Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction
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, c...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/21/19/7102 |
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| author | Fabian Mayr Gabriele Möller Ulrike Garscha Jana Fischer Patricia Rodríguez Castaño Silvia G. Inderbinen Veronika Temml Birgit Waltenberger Stefan Schwaiger Rolf W. Hartmann Christian Gege Stefan Martens Alex Odermatt Amit V. Pandey Oliver Werz Jerzy Adamski Hermann Stuppner Daniela Schuster |
| author_facet | Fabian Mayr Gabriele Möller Ulrike Garscha Jana Fischer Patricia Rodríguez Castaño Silvia G. Inderbinen Veronika Temml Birgit Waltenberger Stefan Schwaiger Rolf W. Hartmann Christian Gege Stefan Martens Alex Odermatt Amit V. Pandey Oliver Werz Jerzy Adamski Hermann Stuppner Daniela Schuster |
| author_sort | Fabian Mayr |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T16:02:02Z |
| format | Article |
| id | doaj.art-bb23ea7804564fa790babde137dcf0e0 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-10T16:02:02Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-bb23ea7804564fa790babde137dcf0e02023-11-20T15:11:35ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672020-09-012119710210.3390/ijms21197102Finding New Molecular Targets of Familiar Natural Products Using In Silico Target PredictionFabian Mayr0Gabriele Möller1Ulrike Garscha2Jana Fischer3Patricia Rodríguez Castaño4Silvia G. Inderbinen5Veronika Temml6Birgit Waltenberger7Stefan Schwaiger8Rolf W. Hartmann9Christian Gege10Stefan Martens11Alex Odermatt12Amit V. Pandey13Oliver Werz14Jerzy Adamski15Hermann Stuppner16Daniela Schuster17Institute of Pharmacy/Pharmacognosy, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, AustriaResearch Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, GermanyDepartment of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, University Greifswald, Friedrich-Ludwig-Jahn-Straße 17, 17489 Greifswald, GermanyDepartment of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, University Greifswald, Friedrich-Ludwig-Jahn-Straße 17, 17489 Greifswald, GermanyPediatric Endocrinology, Diabetology and Metabolism, University Children’s Hospital Bern, Freiburgstrasse 15, 3010 Bern, SwitzerlandDivision of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, SwitzerlandInstitute of Pharmacy/Pharmacognosy, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, AustriaInstitute of Pharmacy/Pharmacognosy, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, AustriaInstitute of Pharmacy/Pharmacognosy, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, AustriaHelmholtz Institute of Pharmaceutical Research Saarland (HIPS), Department for Drug Design and Optimization, Campus E8.1, 66123 Saarbrücken, GermanyUniversity of Heidelberg, Institute of Pharmacy and Molecular Biotechnology (IPMB), Medicinal Chemistry, Im Neuenheimer Feld 364, 69120 Heidelberg, GermanyResearch and Innovation Centre, Fondazione Edmund Mach (FEM), Via Mach 1, 38010 San Michele all’Adige, ItalyDivision of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, SwitzerlandPediatric Endocrinology, Diabetology and Metabolism, University Children’s Hospital Bern, Freiburgstrasse 15, 3010 Bern, SwitzerlandDepartment of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, Friedrich-Schiller-University Jena, Philosophenweg 14, 07743 Jena, GermanyResearch Unit Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, GermanyInstitute of Pharmacy/Pharmacognosy, Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80/82, 6020 Innsbruck, AustriaInstitute of Pharmacy, Department of Pharmaceutical and Medicinal Chemistry, Paracelsus Medical University Salzburg, Strubergasse 21, 5020 Salzburg, AustriaNatural 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.https://www.mdpi.com/1422-0067/21/19/7102in silico target predictiondihydrochalconesSEASwissTargetPredictionSuperPredpolypharmacology |
| spellingShingle | Fabian Mayr Gabriele Möller Ulrike Garscha Jana Fischer Patricia Rodríguez Castaño Silvia G. Inderbinen Veronika Temml Birgit Waltenberger Stefan Schwaiger Rolf W. Hartmann Christian Gege Stefan Martens Alex Odermatt Amit V. Pandey Oliver Werz Jerzy Adamski Hermann Stuppner Daniela Schuster Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction International Journal of Molecular Sciences in silico target prediction dihydrochalcones SEA SwissTargetPrediction SuperPred polypharmacology |
| title | Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction |
| title_full | Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction |
| title_fullStr | Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction |
| title_full_unstemmed | Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction |
| title_short | Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction |
| title_sort | finding new molecular targets of familiar natural products using in silico target prediction |
| topic | in silico target prediction dihydrochalcones SEA SwissTargetPrediction SuperPred polypharmacology |
| url | https://www.mdpi.com/1422-0067/21/19/7102 |
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