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      Dual binding mode of “bitter sugars” to their human bitter taste receptor target

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          Abstract

          The 25 human bitter taste receptors (hTAS2Rs) are responsible for detecting bitter molecules present in food, and they also play several physiological and pathological roles in extraoral compartments. Therefore, understanding their ligand specificity is important both for food research and for pharmacological applications. Here we provide a molecular insight into the exquisite molecular recognition of bitter β-glycopyranosides by one of the members of this receptor subclass, hTAS2R16. Most of its agonists have in common the presence of a β-glycopyranose unit along with an extremely structurally diverse aglycon moiety. This poses the question of how hTAS2R16 can recognize such a large number of “bitter sugars”. By means of hybrid molecular mechanics/coarse grained molecular dynamics simulations, here we show that the three hTAS2R16 agonists salicin, arbutin and phenyl-β-D-glucopyranoside interact with the receptor through a previously unrecognized dual binding mode. Such mechanism may offer a seamless way to fit different aglycons inside the binding cavity, while maintaining the sugar bound, similar to the strategy used by several carbohydrate-binding lectins. Our prediction is validated a posteriori by comparison with mutagenesis data and also rationalizes a wealth of structure-activity relationship data. Therefore, our findings not only provide a deeper molecular characterization of the binding determinants for the three ligands studied here, but also give insights applicable to other hTAS2R16 agonists. Together with our results for other hTAS2Rs, this study paves the way to improve our overall understanding of the structural determinants of ligand specificity in bitter taste receptors.

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          The Pfam protein families database.

          Pfam is a large collection of protein families and domains. Over the past 2 years the number of families in Pfam has doubled and now stands at 6190 (version 10.0). Methodology improvements for searching the Pfam collection locally as well as via the web are described. Other recent innovations include modelling of discontinuous domains allowing Pfam domain definitions to be closer to those found in structure databases. Pfam is available on the web in the UK (http://www.sanger.ac.uk/Software/Pfam/), the USA (http://pfam.wustl.edu/), France (http://pfam.jouy.inra.fr/) and Sweden (http://Pfam.cgb.ki.se/).
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            Protein homology detection by HMM-HMM comparison.

            Protein homology detection and sequence alignment are at the basis of protein structure prediction, function prediction and evolution. We have generalized the alignment of protein sequences with a profile hidden Markov model (HMM) to the case of pairwise alignment of profile HMMs. We present a method for detecting distant homologous relationships between proteins based on this approach. The method (HHsearch) is benchmarked together with BLAST, PSI-BLAST, HMMER and the profile-profile comparison tools PROF_SIM and COMPASS, in an all-against-all comparison of a database of 3691 protein domains from SCOP 1.63 with pairwise sequence identities below 20%.Sensitivity: When the predicted secondary structure is included in the HMMs, HHsearch is able to detect between 2.7 and 4.2 times more homologs than PSI-BLAST or HMMER and between 1.44 and 1.9 times more than COMPASS or PROF_SIM for a rate of false positives of 10%. Approximately half of the improvement over the profile-profile comparison methods is attributable to the use of profile HMMs in place of simple profiles. Alignment quality: Higher sensitivity is mirrored by an increased alignment quality. HHsearch produced 1.2, 1.7 and 3.3 times more good alignments ('balanced' score >0.3) than the next best method (COMPASS), and 1.6, 2.9 and 9.4 times more than PSI-BLAST, at the family, superfamily and fold level, respectively.Speed: HHsearch scans a query of 200 residues against 3691 domains in 33 s on an AMD64 2GHz PC. This is 10 times faster than PROF_SIM and 17 times faster than COMPASS.
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              High-resolution crystal structure of an engineered human beta2-adrenergic G protein-coupled receptor.

              Heterotrimeric guanine nucleotide-binding protein (G protein)-coupled receptors constitute the largest family of eukaryotic signal transduction proteins that communicate across the membrane. We report the crystal structure of a human beta2-adrenergic receptor-T4 lysozyme fusion protein bound to the partial inverse agonist carazolol at 2.4 angstrom resolution. The structure provides a high-resolution view of a human G protein-coupled receptor bound to a diffusible ligand. Ligand-binding site accessibility is enabled by the second extracellular loop, which is held out of the binding cavity by a pair of closely spaced disulfide bridges and a short helical segment within the loop. Cholesterol, a necessary component for crystallization, mediates an intriguing parallel association of receptor molecules in the crystal lattice. Although the location of carazolol in the beta2-adrenergic receptor is very similar to that of retinal in rhodopsin, structural differences in the ligand-binding site and other regions highlight the challenges in using rhodopsin as a template model for this large receptor family.
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                Author and article information

                Contributors
                m.alfonso-prieto@fz-juelich.de
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                11 June 2019
                11 June 2019
                2019
                : 9
                : 8437
                Affiliations
                [1 ]ISNI 0000 0001 2297 375X, GRID grid.8385.6, Computational Biomedicine, , Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich, ; Jülich, Germany
                [2 ]ISNI 0000 0001 0728 696X, GRID grid.1957.a, Department of Biology, , Rheinisch-Westfälische Technische Hochschule Aachen, ; Aachen, Germany
                [3 ]ISNI 0000 0004 1763 1124, GRID grid.5611.3, Department of Biotechnology, , University of Verona, ; Verona, Italy
                [4 ]ISNI 0000 0001 2297 375X, GRID grid.8385.6, JARA–HPC, IAS-5/INM-9 Computational Biomedicine, Forschungszentrum Jülich GmbH, ; Jülich, 52425 Germany
                [5 ]ISNI 0000 0001 0728 696X, GRID grid.1957.a, Department of Physics, , Rheinisch-Westfälische Technische Hochschule Aachen, ; Aachen, Germany
                [6 ]GRID grid.493130.c, VNU Key Laboratory “Multiscale Simulation of Complex Systems”, , VNU University of Science, Vietnam National University, ; Hanoi, Vietnam
                [7 ]ISNI 0000 0001 2167 7588, GRID grid.11749.3a, Center for Integrative Physiology and Molecular Medicine (CIPMM), , Saarland University, ; Homburg, Germany
                [8 ]ISNI 0000 0001 2176 9917, GRID grid.411327.2, Cécile and Oskar Vogt Institute for Brain Research, Medical Faculty, , Heinrich Heine University Düsseldorf, ; Düsseldorf, Germany
                Author information
                http://orcid.org/0000-0003-0319-5707
                Article
                44805
                10.1038/s41598-019-44805-z
                6560132
                31186454
                134119e9-9507-4a6e-891b-ecab586b5797
                © The Author(s) 2019

                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
                : 5 February 2019
                : 22 May 2019
                Funding
                Funded by: Ernesto Illy Foundation, Trieste, Italy
                Categories
                Article
                Custom metadata
                © The Author(s) 2019

                Uncategorized
                computational biophysics,protein structure predictions,molecular modelling,g protein-coupled receptors,sensory processing

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