Onametostat, a PfPRMT5 inhibitor, exhibits antimalarial activity to Plasmodium falciparum.

Protein arginine methyltransferases (PRMTs) play critical roles in Plasmodium falciparum, a protozoan causing the deadliest form of malaria, making them potential targets for novel antimalarial drugs. Here, we screened 11 novel PRMT inhibitors against P. falciparum asexual growth and found that onametostat, an inhibitor for type II PRMTs, exhibited strong antimalarial activity with a half-maximal inhibitory concentration (IC50) value of 1.69 ± 0.04 µM. In vitro methyltransferase activities of purified PfPRMT5 were inhibited by onametostat, and a shift of IC50 to onametostat was found in the PfPRTM5 disruptant parasite line, indicating that PfPRTM5 is the primary target of onametostat. Consistent with the function of PfPRMT5 in mediating symmetric dimethylation of histone H3R2 (H3R2me2s) and in regulating invasion-related genes, onametostat treatment led to the reduction of H3R2me2s level in P. falciparum and caused the defects on the parasite's invasion of red blood cells. This study provides a starting point for identifying specific PRMT inhibitors with the potential to serve as novel antimalarial drugs.

Elucidating the complex membrane binding of a protein with multiple anchoring domains using extHMMM.

Membrane binding is a crucial mechanism for many proteins, but understanding the specific interactions between proteins and membranes remains a challenging endeavor. Coagulation factor Va (FVa) is a large protein whose membrane interactions are complicated due to the presence of multiple anchoring domains that individually can bind to lipid membranes. Using molecular dynamics simulations, we investigate the membrane binding of FVa and identify the key mechanisms that govern its interaction with membranes. Our results reveal that FVa can either adopt an upright or a tilted molecular orientation upon membrane binding. We further find that the domain organization of FVa deviates (sometimes significantly) from its crystallographic reference structure, and that the molecular orientation of the protein matches with domain reorganization to align the C2 domain toward its favored membrane-normal orientation. We identify specific amino acid residues that exhibit contact preference with phosphatidylserine lipids over phosphatidylcholine lipids, and we observe that mostly electrostatic effects contribute to this preference. The observed lipid-binding process and characteristics, specific to FVa or common among other membrane proteins, in concert with domain reorganization and molecular tilt, elucidate the complex membrane binding dynamics of FVa and provide important insights into the molecular mechanisms of protein-membrane interactions. An updated version of the HMMM model, termed extHMMM, is successfully employed for efficiently observing membrane bindings of systems containing the whole FVa molecule.

Membrane binding and lipid-protein interaction of the C2 domain from coagulation factor V.

Anchoring of coagulation factors to anionic regions of the membrane involves the C2 domain as a key player. The rate of enzymatic reactions of the coagulation factors is increased by several orders of magnitude upon membrane binding. However, the precise mechanisms behind the rate acceleration remain unclear, primarily because of a lack of understanding of the conformational dynamics of the C2-containing factors and corresponding complexes. We elucidate the membrane-bound form of the C2 domain from human coagulation factor V (FV-C2) by characterizing its membrane binding the specific lipid-protein interactions. Employing all-atom molecular dynamics simulations and leveraging the highly mobile membrane-mimetic (HMMM) model, we observed spontaneous binding of FV-C2 to a phosphatidylserine (PS)-containing membrane within 2-25 ns across twelve independent simulations. FV-C2 interacted with the membrane through three loops (spikes 1-3), achieving a converged, stable orientation. Multiple HMMM trajectories of the spontaneous membrane binding provided extensive sampling and ample data to examine the membrane-induced effects on the conformational dynamics of C2 as well as specific lipid-protein interactions. Despite existing crystal structures representing presumed "open" and "closed" states of FV-C2, our results revealed a continuous distribution of structures between these states, with the most populated structures differing from both "open" and "closed" states observed in crystal environments. Lastly, we characterized a putative PS-specific binding site formed by K23, Q48, and S78 located in the groove enclosed by spikes 1-3 (PS-specificity pocket), suggesting a different orientation of a bound headgroup moiety compared to previous proposals based upon analysis of static crystal structures.

Optimizing Coarse-Grained Models for Large-Scale Membrane Protein Simulation.

