G protein-coupled receptors (GPCRs) are pivotal therapeutic targets that signal through heterotrimeric G proteins. While structural insights into Gα s and Gα i/o subfamilies have advanced significantly, characterizing the Gα q/11 subfamily remains challenging due to poor soluble expression in prokaryotic systems, precluding high-yield isotope labeling needed for NMR studies. Here, we present an efficient expression strategy for Gα q protein in Escherichia coli . We identified the H5 helix as a critical determinant of folding and solubility. By swapping this helix with its Gα i/o counterpart and employing low-temperature co-expression with bacterial chaperones, we significantly enhance soluble yields while retaining GTPase activity. The 15 N-labeled protein exhibited well-dispersed 1 H– 15 N HSQC spectra, validating the suitability of this expression strategy for future high-resolution NMR investigations.
G protein-coupled receptors (GPCRs) are pivotal therapeutic targets that signal through heterotrimeric G proteins. While structural insights into Gαs and Gαi/o subfamilies have advanced significantly, characterizing the Gαq/11 subfamily remains challenging due to poor soluble expression in prokaryotic systems, precluding high-yield isotope labeling needed for NMR studies. Here, we present an efficient expression strategy for Gαq protein in Escherichia coli. We identified the H5 helix as a critical determinant of folding and solubility. By swapping this helix with its Gαi/o counterpart and employing low-temperature co-expression with bacterial chaperones, we significantly enhance soluble yields while retaining GTPase activity. The 15N-labeled protein exhibited well-dispersed 1H–15N HSQC spectra, validating the suitability of this expression strategy for future high-resolution NMR investigations.
Nur77 (NR4A1) is an orphan nuclear receptor whose allosteric mechanism departs fundamentally from that of classical nuclear receptors. Its ligand-binding pocket is constitutively occupied by endogenous hydrophobic side chains, obscuring the structural and allosteric basis of its conformational regulation. As a result, drug design targeting Nur77 has always posed greater challenges. Using NMR spectroscopy, we identified two conformational substates defined by a pocket enclosed by the H1–H3 loop of the ligand-binding domain. These substates interconvert slowly beyond the NMR detection timescale, reflecting a substantial free energy barrier. Solvent-induced isotope shift (SIIS) experiments reveal differential hydrophobic exposure of these two substates, with the minor substate displaying enhanced solvent accessibility. Integrating multiple sequence alignment (MSA) with mutational dissection, we identified a network of long-range weak interactions that acts as a cryptic allosteric hub governing substate equilibrium. Notably, the two substates exhibit distinct ligand-binding affinities, implying functional specialization. Therefore, this study reveals a non-classical allosteric framework of Nur77, providing a structural basis for the design of drugs that target allosteric regulation.
Predicting task-induced brain activation maps from resting-state fMRI (rs-fMRI) is an important challenge in computational neuroimaging, with potential applications for studying brain function without task-based scanning. However, most existing approaches rely primarily on functional connectivity representations, which often overlook the rich temporal dynamics embedded within rs-fMRI signals. To this end, we propose a novel hierarchical framework that uniquely combines spatio-temporal fingerprinting with residual refinement to predict individualized task-activation maps directly from rs-fMRI. The framework first captures complementary spatial and temporal characteristics of resting-state brain activity and subsequently employs hierarchical residual learning to progressively refine the predicted activation maps. Evaluation on the Human Connectome Project (HCP) dataset across seven task contrasts demonstrates strong agreement between predicted and ground-truth activation maps, with correlations reaching up to 0.884. Additional evaluation on the Chinese Human Connectome Project (CHCP) dataset further demonstrates the robustness and generalizability of the proposed framework. The results thus highlight the potential of integrating spatio-temporal representations with hierarchical residual learning for individualized prediction of task-related brain activation from resting-state fMRI.
Solid-state nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for characterizing pharmaceutical solids, yet inconsistencies in spectral assignment can hinder accurate structural interpretation. In this study, we revisit the solid-state NMR of efavirenz (EFV), a widely used anti-HIV drug, by employing fast magic-angle spinning (MAS) at 37 kHz. Through a combination of one-dimensional (1H, 19F, 13C) and two-dimensional correlation (13C–1H and 13C–19F) experiments, we resolve previously disputed assignments. Critically, we correct the misassignment of the aromatic C3 and C4 resonances, which is unambiguously confirmed by a 2D 13C–1H correlation spectrum using the key HN–C4 proximity. The 19F spectrum reveals two distinct peaks with an integral ratio of 1:2, directly reflecting the three crystallographically independent molecules in the unit cell and offering a facile diagnostic for molecular asymmetry. 19F-detected 13C–19F correlations further differentiate carbon environments. This work not only rectifies prior assignment errors but also establishes a robust multinuclear approach, integrating 1H, 13C, and 19F NMR, for detailed analysis of fluorinated pharmaceuticals.
