Toward a platelet-activating factor pseudoreceptor 2. Three-dimensional semiempirical models for agonist and antagonist binding

Characterization of the conformational ensemble from bioactive N-acylhydrazone derivatives

The search for bioactive conformations from prototypes is mostly referenced on crystallographic ligand-receptor complexes, in which the molecule conformation is already caged inside its binding site. However, the complexation process is a thermodynamic event depending on both complexed and uncomplexed states. As ligand affinity originates from such equilibrium, the development of novel computational models capable of supplying data on ligand dynamics in biological solutions is potentially applicable in more efficient methods for prediction of compounds binding and affinity. In this context, the current work employs a series of molecular dynamics simulations on three N-acylhydrazone derivatives, already shown to present promising cardioinotropic and vasodilatory activities, in order to obtain a precise characterization of each compound conformational ensemble in aqueous solutions, instead of a single minimum energy conformation. Consequently, we were able to observe the influence of each functional group of the studied molecules on the conformation of the entire compounds and thus on the exposure of functional groups that might potentially bind to target receptors. Additionally, the differences between the molecules conformational behavior were characterized, supporting a spatial and temporal image of each ligand, which may be potentially correlated to their biological activities. So in the context of conformational selection, such strategy may represent a useful methodology to contribute in the choice of ligands conformations for both 3D-QSAR and docking calculations.

The impact of variable selection on the modelling of oestrogenicity

Many oestrogenic chemicals exert their activity via specific interactions with the oestrogen receptor (ER). The objective of the present study was to identify significant descriptors associated with the ER binding affinities of a large and diverse set of compounds to drive quantitative structure-activity relationships (QSARs). To this end, a variety of statistical methods were employed for variable selection. These included stepwise regression and partial least squares (PLS) analyses, as well as a non-linear recursive partitioning method (Formal Inference-based Recursive Modelling). A total of 157 molecular descriptors including quantum mechanical, graph theoretical, indicator variables and log P were used in the study. Furthermore, cluster analysis of variables was performed to identify groups of descriptors representing similar molecular features. Hierarchical PLS analyses were performed, where the scores of the significant components of either PLS or principle component analysis (PCA), performed separately on each cluster, were used as the variables for the top model. This reduced the number of the variables representing the larger clusters, leading to a similar number of descriptors for each distinct molecular feature. The results showed that the most important molecular properties for stronger ER binding affinity are molecular size and shape, the presence of a phenol moiety as well as other aromatic groups, hydrophobicity and presence of double bonds. The best PLS model obtained, in terms of predictive ability, was a hierarchical PLS model. However, a rigorous validation study showed that the MLR model using descriptors selected by stepwise regression has greater predictive power than the PLS models.