Coarse-grained (CG) models have been developed for studying membrane proteins at physiologically relevant scales. Such methods, including popular CG lipid models, exhibit stability and efficiency at moderate scales, but they can become impractical or even unusable beyond a critical size due to various technical issues. Here, we report that these scale-dependent issues can arise from progressively slower relaxation dynamics and become confounded by unforeseen instabilities observed only at larger scales. To address these issues, we systemically optimized a 4-site solvent-free CG lipid model that is suitable for conducting micron-scale molecular dynamics simulations of membrane proteins under various membrane properties. We applied this lipid model to explore the long-range membrane deformation induced by a large mechanosensitive ion channel, PIEZO. We show that the optimized CG models are powerful in elucidating the structural and dynamic interplay between PIEZO and the membrane. Furthermore, we anticipate that our methodological insights can prove useful for resolving issues stemming from scale-dependent limitations of similar CG methodologies.

Evaluating Polarizable Biomembrane Simulations against Experiments.

Owing to the increase of available computational capabilities and the potential for providing a more accurate description, polarizable molecular dynamics force fields are gaining popularity in modeling biomolecular systems. It is, however, crucial to evaluate how much precision is truly gained with increasing cost and complexity of the simulation. Here, we leverage the NMRlipids open collaboration and Databank to assess the performance of available polarizable lipid models─the CHARMM-Drude and the AMOEBA-based parameters─against high-fidelity experimental data and compare them to the top-performing nonpolarizable models. While some improvement in the description of ion binding to membranes is observed in the most recent CHARMM-Drude parameters, and the conformational dynamics of AMOEBA-based parameters are excellent, the best nonpolarizable models tend to outperform their polarizable counterparts for each property we explored. The identified shortcomings range from inaccuracies in describing the conformational space of lipids to excessively slow conformational dynamics. Our results provide valuable insights for the further refinement of polarizable lipid force fields and for selecting the best simulation parameters for specific applications.

Distal Protein-Protein Interactions Contribute to SARS-CoV-2 Main Protease Substrate Binding and Nirmatrelvir Resistance.

SARS-CoV-2 main protease, M pro , is responsible for the processing of the viral polyproteins into individual proteins, including the protease itself. M pro is a key target of anti-COVID-19 therapeutics such as nirmatrelvir (the active component of Paxlovid). Resistance mutants identified clinically and in viral passage assays contain a combination of active site mutations (e.g. E166V, E166A, L167F), which reduce inhibitor binding and enzymatic activity, and non-active site mutations (e.g. P252L, T21I, L50F), which restore the fitness of viral replication. Although the mechanism of resistance for the active site mutations is apparent, the role of the non-active site mutations in fitness rescue remains elusive. In this study, we use the model system of a M pro triple mutant (L50F/E166A/L167F) that confers not only nirmatrelvir drug resistance but also a similar fitness of replication compared to the wild-type both in vitro and in vivo. By comparing peptide and full-length M pro protein as substrates, we demonstrate that the binding of M pro substrate involves more than residues in the active site. In particular, L50F and other non-active site mutations can enhance the M pro dimer-dimer interactions and help place the nsp5-6 substrate at the enzyme catalytic center. The structural and enzymatic activity data of M pro L50F, L50F/E166A/L167F, and others underscore the importance of considering the whole substrate protein in studying M pro and substrate interactions, and offers important insights into M pro function, resistance development, and inhibitor design.

Staphylococcus aureus FtsZ and PBP4 bind to the conformationally dynamic N-terminal domain of GpsB.

In the Firmicutes phylum, GpsB is a membrane associated protein that coordinates peptidoglycan synthesis with cell growth and division. Although GpsB has been studied in several bacteria, the structure, function, and interactome of Staphylococcus aureus GpsB is largely uncharacterized. To address this knowledge gap, we solved the crystal structure of the N-terminal domain of S. aureus GpsB, which adopts an atypical, asymmetric dimer, and demonstrates major conformational flexibility that can be mapped to a hinge region formed by a three-residue insertion exclusive to Staphylococci. When this three-residue insertion is excised, its thermal stability increases, and the mutant no longer produces a previously reported lethal phenotype when overexpressed in Bacillus subtilis. In S. aureus, we show that these hinge mutants are less functional and speculate that the conformational flexibility imparted by the hinge region may serve as a dynamic switch to fine-tune the function of the GpsB complex and/or to promote interaction with its various partners. Furthermore, we provide the first biochemical, biophysical, and crystallographic evidence that the N-terminal domain of GpsB binds not only PBP4, but also FtsZ, through a conserved recognition motif located on their C-termini, thus coupling peptidoglycan synthesis to cell division. Taken together, the unique structure of S. aureus GpsB and its direct interaction with FtsZ/PBP4 provide deeper insight into the central role of GpsB in S. aureus cell division.

