Statistical methods and molecular docking for the prediction of thyroid hormone receptor subtype binding affinity and selectivity

Abstract

The development of compounds that selectively modulate thyroid hormone action by serving as subtype-selective ligands of the thyroid hormone receptors (TRs) would be useful for clinical therapy. In the present work, quantitative structure-activity relationship (QSAR) models by adopting molecular descriptors to predict the TR binding activity were established based on a data set of TR ligands. The linear (multiple linear regression (MLR) and partial least squares regression (PLSR)) and nonlinear (support vector machine regression (SVR)) methods were employed to investigate the relationship between structural properties and binding activities. The proposed PLSR model was slightly superior to the MLR model and SVR model, as indicated by the reasonable statistical properties (TRβ: Rtr2 = 0.9594, Qte2 = 0.8091. TRα: Rtr2 = 0.9705, Qte2 = 0.8057). Additionally, molecular docking simulations were also performed to study the probable binding modes of the ligands and the TR subtype selectivity. The results indicate that substituents located in region A, region B, and region C and the orientation of these groups might result in the subtype selectivity based on the hydrogen bonding and electrostatic interactions. The derived QSAR models together with the molecular docking results have good potential in facilitating the discovery of novel TR ligands with improved activity and subtype selectivity.