Bias in FGFR1 signaling in response to FGF4, FGF8, and FGF9

FGFR1 signals differently in response to the FGF ligands FGF4, FGF8 and FGF9, but the mechanism behind the differential ligand recognition is poorly understood. Here, we use biophysical tools to quantify multiple aspects of FGFR1 signaling in response to the three FGFs: potency, efficacy, ligand-induced oligomerization and downregulation, and conformation of the active FGFR1 dimers. We show that FGF4, FGF8, and FGF9 are biased ligands, and that bias can explain differences in FGF8 and FGF9-mediated cellular responses. Our data suggest that ligand bias arises due to structural differences in the ligand-bound FGFR1 dimers, which impact the interactions of the FGFR1 transmembrane helices, leading to differential recruitment and activation of the downstream signaling adaptor FRS2. This study expands the mechanistic understanding of FGF signaling during development and brings the poorly understood concept of receptor tyrosine kinase ligand bias into the spotlight.

International Union of Basic and Clinical Pharmacology. Guidelines for GPCR ligand bias

G protein-coupled receptors modulate a plethora of physiological processes and mediate the effects of one-third of FDA-approved drugs. Notably, depending on which ligand has activated a particular receptor, it can engage different intracellular transducers. This paradigm of ligand-dependent ‘biased signaling’ dictates a need to advance beyond the level of receptors to consider the combined ligand-receptor pair in order to understand physiological signaling. Bias signaling also has the potential to improve medicines by reducing adverse effects. However, this is challenged by inconsistent interpretation of results and lack of commonly agreed guidelines. Here, we present recommended terminology and guidelines to conduct, report and quantify bias in a comparable and reproducible fashion. We expect these recommendations will facilitate a common understanding of experiments and findings across basic receptor research and drug discovery, while the area and the analytical methods to measure bias are still evolving, especially in complex cellular, tissue and organismal systems.

International Union of Basic and Clinical Pharmacology. Recommendations for GPCR ligand bias

G protein-coupled receptors modulate a plethora of physiological processes and mediate the effects of one-third of FDA-approved drugs. Notably, depending on which ligand has activated a particular receptor, it can engage different intracellular transducers. This paradigm of ligand-dependent ‘biased signaling’ dictates a need to advance beyond the level of receptors to consider the combined ligand-receptor pair in order to understand physiological signaling. Bias signaling also has the potential to improve medicines by reducing adverse effects. However, this is challenged by inconsistent interpretation of results and lack of commonly agreed guidelines. Here, we present recommended terminology and guidelines to conduct, report and quantify bias in a comparable and reproducible fashion. We expect these recommendations will facilitate a common understanding of experiments and findings across basic receptor research and drug discovery, while the area and the analytical methods to measure bias are still evolving, especially in complex cellular, tissue and organismal systems.

Histamine receptors in GtoPdb v.2021.3

Histamine receptors (nomenclature as agreed by the NC-IUPHAR Subcommittee on Histamine Receptors [80, 173]) are activated by the endogenous ligand histamine. Marked species differences exist between histamine receptor orthologues [80]. The human and rat H3 receptor genes are subject to significant splice variance [12]. The potency order of histamine at histamine receptor subtypes is H3 = H4 > H2 > H1 [173]. Some agonists at the human H3 receptor display significant ligand bias [182]. Antagonists of all 4 histamine receptors have clinical uses: H1 antagonists for allergies (e.g. cetirizine), H2 antagonists for acid-reflux diseases (e.g. ranitidine), H3 antagonists for narcolepsy (e.g. pitolisant/WAKIX; Registered) and H4 antagonists for atopic dermatitis (e.g. adriforant; Phase IIa) [173] and vestibular neuritis (AUV) (SENS-111 (Seliforant, previously UR-63325), entered and completed vestibular neuritis (AUV) Phase IIa efficacy and safety trials, respectively) [216, 8].

Clathrin regulates the β-arrestin pathway regardless of ligand bias

μ-opioid receptors (MOP) are thought to activate the G protein-mediated analgesic pathway and β-arrestin 2-mediated side effect pathway; however, ligands that recruit β-arrestin 2 only minimally to MOP may also cause opioid side effects. Such side effects are also induced in mutant mice lacking β-arrestin 2 or expressing phosphorylation-deficient MOP that do not recruit β-arrestin 2. These findings critically questioned whether β-arrestin 2 recruitment to MOP triggers side effects. Here, we show that β-arrestin 1 partially compensates for the lack of β-arrestin 2 in a neuronal cell line and thus might be involved in triggering such side effects in β-arrestin 2-null mice. Moreover, the magnitude of β-arrestin-mediated signals is not correlated with β-arrestin recruitment to MOP via phosphorylation of the carboxyl-terminal of MOP, which has long been used to evaluate β-arrestin bias of a ligand. Instead, β-arrestin activates downstream signals by binding with the clathrin heavy chain in the process of clathrin-coated pit formation. Our findings provide not only a novel insight into G protein-coupled receptor-mediated signalling to overcome opioid side effects but also an unexpected concept that the accumulation of molecules required for endocytosis is a key for activating the intracellular signalling.

