Cannabinoid receptor 1 antagonist genistein attenuates marijuana-induced vascular inflammation.

Epidemiological studies reveal that marijuana increases the risk of cardiovascular disease (CVD); however, little is known about the mechanism. Δ9-tetrahydrocannabinol (Δ9-THC), the psychoactive component of marijuana, binds to cannabinoid receptor 1 (CB1/CNR1) in the vasculature and is implicated in CVD. A UK Biobank analysis found that cannabis was an risk factor for CVD. We found that marijuana smoking activated inflammatory cytokines implicated in CVD. In silico virtual screening identified genistein, a soybean isoflavone, as a putative CB1 antagonist. Human-induced pluripotent stem cell-derived endothelial cells were used to model Δ9-THC-induced inflammation and oxidative stress via NF-κB signaling. Knockdown of the CB1 receptor with siRNA, CRISPR interference, and genistein attenuated the effects of Δ9-THC. In mice, genistein blocked Δ9-THC-induced endothelial dysfunction in wire myograph, reduced atherosclerotic plaque, and had minimal penetration of the central nervous system. Genistein is a CB1 antagonist that attenuates Δ9-THC-induced atherosclerosis.

How α-Helical Motifs Form Functionally Diverse Lipid-Binding Compartments.

Lipids are produced site-specifically in cells and then distributed nonrandomly among membranes via vesicular and nonvesicular trafficking mechanisms. The latter involves soluble amphitropic proteins extracting specific lipids from source membranes to function as molecular solubilizers that envelope their insoluble cargo before transporting it to destination sites. Lipid-binding and lipid transfer structural motifs range from multi-ß-strand barrels, to ß-sheet cups and baskets covered by α-helical lids, to multi-α-helical bundles and layers. Here, we focus on how α-helical proteins use amphipathic helical layering and bundling to form modular lipid-binding compartments and discuss the functional consequences. Preformed compartments generally rely on intramolecular disulfide bridging to maintain conformation (e.g., albumins, nonspecific lipid transfer proteins, saposins, nematode polyprotein allergens/antigens). Insights into nonpreformed hydrophobic compartments that expand and adapt to accommodate a lipid occupant are few and provided mostly by the three-layer, α-helical ligand-binding domain of nuclear receptors. The simple but elegant and nearly ubiquitous two-layer, α-helical glycolipid transfer protein (GLTP)-fold now further advances understanding.

Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes.

CD4+ T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on class II major histocompatibility complex (MHC-II) molecules. The high polymorphism of MHC-II genes represents an important hurdle toward accurate prediction and identification of CD4+ T cell epitopes. Here we collected and curated a dataset of 627,013 unique MHC-II ligands identified by mass spectrometry. This enabled us to precisely determine the binding motifs of 88 MHC-II alleles across humans, mice, cattle, and chickens. Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC-II motifs and revealed a widespread reverse-binding mode in HLA-DP ligands. We then developed a machine-learning framework to accurately predict binding specificities and ligands of any MHC-II allele. This tool improves and expands predictions of CD4+ T cell epitopes and enables us to discover viral and bacterial epitopes following the aforementioned reverse-binding mode.

Molecular dynamics-based identification of binding pathways and two distinct high-affinity sites for succinate in succinate receptor 1/GPR91.

SUCNR1 is an auto- and paracrine sensor of the metabolic stress signal succinate. Using unsupervised molecular dynamics (MD) simulations (170.400 ns) and mutagenesis across human, mouse, and rat SUCNR1, we characterize how a five-arginine motif around the extracellular pole of TM-VI determines the initial capture of succinate in the extracellular vestibule (ECV) to either stay or move down to the orthosteric site. Metadynamics demonstrate low-energy succinate binding in both sites, with an energy barrier corresponding to an intermediate stage during which succinate, with an associated water cluster, unlocks the hydrogen-bond-stabilized conformationally constrained extracellular loop (ECL)-2b. Importantly, simultaneous binding of two succinate molecules through either a "sequential" or "bypassing" mode is a frequent endpoint. The mono-carboxylate NF-56-EJ40 antagonist enters SUCNR1 between TM-I and -II and does not unlock ECL-2b. It is proposed that occupancy of both high-affinity sites is required for selective activation of SUCNR1 by high local succinate concentrations.

