TY - JOUR
T1 - Extensions to in silico bioactivity predictions using pathway annotations and differential pharmacology analysis: Application to Xenopus laevis phenotypic readouts
AU - Liggi, Sonia
AU - Drakakis, Georgios
AU - Hendry, Adam E.
AU - Hanson, Kimberley M.
AU - Brewerton, Suzanne C.
AU - Wheeler, Grant N.
AU - Bodkin, Michael J.
AU - Evans, David A.
AU - Bender, Andreas
PY - 2013/12
Y1 - 2013/12
N2 - 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.
AB - 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.
KW - In silico bioactivity prediction
KW - Cheminoformatics
KW - Mechanism of action
KW - Xenopus laevis
KW - Pigmentation
KW - Cheminformatics
UR - http://www.scopus.com/inward/record.url?scp=84894539238&partnerID=8YFLogxK
U2 - 10.1002/minf.201300102
DO - 10.1002/minf.201300102
M3 - Article
VL - 32
SP - 1009
EP - 1024
JO - Molecular Informatics
JF - Molecular Informatics
SN - 1868-1743
IS - 11-12
ER -