Decoding Angiotensin II Type 1 Receptor Allosteric Communication to Gq and β-arrestin

The allosteric communication between the agonist binding site and the G protein or beta-arrestin coupling sites in G protein-coupled receptors (GPCRs) play an important role in determining ligand efficacy towards these two signaling pathways and hence the ligand bias. Knowledge of the amino acid residue networks involved in the allosteric communication will aid understanding GPCR signaling and the design of biased ligands. Angiotensin II type I receptor (AT1R) is an ideal model GPCR to study the molecular basis of ligand bias as it has multiple beta-arrestin2 and Gq protein biased agonists as well as three-dimensional structures. Using Molecular Dynamics simulations, dynamic allostery analysis, and functional BRET assays, we identified a network of residues involved in allosteric communication from the angiotensin II binding site to the putative Gq coupling sites and another network to the beta-arrestin2 coupling sites, with 6 residues common to both pathways located in TM3, TM5 and TM6. Our findings unveil unique and common allosteric communication residue hubs for Gq and beta-arr2 coupling by AngII ligands and suggests that some of these residues can be targeted to design biased AT1R ligands. Finally, we show through analysis of the inter-residue distance distributions of the activation microswitches involved in class A GPCR activation for ten different agonists, that these microswitches behave like rheostats with different relative strengths of activation, which we speculate could modulate the relative efficacy of these agonists toward the two signaling pathways.

BiasNet: A model to predict ligand bias towards GPCR signaling

<p>Signaling bias is a feature of many G–protein coupled receptor (GPCR) modulating drugs with clinical implications. Whether it is therapeutically advantageous for a drug to be G Protein biased or <i>β</i>-Arrestin (<i>β</i>-Arr) biased, depends on the context of the signaling pathway. Here, we explored GPCR ligands that exhibit biased signaling to gain insights into scaffolds and pharmacophores that leads to bias. More specifically, we used BiasDB, a database containing information about GPCR biased ligands and all ligands which show a (<i>β</i>-Arr) / G protein bias or a G protein / <i>β</i>-Arr bias are considered for the study. Four machine learning models were trained on these ligands to classify them. The features which were most important for training the models were analyzed. Two of these features (number of secondary amines and number of aromatic amines) were more prevalent in <i>β</i>-Arr biased ligands. After training a Random Forest model on HierS scaffolds, we found five scaffolds which demonstrated G protein or <i>β</i>-Arr bias. We also conducted t-SNE clustering, observing correspondence between unsupervised and supervised machine learning methods. To increase the applicability of our work, we developed a web implementation of our models which can predict bias based on a user-provided SMILES patterns. Our web implementation is available at: drugdiscovery.utep.edu/biasnet.</p>

Biased Ligands Differentially Shape the Conformation of the Extracellular Loop Region in 5-HT2B Receptors

G protein-coupled receptors are linked to various intracellular transducers, each pathway associated with different physiological effects. Biased ligands, capable of activating one pathway over another, are gaining attention for their therapeutic potential, as they could selectively activate beneficial pathways whilst avoiding those responsible for adverse effects. We performed molecular dynamics simulations with known β-arrestin-biased ligands like lysergic acid diethylamide and ergotamine in complex with the 5-HT2B receptor and discovered that the extent of ligand bias is directly connected with the degree of closure of the extracellular loop region. Given a loose allosteric coupling of extracellular and intracellular receptor regions, we delineate a concept for biased signaling at serotonin receptors, by which conformational interference with binding pocket closure restricts the signaling repertoire of the receptor. Molecular docking studies of biased ligands gathered from the BiasDB demonstrate that larger ligands only show plausible docking poses in the ergotamine-bound structure, highlighting the conformational constraints associated with bias. This emphasizes the importance of selecting the appropriate receptor conformation on which to base virtual screening workflows in structure-based drug design of biased ligands. As this mechanism of ligand bias has also been observed for muscarinic receptors, our studies provide a general mechanism of signaling bias transferable between aminergic receptors.

Ligand bias in receptor tyrosine kinase signaling

Ligand bias is the ability of ligands to differentially activate certain receptor signaling responses compared with others. It reflects differences in the responses of a receptor to specific ligands and has implications for the development of highly specific therapeutics. Whereas ligand bias has been studied primarily for G protein–coupled receptors (GPCRs), there are also reports of ligand bias for receptor tyrosine kinases (RTKs). However, the understanding of RTK ligand bias is lagging behind the knowledge of GPCR ligand bias. In this review, we highlight how protocols that were developed to study GPCR signaling can be used to identify and quantify RTK ligand bias. We also introduce an operational model that can provide insights into the biophysical basis of RTK activation and ligand bias. Finally, we discuss possible mechanisms underpinning RTK ligand bias. Thus, this review serves as a primer for researchers interested in investigating ligand bias in RTK signaling.