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.; 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. International journal of molecular sciences, 21(19), p. 7102. MDPI 10.3390/ijms21197102

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > Unit Childrens Hospital > Forschungsgruppe Endokrinologie / Diabetologie / Metabolik (Pädiatrie)
04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Paediatric Medicine
04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Paediatric Medicine > Endocrinology/Metabolic Disorders

UniBE Contributor:

Pandey, Amit Vikram

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology

ISSN:

1422-0067

Publisher:

MDPI

Language:

English

Submitter:

Amit Vikram Pandey

Date Deposited:

19 Jan 2021 16:23

Last Modified:

19 Jan 2021 16:23

Publisher DOI:

10.3390/ijms21197102

Related URLs:

PubMed ID:

32993084

BORIS DOI:

10.48350/150393

URI:

https://boris.unibe.ch/id/eprint/150393

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