3D proteochemometrics: using three-dimensional information of proteins and ligands to address aspects of the selectivity of serine proteases.

The high similarity between certain sub-pockets of serine proteases may lead to low selectivity of protease inhibitors. Therefore the application of proteochemometrics (PCM), which quantifies the relationship between protein/ligand descriptors and affinity for multiple ligands and targets simultaneously, is useful to understand and improve the selectivity profiles of potential inhibitors. In this study, protein field-based PCM that uses knowledge-based and WaterMap derived fields to describe proteins in combination with 2D (RDKit and MOE fingerprints) and 3D (4 point pharmacophoric fingerprints and GRIND) ligand descriptors was used to model the bioactivities of 24 homologous serine proteases and 5863 inhibitors in an integrated fashion. Of the multiple field-based PCM models generated based on different ligand descriptors, RDKit fingerprints showed the best performance in terms of external prediction with Rtest2 of 0.72 and RMSEP of 0.81. Further, visual interpretation of the models highlights sub-pocket specific regions that influence affinity and selectivity of serine protease inhibitors.

Visually interpretable models of kinase selectivity related features derived from field-based proteochemometrics.

Achieving selectivity for small organic molecules toward biological targets is a main focus of drug discovery but has been proven difficult, for example, for kinases because of the high similarity of their ATP binding pockets. To support the design of more selective inhibitors with fewer side effects or with altered target profiles for improved efficacy, we developed a method combining ligand- and receptor-based information. Conventional QSAR models enable one to study the interactions of multiple ligands toward a single protein target, but in order to understand the interactions between multiple ligands and multiple proteins, we have used proteochemometrics, a multivariate statistics method that aims to combine and correlate both ligand and protein descriptions with affinity to receptors. The superimposed binding sites of 50 unique kinases were described by molecular interaction fields derived from knowledge-based potentials and Schrödinger's WaterMap software. Eighty ligands were described by Mold(2), Open Babel, and Volsurf descriptors. Partial least-squares regression including cross-terms, which describe the selectivity, was used for model building. This combination of methods allows interpretation and easy visualization of the models within the context of ligand binding pockets, which can be translated readily into the design of novel inhibitors.

Field-based comparison of ligand and coactivator binding sites of nuclear receptors.

A structure-based comparison of the ligand-binding domains of 35 nuclear receptors from five different subfamilies is presented. Their ligand and coactivator binding sites are characterized using knowledge-based contact preference fields for hydrophobic and hydrophilic interactions implemented in the MOE modeling environment. Additionally, for polar knowledge-based field points the preference for negative or positive electrostatic interactions is estimated using the Poisson-Boltzmann equation. These molecular-interaction fields are used to cluster the nuclear receptor family based on similarities of their binding sites. By analyzing the similarities and differences of hydrophobic and polar fields in binding pockets of related receptors it is possible to identify conserved interactions in ligand and coactivator binding pockets, which support e.g. design of specific ligands during lead optimization or virtual screening as docking filter. Examples of remarkable similarities between ligand binding sites of members from phylogenetically different nuclear receptor families (RXR, RAR, HNF4, NR5) and differences between closely related subtypes (LXR, RAR, TR) are discussed in more detail. Significant similarities and differences of coactivator binding sites are shown for NR3Cs, LXRs and PPARs.