Linear and non-linear QSAR modelling of juvenile hormone esterase inhibitors

A tight control of juvenile hormone (JH) titre is crucial during the life cycle of a holometabolous insect. JH metabolism is made through the action of enzymes, particularly the juvenile hormone esterase (JHE). Trifluoromethylketones (TFKs) are able to inhibit this enzyme to disrupt the endocrine fu...

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Bibliographic Details
Published in:SAR and QSAR in environmental research 2012-04, Vol.23 (3-4), p.357-369
Main Authors: Devillers, J., Doucet, J.P., Doucet-Panaye, A., Decourtye, A., Aupinel, P.
Format: Article
Language:English
Subjects:
Animals
artificial neural network
Basis functions
Carboxylic Ester Hydrolases - antagonists & inhibitors
Carboxylic Ester Hydrolases - chemistry
Esterase
Hemolymph - chemistry
Inhibition
Insects
Juvenile hormone esterase
Ketones - chemistry
Larva - enzymology
Life cycle engineering
Life cycles
Life Sciences
Linear Models
Metabolism
Models, Molecular
Moths - enzymology
Nonlinear Dynamics
Quantitative Structure-Activity Relationship
Radial basis function
Regression analysis
spline regression analysis
Statistical analysis
Statistics
Structure-activity relationships
Support vector machines
support vector regression
Trichoplusia ni
trifluoromethylketones
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Summary:A tight control of juvenile hormone (JH) titre is crucial during the life cycle of a holometabolous insect. JH metabolism is made through the action of enzymes, particularly the juvenile hormone esterase (JHE). Trifluoromethylketones (TFKs) are able to inhibit this enzyme to disrupt the endocrine function of the targeted insect. In this context, a set of 96 TFKs, tested on Trichoplusia ni for their JHE inhibition, was split into a training set (n = 77) and a test set (n = 19) to derive a QSAR model. TFKs were initially described by 42 CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) descriptors, but a feature selection process allowed us to consider only five descriptors encoding the structural characteristics of the TFKs and their reactivity. A classical and spline regression analysis, a three-layer perceptron, a radial basis function network and a support vector regression were experienced as statistical tools. The best results were obtained with the support vector regression (r 2 and  = 0.91). The model provides information on the structural features and properties responsible for the high JHE inhibition activity of TFKs.
ISSN:1062-936X
1029-046X
DOI:10.1080/1062936X.2012.664562