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1. In silico analysis of crustacean hyperglycemic hormone family G protein-coupled receptor candidates

   https://doi.org/10.3389/fendo.2023.1322800  

   Kozma, Mihika T.; Pérez-Moreno, Jorge L.; Gandhi, Neha S.; Hernandez Jeppesen, Luisanna; Durica, David S.; Ventura, Tomer; Mykles, Donald L. (January 2024, Frontiers in Endocrinology)

   Vrecl, M. (Ed.)

   Ecdysteroid molting hormone synthesis is directed by a pair of molting glands or Y-organs (YOs), and this synthesis is inhibited by molt-inhibiting hormone (MIH). MIH is a member of the crustacean hyperglycemic hormone (CHH) neuropeptide superfamily, which includes CHH and insect ion transport peptide (ITP). It is hypothesized that the MIH receptor is a Class A (Rhodopsin-like) G protein-coupled receptor (GPCR). The YO of the blackback land crab,Gecarcinus lateralis, expresses 49 Class A GPCRs, three of which (Gl-CHHR-A9, -A10, and -A12) were provisionally assigned as CHH-like receptors. CrusTome, a transcriptome database assembled from 189 crustaceans and 12 ecdysozoan outgroups, was used to deorphanize candidate MIH/CHH GPCRs, relying on sequence homology to three functionally characterized ITP receptors (BNGR-A2, BNGR-A24, and BNGR-A34) in the silk moth,Bombyx mori. Phylogenetic analysis and multiple sequence alignments across major taxonomic groups revealed extensive expansion and diversification of crustacean A2, A24, and A34 receptors, designatedCHHFamilyReceptorCandidates (CFRCs). The A2 clade was divided into three subclades; A24 clade was divided into five subclades; and A34 was divided into six subclades. The subclades were distinguished by conserved motifs in extracellular loop (ECL) 2 and ECL3 in the ligand-binding region. Eleven of the 14 subclades occurred in decapod crustaceans. InG. lateralis, seven CFRC sequences, designated Gl-CFRC-A2α1, -A24α, -A24β1, -A24β2, -A34α2, -A34β1, and -A34β2, were identified; the three A34 sequences corresponded to Gl-GPCR-A12, -A9, and A10, respectively. ECL2 in all the CFRC sequences had a two-stranded β-sheet structure similar to human Class A GPCRs, whereas the ECL2 of decapod CFRC-A34β1/β2 had an additional two-stranded β-sheet. We hypothesize that this second β-sheet on ECL2 plays a role in MIH/CHH binding and activation, which will be investigated further with functional assays. 

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2. Dissecting the genetic landscape of GPCR signaling through phenotypic profiling in C. elegans

   https://doi.org/10.1038/s41467-023-44177-z  

   Pu, Longjun; Wang, Jing; Lu, Qiongxuan; Nilsson, Lars; Philbrook, Alison; Pandey, Anjali; Zhao, Lina; Schendel, Robin van; Koh, Alan; Peres, Tanara V; et al (December 2023, Nature Communications)

   Abstract G protein-coupled receptors (GPCRs) mediate responses to various extracellular and intracellular cues. However, the large number of GPCR genes and their substantial functional redundancy make it challenging to systematically dissect GPCR functions in vivo. Here, we employ a CRISPR/Cas9-based approach, disrupting 1654 GPCR-encoding genes in 284 strains and mutating 152 neuropeptide-encoding genes in 38 strains inC. elegans. These two mutant libraries enable effective deorphanization of chemoreceptors, and characterization of receptors for neuropeptides in various cellular processes. Mutating a set of closely related GPCRs in a single strain permits the assignment of functions to GPCRs with functional redundancy. Our analyses identify a neuropeptide that interacts with three receptors in hypoxia-evoked locomotory responses, unveil a collection of regulators in pathogen-induced immune responses, and define receptors for the volatile food-related odorants. These results establish our GPCR and neuropeptide mutant libraries as valuable resources for theC. eleganscommunity to expedite studies of GPCR signaling in multiple contexts. 

