Evolutionary information helps understand distinctive features of the angiotensin II receptors AT1 and AT2 in amniota.

In vertebrates, the octopeptide angiotensin II (AngII) is an important in vivo regulator of the cardiovascular system. It acts mainly through two G protein-coupled receptors, AT1 and AT2. To better understand distinctive features of these receptors, we carried out a phylogenetic analysis that revealed a mirror evolution of AT1 and AT2, each one split into two clades, separating fish from terrestrial receptors. It also revealed that hallmark mutations occurred at, or near, the sodium binding site in both AT1 and AT2. Electrostatics computations and molecular dynamics simulations support maintained sodium binding to human AT1 with slow ingress from the extracellular side and an electrostatic component of the binding free energy around -3kT, to be compared to around -2kT for human AT2 and the δ opioid receptor. Comparison of the sodium binding modes in wild type and mutated AT1 and AT2 from humans and eels indicates that the allosteric control by sodium in both AT1 and AT2 evolved during the transition from fish to amniota. The unusual S7.46N mutation in AT1 is mirrored by a L3.36M mutation in AT2. In the presence of sodium, the N7.46 pattern in amniota AT1 stabilizes the inward orientation of N3.35 in the apo receptor, which should contribute to efficient N3.35 driven biased signaling. The M3.36 pattern in amniota AT2 favours the outward orientation of N3.35 and the receptor promiscuity. Both mutations have physiological consequences for the regulation of the renin-angiotensin system.

Homology Modeling in the Twilight Zone: Improved Accuracy by Sequence Space Analysis.

The analysis of the relationship between sequence and structure similarities during the evolution of a protein family has revealed a limit of sequence divergence for which structural conservation can be confidently assumed and homology modeling is reliable. Below this limit, the twilight zone corresponds to sequence divergence for which homology modeling becomes increasingly difficult and requires specific methods. Either with conventional threading methods or with recent deep learning methods, such as AlphaFold, the challenge relies on the identification of a template that shares not only a common ancestor (homology) but also a conserved structure with the query. As both homology and structural conservation are transitive properties, mining of sequence databases followed by multidimensional scaling (MDS) of the query sequence space can reveal intermediary sequences to infer homology and structural conservation between the query and the template. Here, as a case study, we studied the plethodontid receptivity factor isoform 1 (PRF1) from Plethodon jordani, a member of a pheromone protein family present only in lungless salamanders and weakly related to cytokines of the IL6 family. A variety of conventional threading methods led to the cytokine CNTF as a template. Sequence mining, followed by phylogenetic and MDS analysis, provided missing links between PRF1 and CNTF and allowed reliable homology modeling. In addition, we compared automated models obtained from web servers to a customized model to show how modeling can be improved by expert information.

Identification of Dysregulated Expression of G Protein Coupled Receptors in Endocrine Tumors by Bioinformatics Analysis: Potential Drug Targets?

BACKGROUND: Many studies link G protein-coupled receptors (GPCRs) to cancer. Some endocrine tumors are unresponsive to standard treatment and/or require long-term and poorly tolerated treatment. This study explored, by bioinformatics analysis, the tumoral profiling of the GPCR transcriptome to identify potential targets in these tumors aiming at drug repurposing. METHODS: We explored the GPCR differentially expressed genes (DEGs) from public datasets (Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA)). The GEO datasets were available for two medullary thyroid cancers (MTCs), eighty-seven pheochromocytomas (PHEOs), sixty-one paragangliomas (PGLs), forty-seven pituitary adenomas and one-hundred-fifty adrenocortical cancers (ACCs). The TCGA dataset covered 92 ACCs. We identified GPCRs targeted by approved drugs from pharmacological databases (ChEMBL and DrugBank). RESULTS: The profiling of dysregulated GPCRs was tumor specific. In MTC, we found 14 GPCR DEGs, including an upregulation of the dopamine receptor (DRD2) and adenosine receptor (ADORA2B), which were the target of many drugs. In PGL, seven GPCR genes were downregulated, including vasopressin receptor (AVPR1A) and PTH receptor (PTH1R), which were targeted by approved drugs. In ACC, PTH1R was also downregulated in both the GEO and TCGA datasets and was the target of osteoporosis drugs. CONCLUSIONS: We highlight specific GPCR signatures across the major endocrine tumors. These data could help to identify new opportunities for drug repurposing.

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.

G Protein-coupled Receptors in Radioiodine-refractory Thyroid Cancer in the Era of Precision Medicine.

CONTEXT: Radioiodine-refractory thyroid cancers have poor outcomes and limited therapeutic options (tyrosine kinase inhibitors) due to transient efficacy and toxicity of treatments. Therefore, combinatorial treatments with new therapeutic approaches are needed. Many studies link G protein-coupled receptors (GPCRs) to cancer cell biology. OBJECTIVE: To perform a specific atlas of GPCR expression in progressive and refractory thyroid cancer to identify potential targets among GPCRs aiming at drug repositioning. METHODS: We analyzed samples from tumor and normal thyroid tissues from 17 patients with refractory thyroid cancer (12 papillary thyroid cancers [PTCs] and 5 follicular thyroid cancers [FTCs]). We assessed GPCR mRNA expression using NanoString technology with a custom panel of 371 GPCRs. The data were compared with public repositories and pharmacological databases to identify eligible drugs. The analysis of prognostic value of genes was also performed with TCGA datasets. RESULTS: With our transcriptomic analysis, 4 receptors were found to be downregulated in FTC (VIPR1, ADGRL2/LPHN2, ADGRA3, and ADGRV1). In PTC, 24 receptors were deregulated, 7 of which were also identified by bioinformatics analyses of publicly available datasets on primary thyroid cancers (VIPR1, ADORA1, GPRC5B, P2RY8, GABBR2, CYSLTR2, and LPAR5). Among all the differentially expressed genes, 22 GPCRs are the target of approved drugs and some GPCRs are also associated with prognostic factors. DISCUSSION: For the first time, we performed GPCR mRNA expression profiling in progressive and refractory thyroid cancers. These findings provide an opportunity to identify potential therapeutic targets for drug repositioning and precision medicine in radioiodine-refractory thyroid cancer.