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Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction.
Mayr, Fabian Möller, Gabriele Garscha, Ulrike Fischer, Jana Rodríguez Castaño, Patricia Inderbinen, Silvia G Temml, Veronika Waltenberger, Birgit Schwaiger, Stefan Hartmann, Rolf W
Mayr, Fabian
Möller, Gabriele
Garscha, Ulrike
Fischer, Jana
Rodríguez Castaño, Patricia
Inderbinen, Silvia G
Temml, Veronika
Waltenberger, Birgit
Schwaiger, Stefan
Hartmann, Rolf W
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2020-09-26
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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.
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Int J Mol Sci. 2020 Sep 26;21(19):7102. doi: 10.3390/ijms21197102.
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Article
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en
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1422-0067
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Attribution-NonCommercial-ShareAlike 4.0 International