Characterization of the ligand-binding domain of the ecdysteroid receptor from Drosophila melanogaster

Mutants created by site-directed mutagenesis were used to elucidate the function of amino acids involved in ligand binding to ecdysteroid receptor (EcR) and heterodimer formation with ultraspiracle (USP). The results demonstrate the importance of the C-terminal part of the D-domain and helix 12 of EcR for hormone binding. Some amino acids are involved either in ligand binding to EcR (E476, M504, D572, I617, N626) or ligand-dependent heterodimerization as determined by gel mobility shift assays (A612, L615, T619), while others are involved in both functions (K497, E648). Some amino acids are suboptimal for ligand binding (L615, T619), but mediate ligand-dependent dimerization. We conclude that the enhanced regulatory potential by ligand-dependent modulation of dimerization in the wild type is achieved at the expense of optimal ligand binding. Mutation of amino acids (K497, E648) involved in the salt bridge between helix 4 and 12 impair ligand binding to EcR more severely than hormone binding to the heterodimer, indicating that to some extent heterodimerization compensates for the deleterious effect of certain mutations. Different effects of the same point mutations on ligand binding to EcR and EcR/USP (R511, A612, L615, I617, T619, N626) indicate that the ligand-binding pocket is modified by heterodimerization

Dibenzoylhydrazines as Insect Growth Modulators: Topology-Based QSAR Modelling

Dibenzoylhydrazines Xa-(C6H5)a-CO-N-(t-Bu)-NH-CO-(C6H5)b-Yb are efficient insect growth regulators with high activity and selectivity toward lepidopteran and coleopteran pests. For 123 congeneric molecules, a quantitative structure activity relationship model was built in the framework of the QSARINS package using 2D, Topology-based, PaDEL descriptors. Variable selection by GA-MLR allows building an efficient multilinear regression linking pEC50 values to nine structural variables. Robustness and quality of the model were carefully examined at various levels: data-fitting (recall), leave-one (or some) - out, internal and external validation (including random splitting), points not in depth investigated in previous works. Various Machine Learning approaches (Partial Least Squares Regression, Projection Pursuit Regression, Linear Support Vector Machine or Three Layer Perceptron Artificial Neural Network) confirm the validity of the analysis, giving highly consistent results of comparable quality, with only a slight advantage for the three-layer perceptron

Ecdysteroid 7,9(11)-dien-6-ones as potential photoaffinity labels for ecdysteroid binding proteins

Three ecdysteroid 7,9(11)-dien-7-ones (dacryhainansterone, 25-hydroxydacryhainansterone and kaladasterone) were prepared by dehydration of the corresponding 11a-hydroxy ecdysteroids (ajugasterone C, turkesterone and muristerone A, respectively). The biological activities of the dienones in the Drosophila melanogaster B(II) cell bioassay, which reflect the affinity for the ecdysteroid receptor complex, showed that the dienones retain high biological activity. Irradiation at 350 nm of the ecdysteroid dienones (100 nM) with bacterially-expressed dipteran and lepidopteran ecdysteroid receptor proteins (DmEcR/DmUSP or CfEcR/CfUSP), followed by loading with [(3)H]ponasterone A revealed that irradiation of dacryhainansterone or kaladasterone resulted in blocking of >70% of the specific binding sites. Thus, ecdysteroid dienones show considerable potential as photoaffinity analogues for ecdysteroid binding proteins