Irisin acts through its integrin receptor in a two-step process involving extracellular Hsp90α.

Exercise benefits the human body in many ways. Irisin is secreted by muscle, increased with exercise, and conveys physiological benefits, including improved cognition and resistance to neurodegeneration. Irisin acts via αV integrins; however, a mechanistic understanding of how small polypeptides like irisin can signal through integrins is poorly understood. Using mass spectrometry and cryo-EM, we demonstrate that the extracellular heat shock protein 90α (eHsp90α) is secreted by muscle with exercise and activates integrin αVß5. This allows for high-affinity irisin binding and signaling through an Hsp90α/αV/ß5 complex. By including hydrogen/deuterium exchange data, we generate and experimentally validate a 2.98 Å RMSD irisin/αVß5 complex docking model. Irisin binds very tightly to an alternative interface on αVß5 distinct from that used by known ligands. These data elucidate a non-canonical mechanism by which a small polypeptide hormone like irisin can function through an integrin receptor.

Ligand-induced protein transition state stabilization switches the binding pathway from conformational selection to induced fit.

Protein-ligand complex formation is fundamental to biological function. A central question is whether proteins spontaneously adopt binding-competent conformations to which ligands bind conformational selection (CS) or whether ligands induce the binding-competent conformation induced fit (IF). Here, we resolve the CS and IF binding pathways by characterizing protein conformational dynamics over a wide range of ligand concentrations using NMR relaxation dispersion. We determined the relative flux through the two pathways using a four-state binding model that includes both CS and IF. Experiments conducted without ligand show that galectin-3 exchanges between the ground-state conformation and a high-energy conformation similar to the ligand-bound conformation, demonstrating that CS is a plausible pathway. Near-identical crystal structures of the apo and ligand-bound states suggest that the high-energy conformation in solution corresponds to the apo crystal structure. Stepwise additions of the ligand lactose induce progressive changes in the relaxation dispersions that we fit collectively to the four-state model, yielding all microscopic rate constants and binding affinities. The ligand affinity is higher for the bound-like conformation than for the ground state, as expected for CS. Nonetheless, the IF pathway contributes greater than 70% of the total flux even at low ligand concentrations. The higher flux through the IF pathway is explained by considerably higher rates of exchange between the two protein conformations in the ligand-associated state. Thus, the ligand acts to decrease the activation barrier between protein conformations in a manner reciprocal to enzymatic transition-state stabilization of reactions involving ligand transformation.

Cryo-EM structures elucidate the multiligand receptor nature of megalin.

Megalin (low-density lipoprotein receptor-related protein 2) is a giant glycoprotein of about 600 kDa, mediating the endocytosis of more than 60 ligands, including those of proteins, peptides, and drug compounds [S. Goto, M. Hosojima, H. Kabasawa, A. Saito, Int. J. Biochem. Cell Biol. 157, 106393 (2023)]. It is expressed predominantly in renal proximal tubule epithelial cells, as well as in the brain, lungs, eyes, inner ear, thyroid gland, and placenta. Megalin is also known to mediate the endocytosis of toxic compounds, particularly those that cause renal and hearing disorders [Y. Hori et al., J. Am. Soc. Nephrol. 28, 1783-1791 (2017)]. Genetic megalin deficiency causes Donnai-Barrow syndrome/facio-oculo-acoustico-renal syndrome in humans. However, it is not known how megalin interacts with such a wide variety of ligands and plays pathological roles in various organs. In this study, we elucidated the dimeric architecture of megalin, purified from rat kidneys, using cryoelectron microscopy. The maps revealed the densities of endogenous ligands bound to various regions throughout the dimer, elucidating the multiligand receptor nature of megalin. We also determined the structure of megalin in complex with receptor-associated protein, a molecular chaperone for megalin. The results will facilitate further studies on the pathophysiology of megalin-dependent multiligand endocytic pathways in multiple organs and will also be useful for the development of megalin-targeted drugs for renal and hearing disorders, Alzheimer's disease [B. V. Zlokovic et al., Proc. Natl. Acad. Sci. U.S.A. 93, 4229-4234 (1996)], and other illnesses.

Prediction of protein-ligand binding affinity via deep learning models.

