische Heilpflanze Terminalia dhofarica unter die phytochemische Lupe genommen. Die Pflanze ist reich an…
Geschmack ist vorhersagbar: Mit FlavorMiner. FlavorMiner heißt das Tool, das IPB-Chemiker und Partner aus Kolumbien jüngst entwickelt haben. Das Programm kann, basierend auf maschinellem Lernen (KI), anhand der…
Olkhov, R. V.; Weissenborn, M. J.; Flitsch, S. L.; Shaw, A. M.;Glycosylation Characterization of Human and Porcine Fibrinogen Proteins by Lectin-Binding Biophotonic Microarray ImagingAnal. Chem.86621-628(2014)DOI: 10.1021/ac402872t
Lectin binding has been studied using the particle plasmon light-scattering properties of gold nanoparticles printed into an array format. Performance of the kinetic assay is evaluated from a detailed analysis of the binding of concanavalin A (ConA) and wheat germ agglutinin (WGA) to their target monosaccharides indicating affinity constants in the order of KD ∼10 nM for the lectin-monosaccharide interaction. The detection limits for the lectins following a 200 s injection time were determined as 10 ng/mL or 0.23 nM and 100 ng/mL or 0.93 nM, respectively. Subsequently, a nine-lectin screen was performed on the porcine and human fibrinogen glycoproteins. The observed spectra of lectin-protein specific binding rates result in characteristic patterns that evidently correlate with the structure of the glycans and allow one to distinguish between glycosylation of the porcine and human fibrinogens. The array technology has the potential to perform a multilectin screen of large numbers of proteins providing information on protein glycosylation and their microheterogeneity.
Publikation
Broeckling, C. D.; Afsar, F. A.; Neumann, S.; Ben-Hur, A.; Prenni, J. E.;RAMClust: A Novel Feature Clustering Method Enables Spectral-Matching-Based Annotation for Metabolomics DataAnal. Chem.866812-6817(2014)DOI: 10.1021/ac501530d
Metabolomic data are frequently acquired using chromatographically coupled mass spectrometry (MS) platforms. For such datasets, the first step in data analysis relies on feature detection, where a feature is defined by a mass and retention time. While a feature typically is derived from a single compound, a spectrum of mass signals is more a more-accurate representation of the mass spectrometric signal for a given metabolite. Here, we report a novel feature grouping method that operates in an unsupervised manner to group signals from MS data into spectra without relying on predictability of the in-source phenomenon. We additionally address a fundamental bottleneck in metabolomics, annotation of MS level signals, by incorporating indiscriminant MS/MS (idMS/MS) data implicitly: feature detection is performed on both MS and idMS/MS data, and feature–feature relationships are determined simultaneously from the MS and idMS/MS data. This approach facilitates identification of metabolites using in-source MS and/or idMS/MS spectra from a single experiment, reduces quantitative analytical variation compared to single-feature measures, and decreases false positive annotations of unpredictable phenomenon as novel compounds. This tool is released as a freely available R package, called RAMClustR, and is sufficiently versatile to group features from any chromatographic-spectrometric platform or feature-finding software.