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...
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Autor*in:	Fabian Mayr [verfasserIn] Gabriele Möller [verfasserIn] Ulrike Garscha [verfasserIn] Jana Fischer [verfasserIn] Patricia Rodríguez Castaño [verfasserIn] Silvia G. Inderbinen [verfasserIn] Veronika Temml [verfasserIn] Birgit Waltenberger [verfasserIn] Stefan Schwaiger [verfasserIn] Rolf W. Hartmann [verfasserIn] Christian Gege [verfasserIn] Stefan Martens [verfasserIn] Alex Odermatt [verfasserIn] Amit V. Pandey [verfasserIn] Oliver Werz [verfasserIn] Jerzy Adamski [verfasserIn] Hermann Stuppner [verfasserIn] Daniela Schuster [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	in silico target prediction dihydrochalcones SEA SwissTargetPrediction SuperPred polypharmacology

Übergeordnetes Werk:	In: International Journal of Molecular Sciences - MDPI AG, 2003, 21(2020), 19, p 7102
Übergeordnetes Werk:	volume:21 ; year:2020 ; number:19, p 7102

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.3390/ijms21197102

Katalog-ID:	DOAJ07549891X

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spelling	10.3390/ijms21197102 doi (DE-627)DOAJ07549891X (DE-599)DOAJbb23ea7804564fa790babde137dcf0e0 DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Fabian Mayr verfasserin aut Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. in silico target prediction dihydrochalcones SEA SwissTargetPrediction SuperPred polypharmacology Biology (General) Chemistry Gabriele Möller verfasserin aut Ulrike Garscha verfasserin aut Jana Fischer verfasserin aut Patricia Rodríguez Castaño verfasserin aut Silvia G. Inderbinen verfasserin aut Veronika Temml verfasserin aut Birgit Waltenberger verfasserin aut Stefan Schwaiger verfasserin aut Rolf W. Hartmann verfasserin aut Christian Gege verfasserin aut Stefan Martens verfasserin aut Alex Odermatt verfasserin aut Amit V. Pandey verfasserin aut Oliver Werz verfasserin aut Jerzy Adamski verfasserin aut Hermann Stuppner verfasserin aut Daniela Schuster verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 21(2020), 19, p 7102 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:21 year:2020 number:19, p 7102 https://doi.org/10.3390/ijms21197102 kostenfrei https://doaj.org/article/bb23ea7804564fa790babde137dcf0e0 kostenfrei https://www.mdpi.com/1422-0067/21/19/7102 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2020 19, p 7102
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allfieldsSound	10.3390/ijms21197102 doi (DE-627)DOAJ07549891X (DE-599)DOAJbb23ea7804564fa790babde137dcf0e0 DE-627 ger DE-627 rakwb eng QH301-705.5 QD1-999 Fabian Mayr verfasserin aut Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. in silico target prediction dihydrochalcones SEA SwissTargetPrediction SuperPred polypharmacology Biology (General) Chemistry Gabriele Möller verfasserin aut Ulrike Garscha verfasserin aut Jana Fischer verfasserin aut Patricia Rodríguez Castaño verfasserin aut Silvia G. Inderbinen verfasserin aut Veronika Temml verfasserin aut Birgit Waltenberger verfasserin aut Stefan Schwaiger verfasserin aut Rolf W. Hartmann verfasserin aut Christian Gege verfasserin aut Stefan Martens verfasserin aut Alex Odermatt verfasserin aut Amit V. Pandey verfasserin aut Oliver Werz verfasserin aut Jerzy Adamski verfasserin aut Hermann Stuppner verfasserin aut Daniela Schuster verfasserin aut In International Journal of Molecular Sciences MDPI AG, 2003 21(2020), 19, p 7102 (DE-627)316340715 (DE-600)2019364-6 14220067 nnns volume:21 year:2020 number:19, p 7102 https://doi.org/10.3390/ijms21197102 kostenfrei https://doaj.org/article/bb23ea7804564fa790babde137dcf0e0 kostenfrei https://www.mdpi.com/1422-0067/21/19/7102 kostenfrei https://doaj.org/toc/1661-6596 Journal toc kostenfrei https://doaj.org/toc/1422-0067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2020 19, p 7102
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authorswithroles_txt_mv	Fabian Mayr @@aut@@ Gabriele Möller @@aut@@ Ulrike Garscha @@aut@@ Jana Fischer @@aut@@ Patricia Rodríguez Castaño @@aut@@ Silvia G. Inderbinen @@aut@@ Veronika Temml @@aut@@ Birgit Waltenberger @@aut@@ Stefan Schwaiger @@aut@@ Rolf W. Hartmann @@aut@@ Christian Gege @@aut@@ Stefan Martens @@aut@@ Alex Odermatt @@aut@@ Amit V. Pandey @@aut@@ Oliver Werz @@aut@@ Jerzy Adamski @@aut@@ Hermann Stuppner @@aut@@ Daniela Schuster @@aut@@
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callnumber-first	Q - Science
author	Fabian Mayr
spellingShingle	Fabian Mayr misc QH301-705.5 misc QD1-999 misc in silico target prediction misc dihydrochalcones misc SEA misc SwissTargetPrediction misc SuperPred misc polypharmacology misc Biology (General) misc Chemistry Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction
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topic_title	QH301-705.5 QD1-999 Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction in silico target prediction dihydrochalcones SEA SwissTargetPrediction SuperPred polypharmacology
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title	Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction
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title_sort	finding new molecular targets of familiar natural products using in silico target prediction
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title_auth	Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction
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.
abstractGer	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.
abstract_unstemmed	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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title_short	Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction
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up_date	2024-07-03T15:14:52.112Z
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Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction

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