The conformational landscape of human transthyretin revealed by cryo-EM
Related Articles
Modulation of the human GlyT1 by clinical drugs and cholesterol
Glycine transporter 1 (GlyT1) is a key player in shaping extracellular glutamatergic signaling processes and holds promise for treating cognitive impairments associated with schizophrenia by inhibiting its activity and thus enhancing the function of NMDA receptors. Despite its significant role in physiological and pharmacology, its modulation mechanism by clinical drugs and internal lipids remains elusive. Here, we determine cryo-EM structures of GlyT1 in its apo state and in complex with clinical trial drugs iclepertin and sarcosine. The GlyT1 in its apo state is determined in three distinct conformations, exhibiting a conformational equilibrium of the transport cycle. The complex structures with inhibitor iclepertin and sarcosine elucidate their unique binding poses with GlyT1. Three binding sites of cholesterol are determined in GlyT1, two of which are conformation-dependent. Transport kinetics studies reveal that a delicate binding equilibrium for cholesterol is crucial for the conformational transition of GlyT1. This study significantly enhances our understanding of the physiological and pharmacological aspects of GlyT1.
Energy landscape of conformational changes for a single unmodified protein
Resolving the free energy landscapes that govern protein biophysics has been obscured by ensemble averaging. While the folding dynamics of single proteins have been observed using fluorescent labels and/or tethers, a simpler and more direct measurement of the conformational changes would not require modifications to the protein. We use nanoaperture optical tweezers to resolve the energy landscape of a single unmodified protein, Bovine Serum Albumin (BSA), and quantify changes in the three-state conformation dynamics with temperature. A Markov model with Kramers’ theory transition rates is used to model the dynamics, showing good agreement with the observed state transitions. This first look at the intrinsic energy landscape of proteins provides a transformative tool for protein biophysics and may be applied broadly, including mapping out the energy landscape of particularly challenging intrinsically disordered proteins.
Deep learning-driven semantic segmentation and spatial analysis of quarry relic landscapes using point cloud data: insights from the Shaoxing quarry relics
Quarry relic landscapes hold significant historical and cultural value, yet current research often lacks the depth to understand their complex spatial structure. This study addresses this gap by utilizing 3D point cloud data and deep learning to analyze quarry relic landscapes, focusing on the Shaoxing quarry relics. In this paper, point cloud models of four quarry relic landscapes were established, as well as the performance of the PointNet + + network in segmenting complex and variable quarry relic landscape spaces. Based on the semantic segmentation results, quantitative and clustering analyses were conducted on various landscape elements of the four quarry relics, thereby exploring the cultural value of Shaoxing quarry relic’s heritage. The study demonstrates the following key findings: 1. The feasibility of combining 3D laser scanning and UAV photogrammetry to gather detailed site data for documenting quarry relic landscapes has been proven. 2. The PointNet + + deep learning network is particularly effective for the semantic segmentation of landscape elements associated with quarry relics. 3. The Shaoxing quarry relic exhibits a composite spatial form, with a nearly equal ratio of positive to negative space (approximately 1:1). Plants and bare rocks predominantly occupy the positive space, while rocks and stone pits exhibit the highest heritage value. 4. The development of the QLIM&PMS system has facilitated the comprehensive digitalization of the quarry relic landscape, offering examples and technical support for the preservation and utilization of quarry relic sites.
Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data
Artificial intelligence (AI), specifically Deep learning (DL), has revolutionized biomedical image analysis, but its efficacy is limited by the need for representative, high-quality large datasets with manual annotations. While latest research on synthetic data using AI-based generative models has shown promising results to tackle this problem, several challenges such as lack of interpretability and need for vast amounts of real data remain. This study aims to introduce a new approach—SYNTA—for the generation of photo-realistic synthetic biomedical image data to address the challenges associated with state-of-the art generative models and DL-based image analysis.
The structural basis for the human procollagen lysine hydroxylation and dual-glycosylation
The proper assembly and maturation of collagens necessitate the orchestrated hydroxylation and glycosylation of multiple lysyl residues in procollagen chains. Dysfunctions in this multistep modification process can lead to severe collagen-associated diseases. To elucidate the coordination of lysyl processing activities, we determine the cryo-EM structures of the enzyme complex formed by LH3/PLOD3 and GLT25D1/ColGalT1, designated as the KOGG complex. Our structural analysis reveals a tetrameric complex comprising dimeric LH3/PLOD3s and GLT25D1/ColGalT1s, assembled with interactions involving the N-terminal loop of GLT25D1/ColGalT1 bridging another GLT25D1/ColGalT1 and LH3/PLOD3. We further elucidate the spatial configuration of the hydroxylase, galactosyltransferase, and glucosyltransferase sites within the KOGG complex, along with the key residues involved in substrate binding at these enzymatic sites. Intriguingly, we identify a high-order oligomeric pattern characterized by the formation of a fiber-like KOGG polymer assembled through the repetitive incorporation of KOGG tetramers as the biological unit.
Responses