Accurately predicting the binding affinity between proteins and ligands is crucial in drug screening and optimization, but it is still a challenge in computer-aided drug design. The recent success of AlphaFold2 in predicting protein structures has brought new hope for deep learning (DL) models to accurately predict protein-ligand binding affinity. However, the current DL models still face limitations due to the low-quality database, inaccurate input representation and inappropriate model architecture. In this work, we review the computational methods, specifically DL-based models, used to predict protein-ligand binding affinity. We start with a brief introduction to protein-ligand binding affinity and the traditional computational methods used to calculate them. We then introduce the basic principles of DL models for predicting protein-ligand binding affinity. Next, we review the commonly used databases, input representations and DL models in this field. Finally, we discuss the potential challenges and future work in accurately predicting protein-ligand binding affinity via DL models.

Structures of the Human PGD2 Receptor CRTH2 Reveal Novel Mechanisms for Ligand Recognition.

The signaling of prostaglandin D2 (PGD2) through G-protein-coupled receptor (GPCR) CRTH2 is a major pathway in type 2 inflammation. Compelling evidence suggests the therapeutic benefits of blocking CRTH2 signaling in many inflammatory disorders. Currently, a number of CRTH2 antagonists are under clinical investigation, and one compound, fevipiprant, has advanced to phase 3 clinical trials for asthma. Here, we present the crystal structures of human CRTH2 with two antagonists, fevipiprant and CAY10471. The structures, together with docking and ligand-binding data, reveal a semi-occluded pocket covered by a well-structured amino terminus and different binding modes of chemically diverse CRTH2 antagonists. Structural analysis suggests a ligand entry port and a binding process that is facilitated by opposite charge attraction for PGD2, which differs significantly from the binding pose and binding environment of lysophospholipids and endocannabinoids, revealing a new mechanism for lipid recognition by GPCRs.

Basis for high-affinity ethylene binding by the ethylene receptor ETR1 of Arabidopsis.

The gaseous hormone ethylene is perceived in plants by membrane-bound receptors, the best studied of these being ETR1 from Arabidopsis. Ethylene receptors can mediate a response to ethylene concentrations at less than one part per billion; however, the mechanistic basis for such high-affinity ligand binding has remained elusive. Here we identify an Asp residue within the ETR1 transmembrane domain that plays a critical role in ethylene binding. Site-directed mutation of the Asp to Asn results in a functional receptor that has a reduced affinity for ethylene, but still mediates ethylene responses in planta. The Asp residue is highly conserved among ethylene receptor-like proteins in plants and bacteria, but Asn variants exist, pointing to the physiological relevance of modulating ethylene-binding kinetics. Our results also support a bifunctional role for the Asp residue in forming a polar bridge to a conserved Lys residue in the receptor to mediate changes in signaling output. We propose a new structural model for the mechanism of ethylene binding and signal transduction, one with similarities to that found in a mammalian olfactory receptor.

Measures of epitope binding degeneracy from T cell receptor repertoires.

Adaptive immunity is driven by specific binding of hypervariable receptors to diverse molecular targets. The sequence diversity of receptors and targets are both individually known but because multiple receptors can recognize the same target, a measure of the effective "functional" diversity of the human immune system has remained elusive. Here, we show that sequence near-coincidences within T cell receptors that bind specific epitopes provide a new window into this problem and allow the quantification of how binding probability covaries with sequence. We find that near-coincidence statistics within epitope-specific repertoires imply a measure of binding degeneracy to amino acid changes in receptor sequence that is consistent across disparate experiments. Paired data on both chains of the heterodimeric receptor are particularly revealing since simultaneous near-coincidences are rare and we show how they can be exploited to estimate the number of epitope responses that created the memory compartment. In addition, we find that paired-chain coincidences are strongly suppressed across donors with different human leukocyte antigens, evidence for a central role of antigen-driven selection in making paired chain receptors public. These results demonstrate the power of coincidence analysis to reveal the sequence determinants of epitope binding in receptor repertoires.

A nonequilibrium allosteric model for receptor-kinase complexes: The role of energy dissipation in chemotaxis signaling.

