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…
Seit Februar 2021 bietet Wolfgang Brandt, ehemaliger Leiter der Arbeitsgruppe Computerchemie am IPB, sein Citizen Science-Projekt zur Pilzbestimmung an. Dafür hat er in regelmäßigen Abständen öffentliche Vorträge zur Vielfalt…
Stanstrup, J.; Neumann, S.; Vrhovšek, U.;PredRet: Prediction of Retention Time by Direct Mapping between Multiple Chromatographic SystemsAnal. Chem.879421-9428(2015)DOI: 10.1021/acs.analchem.5b02287
Demands in research investigating small molecules by applying untargeted approaches have been a key motivator for the development of repositories for mass spectrometry spectra and automated tools to aid compound identification. Comparatively little attention has been afforded to using retention times (RTs) to distinguish compounds and for liquid chromatography there are currently no coordinated efforts to share and exploit RT information. We therefore present PredRet; the first tool that makes community sharing of RT information possible across laboratories and chromatographic systems (CSs). At http://predret.org, a database of RTs from different CSs is available and users can upload their own experimental RTs and download predicted RTs for compounds which they have not experimentally determined in their own experiments. For each possible pair of CSs in the database, the RTs are used to construct a projection model between the RTs in the two CSs. The number of compounds for which RTs can be predicted and the accuracy of the predictions are dependent upon the compound coverage overlap between the CSs used for construction of projection models. At the moment, it is possible to predict up to 400 RTs with a median error between 0.01 and 0.28 min depending on the CS and the median width of the prediction interval ranging from 0.08 to 1.86 min. By comparing experimental and predicted RTs, the user can thus prioritize which isomers to target for further characterization and potentially exclude some structures completely. As the database grows, the number and accuracy of predictions will increase.
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.
Publikation
Rampon, D. S.; Wessjohann, L. A.; Schneider, P. H.;Palladium-Catalyzed Direct Arylation of SelenopheneJ. Org. Chem.795987-5992(2014)DOI: 10.1021/jo500094t
An efficient and convenient method was developed for the regioselective formation of 2-aryl- or 2,5-diarylselenophenes via a palladium-catalyzed direct arylation. This protocol is suitable for a wide range of aryl halides containing different functional groups. The 2-arylated substrates can undergo an additional regioselective direct arylation event furnishing symmetrical or unsymmetrical 2,5-diaryl selenophenes in good yield. Competition experiments and the role of the acid additive are in agreement with a concerted metalation deprotonation (CMD) pathway.
Publikation
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.