The formyl peptide receptor 1 (FPR1) is primarily responsible for detection of short peptides bearing N-formylated methionine (fMet) that are characteristic of protein synthesis in bacteria and mitochondria. As a result, FPR1 is critical to phagocyte migration and activation in bacterial infection, tissue injury and inflammation. How FPR1 distinguishes between formyl peptides and non-formyl peptides remains elusive. Here we report cryo-EM structures of human FPR1-Gi protein complex bound to S. aureus-derived peptide fMet-Ile-Phe-Leu (fMIFL) and E. coli-derived peptide fMet-Leu-Phe (fMLF). Both structures of FPR1 adopt an active conformation and exhibit a binding pocket containing the R201XXXR205 (RGIIR) motif for formyl group interaction and receptor activation. This motif works together with D106 for hydrogen bond formation with the N-formyl group and with fMet, a model supported by MD simulation and functional assays of mutant receptors with key residues for recognition substituted by alanine. The cryo-EM model of agonist-bound FPR1 provides a structural basis for recognition of bacteria-derived chemotactic peptides with potential applications in developing FPR1-targeting agents.
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http://dx.doi.org/10.1038/s41467-022-32822-y | DOI Listing |
Int J Biol Macromol
January 2025
College of Pharmacy, Shaanxi University of Chinese Medicine, Shiji Ave., Xi'an-xianyang New Economic Zone, Shaanxi Province 712046, People's Republic of China. Electronic address:
Ophiopogonis japonicus is a famous medicinal plant in China with a long history of medicinal and food origin. It contains various chemical components, such as polysaccharides, steroidal saponins, alkaloids, flavonoids, etc. According to traditional Chinese medicine (TCM) theory, it has the efficacy of moistening the lungs and nourishing the yin, benefiting the stomach by generating fluids, and clearing the heart to get rid of vexation.
View Article and Find Full Text PDFPediatr Neurol
January 2025
Faculty of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain; Pediatrics Research Group, Institut de Recerca Sant Pau (IR-Sant Pau), Barcelona, Spain; Pediatric Neurology Unit, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.
Background: Dravet syndrome (DS) is a severe developmental and epileptic encephalopathy associated with loss-of-function variants in the SCN1A gene. Although predominantly expressed in the central nervous system, SCN1A is also expressed in the heart, suggesting a potential link between neuronal and cardiac channelopathies. Additionally, DS carries a high risk of sudden unexpected death in epilepsy (SUDEP).
View Article and Find Full Text PDFInt J Med Inform
January 2025
ISCTE-Instituto Universitário de Lisboa, Lisbon, Portugal; Universidade da Beira Interior Faculdade de Ciências da Saúde, Covilha, Portugal.
Introduction: In the WHO European Region, 44 of 53 reporting Member States (MS) have a national digital health strategy (NDHS) or policy. Their formulation is heterogenous and evolving and should best reflect public common interest. This research aims to explore how a public value approach improves the relevance of digital health policies and services, increasing their capacity to better serve the diverse range of societal interests.
View Article and Find Full Text PDFMol Divers
January 2025
Key Laboratory for Macromolecular Science of Shaanxi Province, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an, 710119, People's Republic of China.
Molecular Property Prediction (MPP) is a fundamental task in important research fields such as chemistry, materials, biology, and medicine, where traditional computational chemistry methods based on quantum mechanics often consume substantial time and computing power. In recent years, machine learning has been increasingly used in computational chemistry, in which graph neural networks have shown good performance in molecular property prediction tasks, but they have some limitations in terms of generalizability, interpretability, and certainty. In order to address the above challenges, a Multiscale Molecular Structural Neural Network (MMSNet) is proposed in this paper, which obtains rich multiscale molecular representations through the information fusion between bonded and non-bonded "message passing" structures at the atomic scale and spatial feature information "encoder-decoder" structures at the molecular scale; a multi-level attention mechanism is introduced on the basis of theoretical analysis of molecular mechanics in order to enhance the model's interpretability; the prediction results of MMSNet are used as label values and clustered in the molecular library by the K-NN (K-Nearest Neighbors) algorithm to reverse match the spatial structure of the molecules, and the certainty of the model is quantified by comparing virtual screening results across different K-values.
View Article and Find Full Text PDFClin Oral Investig
January 2025
State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, China.
Objectives: To develop a platform including a deep convolutional neural network (DCNN) for automatic segmentation of the maxillary sinus (MS) and adjacent structures, and automatic algorithms for measuring 3-dimensional (3D) clinical parameters.
Materials And Methods: 175 CBCTs containing 242 MS were used as the training, validating and testing datasets at the ratio of 7:1:2. The datasets contained healthy MS and MS with mild (2-4 mm), moderate (4-10 mm) and severe (10- mm) mucosal thickening.
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