Abstract details
	Daniela Schuster
	Institute of Pharmacy / Pharmaceutical Chemistry, Univ. of Innsbruck
	Innsbruck  [Austria]
	Semi-automatic, computational target prediction for dihydrochalcones using publicly available profiling tools
	Purpose: Computational target prediction programs are valuable tools for the focused identification of biological activities for a given substance. This is especially important for natural product-based projects, in which often only low amounts of pure compound are available for in vitro testing. For the prediction of targets, several open tools are available online. However, many free tools work on a one-compound-per-calculation only basis and the extraction of information and comparison between the predictions based on different programs is tedious. Methods: We therefore compiled a KNIME-based workflow (www.knime.com) collecting and comparing virtual profiling results from the complementary open platforms Similarity Ensemble Approach (sea.bkslab.org/), Swiss Target Prediction (www.swisstargetprediction.ch/), Endocrine Disruptome (endocrinedisruptome.ki.si/) and Superpred (prediction.charite.de/), and also included our in-house pharmacophore model collection screening results. The report created by KNIME can be customized to report results on specific targets of interest or focus on consensus hits, which may have a higher probability to be active in vitro. Results: As an application example, a random series of ten dihydrochalcones (DHCs) was profiled and analyzed for their predicted activities towards mushroom tyrosinase, for which an in-house in vitro test system is available. Six DHCs were predicted as active, of which four were confirmed by literature data (1-3). The other two hits were tested in vitro and found to modulate the enzymes activity. Conclusion: These results and the user-friendliness of the workflow encouraged us to further refine and apply the KNIME tool to other targets as well. References (1) Mapunya M. B. et al. (2011) Phytomedicine 18 (11) 1006-1012 (2) Ortiz-Ruiz C. V. et al (2015) Bioorg. Med. Chem. 23 (13) 3738-3746 (3) Lin Y. et al. (2007) Phytochemistry 68 (8) 1189-1199 Acknowledgements: Euregio Science Fund (IPN55, ExPoApple2)
	Mayr Fabian1 Waltenberger Birgit1 Stuppner Hermann1 Schuster Daniela2 1 Institute of Pharmacy / Pharmacognosy, University of Innsbruck, Austria 2 Institute of Pharmacy / Pharmaceutical Chemistry, University of Innsbruck, Austria
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