Overlay databank unlocks data-driven analyses of biomolecules for all.

Tools based on artificial intelligence (AI) are currently revolutionising many fields, yet their applications are often limited by the lack of suitable training data in programmatically accessible format. Here we propose an effective solution to make data scattered in various locations and formats accessible for data-driven and machine learning applications using the overlay databank format. To demonstrate the practical relevance of such approach, we present the NMRlipids Databank-a community-driven, open-for-all database featuring programmatic access to quality-evaluated atom-resolution molecular dynamics simulations of cellular membranes. Cellular membrane lipid composition is implicated in diseases and controls major biological functions, but membranes are difficult to study experimentally due to their intrinsic disorder and complex phase behaviour. While MD simulations have been useful in understanding membrane systems, they require significant computational resources and often suffer from inaccuracies in model parameters. Here, we demonstrate how programmable interface for flexible implementation of data-driven and machine learning applications, and rapid access to simulation data through a graphical user interface, unlock possibilities beyond current MD simulation and experimental studies to understand cellular membranes. The proposed overlay databank concept can be further applied to other biomolecules, as well as in other fields where similar barriers hinder the AI revolution.

Dynamic Nature of Staphylococcus aureus Type I Signal Peptidases.

Molecular dynamics simulations are used to interrogate the dynamic nature of Staphylococcus aureus Type I signal peptidases, SpsA and SpsB, including the impact of the P29S mutation of SpsB. Fluctuations and plasticity- rigidity characteristics vary among the proteins, particularly in the extracellular domain. Intriguingly, the P29S mutation, which influences susceptibility to arylomycin antibiotics, affect the mechanically coupled motions in SpsB. The integrity of the active site is crucial for catalytic competency, and variations in sampled structural conformations among the proteins are consistent with diverse peptidase capabilities. We also explored the intricate interactions between the proteins and the model S. aureus membrane. It was observed that certain membrane-inserted residues in the loop around residue 50 (50s) and C-terminal loops, beyond the transmembrane domain, give rise to direct interactions with lipids in the bilayer membrane. Our findings are discussed in the context of functional knowledge about these signal peptidases, offering additional understanding of dynamic aspects relevant to some cellular processes with potential implications for drug targeting strategies.

Cholesterol and M2 Rendezvous in Budding and Scission of Influenza A Virus.

The cholesterol of the host cell plasma membrane and viral M2 protein plays a crucial role in multiple stages of infection and replication of the influenza A virus. Cholesterol is required for the formation of heterogeneous membrane microdomains (or rafts) in the budozone of the host cell that serves as assembly sites for the viral components. The raft microstructures act as scaffolds for several proteins. Cholesterol may further contribute to the mechanical forces necessary for membrane scission in the last stage of budding and help to maintain the stability of the virus envelope. The M2 protein has been shown to cause membrane scission in model systems by promoting the formation of curved lipid bilayer structures that, in turn, can lead to membrane vesicles budding off or scission intermediates. Membrane remodeling by M2 is intimately linked with cholesterol as it affects local lipid composition, fluidity, and stability of the membrane. Thus, both cholesterol and M2 protein contribute to the efficient and proper release of newly formed influenza viruses from the virus-infected cells.

JOINTLY: interpretable joint clustering of single-cell transcriptomes.

Single-cell and single-nucleus RNA-sequencing (sxRNA-seq) is increasingly being used to characterise the transcriptomic state of cell types at homeostasis, during development and in disease. However, this is a challenging task, as biological effects can be masked by technical variation. Here, we present JOINTLY, an algorithm enabling joint clustering of sxRNA-seq datasets across batches. JOINTLY performs on par or better than state-of-the-art batch integration methods in clustering tasks and outperforms other intrinsically interpretable methods. We demonstrate that JOINTLY is robust against over-correction while retaining subtle cell state differences between biological conditions and highlight how the interpretation of JOINTLY can be used to annotate cell types and identify active signalling programs across cell types and pseudo-time. Finally, we use JOINTLY to construct a reference atlas of white adipose tissue (WATLAS), an expandable and comprehensive community resource, in which we describe four adipocyte subpopulations and map compositional changes in obesity and between depots.

