Structural insights into ligand recognition, selectivity, and activation of bombesin receptor subtype-3.

Bombesin receptor subtype-3 (BRS3) is an important orphan G protein-coupled receptor that regulates energy homeostasis and insulin secretion. As a member of the bombesin receptor (BnR) family, the lack of known endogenous ligands and high-resolution structure has hindered the understanding of BRS3 signaling and function. We present two cryogenic electron microscopy (cryo-EM) structures of BRS3 in complex with the heterotrimeric Gq protein in its active states: one bound to the pan-BnR agonist BA1 and the other bound to the synthetic BRS3-specific agonist MK-5046. These structures reveal the architecture of the orthosteric ligand pocket underpinning molecular recognition and provide insights into the structural basis for BRS3's selectivity and low affinity for bombesin peptides. Examination of conserved micro-switches suggests a shared activation mechanism among BnRs. Our findings shed light on BRS3's ligand selectivity and signaling mechanisms, paving the way for exploring its therapeutic potential for diabetes, obesity, and related metabolic disorders.

Structural basis for recognition of 26RFa by the pyroglutamylated RFamide peptide receptor.

The neuropeptide 26RFa, a member of the RF-amide peptide family, activates the pyroglutamylated RF-amide peptide receptor (QRFPR), a class A GPCR. The 26RFa/QRFPR system plays critical roles in energy homeostasis, making QRFPR an attractive drug target for treating obesity, diabetes, and eating disorders. However, the lack of structural information has hindered our understanding of the peptide recognition and regulatory mechanism of QRFPR, impeding drug design efforts. In this study, we determined the cryo-EM structure of the Gq-coupled QRFPR bound to 26RFa. The structure reveals a unique assembly mode of the extracellular region of the receptor and the N-terminus of the peptide, and elucidates the recognition mechanism of the C-terminal heptapeptide of 26RFa by the transmembrane binding pocket of QRFPR. The study also clarifies the similarities and distinctions in the binding pattern of the RF-amide moiety in five RF-amide peptides and the RY-amide segment in neuropeptide Y. These findings deepen our understanding of the RF-amide peptide recognition, aiding in the rational design of drugs targeting QRFPR and other RF-amide peptide receptors.

Highly accurate carbohydrate-binding site prediction with DeepGlycanSite.

As the most abundant organic substances in nature, carbohydrates are essential for life. Understanding how carbohydrates regulate proteins in the physiological and pathological processes presents opportunities to address crucial biological problems and develop new therapeutics. However, the diversity and complexity of carbohydrates pose a challenge in experimentally identifying the sites where carbohydrates bind to and act on proteins. Here, we introduce a deep learning model, DeepGlycanSite, capable of accurately predicting carbohydrate-binding sites on a given protein structure. Incorporating geometric and evolutionary features of proteins into a deep equivariant graph neural network with the transformer architecture, DeepGlycanSite remarkably outperforms previous state-of-the-art methods and effectively predicts binding sites for diverse carbohydrates. Integrating with a mutagenesis study, DeepGlycanSite reveals the guanosine-5'-diphosphate-sugar-recognition site of an important G-protein coupled receptor. These findings demonstrate DeepGlycanSite is invaluable for carbohydrate-binding site prediction and could provide insights into molecular mechanisms underlying carbohydrate-regulation of therapeutically important proteins.

Structural basis for hormone recognition and distinctive Gq protein coupling by the kisspeptin receptor.

Kisspeptin signaling through its G protein-coupled receptor, KISS1R, plays an indispensable role in regulating reproduction via the hypothalamic-pituitary-gonadal axis. Dysregulation of this pathway underlies severe disorders like infertility and precocious puberty. Here, we present cryo-EM structures of KISS1R bound to the endogenous agonist kisspeptin-10 and a synthetic analog TAK-448. These structures reveal pivotal interactions between peptide ligands and KISS1R extracellular loops for receptor activation. Both peptides exhibit a conserved binding mode, unveiling their common activation mechanism. Intriguingly, KISS1R displays a distinct 40° angular deviation in its intracellular TM6 region compared to other Gq-coupled receptors, enabling distinct interactions with Gq. This study reveals the molecular intricacies governing ligand binding and activation of KISS1R, while highlighting its exceptional ability to couple with Gq. Our findings pave the way for structure-guided design of therapeutics targeting this physiologically indispensable receptor.

Molecular basis for the activation of PAF receptor by PAF.

