'Inductive' charges on atoms in proteins: comparative docking with the extended steroid benchmark set and discovery of a novel SHBG ligand

We have developed a novel iterative approach for calculation of partial charges in proteins within the framework of the 'molecular capacitance' model. The method operates by an effective 'inductive' electronegativity scale derived from a number of the conventional charge systems including CHARMM, AMBER, MMFF, OPLS, and PEOE among others. Our novel 'inductive' electronegativity equalization procedure allows rapid and conformation sensitive computation of adequate partial charges in proteins. Accuracy of the 'inductive' values was confirmed by their correlation with DFT-computed partial charges in common amino acids. A comparative docking study with an extended steroid data set not only illustrated the adequacy of 'inductive' protein charges but also demonstrated their superior performance compared to several conventional protein charging systems. Subsequent docking with 'inductive' charges resulted in identification of five potential leads as human Sex Hormone Binding Globulin (SHBG) ligands from a commercial library of natural compounds. When the selected substances were evaluated for their ability to bind SHBG in vitro, three of them displaced testosterone from the SHBG steroid-binding site, and with one compound this was achieved at micromolar concentrations.     

Using particle swarms for the development of QSAR models based on K-nearest neighbor and kernel regression

We describe the application of particle swarms for the development of quantitative structure-activity relationship (QSAR) models based on k-nearest neighbor and kernel regression. Particle swarms is a population-based stochastic search method based on the principles of social interaction. Each individual explores the feature space guided by its previous success and that of its neighbors. Success is measured using leave-one-out (LOO) cross validation on the resulting model as determined by k-nearest neighbor kernel regression. The technique is shown to compare favorably to simulated annealing using three classical data sets from the QSAR literature.