Deciphering specificity and cross-reactivity in tachykinin NK1 and NK2 receptors.

The tachykinin receptors neurokinin 1 (NK1R) and neurokinin 2 (NK2R) are G protein-coupled receptors that bind preferentially to the natural peptide ligands substance P and neurokinin A, respectively, and have been targets for drug development. Despite sharing a common C-terminal sequence of Phe-X-Gly-Leu-Met-NH2 that helps direct biological function, the peptide ligands exhibit some degree of cross-reactivity toward each other's non-natural receptor. Here, we investigate the detailed structure-activity relationships of the ligand-bound receptor complexes that underlie both potent activation by the natural ligand and cross-reactivity. We find that the specificity and cross-reactivity of the peptide ligands can be explained by the interactions between the amino acids preceding the FxGLM consensus motif of the bound peptide ligand and two regions of the receptor: the ß-hairpin of the extracellular loop 2 (ECL2) and a N-terminal segment leading into transmembrane helix 1. Positively charged sidechains of the ECL2 (R177 of NK1R and K180 of NK2R) are seen to play a vital role in the interaction. The N-terminal positions 1 to 3 of the peptide ligand are entirely dispensable. Mutated and chimeric receptor and ligand constructs neatly swap around ligand specificity as expected, validating the structure-activity hypotheses presented. These findings will help in developing improved agonists or antagonists for NK1R and NK2R.

Automatic quality control of single-cell and single-nucleus RNA-seq using valiDrops.

Single-cell and single-nucleus RNA-sequencing (sxRNA-seq) measures gene expression in individual cells or nuclei enabling comprehensive characterization of cell types and states. However, isolation of cells or nuclei for sxRNA-seq releases contaminating RNA, which can distort biological signals, through, for example, cell damage and transcript leakage. Thus, identifying barcodes containing high-quality cells or nuclei is a critical analytical step in the processing of sxRNA-seq data. Here, we present valiDrops, an automated method to identify high-quality barcodes and flag dead cells. In valiDrops, barcodes are initially filtered using data-adaptive thresholding on community-standard quality metrics, and subsequently, valiDrops uses a novel clustering-based approach to identify barcodes with distinct biological signals. We benchmark valiDrops and show that biological signals from cell types and states are more distinct, easier to separate and more consistent after filtering by valiDrops compared to existing tools. Finally, we show that valiDrops can predict and flag dead cells with high accuracy. This novel classifier can further improve data quality or be used to identify dead cells to interrogate the biology of cell death. Thus, valiDrops is an effective and easy-to-use method to improve data quality and biological interpretation. Our method is openly available as an R package at www.github.com/madsen-lab/valiDrops.

Rare variant association analysis in 51,256 type 2 diabetes cases and 370,487 controls informs the spectrum of pathogenicity of monogenic diabetes genes.

We meta-analyzed array data imputed with the TOPMed reference panel and whole-genome sequence (WGS) datasets and performed the largest, rare variant (minor allele frequency as low as 5×10-5) GWAS meta-analysis of type 2 diabetes (T2D) comprising 51,256 cases and 370,487 controls. We identified 52 novel variants at genome-wide significance (p<5 × 10-8), including 8 novel variants that were either rare or ancestry-specific. Among them, we identified a rare missense variant in HNF4A p.Arg114Trp (OR=8.2, 95% confidence interval [CI]=4.6-14.0, p = 1.08×10-13), previously reported as a variant implicated in Maturity Onset Diabetes of the Young (MODY) with incomplete penetrance. We demonstrated that the diabetes risk in carriers of this variant was modulated by a T2D common variant polygenic risk score (cvPRS) (carriers in the top PRS tertile [OR=18.3, 95%CI=7.2-46.9, p=1.2×10-9] vs carriers in the bottom PRS tertile [OR=2.6, 95% CI=0.97-7.09, p = 0.06]. Association results identified eight variants of intermediate penetrance (OR>5) in monogenic diabetes (MD), which in aggregate as a rare variant PRS were associated with T2D in an independent WGS dataset (OR=4.7, 95% CI=1.86-11.77], p = 0.001). Our data also provided support evidence for 21% of the variants reported in ClinVar in these MD genes as benign based on lack of association with T2D. Our work provides a framework for using rare variant imputation and WGS analyses in large-scale population-based association studies to identify large-effect rare variants and provide evidence for informing variant pathogenicity.

