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      Allosteric nanobodies uncover a role of hippocampal mGlu2 receptor homodimers in contextual fear consolidation

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          Abstract

          Antibodies have enormous therapeutic and biotechnology potential. G protein-coupled receptors (GPCRs), the main targets in drug development, are of major interest in antibody development programs. Metabotropic glutamate receptors are dimeric GPCRs that can control synaptic activity in a multitude of ways. Here we identify llama nanobodies that specifically recognize mGlu2 receptors, among the eight subtypes of mGluR subunits. Among these nanobodies, DN10 and 13 are positive allosteric modulators (PAM) on homodimeric mGlu2, while DN10 displays also a significant partial agonist activity. DN10 and DN13 have no effect on mGlu2-3 and mGlu2-4 heterodimers. These PAMs enhance the inhibitory action of the orthosteric mGlu2/mGlu3 agonist, DCG-IV, at mossy fiber terminals in the CA3 region of hippocampal slices. DN13 also impairs contextual fear memory when injected in the CA3 region of hippocampal region. These data highlight the potential of developing antibodies with allosteric actions on GPCRs to better define their roles in vivo.

          Abstract

          G protein-coupled receptors are considered promising therapeutic targets. Here, the authors have identified nanobodies, or single-domain llama antibodies, that specifically enhance agonist-induced activity of a type of G protein-coupled receptor, the mGlu2 receptor.

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          Most cited references48

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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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            MMTSB Tool Set: enhanced sampling and multiscale modeling methods for applications in structural biology.

            We describe the Multiscale Modeling Tools for Structural Biology (MMTSB) Tool Set (https://mmtsb.scripps.edu/software/mmtsbToolSet.html), which is a novel set of utilities and programming libraries that provide new enhanced sampling and multiscale modeling techniques for the simulation of proteins and nucleic acids. The tool set interfaces with the existing molecular modeling packages CHARMM and Amber for classical all-atom simulations, and with MONSSTER for lattice-based low-resolution conformational sampling. In addition, it adds new functionality for the integration and translation between both levels of detail. The replica exchange method is implemented to allow enhanced sampling of both the all-atom and low-resolution models. The tool set aims at applications in structural biology that involve protein or nucleic acid structure prediction, refinement, and/or extended conformational sampling. With structure prediction applications in mind, the tool set also implements a facility that allows the control and application of modeling tasks on a large set of conformations in what we have termed ensemble computing. Ensemble computing encompasses loosely coupled, parallel computation on high-end parallel computers, clustered computational grids and desktop grid environments. This paper describes the design and implementation of the MMTSB Tool Set and illustrates its utility with three typical examples--scoring of a set of predicted protein conformations in order to identify the most native-like structures, ab initio folding of peptides in implicit solvent with the replica exchange method, and the prediction of a missing fragment in a larger protein structure.
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              Accelerating Protein Docking in ZDOCK Using an Advanced 3D Convolution Library

              Computational prediction of the 3D structures of molecular interactions is a challenging area, often requiring significant computational resources to produce structural predictions with atomic-level accuracy. This can be particularly burdensome when modeling large sets of interactions, macromolecular assemblies, or interactions between flexible proteins. We previously developed a protein docking program, ZDOCK, which uses a fast Fourier transform to perform a 3D search of the spatial degrees of freedom between two molecules. By utilizing a pairwise statistical potential in the ZDOCK scoring function, there were notable gains in docking accuracy over previous versions, but this improvement in accuracy came at a substantial computational cost. In this study, we incorporated a recently developed 3D convolution library into ZDOCK, and additionally modified ZDOCK to dynamically orient the input proteins for more efficient convolution. These modifications resulted in an average of over 8.5-fold improvement in running time when tested on 176 cases in a newly released protein docking benchmark, as well as substantially less memory usage, with no loss in docking accuracy. We also applied these improvements to a previous version of ZDOCK that uses a simpler non-pairwise atomic potential, yielding an average speed improvement of over 5-fold on the docking benchmark, while maintaining predictive success. This permits the utilization of ZDOCK for more intensive tasks such as docking flexible molecules and modeling of interactomes, and can be run more readily by those with limited computational resources.
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                Author and article information

                Contributors
                patrick.chames@inserm.fr
                philippe.rondard@igf.cnrs.fr
                jean-philippe.pin@igf.cnrs.fr
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                6 December 2017
                6 December 2017
                2017
                : 8
                : 1967
                Affiliations
                [1 ]ISNI 0000 0004 0383 2080, GRID grid.461890.2, Institut de Génomique Fonctionnelle, CNRS, INSERM, Univ. Montpellier, ; F-34094 Montpellier, France
                [2 ]Cisbio Bioassays, F-30200 Codolet, France
                [3 ]ISNI 0000 0004 0572 0656, GRID grid.463833.9, Aix Marseille Univ, CNRS, INSERM, Institut Paoli-Calmettes, CRCM, ; F-13009 Marseille, France
                [4 ]ISNI 0000 0001 2171 2558, GRID grid.5842.b, CNRS UMR9197, Université Paris-Sud, Institut des Neurosciences Paris-Saclay, ; F-91405 Orsay, France
                Author information
                http://orcid.org/0000-0002-9764-9927
                http://orcid.org/0000-0002-2256-577X
                http://orcid.org/0000-0002-6104-6286
                http://orcid.org/0000-0003-1134-2738
                http://orcid.org/0000-0002-1423-345X
                Article
                1489
                10.1038/s41467-017-01489-1
                5719040
                29213077
                020a09d6-fa92-4d30-a960-3c6c0b218b8f
                © The Author(s) 2017

                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
                : 9 March 2017
                : 15 September 2017
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