Katalog Plus
Bibliothek der Frankfurt UAS
Bald neuer Katalog: sichern Sie sich schon vorab Ihre persönlichen Merklisten im Nutzerkonto: Anleitung.
Dieses Ergebnis aus BASE kann Gästen nicht angezeigt werden.  Login für vollen Zugriff.

Dereplication strategy and purification of plant's active secondary metabolites through bio-guided fractionation, LC-(HR)MS and data mining

Title: Dereplication strategy and purification of plant's active secondary metabolites through bio-guided fractionation, LC-(HR)MS and data mining
Authors: Fauquet, Jason; Karasiewicz, Tania; Wells, Mathilde; Duez, Pierre; Blankert, Bertrand; Nachtergael, Amandine
Contributors: M136 - Chimie thérapeutique et Pharmacognosie; M130 - Analyse pharmaceutique; S828 - Protéomie et Microbiologie; R550 - Institut des Sciences et Technologies de la Santé
Source: MSBM XVI - 16th Mass Spectrometry School in Biotechnology and Medicine, Dubrovnik, Croatia [HR], 7 juillet - 13 juillet 2024
Publication Year: 2024
Subject Terms: Metabolomics; dereplication; secondary metabolites; fractionation; Flash chromatography; GNPS; SIRIUS; molecular network; workflow; Mass spectrometry; MS/MS; MZmine; Chromatography; Structure prediction; Human health sciences; Pharmacy; pharmacology & toxicology; Sciences de la santé humaine; Pharmacie; pharmacologie & toxicologie
Description: Identifying bioactive secondary metabolites through bio-guided fractionation is a challenging and resource-intensive process that can take several years. Often, this involves identifying known compounds or those that ultimately prove to be Pan Assay INterference compoundS (PAINS). To overcome this hurdle, our approach is to use a strategy of dereplication that involves short bio-guided fractionation and LC-(HR)MS coupled with data mining (using databases and AI) to isolate only active and unknown compounds. The dereplication strategy will first involve the recovery of bioactive fractions through Flash-Chromatography and LC-HMRS analysis, pre-processing of the data using tools or platforms such as MZmine 4, MetaboAnalyst 6.0, Galaxy, Workflow4Metabolomics, etc. The datasets will then be submitted to databases, such as GNPS, to match the spectra with those already known. SIRIUS will be used to predict the structure of unknown compounds, and their ADMETox properties will be predicted in sillico to ensure only bioactive compounds with favourable properties are isolated. Their structures will then be confirmed through NMR.
Document Type: conference object
Language: English
Relation: https://orbi.umons.ac.be/handle/20.500.12907/50721; info:hdl:20.500.12907/50721
Availability: https://orbi.umons.ac.be/handle/20.500.12907/50721; https://hdl.handle.net/20.500.12907/50721; https://orbi.umons.ac.be/bitstream/20.500.12907/50721/1/Abstract%20Poster%20MSBM%20XVI%20-%202024.docx
Rights: open access ; http://purl.org/coar/access_right/c_abf2 ; info:eu-repo/semantics/openAccess
Accession Number: edsbas.9A892F40
Database: BASE