A 19F-qNMR-Guided Mathematical Model for G Protein-Coupled Receptor Signaling.

G protein-coupled receptors (GPCRs) exhibit a wide range of pharmacological efficacies, yet the molecular mechanisms responsible for the differential efficacies in response to various ligands remain poorly understood. This lack of understanding has hindered the development of a solid foundation for establishing a mathematical model for signaling efficacy. However, recent progress has been made in delineating and quantifying receptor conformational states and associating function with these conformations. This progress has allowed us to construct a mathematical model for GPCR signaling efficacy that goes beyond the traditional ON/OFF binary switch model. In this study, we present a quantitative conformation-based mathematical model for GPCR signaling efficacy using the adenosine A2A receptor (A2AR) as a model system, under the guide of 19F quantitative nuclear magnetic resonance experiments. This model encompasses two signaling states, a fully activated state and a partially activated state, defined as being able to regulate the cognate Gα s nucleotide exchange with respective G protein recognition capacity. By quantifying the population distribution of each state, we can now in turn examine GPCR signaling efficacy. This advance provides a foundation for assessing GPCR signaling efficacy using a conformation-based mathematical model in response to ligand binding. SIGNIFICANCE STATEMENT: Mathematical models to describe signaling efficacy of GPCRs mostly suffer from considering only two states (ON/OFF). However, research indicates that a GPCR possesses multiple active-(like) states that can interact with Gαßγ independently, regulating varied nucleotide exchanges. With the guide of 19F-qNMR, the transitions among these states are quantified as a function of ligand and Gαßγ, serving as a foundation for a novel conformation-based mathematical signaling model.

Quantitative Comparison against Experiments Reveals Imperfections in Force Fields' Descriptions of POPC-Cholesterol Interactions.

Cholesterol is a central building block in biomembranes, where it induces orientational order, slows diffusion, renders the membrane stiffer, and drives domain formation. Molecular dynamics (MD) simulations have played a crucial role in resolving these effects at the molecular level; yet, it has recently become evident that different MD force fields predict quantitatively different behavior. Although easily neglected, identifying such limitations is increasingly important as the field rapidly progresses toward simulations of complex membranes mimicking the in vivo conditions: pertinent multicomponent simulations must capture accurately the interactions between their fundamental building blocks, such as phospholipids and cholesterol. Here, we define quantitative quality measures for simulations of binary lipid mixtures in membranes against the C-H bond order parameters and lateral diffusion coefficients from NMR spectroscopy as well as the form factors from X-ray scattering. Based on these measures, we perform a systematic evaluation of the ability of commonly used force fields to describe the structure and dynamics of binary mixtures of palmitoyloleoylphosphatidylcholine (POPC) and cholesterol. None of the tested force fields clearly outperforms the others across the tested properties and conditions. Still, the Slipids parameters provide the best overall performance in our tests, especially when dynamic properties are included in the evaluation. The quality evaluation metrics introduced in this work will, particularly, foster future force field development and refinement for multicomponent membranes using automated approaches.

The metabolome of pink-footed goose: Heavy metals and lipid metabolism.

Wildlife is exposed to mixtures of environmental contaminants that affect health and population dynamics. Exposure to toxic heavy metals originating from anthropogenic sources may exert metabolic effects at even low exposure concentrations. Here we investigated the relationships between heavy metal exposure and metabolic changes in the migratory bird pink-footed goose (Anser brachyrhynchus). We used blood pellet and blood plasma samples from 27 free-ranging pink-footed geese to study heavy metal (Cd, Cr, Hg, and Pb) exposure in relation to the metabolome. The results relate blood concentrations of Cd (range: 0.218-1.09 ng/g), Cr (range: 0.299-5.60 ng/g), and Hg (range: 2.63-6.00 ng/g) to signal areas of fatty acids and other lipids, while no correlations were identified for Pb level (range: 21.0-64.2 ng/g) exposure. Lipid signal areas were negatively associated with concentrations of Cr and positively associated with Hg exposure (both p < 0.05). α-Linolenic acid and 9-oxononanoic acid were negatively correlated to Cr exposure (both p < 0.05) and were related in the α-linolenic acid metabolism pathway. Compared to known thresholds for aviary species, the heavy metal concentrations are below levels of toxicity, which may explain the low number of metabolites that significantly change. Nevertheless, the heavy metal exposure is still correlated to changes in the lipid metabolism that may reduce migrating birds' breeding success and increase mortality for an exposed part of the population.

