Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction.
dc.contributor.author | Mayr, Fabian | |
dc.contributor.author | Möller, Gabriele | |
dc.contributor.author | Garscha, Ulrike | |
dc.contributor.author | Fischer, Jana | |
dc.contributor.author | Rodríguez Castaño, Patricia | |
dc.contributor.author | Inderbinen, Silvia G | |
dc.contributor.author | Temml, Veronika | |
dc.contributor.author | Waltenberger, Birgit | |
dc.contributor.author | Schwaiger, Stefan | |
dc.contributor.author | Hartmann, Rolf W | |
dc.contributor.author | Gege, Christian | |
dc.contributor.author | Martens, Stefan | |
dc.contributor.author | Odermatt, Alex | |
dc.contributor.author | Pandey, Amit V | |
dc.contributor.author | Werz, Oliver | |
dc.contributor.author | Adamski, Jerzy | |
dc.contributor.author | Stuppner, Hermann | |
dc.contributor.author | Schuster, Daniela | |
dc.date.accessioned | 2020-11-06T10:53:51Z | |
dc.date.available | 2020-11-06T10:53:51Z | |
dc.date.issued | 2020-09-26 | |
dc.identifier.citation | Int J Mol Sci. 2020 Sep 26;21(19):7102. doi: 10.3390/ijms21197102. | en_US |
dc.identifier.pmid | 32993084 | |
dc.identifier.doi | 10.3390/ijms21197102 | |
dc.identifier.uri | http://hdl.handle.net/10033/622559 | |
dc.description.abstract | 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, 17β-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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Attribution-NonCommercial-ShareAlike 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
dc.subject | SEA | en_US |
dc.subject | SuperPred | en_US |
dc.subject | SwissTargetPrediction | en_US |
dc.subject | dihydrochalcones | en_US |
dc.subject | in silico target prediction | en_US |
dc.subject | polypharmacology | en_US |
dc.subject | virtual screening | en_US |
dc.title | Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction. | en_US |
dc.type | Article | en_US |
dc.identifier.eissn | 1422-0067 | |
dc.contributor.department | HIPS, Helmholtz-Institut für Pharmazeutische Forschung Saarland, Universitätscampus E8.1 66123 Saarbrücken, Germany. | en_US |
dc.identifier.journal | International journal of molecular sciences | en_US |
dc.source.volume | 21 | |
dc.source.issue | 19 | |
refterms.dateFOA | 2020-11-06T10:53:52Z | |
dc.source.journaltitle | International journal of molecular sciences | |
dc.source.country | Switzerland |