The Escherichia coli chemotaxis signaling pathway has served as a model system for the adaptive sensing of environmental signals by large protein complexes. The chemoreceptors control the kinase activity of CheA in response to the extracellular ligand concentration and adapt across a wide concentration range by undergoing methylation and demethylation. Methylation shifts the kinase response curve by orders of magnitude in ligand concentration while incurring a much smaller change in the ligand binding curve. Here, we show that the disproportionate shift in binding and kinase response is inconsistent with equilibrium allosteric models. To resolve this inconsistency, we present a nonequilibrium allosteric model that explicitly includes the dissipative reaction cycles driven by adenosine triphosphate (ATP) hydrolysis. The model successfully explains all existing joint measurements of ligand binding, receptor conformation, and kinase activity for both aspartate and serine receptors. Our results suggest that the receptor complex acts as an enzyme: Receptor methylation modulates the ON-state kinetics of the kinase (e.g., phosphorylation rate), while ligand binding controls the equilibrium balance between kinase ON/OFF states. Furthermore, sufficient energy dissipation is responsible for maintaining and enhancing the sensitivity range and amplitude of the kinase response. We demonstrate that the nonequilibrium allosteric model is broadly applicable to other sensor-kinase systems by successfully fitting previously unexplained data from the DosP bacterial oxygen-sensing system. Overall, this work provides a nonequilibrium physics perspective on cooperative sensing by large protein complexes and opens up research directions for understanding their microscopic mechanisms through simultaneous measurements and modeling of ligand binding and downstream responses.

Exploiting conformational dynamics to modulate the function of designed proteins.

With the recent success in calculating protein structures from amino acid sequences using artificial intelligence-based algorithms, an important next step is to decipher how dynamics is encoded by the primary protein sequence so as to better predict function. Such dynamics information is critical for protein design, where strategies could then focus not only on sequences that fold into particular structures that perform a given task, but would also include low-lying excited protein states that could influence the function of the designed protein. Herein, we illustrate the importance of dynamics in modulating the function of C34, a designed α/ß protein that captures ß-strands of target ligands and is a member of a family of proteins designed to sequester ß-strands and ß hairpins of aggregation-prone molecules that lead to a variety of pathologies. Using a strategy to "see" regions of apo C34 that are invisible to NMR spectroscopy as a result of pervasive conformational exchange, as well as a mutagenesis approach whereby C34 molecules are stabilized into a single conformer, we determine the structures of the predominant conformations that are sampled by C34 and show that these attenuate the affinity for cognate peptide. Subsequently, the observed motion is exploited to develop an allosterically regulated peptide binder whose binding affinity can be controlled through the addition of a second molecule. Our study emphasizes the unique role that NMR can play in directing the design process and in the construction of new molecules with more complex functionality.

A rapid, tag-free way to purify functional GPCRs.

G protein-coupled receptors (GPCRs) play diverse signaling roles and represent major pharmaceutical targets. Consequently, they are the focus of intense study, and numerous advances have been made in their handling and analysis. However, a universal way to purify GPCRs has remained elusive, in part because of their inherent instability when isolated from cells. To address this, we have developed a general, rapid, and tag-free way to purify GPCRs. The method uses short peptide analogs of the Gα subunit C terminus (Gα-CT) that are attached to chromatography beads (Gα-CT resin). Because the Gα-CT peptides bind active GPCRs with high affinity, the Gα-CT resin selectively purifies only active functional receptors. We use this method to purify both rhodopsin and the ß2-adrenergic receptor and show they can be purified in either active conformations or inactive conformations, simply by varying elution conditions. While simple in concept-leveraging the conserved GPCR-Gα-CT binding interaction for the purpose of GPCR purification-we think this approach holds excellent potential to isolate functional receptors for a myriad of uses, from structural biology to proteomics.

Identification of small-molecule ligand-binding sites on and in the ARNT PAS-B domain.