Platelet-activating factor (PAF) is a potent phospholipid mediator crucial in multiple inflammatory and immune responses through binding and activating the PAF receptor (PAFR). However, drug development targeting the PAFR has been limited, partly due to an incomplete understanding of its activation mechanism. Here, we present a 2.9-Å structure of the PAF-bound PAFR-Gi complex. Structural and mutagenesis analyses unveil a specific binding mode of PAF, with the choline head forming cation-π interactions within PAFR hydrophobic pocket, while the alkyl tail penetrates deeply into an aromatic cleft between TM4 and TM5. Binding of PAF modulates conformational changes in key motifs of PAFR, triggering the outward movement of TM6, TM7, and helix 8 for G protein coupling. Molecular dynamics simulation suggests a membrane-side pathway for PAF entry into PAFR via the TM4-TM5 cavity. By providing molecular insights into PAFR signaling, this work contributes a foundation for developing therapeutic interventions targeting PAF signal axis.

Mechanisms of ligand recognition and activation of melanin-concentrating hormone receptors.

Melanin-concentrating hormone (MCH) is a cyclic neuropeptide that regulates food intake, energy balance, and other physiological functions by stimulating MCHR1 and MCHR2 receptors, both of which are class A G protein-coupled receptors. MCHR1 predominately couples to inhibitory G protein, Gi/o, and MCHR2 can only couple to Gq/11. Here we present cryo-electron microscopy structures of MCH-activated MCHR1 with Gi and MCH-activated MCHR2 with Gq at the global resolutions of 3.01 Å and 2.40 Å, respectively. These structures reveal that MCH adopts a consistent cysteine-mediated hairpin loop configuration when bound to both receptors. A central arginine from the LGRVY core motif between the two cysteines of MCH penetrates deeply into the transmembrane pocket, triggering receptor activation. Integrated with mutational and functional insights, our findings elucidate the molecular underpinnings of ligand recognition and MCH receptor activation and offer a structural foundation for targeted drug design.

Dimerization and antidepressant recognition at noradrenaline transporter.

The noradrenaline transporter has a pivotal role in regulating neurotransmitter balance and is crucial for normal physiology and neurobiology1. Dysfunction of noradrenaline transporter has been implicated in numerous neuropsychiatric diseases, including depression and attention deficit hyperactivity disorder2. Here we report cryo-electron microscopy structures of noradrenaline transporter in apo and substrate-bound forms, and as complexes with six antidepressants. The structures reveal a noradrenaline transporter dimer interface that is mediated predominantly by cholesterol and lipid molecules. The substrate noradrenaline binds deep in the central binding pocket, and its amine group interacts with a conserved aspartate residue. Our structures also provide insight into antidepressant recognition and monoamine transporter selectivity. Together, these findings advance our understanding of noradrenaline transporter regulation and inhibition, and provide templates for designing improved antidepressants to treat neuropsychiatric disorders.

Cryo-electron microscopy for GPCR research and drug discovery in endocrinology and metabolism.

G protein-coupled receptors (GPCRs) are the largest family of cell surface receptors, with many GPCRs having crucial roles in endocrinology and metabolism. Cryogenic electron microscopy (cryo-EM) has revolutionized the field of structural biology, particularly regarding GPCRs, over the past decade. Since the first pair of GPCR structures resolved by cryo-EM were published in 2017, the number of GPCR structures resolved by cryo-EM has surpassed the number resolved by X-ray crystallography by 30%, reaching >650, and the number has doubled every ~0.63 years for the past 6 years. At this pace, it is predicted that the structure of 90% of all human GPCRs will be completed within the next 5-7 years. This Review highlights the general structural features and principles that guide GPCR ligand recognition, receptor activation, G protein coupling, arrestin recruitment and regulation by GPCR kinases. The Review also highlights the diversity of GPCR allosteric binding sites and how allosteric ligands could dictate biased signalling that is selective for a G protein pathway or an arrestin pathway. Finally, the authors use the examples of glycoprotein hormone receptors and glucagon-like peptide 1 receptor to illustrate the effect of cryo-EM on understanding GPCR biology in endocrinology and metabolism, as well as on GPCR-related endocrine diseases and drug discovery.

Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing.

Geometric deep learning has been revolutionizing the molecular modeling field. Despite the state-of-the-art neural network models are approaching ab initio accuracy for molecular property prediction, their applications, such as drug discovery and molecular dynamics (MD) simulation, have been hindered by insufficient utilization of geometric information and high computational costs. Here we propose an equivariant geometry-enhanced graph neural network called ViSNet, which elegantly extracts geometric features and efficiently models molecular structures with low computational costs. Our proposed ViSNet outperforms state-of-the-art approaches on multiple MD benchmarks, including MD17, revised MD17 and MD22, and achieves excellent chemical property prediction on QM9 and Molecule3D datasets. Furthermore, through a series of simulations and case studies, ViSNet can efficiently explore the conformational space and provide reasonable interpretability to map geometric representations to molecular structures.