The Link module from human TSG-6, a hyaladherin with roles in ovulation and inflammation, has a hyaluronan (HA)-binding groove containing two adjacent tyrosine residues that are likely to form CH-pi stacking interactions with sequential rings in the sugar. We have used this observation to construct a model of a protein.HA complex, which was then tested against existing experimental information and by acquisition of new NMR data sets of [(13)C, (15)N]HA (8-mer) complexed with unlabeled protein. A major finding of this analysis was that acetamido side chains of two GlcNAc rings fit into hydrophobic pockets on either side of the adjacent tyrosines, providing a selectivity mechanism of HA over other polysaccharides. Furthermore, two basic residues have a separation that matches that of glucuronic acids in the sugar, consistent with the formation of salt bridges; NMR experiments at a range of pH values identified protein groups that titrate due to their proximity to a free carboxylate in HA. Sequence alignment and construction of homology models for all human Link modules in their HA-bound states revealed that many of these features are conserved across the superfamily, thus allowing the prediction of functionally important residues. In the case of cartilage link protein, its two Link modules were docked together (using bound HA as a guide), identifying hydrophobic residues likely to form an intra-Link module interface as well as amino acids that could be involved in supporting intermolecular interactions between link proteins and chondroitin sulfate proteoglycans. Here, we propose a mechanism for ternary complex formation that generates higher order helical structures, as may exist in cartilage aggregates.
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http://dx.doi.org/10.1074/jbc.M414343200 | DOI Listing |
J Mol Neurosci
January 2025
Department of Biophysics, School of Life Sciences, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
Alzheimer's disease (AD), a prevalent neurodegenerative disorder, is characterized by mitochondrial dysfunction and immune dysregulation. This study is aimed at developing a risk prediction model for AD by integrating multi-omics data and exploring the interplay between mitochondrial energy metabolism-related genes (MEMRGs) and immune cell dynamics. We integrated four GEO datasets (GSE132903, GSE29378, GSE33000, GSE5281) for differential gene expression analysis, functional enrichment, and weighted gene co-expression network analysis (WGCNA).
View Article and Find Full Text PDFJ Nutr Health Aging
January 2025
Department of Clinical Nutrition, King Abdulaziz University, Jeddah, Saudi Arabia; Department of Nutritional Sciences, University of Michigan, Ann Arbor, MI, United States. Electronic address:
Womens Health (Lond)
January 2025
Global Health, and Department Pediatrics, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Sci Rep
December 2024
School of Literature, Law and Art, East China University of Technology, Nanchang, 330013, China.
The purpose of this study is to put forward a new evaluation model of dance movement quality to deal with the subjectivity and inconsistency in traditional evaluation methods. In view of the complexity and diversity of dance art and the widespread popularity of dance videos on social media, it is particularly urgent to develop an automatic and efficient tool for evaluating the quality of dance movements. Therefore, this study puts forward the Transformer Convolutional Neural Network with Dynamic and Static Streams (TransCNN-DSSS) model, which combines the analysis of dynamic flow and static flow, and makes use of the advantages of Transformer and Convolutional Neural Network (CNN) to deeply analyze and evaluate the dance movements.
View Article and Find Full Text PDFSci Rep
December 2024
Guangzhou University, School of Computer Science and Cyber Engineering, Guangzhou, 510006, China.
Underwater image enhancement (UIE) is challenging since image degradation in aquatic environments is complicated and changing over time. Existing mainstream methods rely on either physical-model or data-driven, suffering from performance bottlenecks due to changes in imaging conditions or training instability. In this article, we attempt to adapt the diffusion model to the UIE task and propose a Content-Preserving Diffusion Model (CPDM) to address the above challenges.
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