TY - JOUR
T1 - A platform for target prediction of phenotypic screening hit molecules
AU - Homeyer, Nadine
AU - van Deursen, Ruud
AU - Ochoa-Montaño, Bernardo
AU - Heikamp, Kathrin
AU - Ray, Peter
AU - Zuccotto, Fabio
AU - Blundell, Tom L.
AU - Gilbert, Ian H.
N1 - We acknowledge the Bill and Melinda Gates Foundation forsupport of this project (OPP1096928)
Copyright © 2019. Published by Elsevier Inc.
PY - 2020/3
Y1 - 2020/3
N2 - Many drug discovery programmes, particularly for infectious diseases, are conducted phenotypically. Identifying the targets of phenotypic screening hits experimentally can be complex, time-consuming, and expensive. However, it would be valuable to know what the molecular target(s) is, as knowledge of the binding pose of the hit molecule in the binding site can facilitate the compound optimisation. Furthermore, knowing the target would allow de-prioritisation of less attractive chemical series or molecular targets. To generate target-hypotheses for phenotypic active compounds, an in silico platform was developed that utilises both ligand and protein-structure information to generate a ranked set of predicted molecular targets. As a result of the web-based workflow the user obtains a set of 3D structures of the predicted targets with the active molecule bound. The platform was exemplified using Mycobacterium tuberculosis, the causative organism of tuberculosis. In a test that we performed, the platform was able to predict the targets of 60% of compounds investigated, where there was some similarity to a ligand in the protein database.
AB - Many drug discovery programmes, particularly for infectious diseases, are conducted phenotypically. Identifying the targets of phenotypic screening hits experimentally can be complex, time-consuming, and expensive. However, it would be valuable to know what the molecular target(s) is, as knowledge of the binding pose of the hit molecule in the binding site can facilitate the compound optimisation. Furthermore, knowing the target would allow de-prioritisation of less attractive chemical series or molecular targets. To generate target-hypotheses for phenotypic active compounds, an in silico platform was developed that utilises both ligand and protein-structure information to generate a ranked set of predicted molecular targets. As a result of the web-based workflow the user obtains a set of 3D structures of the predicted targets with the active molecule bound. The platform was exemplified using Mycobacterium tuberculosis, the causative organism of tuberculosis. In a test that we performed, the platform was able to predict the targets of 60% of compounds investigated, where there was some similarity to a ligand in the protein database.
KW - Fragment-based target prediction
KW - Ligand similarity
KW - Scaffold hopping
KW - Cavity comparison
KW - Hit docking with constraints
UR - http://www.scopus.com/inward/record.url?scp=85076488577&partnerID=8YFLogxK
U2 - 10.1016/j.jmgm.2019.107485
DO - 10.1016/j.jmgm.2019.107485
M3 - Article
C2 - 31836397
VL - 95
SP - 1
EP - 9
JO - Journal of Molecular Graphics & Modelling
JF - Journal of Molecular Graphics & Modelling
SN - 1093-3263
M1 - 107485
ER -