Modeling and Deorphanization of Orphan GPCRs

Decrypting orphan GPCR drug discovery via multitask learning

The drug discovery of G protein-coupled receptors (GPCRs) superfamily using computational models is often limited by the availability of protein three-dimensional (3D) structures and chemicals with experimentally measured bioactivities. Orphan GPCRs without known ligands further complicate the process. To enable drug discovery for human orphan GPCRs, multitask models were proposed for predicting half maximal effective concentrations (EC50) of the pairs of chemicals and GPCRs. Protein multiple sequence alignment features, and physicochemical properties and fingerprints of chemicals were utilized to encode the protein and chemical information, respectively. The protein features enabled the transfer of data-rich GPCRs to orphan receptors and the transferability based on the similarity of protein features. The final model was trained using both agonist and antagonist data from 200 GPCRs and showed an excellent mean squared error (MSE) of 0.24 in the validation dataset. An independent test using the orphan dataset consisting of 16 receptors associated with less than 8 bioactivities showed a reasonably good MSE of 1.51 that can be further improved to 0.53 by considering the transferability based on protein features. The informative features were identified and mapped to corresponding 3D structures to gain insights into the mechanism of GPCR-ligand interactions across the GPCR family. The proposed method provides a novel perspective on learning ligand bioactivity within the diverse human GPCR superfamily and can potentially accelerate the discovery of therapeutic agents for orphan GPCRs.

Fine tuning for success in structure-based virtual screening

Structure-based virtual screening plays a significant role in drug-discovery. The method virtually docks millions of compounds from corporate or public libraries into a binding site of a disease-related protein structure, allowing for the selection of a small list of potential ligands for experimental testing. Many algorithms are available for docking and assessing the affinity of compounds for a targeted protein site. The performance of affinity estimation calculations is highly dependent on the size and nature of the site, therefore a rationale for selecting the best protocol is required. To address this issue, we have developed an automated calibration process, implemented in a Knime workflow. It consists of four steps: preparation of a protein test set with structures and models of the target, preparation of a compound test set with target-related ligands and decoys, automatic test of 24 scoring/rescoring protocols for each target structure and model, and graphical display of results. The automation of the process combined with execution on high performance computing resources greatly reduces the duration of the calibration phase, and the test of many combinations of algorithms on various target conformations results in a rational and optimal choice of the best protocol. Here, we present this tool and exemplify its application in setting-up an optimal protocol for SBVS against Retinoid X Receptor alpha.

Homology Modeling of Class A G-Protein-Coupled Receptors in the Age of the Structure Boom

With 700 members, G protein-coupled receptors (GPCRs) of the rhodopsin family (class A) form the largest membrane receptor family in humans and are the target of about 30% of presently available pharmaceutical drugs. The recent boom in GPCR structures led to the structural resolution of 57 unique receptors in different states (39 receptors in inactive state only, 2 receptors in active state only and 16 receptors in different activation states). In spite of these tremendous advances, most computational studies on GPCRs, including molecular dynamics simulations, virtual screening and drug design, rely on GPCR models obtained by homology modeling. In this protocol, we detail the different steps of homology modeling with the MODELLER software, from template selection to model evaluation. The present structure boom provides closely related templates for most receptors. If, in these templates, some of the loops are not resolved, in most cases, the numerous available structures enable to find loop templates with similar length for equivalent loops. However, simultaneously, the large number of putative templates leads to model ambiguities that may require additional information based on multiple sequence alignments or molecular dynamics simulations to be resolved. Using the modeling of the human bradykinin receptor B1 as a case study, we show how several templates are managed by MODELLER, and how the choice of template(s) and of template fragments can improve the quality of the models. We also give examples of how additional information and tools help the user to resolve ambiguities in GPCR modeling.

Fatty Acid Signaling Mechanisms in Neural Cells: Fatty Acid Receptors

Fatty acids (FAs) are typically associated with structural and metabolic roles, as they can be stored as triglycerides, degraded by β-oxidation or used in phospholipids’ synthesis, the main components of biological membranes. It has been shown that these lipids exhibit also regulatory functions in different cell types. FAs can serve as secondary messengers, as well as modulators of enzymatic activities and substrates for cytokines synthesis. More recently, it has been documented a direct activity of free FAs as ligands of membrane, cytosolic, and nuclear receptors, and cumulative evidence has emerged, demonstrating its participation in a wide range of physiological and pathological conditions. It has been long known that the central nervous system is enriched with poly-unsaturated FAs, such as arachidonic (C20:4ω-6) or docosohexaenoic (C22:6ω-3) acids. These lipids participate in the regulation of membrane fluidity, axonal growth, development, memory, and inflammatory response. Furthermore, a whole family of low molecular weight compounds derived from FAs has also gained special attention as the natural ligands for cannabinoid receptors or key cytokines involved in inflammation, largely expanding the role of FAs as precursors of signaling molecules. Nutritional deficiencies, and alterations in lipid metabolism and lipid signaling have been associated with developmental and cognitive problems, as well as with neurodegenerative diseases. The molecular mechanism behind these effects still remains elusive. But in the last two decades, different families of proteins have been characterized as receptors mediating FAs signaling. This review focuses on different receptors sensing and transducing free FAs signals in neural cells: (1) membrane receptors of the family of G Protein Coupled Receptors known as Free Fatty Acid Receptors (FFARs); (2) cytosolic transport Fatty Acid-Binding Proteins (FABPs); and (3) transcription factors Peroxisome Proliferator-Activated Receptors (PPARs). We discuss how these proteins modulate and mediate direct regulatory functions of free FAs in neural cells. Finally, we briefly discuss the advantages of evaluating them as potential targets for drug design in order to manipulate lipid signaling. A thorough characterization of lipid receptors of the nervous system could provide a framework for a better understanding of their roles in neurophysiology and, potentially, help for the development of novel drugs against aging and neurodegenerative processes.