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

Mayr, F.; Möller, G.; Garscha, U.; Fischer, J.; Rodríguez Castaño, P.; 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. (2020). Finding new molecular targets of familiar natural products using in silico target prediction. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 21 (19): 1-18: 7102. doi: 10.3390/ijms21197102 handle: http://hdl.handle.net/10449/64048

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

Martens, S.;
2020

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
Settore BIO/15 - BIOLOGIA FARMACEUTICA
Mayr, F.; Möller, G.; Garscha, U.; Fischer, J.; Rodríguez Castaño, P.; 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. (2020). Finding new molecular targets of familiar natural products using in silico target prediction. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 21 (19): 1-18: 7102. doi: 10.3390/ijms21197102 handle: http://hdl.handle.net/10449/64048
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