Rapid formation of new migration route and breeding area by Arctic geese.

Many Arctic-breeding animals are at risk from local extirpation associated with habitat constriction and alterations in phenology in their Arctic environment as a result of rapid global warming.1 Migratory species face additional increasing anthropogenic pressures along their migratory routes such as habitat destruction, droughts, creation of barriers, and overexploitation.2,3 Such species can only persist if they adjust their migration, timing of breeding, and range.4 Here, we document both the abrupt (∼10 years) formation of a new migration route and a disjunct breeding population of the pink-footed goose (Anser brachyrhynchus) on Novaya Zemlya, Russia, almost 1,000 km away from the original breeding grounds in Svalbard. The population has grown to 3,000-4,000 birds, explained by intrinsic growth and continued immigration from the original route. The colonization was enabled by recent warming on Novaya Zemlya. We propose that social behavior of geese, resulting in cultural transmission of migration behavior among conspecifics as well as in mixed-species flocks, is key to this fast development and acts as a mechanism enabling ecological rescue in a rapidly changing world.

Highly differentiated loci resolve phylogenetic relationships in the Bean Goose complex.

BACKGROUND: Reconstructing phylogenetic relationships with genomic data remains a challenging endeavor. Numerous phylogenomic studies have reported incongruent gene trees when analyzing different genomic regions, complicating the search for a 'true' species tree. Some authors have argued that genomic regions of increased divergence (i.e. differentiation islands) reflect the species tree, although other studies have shown that these regions might produce misleading topologies due to species-specific selective sweeps or ancient introgression events. In this study, we tested the extent to which highly differentiated loci can resolve phylogenetic relationships in the Bean Goose complex, a group of goose taxa that includes the Taiga Bean Goose (Anser fabalis), the Tundra Bean Goose (Anser serrirostris) and the Pink-footed Goose (Anser brachyrhynchus). RESULTS: First, we show that a random selection of genomic loci-which mainly samples the undifferentiated regions of the genome-results in an unresolved species complex with a monophyletic A. brachyrhynchus embedded within a paraphyletic cluster of A. fabalis and A. serrirostris. Next, phylogenetic analyses of differentiation islands converged upon a topology of three monophyletic clades in which A. brachyrhynchus is sister to A. fabalis, and A. serrirostris is sister to the clade uniting these two species. Close inspection of the locus trees within the differentiated regions revealed that this topology was consistently supported over other phylogenetic arrangements. As it seems unlikely that selection or introgression events have impacted all differentiation islands in the same way, we are convinced that this topology reflects the 'true' species tree. Additional analyses, based on D-statistics, revealed extensive introgression between A. fabalis and A. serrirostris, which partly explains the failure to resolve the species complex with a random selection of genomic loci. Recent introgression between these taxa has probably erased the phylogenetic branching pattern across a large section of the genome, whereas differentiation islands were unaffected by the homogenizing gene flow and maintained the phylogenetic patterns that reflect the species tree. CONCLUSIONS: The evolution of the Bean Goose complex can be depicted as a simple bifurcating tree, but this would ignore the impact of introgressive hybridization. Hence, we advocate that the evolutionary relationships between these taxa are best represented as a phylogenetic network.

19F NMR: A promising tool for dynamic conformational studies of G protein-coupled receptors.

Advances in X-ray crystallography and cryoelectron microscopy enabled unprecedented insights into the activation processes of G protein-coupled receptors (GPCRs). However, these static receptor structures provide limited information about dynamics and conformational transitions that play pivotal roles in mediating signaling diversity through the multifaceted interactions between ligands, receptors, and transducers. Developing NMR approaches to probe the dynamics of conformational transitions will push the frontier of receptor science toward a more comprehensive understanding of these signaling processes. Although much progress has been made during the last decades, it remains challenging to delineate receptor conformational states and interrogate the functions of the individual states at a quantitative level. Here we cover the progress of 19F NMR applications in GPCR conformational and dynamic studies during the past 20 years. Current challenges and limitations of 19F NMR for studying GPCR dynamics are also discussed, along with experimental strategies that will drive this field forward.