2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Reversible Control of Native GluN2B-Containing NMDA Receptors with Visible Light

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          NMDA receptors (NMDARs) are glutamate-gated ion channels playing a central role in synaptic transmission and plasticity. NMDAR dysregulation is linked to various neuropsychiatric disorders. This is particularly true for GluN2B-containing NMDARs (GluN2B-NMDARs), which have major pro-cognitive, but also pro-excitotoxic roles, although their exact involvement in these processes remains debated. Traditional GluN2B-selective antagonists suffer from slow and irreversible effects, limiting their use in native tissues. We therefore developed OptoNAM-3, a photoswitchable negative allosteric modulator selective for GluN2B-NMDARs. OptoNAM-3 provided light-induced reversible inhibition of GluN2B-NMDAR activity with precise temporal control both in vitro and in vivo on the behavior of freely moving Xenopus tadpoles. When bound to GluN2B-NMDARs, OptoNAM-3 displayed remarkable red-shifting of its photoswitching properties allowing the use of blue light instead of UV light to turn-off its activity, which we attributed to geometric constraints imposed by the binding site onto the azobenzene moiety of the ligand. This study therefore highlights the importance of the binding site in shaping the photochemical properties of azobenzene-based photoswitches. In addition, by enabling selective, fast, and reversible photocontrol of native GluN2B-NMDARs with in vivo compatible photochemical properties (visible light), OptoNAM-3 should be a useful tool for the investigation of the GluN2B-NMDAR physiology in native tissues.

          Related collections

          Most cited references120

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu.

            The method of dispersion correction as an add-on to standard Kohn-Sham density functional theory (DFT-D) has been refined regarding higher accuracy, broader range of applicability, and less empiricism. The main new ingredients are atom-pairwise specific dispersion coefficients and cutoff radii that are both computed from first principles. The coefficients for new eighth-order dispersion terms are computed using established recursion relations. System (geometry) dependent information is used for the first time in a DFT-D type approach by employing the new concept of fractional coordination numbers (CN). They are used to interpolate between dispersion coefficients of atoms in different chemical environments. The method only requires adjustment of two global parameters for each density functional, is asymptotically exact for a gas of weakly interacting neutral atoms, and easily allows the computation of atomic forces. Three-body nonadditivity terms are considered. The method has been assessed on standard benchmark sets for inter- and intramolecular noncovalent interactions with a particular emphasis on a consistent description of light and heavy element systems. The mean absolute deviations for the S22 benchmark set of noncovalent interactions for 11 standard density functionals decrease by 15%-40% compared to the previous (already accurate) DFT-D version. Spectacular improvements are found for a tripeptide-folding model and all tested metallic systems. The rectification of the long-range behavior and the use of more accurate C(6) coefficients also lead to a much better description of large (infinite) systems as shown for graphene sheets and the adsorption of benzene on an Ag(111) surface. For graphene it is found that the inclusion of three-body terms substantially (by about 10%) weakens the interlayer binding. We propose the revised DFT-D method as a general tool for the computation of the dispersion energy in molecules and solids of any kind with DFT and related (low-cost) electronic structure methods for large systems.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.

              AutoDock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed-up compared with the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multicore machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user. Copyright 2009 Wiley Periodicals, Inc.
                Bookmark

                Author and article information

                Journal
                ACS Chem Neurosci
                ACS Chem Neurosci
                cn
                acncdm
                ACS Chemical Neuroscience
                American Chemical Society
                1948-7193
                06 September 2024
                18 September 2024
                : 15
                : 18
                : 3321-3343
                Affiliations
                []Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, CNRS, INSERM, Université PSL , Paris F-75005, France
                []Laboratoire d’Archéologie Moléculaire et Structurale (LAMS), CNRS UMR 8220, Sorbonne Université , Paris 75005, France
                [§ ]PASTEUR, Département de chimie, École normale supérieure, CNRS, Université PSL, Sorbonne Université , Paris 75005, France
                []Laboratoire de Chimie et Biochimie Pharmacologiques et Toxicologiques, CNRS UMR8601, Université Paris Cité , Paris 75006, France
                Author notes
                Author information
                https://orcid.org/0000-0002-4090-5897
                https://orcid.org/0000-0001-7447-8806
                https://orcid.org/0009-0001-4324-5716
                https://orcid.org/0000-0001-7753-6231
                Article
                10.1021/acschemneuro.4c00247
                11413854
                39242213
                c66d1243-03bc-40ef-ba15-4a4de8e01dd2
                © 2024 The Authors. Published by American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 22 April 2024
                : 26 June 2024
                : 25 June 2024
                Funding
                Funded by: H2020 European Research Council, doi 10.13039/100010663;
                Award ID: 693021
                Funded by: Université de Recherche Paris Sciences et Lettres, doi 10.13039/501100009517;
                Award ID: PSL-QLife ANR-17-CONV-0005
                Funded by: HORIZON EUROPE Marie Sklodowska-Curie Actions, doi 10.13039/100018694;
                Award ID: 701467
                Categories
                Research Article
                Custom metadata
                cn4c00247
                cn4c00247

                Neurosciences
                glutamate,nmda receptors,optopharmacology,allostery,glun2b-selective antagonists,azobenzenes

                Comments

                Comment on this article