Small molecule chemokine mimetics suggest a molecular basis for the observation that CXCL10 and CXCL11 are allosteric ligands of CXCR3.

BACKGROUND AND PURPOSE: The chemokine receptor CXCR3 directs migration of T-cells in response to the ligands CXCL9/Mig, CXCL10/IP-10 and CXCL11/I-TAC. Both ligands and receptors are implicated in the pathogenesis of inflammatory disorders, including atherosclerosis and rheumatoid arthritis. Here, we describe the molecular mechanism by which two synthetic small molecule agonists activate CXCR3. EXPERIMENTAL APPROACH: As both small molecules are basic, we hypothesized that they formed electrostatic interactions with acidic residues within CXCR3. Nine point mutants of CXCR3 were generated in which an acidic residue was mutated to its amide counterpart. Following transient expression, the ability of the constructs to bind and signal in response to natural and synthetic ligands was examined. KEY RESULTS: The CXCR3 mutants D112N, D195N and E196Q were efficiently expressed and responsive in chemotaxis assays to CXCL11 but not to CXCL10 or to either of the synthetic agonists, confirmed with radioligand binding assays. Molecular modelling of both CXCL10 and CXCR3 suggests that the small molecule agonists mimic a region of the '30s loop' (residues 30-40 of CXCL10) which interacts with the intrahelical CXCR3 residue D112, leading to receptor activation. D195 and E196 are located in the second extracellular loop and form putative intramolecular salt bridges required for a CXCR3 conformation that recognizes CXCL10. In contrast, CXCL11 recognition by CXCR3 is largely independent of these residues. CONCLUSION AND IMPLICATIONS: We provide here a molecular basis for the observation that CXCL10 and CXCL11 are allosteric ligands of CXCR3. Such findings may have implications for the design of CXCR3 antagonists

Evaluation of machine-learning methods for ligand-based virtual screening

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed

Role of Lipids in Spheroidal High Density Lipoproteins

We study the structure and dynamics of spherical high density lipoprotein (HDL) particles through coarse-grained multi-microsecond molecular dynamics simulations. We simulate both a lipid droplet without the apolipoprotein A-I (apoA-I) and the full HDL particle including two apoA-I molecules surrounding the lipid compartment. The present models are the first ones among computational studies where the size and lipid composition of HDL are realistic, corresponding to human serum HDL. We focus on the role of lipids in HDL structure and dynamics. Particular attention is paid to the assembly of lipids and the influence of lipid-protein interactions on HDL properties. We find that the properties of lipids depend significantly on their location in the particle (core, intermediate region, surface). Unlike the hydrophobic core, the intermediate and surface regions are characterized by prominent conformational lipid order. Yet, not only the conformations but also the dynamics of lipids are found to be distinctly different in the different regions of HDL, highlighting the importance of dynamics in considering the functionalization of HDL. The structure of the lipid droplet close to the HDL-water interface is altered by the presence of apoA-Is, with most prominent changes being observed for cholesterol and polar lipids. For cholesterol, slow trafficking between the surface layer and the regimes underneath is observed. The lipid-protein interactions are strongest for cholesterol, in particular its interaction with hydrophobic residues of apoA-I. Our results reveal that not only hydrophobicity but also conformational entropy of the molecules are the driving forces in the formation of HDL structure. The results provide the first detailed structural model for HDL and its dynamics with and without apoA-I, and indicate how the interplay and competition between entropy and detailed interactions may be used in nanoparticle and drug design through self-assembly

Challenges Predicting Ligand-Receptor Interactions of Promiscuous Proteins: The Nuclear Receptor PXR

Transcriptional regulation of some genes involved in xenobiotic detoxification and apoptosis is performed via the human pregnane X receptor (PXR) which in turn is activated by structurally diverse agonists including steroid hormones. Activation of PXR has the potential to initiate adverse effects, altering drug pharmacokinetics or perturbing physiological processes. Reliable computational prediction of PXR agonists would be valuable for pharmaceutical and toxicological research. There has been limited success with structure-based modeling approaches to predict human PXR activators. Slightly better success has been achieved with ligand-based modeling methods including quantitative structure-activity relationship (QSAR) analysis, pharmacophore modeling and machine learning. In this study, we present a comprehensive analysis focused on prediction of 115 steroids for ligand binding activity towards human PXR. Six crystal structures were used as templates for docking and ligand-based modeling approaches (two-, three-, four- and five-dimensional analyses). The best success at external prediction was achieved with 5D-QSAR. Bayesian models with FCFP_6 descriptors were validated after leaving a large percentage of the dataset out and using an external test set. Docking of ligands to the PXR structure co-crystallized with hyperforin had the best statistics for this method. Sulfated steroids (which are activators) were consistently predicted as non-activators while, poorly predicted steroids were docked in a reverse mode compared to 5α-androstan-3β-ol. Modeling of human PXR represents a complex challenge by virtue of the large, flexible ligand-binding cavity. This study emphasizes this aspect, illustrating modest success using the largest quantitative data set to date and multiple modeling approaches