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Open AccessArticle

Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction

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Institute of Pharmacy/Pharmacognosy, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria
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Research Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
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Department of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, University Greifswald, Friedrich-Ludwig-Jahn-Straße 17, 17489 Greifswald, Germany
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Pediatric Endocrinology, Diabetology and Metabolism, University Children’s Hospital Bern, Freiburgstrasse 15, 3010 Bern, Switzerland
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Department of Biomedical Research, University of Bern, Freiburgstrasse 15, 3010 Bern, Switzerland
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Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland
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Helmholtz Institute of Pharmaceutical Research Saarland (HIPS), Department for Drug Design and Optimization, Campus E8.1, 66123 Saarbrücken, Germany
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Saarland University, Pharmaceutical and Medicinal Chemistry, Campus E8.1, 66123 Saarbrücken, Germany
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University of Heidelberg, Institute of Pharmacy and Molecular Biotechnology (IPMB), Medicinal Chemistry, Im Neuenheimer Feld 364, 69120 Heidelberg, Germany
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Research and Innovation Centre, Fondazione Edmund Mach (FEM), Via Mach 1, 38010 San Michele all’Adige, Italy
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Department of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, Friedrich-Schiller-University Jena, Philosophenweg 14, 07743 Jena, Germany
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Lehrstuhl für Experimentelle Genetik, Technische Universität München, Emil-Erlenmeyer-Forum 5, 85356 Freising-Weihenstephan, Germany
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Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597, Singapore
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Institute of Pharmacy, Department of Pharmaceutical and Medicinal Chemistry, Paracelsus Medical University Salzburg, Strubergasse 21, 5020 Salzburg, Austria
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Institute of Pharmacy/Pharmaceutical Chemistry, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, Austria
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(19), 7102; https://doi.org/10.3390/ijms21197102
Received: 1 September 2020 / Revised: 19 September 2020 / Accepted: 21 September 2020 / Published: 26 September 2020
(This article belongs to the Special Issue Steroid Metabolism in Human Health and Disease)
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. View Full-Text
Keywords: in silico target prediction; dihydrochalcones; SEA; SwissTargetPrediction; SuperPred; polypharmacology; virtual screening in silico target prediction; dihydrochalcones; SEA; SwissTargetPrediction; SuperPred; polypharmacology; virtual screening
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MDPI and ACS Style

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. Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction. Int. J. Mol. Sci. 2020, 21, 7102. https://doi.org/10.3390/ijms21197102

AMA Style

Mayr F, Möller G, Garscha U, Fischer J, Rodríguez Castaño P, Inderbinen SG, Temml V, Waltenberger B, Schwaiger S, Hartmann RW, Gege C, Martens S, Odermatt A, Pandey AV, Werz O, Adamski J, Stuppner H, Schuster D. Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction. International Journal of Molecular Sciences. 2020; 21(19):7102. https://doi.org/10.3390/ijms21197102

Chicago/Turabian Style

Mayr, Fabian; Möller, Gabriele; Garscha, Ulrike; Fischer, Jana; Rodríguez Castaño, Patricia; Inderbinen, Silvia G.; Temml, Veronika; Waltenberger, Birgit; Schwaiger, Stefan; Hartmann, Rolf W.; Gege, Christian; Martens, Stefan; Odermatt, Alex; Pandey, Amit V.; Werz, Oliver; Adamski, Jerzy; Stuppner, Hermann; Schuster, Daniela. 2020. "Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction" Int. J. Mol. Sci. 21, no. 19: 7102. https://doi.org/10.3390/ijms21197102

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