Extensions to In Silico Bioactivity Predictions Using Pathway Annotations and Differential Pharmacology Analysis: Application to Xenopus laevis Phenotypic Readouts

Authors

  • Sonia Liggi,

    1. Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK telephone: +44(0)1223 762983; fax: +44 (0)1223 763076
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    • These authors contributed equally to this work

  • Georgios Drakakis,

    1. Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK telephone: +44(0)1223 762983; fax: +44 (0)1223 763076
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    • These authors contributed equally to this work

  • Adam E. Hendry,

    1. School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK
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  • Kimberley M. Hanson,

    1. School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK
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  • Suzanne C. Brewerton,

    1. Eli Lilly U.K. Erl Wood Manor, Windlesham, Surrey GU206PH, UK
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  • Grant N. Wheeler,

    1. School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ, UK
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  • Michael J. Bodkin,

    1. Eli Lilly U.K. Erl Wood Manor, Windlesham, Surrey GU206PH, UK
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  • David A. Evans,

    1. Eli Lilly U.K. Erl Wood Manor, Windlesham, Surrey GU206PH, UK
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  • Andreas Bender

    Corresponding author
    1. Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK telephone: +44(0)1223 762983; fax: +44 (0)1223 763076
    • Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK telephone: +44(0)1223 762983; fax: +44 (0)1223 763076

    Search for more papers by this author

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.

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