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Ligand recognition and allosteric regulation of DRD1-Gs signaling complexes

Dopamine receptors, including D1- and D2-like receptors, are important therapeutic targets in a variety of neurological syndromes, as well as cardiovascular and kidney diseases. Here, we present five cryoelectron microscopy (cryo-EM) structures of the dopamine D1 receptor (DRD1) coupled to Gs heterotrimer in complex with three catechol-based agonists, a non-catechol agonist, and a positive allosteric modulator for endogenous dopamine. These structures revealed that a polar interaction network is essential for catecholamine-like agonist recognition, whereas specific motifs in the extended binding pocket were responsible for discriminating D1- from D2-like receptors. Moreover, allosteric binding at a distinct inner surface pocket improved the activity of DRD1 by stabilizing endogenous dopamine interaction at the orthosteric site. DRD1-Gs interface revealed key features that serve as determinants for G protein coupling. Together, our study provides a structural understanding of the ligand recognition, allosteric regulation, and G protein coupling mechanisms of DRD1.

2021-02-10

Cell

West China Hospital, SCU

Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

The implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. Here, we establish a diagnostic tool based on a deep-learning framework for the screening of patients with common treatable blinding retinal diseases. Our framework utilizes transfer learning, which trains a neural network with a fraction of the data of conventional approaches. Applying this approach to a dataset of optical coherence tomography images, we demonstrate performance comparable to that of human experts in classifying age-related macular degeneration and diabetic macular edema. We also provide a more transparent and interpretable diagnosis by highlighting the regions recognized by the neural network. We further demonstrate the general applicability of our AI system for diagnosis of pediatric pneumonia using chest X-ray images. This tool may ultimately aid in expediting the diagnosis and referral of these treatable conditions, thereby facilitating earlier treatment, resulting in improved clinical outcomes. VIDEO ABSTRACT.

2018-02-22

Cell

West China Hospital, SCU