Acid-sensing ion channels (ASICs) play important roles in inflammatory pathways by conducting ions across the neuronal membrane in response to proton binding under acidic conditions. Recent studies have shown that ASICs can be modulated by arachidonic acid (AA), and, in the case of the ASIC3 subtype, even activated by AA at physiological pH. However, the mechanism by which these fatty acids act on the channel is still unknown. Here, we have used multiscale molecular dynamics simulations to predict a putative, general binding region of AA to models of the human ASIC protein. We have identified, in agreement with recent studies, residues in the outer leaflet transmembrane region which interact with AA. In addition, despite their similar modulation, we observe subtle differences in the AA interaction pattern between human ASIC1a and human ASIC3, which can be reversed by mutating three key residues at the outer leaflet portion of TM1. We further probed interactions with these residues in hASIC3 using atomistic simulations and identified possible AA coordinating interactions; salt bridge interactions of AA with R65hASIC3 and R68hASIC3 and AA tail interactions with the Y58hASIC3 aromatic ring. We have shown that longer fatty acid tails with more double bonds have increased relative occupancy in this region of the channel, a finding supported by recent functional studies. We further proposed that the modulatory effect of AA on ASIC does not result from changes in local membrane curvature. Rather, we speculate that it may occur through structural changes to the ion channel upon AA binding.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9836442 | PMC |
http://dx.doi.org/10.1085/jgp.202213259 | DOI Listing |
Alzheimers Dement
January 2025
Department of Pathology & Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA.
Eur J Neurol
February 2025
Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy.
Objective: Disorders of arousal (DoA) are characterized by an intermediate state between wakefulness and deep sleep, leading to incomplete awakenings from NREM sleep. Multimodal studies have shown subtle neurophysiologic alterations even during wakefulness in DoA. The aim of this study was to explore the brain functional connectivity in DoA and the metabolic profile of the anterior and posterior cingulate cortex, given its pivotal role in cognitive and emotional processing.
View Article and Find Full Text PDFJ Food Sci Technol
January 2025
Department of Food Engineering and Technology, School of Food Engineering, Universidade Estadual de Campinas (UNICAMP), Monteiro Lobato 80, 6121, Campinas, SP 3083-862 Brazil.
Unlabelled: The effects of high hydrostatic pressure (HHP) (400-650 MPa) and holding temperature (25-50 °C) in thermally assisted HHP processing on multi-scale structure of starch (granule, crystalline and molecular), techno-functional properties, and digestibility of sorghum starch (SS) were evaluated. Response surface methodology has verified that the process impact on the modification of SS was dependent primarily on the pressure level. As HHP increased, processed SS progressively lost their granular structure and Maltese cross, indicating gradual structural disorder within the granules.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Chemistry, School of Science, Westlake University, Hangzhou, Zhejiang Province, China.
The self-assembly of small molecules through non-covalent interactions is an emerging and promising strategy for building dynamic, stable, and large-scale structures. One remaining challenge is making the non-covalent interactions occur in the ideal positions to generate strength comparable to that of covalent bonds. This work shows that small molecule YAWF can self-assemble into a liquid-crystal hydrogel (LCH), the mechanical properties of which could be controlled by water.
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 PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!