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3. A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs

   https://doi.org/10.3390/molecules24112097  

   Plante, Ambrose; Shore, Derek M.; Morra, Giulia; Khelashvili, George; Weinstein, Harel (June 2019, Molecules)

   G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ligand-specific manner in order to carry out a myriad of functions in diverse cellular contexts. Much has been learned about the molecular mechanisms of ligand-GPCR complexes from Molecular Dynamics (MD) simulations. However, to explore ligand-specific differences in the response of a GPCR to diverse ligands, as is required to understand ligand bias and functional selectivity, necessitates creating very large amounts of data from the needed large-scale simulations. This becomes a Big Data problem for the high dimensionality analysis of the accumulated trajectories. Here we describe a new machine learning (ML) approach to the problem that is based on transforming the analysis of GPCR function-related, ligand-specific differences encoded in the MD simulation trajectories into a representation recognizable by state-of-the-art deep learning object recognition technology. We illustrate this method by applying it to recognize the pharmacological classification of ligands bound to the 5-HT2A and D2 subtypes of class-A GPCRs from the serotonin and dopamine families. The ML-based approach is shown to perform the classification task with high accuracy, and we identify the molecular determinants of the classifications in the context of GPCR structure and function. This study builds a framework for the efficient computational analysis of MD Big Data collected for the purpose of understanding ligand-specific GPCR activity. 

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4. What Makes GPCRs from Different Families Bind to the Same Ligand?

   https://doi.org/10.3390/biom12070863  

   Dankwah, Kwabena Owusu; Mohl, Jonathon E.; Begum, Khodeza; Leung, Ming-Ying (July 2022, Biomolecules)

   G protein-coupled receptors (GPCRs) are the largest class of cell-surface receptor proteins with important functions in signal transduction and often serve as therapeutic drug targets. With the rapidly growing public data on three dimensional (3D) structures of GPCRs and GPCR-ligand interactions, computational prediction of GPCR ligand binding becomes a convincing option to high throughput screening and other experimental approaches during the beginning phases of ligand discovery. In this work, we set out to computationally uncover and understand the binding of a single ligand to GPCRs from several different families. Three-dimensional structural comparisons of the GPCRs that bind to the same ligand revealed local 3D structural similarities and often these regions overlap with locations of binding pockets. These pockets were found to be similar (based on backbone geometry and side-chain orientation using APoc), and they correlate positively with electrostatic properties of the pockets. Moreover, the more similar the pockets, the more likely a ligand binding to the pockets will interact with similar residues, have similar conformations, and produce similar binding affinities across the pockets. These findings can be exploited to improve protein function inference, drug repurposing and drug toxicity prediction, and accelerate the development of new drugs. 

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5. Elucidation of a dynamic interplay between a beta-2 adrenergic receptor, its agonist, and stimulatory G protein

   https://doi.org/10.1073/pnas.2215916120  

   Han, Yanxiao; Dawson, John R.; DeMarco, Kevin R.; Rouen, Kyle C.; Bekker, Slava; Yarov-Yarovoy, Vladimir; Clancy, Colleen E.; Xiang, Yang K.; Vorobyov, Igor (March 2023, Proceedings of the National Academy of Sciences)

   G protein-coupled receptors (GPCRs) represent the largest group of membrane receptors for transmembrane signal transduction. Ligand-induced activation of GPCRs triggers G protein activation followed by various signaling cascades. Understanding the structural and energetic determinants of ligand binding to GPCRs and GPCRs to G proteins is crucial to the design of pharmacological treatments targeting specific conformations of these proteins to precisely control their signaling properties. In this study, we focused on interactions of a prototypical GPCR, beta-2 adrenergic receptor (β 2 AR), with its endogenous agonist, norepinephrine (NE), and the stimulatory G protein (G s ). Using molecular dynamics (MD) simulations, we demonstrated the stabilization of cationic NE, NE(+), binding to β 2 AR by G s protein recruitment, in line with experimental observations. We also captured the partial dissociation of the ligand from β 2 AR and the conformational interconversions of G s between closed and open conformations in the NE(+)–β 2 AR–G s ternary complex while it is still bound to the receptor. The variation of NE(+) binding poses was found to alter G s α subunit (G s α) conformational transitions. Our simulations showed that the interdomain movement and the stacking of G s α α1 and α5 helices are significant for increasing the distance between the G s α and β 2 AR, which may indicate a partial dissociation of G s α The distance increase commences when G s α is predominantly in an open state and can be triggered by the intracellular loop 3 (ICL3) of β 2 AR interacting with G s α, causing conformational changes of the α5 helix. Our results help explain molecular mechanisms of ligand and GPCR-mediated modulation of G protein activation. 

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