Transcription factors are challenging to target with small-molecule inhibitors due to their structural plasticity and lack of catalytic sites. Notable exceptions include naturally ligand-regulated transcription factors, including our prior work with the hypoxia-inducible factor (HIF)-2 transcription factor, showing that small-molecule binding within an internal pocket of the HIF-2α Per-Aryl hydrocarbon Receptor Nuclear Translocator (ARNT)-Sim (PAS)-B domain can disrupt its interactions with its dimerization partner, ARNT. Here, we explore the feasibility of targeting small molecules to the analogous ARNT PAS-B domain itself, potentially opening a promising route to modulate several ARNT-mediated signaling pathways. Using solution NMR fragment screening, we previously identified several compounds that bind ARNT PAS-B and, in certain cases, antagonize ARNT association with the transforming acidic coiled-coil containing protein 3 transcriptional coactivator. However, these ligands have only modest binding affinities, complicating characterization of their binding sites. We address this challenge by combining NMR, molecular dynamics simulations, and ensemble docking to identify ligand-binding "hotspots" on and within the ARNT PAS-B domain. Our data indicate that the two ARNT/transforming acidic coiled-coil containing protein 3 inhibitors, KG-548 and KG-655, bind to a ß-sheet surface implicated in both HIF-2 dimerization and coactivator recruitment. Furthermore, while KG-548 binds exclusively to the ß-sheet surface, KG-655 can additionally bind within a water-accessible internal cavity in ARNT PAS-B. Finally, KG-279, while not a coactivator inhibitor, exemplifies ligands that preferentially bind only to the internal cavity. All three ligands promoted ARNT PAS-B homodimerization, albeit to varying degrees. Taken together, our findings provide a comprehensive overview of ARNT PAS-B ligand-binding sites and may guide the development of more potent coactivator inhibitors for cellular and functional studies.

Structural and functional characterization of the IpaD π-helix reveals critical roles in DOC interaction, T3SS apparatus maturation, and Shigella virulence.

Shigella spp. are highly pathogenic members of the Enterobacteriaceae family, causing ∼269 million cases of bacillary dysentery and >200,000 deaths each year. Like many Gram-negative pathogens, Shigella rely on their type three secretion system (T3SS) to inject effector proteins into eukaryotic host cells, driving both cellular invasion and evasion of host immune responses. Exposure to the bile salt deoxycholate (DOC) significantly enhances Shigella virulence and is proposed to serve as a critical environmental signal present in the small intestine that prepares Shigella's T3SS for efficient infection of the colonic epithelium. Here, we uncover critical mechanistic details of the Shigella-specific DOC signaling process by describing the role of a π-helix secondary structure element within the T3SS tip protein invasion plasmid antigen D (IpaD). Biophysical characterization and high-resolution structures of IpaD mutants lacking the π-helix show that it is not required for global protein structure, but that it defines the native DOC binding site and prevents off target interactions. Additionally, Shigella strains expressing the π-helix deletion mutants illustrate the pathogenic importance of its role in guiding DOC interaction as flow cytometry and gentamycin protection assays show that the IpaD π-helix is essential for DOC-mediated apparatus maturation and enhanced invasion of eukaryotic cells. Together, these findings add to our understanding of the complex Shigella pathogenesis pathway and its evolution to respond to environmental bile salts by identifying the π-helix in IpaD as a critical structural element required for translating DOC exposure to virulence enhancement.

Diverse Molecular Mechanisms Underlying Pathogenic Protein Mutations: Beyond the Loss-of-Function Paradigm.

Most known disease-causing mutations occur in protein-coding regions of DNA. While some of these involve a loss of protein function (e.g., through premature stop codons or missense changes that destabilize protein folding), many act via alternative molecular mechanisms and have dominant-negative or gain-of-function effects. In nearly all cases, these non-loss-of-function mutations can be understood by considering interactions of the wild-type and mutant protein with other molecules, such as proteins, nucleic acids, or small ligands and substrates. Here, we review the diverse molecular mechanisms by which pathogenic mutations can have non-loss-of-function effects, including by disrupting interactions, increasing binding affinity, changing binding specificity, causing assembly-mediated dominant-negative and dominant-positive effects, creating novel interactions, and promoting aggregation and phase separation. We believe that increased awareness of these diverse molecular disease mechanisms will lead to improved diagnosis (and ultimately treatment) of human genetic disorders.

Multi-task bioassay pre-training for protein-ligand binding affinity prediction.

