Extensions to in silico bioactivity predictions using pathway annotations and differential pharmacology analysis: Application to Xenopus laevis phenotypic readouts

Liggi, Sonia, Drakakis, Georgios, Hendry, Adam E., Hanson, Kimberley M., Brewerton, Suzanne C., Wheeler, Grant N. ORCID: https://orcid.org/0000-0002-4335-8577, Bodkin, Michael J., Evans, David A. and Bender, Andreas (2013) Extensions to in silico bioactivity predictions using pathway annotations and differential pharmacology analysis: Application to Xenopus laevis phenotypic readouts. Molecular Informatics, 32 (11-12). pp. 1009-1024. ISSN 1868-1751

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Abstract

The simultaneous increase of computational power and the availability of chemical and biological data have contributed to the recent popularity of in silico bioactivity prediction algorithms. Such methods are commonly used to infer the ‘Mechanism of Action’ of small molecules and they can also be employed in cases where full bioactivity profiles have not been established experimentally. However, protein target predictions by themselves do not necessarily capture information about the effect of a compound on a biological system, and hence merging their output with a systems biology approach can help to better understand the complex network modulation which leads to a particular phenotype. In this work, we review approaches and applications of target prediction, as well as their shortcomings, and demonstrate two extensions of this concept which are exemplified using phenotypic readouts from a chemical genetic screen in Xenopus laevis. In particular, the experimental observations are linked to their predicted bioactivity profiles. Predicted targets are annotated with pathways, which lead to further biological insight. Moreover, we subject the prediction to further machine learning algorithms, namely decision trees, to capture the differential pharmacology of ligand-target interactions in biological systems. Both methodologies hence provide new insight into understanding the Mechanism of Action of compound activities from phenotypic screens.

Item Type: Article
Uncontrolled Keywords: in silico bioactivity prediction,cheminoformatics,mechanism of action,xenopus laevis,pigmentation,cheminformatics,drug discovery,molecular medicine,structural biology,computer science applications,organic chemistry ,/dk/atira/pure/subjectarea/asjc/3000/3002
Faculty \ School: Faculty of Science
Faculty of Science > School of Biological Sciences
UEA Research Groups: Faculty of Science > Research Groups > Wheeler Group
Faculty of Science > Research Groups > Cells and Tissues
Related URLs:
Depositing User: Pure Connector
Date Deposited: 09 Jun 2014 13:20
Last Modified: 19 Mar 2024 08:39
URI: https://ueaeprints.uea.ac.uk/id/eprint/48536
DOI: 10.1002/minf.201300102

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