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      Glutamate and GABA A receptor crosstalk mediates homeostatic regulation of neuronal excitation in the mammalian brain

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          Abstract

          Maintaining a proper balance between the glutamate receptor-mediated neuronal excitation and the A type of GABA receptor (GABA AR) mediated inhibition is essential for brain functioning; and its imbalance contributes to the pathogenesis of many brain disorders including neurodegenerative diseases and mental illnesses. Here we identify a novel glutamate-GABA AR interaction mediated by a direct glutamate binding of the GABA AR. In HEK293 cells overexpressing recombinant GABA ARs, glutamate and its analog ligands, while producing no current on their own, potentiate GABA-evoked currents. This potentiation is mediated by a direct binding at a novel glutamate binding pocket located at the α + subunit interface of the GABA AR. Moreover, the potentiation does not require the presence of a γ subunit, and in fact, the presence of γ subunit significantly reduces the potency of the glutamate potentiation. In addition, the glutamate-mediated allosteric potentiation occurs on native GABA ARs in rat neurons maintained in culture, as evidenced by the potentiation of GABA AR-mediated inhibitory postsynaptic currents and tonic currents. Most importantly, we found that genetic impairment of this glutamate potentiation in knock-in mice resulted in phenotypes of increased neuronal excitability, including decreased thresholds to noxious stimuli and increased seizure susceptibility. These results demonstrate a novel cross-talk between excitatory transmitter glutamate and inhibitory GABA AR. Such a rapid and short feedback loop between the two principal excitatory and inhibitory neurotransmission systems may play a critical homeostatic role in fine-tuning the excitation-inhibition balance (E/I balance), thereby maintaining neuronal excitability in the mammalian brain under both physiological and pathological conditions.

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          Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy.

          Unlike other methods for docking ligands to the rigid 3D structure of a known protein receptor, Glide approximates a complete systematic search of the conformational, orientational, and positional space of the docked ligand. In this search, an initial rough positioning and scoring phase that dramatically narrows the search space is followed by torsionally flexible energy optimization on an OPLS-AA nonbonded potential grid for a few hundred surviving candidate poses. The very best candidates are further refined via a Monte Carlo sampling of pose conformation; in some cases, this is crucial to obtaining an accurate docked pose. Selection of the best docked pose uses a model energy function that combines empirical and force-field-based terms. Docking accuracy is assessed by redocking ligands from 282 cocrystallized PDB complexes starting from conformationally optimized ligand geometries that bear no memory of the correctly docked pose. Errors in geometry for the top-ranked pose are less than 1 A in nearly half of the cases and are greater than 2 A in only about one-third of them. Comparisons to published data on rms deviations show that Glide is nearly twice as accurate as GOLD and more than twice as accurate as FlexX for ligands having up to 20 rotatable bonds. Glide is also found to be more accurate than the recently described Surflex method.
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            Comparative protein modelling by satisfaction of spatial restraints.

            We describe a comparative protein modelling method designed to find the most probable structure for a sequence given its alignment with related structures. The three-dimensional (3D) model is obtained by optimally satisfying spatial restraints derived from the alignment and expressed as probability density functions (pdfs) for the features restrained. For example, the probabilities for main-chain conformations of a modelled residue may be restrained by its residue type, main-chain conformation of an equivalent residue in a related protein, and the local similarity between the two sequences. Several such pdfs are obtained from the correlations between structural features in 17 families of homologous proteins which have been aligned on the basis of their 3D structures. The pdfs restrain C alpha-C alpha distances, main-chain N-O distances, main-chain and side-chain dihedral angles. A smoothing procedure is used in the derivation of these relationships to minimize the problem of a sparse database. The 3D model of a protein is obtained by optimization of the molecular pdf such that the model violates the input restraints as little as possible. The molecular pdf is derived as a combination of pdfs restraining individual spatial features of the whole molecule. The optimization procedure is a variable target function method that applies the conjugate gradients algorithm to positions of all non-hydrogen atoms. The method is automated and is illustrated by the modelling of trypsin from two other serine proteinases.
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              Metabotropic glutamate receptors: physiology, pharmacology, and disease.

              The metabotropic glutamate receptors (mGluRs) are family C G-protein-coupled receptors that participate in the modulation of synaptic transmission and neuronal excitability throughout the central nervous system. The mGluRs bind glutamate within a large extracellular domain and transmit signals through the receptor protein to intracellular signaling partners. A great deal of progress has been made in determining the mechanisms by which mGluRs are activated, proteins with which they interact, and orthosteric and allosteric ligands that can modulate receptor activity. The widespread expression of mGluRs makes these receptors particularly attractive drug targets, and recent studies continue to validate the therapeutic utility of mGluR ligands in neurological and psychiatric disorders such as Alzheimer's disease, Parkinson's disease, anxiety, depression, and schizophrenia.
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                Author and article information

                Contributors
                dongchuanwu@mail.cmu.edu.tw
                ytwang@brain.ubc.ca
                Journal
                Signal Transduct Target Ther
                Signal Transduct Target Ther
                Signal Transduction and Targeted Therapy
                Nature Publishing Group UK (London )
                2095-9907
                2059-3635
                3 October 2022
                3 October 2022
                2022
                : 7
                : 340
                Affiliations
                [1 ]GRID grid.17091.3e, ISNI 0000 0001 2288 9830, DM Centre for Brain Health and Department of Medicine, Vancouver Coastal Health Research Institute, , University of British Columbia, ; Vancouver, BC V6T 2B5 Canada
                [2 ]GRID grid.488412.3, Pediatric Research Institute, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, , Children’s Hospital of Chongqing Medical University, ; Chongqing, 400014 P.R. China
                [3 ]GRID grid.440637.2, ISNI 0000 0004 4657 8879, iHuman Institue, , ShanghaiTech University, ; Shanghai, P.R. China
                Article
                1148
                10.1038/s41392-022-01148-y
                9527238
                34980881
                5e9d32b4-06b6-43bb-9b24-5f9607c9184e
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 28 January 2022
                : 29 May 2022
                : 27 July 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000024, Gouvernement du Canada | Canadian Institutes of Health Research (Instituts de Recherche en Santé du Canada);
                Award ID: FDN-154286
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 82071395
                Award ID: 82001158
                Award ID: 91749116
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100004663, Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan);
                Award ID: MOST 104-2320-B-039-045-MY3
                Award ID: MOST MOST 110-2320-B-039-010-MY3
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                cellular neuroscience,molecular neuroscience
                cellular neuroscience, molecular neuroscience

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