Protein-ligand binding affinity (PLBA) prediction is the fundamental task in drug discovery. Recently, various deep learning-based models predict binding affinity by incorporating the three-dimensional (3D) structure of protein-ligand complexes as input and achieving astounding progress. However, due to the scarcity of high-quality training data, the generalization ability of current models is still limited. Although there is a vast amount of affinity data available in large-scale databases such as ChEMBL, issues such as inconsistent affinity measurement labels (i.e. IC50, Ki, Kd), different experimental conditions, and the lack of available 3D binding structures complicate the development of high-precision affinity prediction models using these data. To address these issues, we (i) propose Multi-task Bioassay Pre-training (MBP), a pre-training framework for structure-based PLBA prediction; (ii) construct a pre-training dataset called ChEMBL-Dock with more than 300k experimentally measured affinity labels and about 2.8M docked 3D structures. By introducing multi-task pre-training to treat the prediction of different affinity labels as different tasks and classifying relative rankings between samples from the same bioassay, MBP learns robust and transferrable structural knowledge from our new ChEMBL-Dock dataset with varied and noisy labels. Experiments substantiate the capability of MBP on the structure-based PLBA prediction task. To the best of our knowledge, MBP is the first affinity pre-training model and shows great potential for future development. MBP web-server is now available for free at: https://huggingface.co/spaces/jiaxianustc/mbp.

Unique peptidic agonists of a juvenile hormone receptor with species-specific effects on insect development and reproduction.

Juvenile hormones (JHs) control insect metamorphosis and reproduction. JHs act through a receptor complex consisting of methoprene-tolerant (Met) and taiman (Tai) proteins to induce transcription of specific genes. Among chemically diverse synthetic JH mimics (juvenoids), some of which serve as insecticides, unique peptidic juvenoids stand out as being highly potent yet exquisitely selective to a specific family of true bugs. Their mode of action is unknown. Here we demonstrate that, like established JH receptor agonists, peptidic juvenoids act upon the JHR Met to halt metamorphosis in larvae of the linden bug, Pyrrhocoris apterus. Peptidic juvenoids induced ligand-dependent dimerization between Met and Tai proteins from P. apterus but, consistent with their selectivity, not from other insects. A cell-based split-luciferase system revealed that the Met-Tai complex assembled within minutes of agonist presence. To explore the potential of juvenoid peptides, we synthesized 120 new derivatives and tested them in Met-Tai interaction assays. While many substituents led to loss of activity, improved derivatives active at sub-nanomolar range outperformed hitherto existing peptidic and classical juvenoids including fenoxycarb. Their potency in inducing Met-Tai interaction corresponded with the capacity to block metamorphosis in P. apterus larvae and to stimulate oogenesis in reproductively arrested adult females. Molecular modeling demonstrated that the high potency correlates with high affinity. This is a result of malleability of the ligand-binding pocket of P. apterus Met that allows larger peptidic ligands to maximize their contact surface. Our data establish peptidic juvenoids as highly potent and species-selective novel JHR agonists.

Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae.

Heme is an oxygen carrier and a cofactor of both industrial enzymes and food additives. The intracellular level of free heme is low, which limits the synthesis of heme proteins. Therefore, increasing heme synthesis allows an increased production of heme proteins. Using the genome-scale metabolic model (GEM) Yeast8 for the yeast Saccharomyces cerevisiae, we identified fluxes potentially important to heme synthesis. With this model, in silico simulations highlighted 84 gene targets for balancing biomass and increasing heme production. Of those identified, 76 genes were individually deleted or overexpressed in experiments. Empirically, 40 genes individually increased heme production (up to threefold). Heme was increased by modifying target genes, which not only included the genes involved in heme biosynthesis, but also those involved in glycolysis, pyruvate, Fe-S clusters, glycine, and succinyl-coenzyme A (CoA) metabolism. Next, we developed an algorithmic method for predicting an optimal combination of these genes by using the enzyme-constrained extension of the Yeast8 model, ecYeast8. The computationally identified combination for enhanced heme production was evaluated using the heme ligand-binding biosensor (Heme-LBB). The positive targets were combined using CRISPR-Cas9 in the yeast strain (IMX581-HEM15-HEM14-HEM3-Δshm1-HEM2-Δhmx1-FET4-Δgcv2-HEM1-Δgcv1-HEM13), which produces 70-fold-higher levels of intracellular heme.