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Mayr, F.* ; Möller, G. ; Garscha, U.* ; Fischer, J.* ; Castaño, P.R.* ; Inderbinen, S.G.* ; Temml, V.* ; Waltenberger, B.* ; Schwaiger, S.* ; Hartmann, R.W.* ; Gege, C.* ; Martens, S.* ; Odermatt, A.* ; Pandey, A.V.* ; Werz, O.* ; Adamski, J. ; Stuppner, H.* ; Schuster, D.*

Finding new molecular targets of familiar natural products using in silico target prediction.

Int. J. Mol. Sci. 21:7102 (2020)
Postprint Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Natural products comprise a rich reservoir for innovative drug leads and are a constant source of bioactive compounds. To find pharmacological targets for new or already known natural products using modern computer-aided methods is a current endeavor in drug discovery. Nature's treasures, however, could be used more effectively. Yet, reliable pipelines for the large-scale target prediction of natural products are still rare. We developed an in silico workflow consisting of four independent, stand-alone target prediction tools and evaluated its performance on dihydrochalcones (DHCs)-a well-known class of natural products. Thereby, we revealed four previously unreported protein targets for DHCs, namely 5-lipoxygenase, cyclooxygenase-1, 17 beta-hydroxysteroid dehydrogenase 3, and aldo-keto reductase 1C3. Moreover, we provide a thorough strategy on how to perform computational target predictions and guidance on using the respective tools.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter In Silico Target Prediction ; Dihydrochalcones ; Sea ; Swisstargetprediction ; Superpred ; Polypharmacology ; Virtual Screening; 17-beta-hydroxysteroid-dehydrogenase Type-3; Macromolecular Targets; Drug Classification; Inhibitors; Potent; Discovery; Identification; Purification; Supertarget; Chalcones
Sprache englisch
Veröffentlichungsjahr 2020
HGF-Berichtsjahr 2020
ISSN (print) / ISBN 1661-6596
e-ISSN 1422-0067
Zeitschrift International Journal of Molecular Sciences
Quellenangaben Band: 21, Heft: 19, Seiten: , Artikelnummer: 7102 Supplement: ,
Verlag MDPI
Verlagsort Basel
Begutachtungsstatus Peer reviewed
Institut(e) Molekulare Endokrinologie und Metabolismus (MEM)
POF Topic(s) 30201 - Metabolic Health
Forschungsfeld(er) Genetics and Epidemiology
PSP-Element(e) G-505600-001
G-505600-003
Förderungen FWF Hertha Firnberg fellowship
GECT Euregio Tirol-Sudtirol-Trentino
DOI 10.3390/ijms21197102
WOS ID WOS:000587244300001
Scopus ID 85091629053
PubMed ID 32993084
Erfassungsdatum 2020-11-12
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