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Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis
Human G-protein coupled receptors (hGPCRs) constitute a large and highly pharmaceutically relevant membrane receptor superfamily. About half of the hGPCRs' family members are chemosensory receptors, involved in bitter taste and olfaction, along with a variety of other physiological processes. H...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5592726/ https://www.ncbi.nlm.nih.gov/pubmed/28932739 http://dx.doi.org/10.3389/fmolb.2017.00063 |
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author | Fierro, Fabrizio Suku, Eda Alfonso-Prieto, Mercedes Giorgetti, Alejandro Cichon, Sven Carloni, Paolo |
author_facet | Fierro, Fabrizio Suku, Eda Alfonso-Prieto, Mercedes Giorgetti, Alejandro Cichon, Sven Carloni, Paolo |
author_sort | Fierro, Fabrizio |
collection | PubMed |
description | Human G-protein coupled receptors (hGPCRs) constitute a large and highly pharmaceutically relevant membrane receptor superfamily. About half of the hGPCRs' family members are chemosensory receptors, involved in bitter taste and olfaction, along with a variety of other physiological processes. Hence these receptors constitute promising targets for pharmaceutical intervention. Molecular modeling has been so far the most important tool to get insights on agonist binding and receptor activation. Here we investigate both aspects by bioinformatics-based predictions across all bitter taste and odorant receptors for which site-directed mutagenesis data are available. First, we observe that state-of-the-art homology modeling combined with previously used docking procedures turned out to reproduce only a limited fraction of ligand/receptor interactions inferred by experiments. This is most probably caused by the low sequence identity with available structural templates, which limits the accuracy of the protein model and in particular of the side-chains' orientations. Methods which transcend the limited sampling of the conformational space of docking may improve the predictions. As an example corroborating this, we review here multi-scale simulations from our lab and show that, for the three complexes studied so far, they significantly enhance the predictive power of the computational approach. Second, our bioinformatics analysis provides support to previous claims that several residues, including those at positions 1.50, 2.50, and 7.52, are involved in receptor activation. |
format | Online Article Text |
id | pubmed-5592726 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-55927262017-09-20 Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis Fierro, Fabrizio Suku, Eda Alfonso-Prieto, Mercedes Giorgetti, Alejandro Cichon, Sven Carloni, Paolo Front Mol Biosci Molecular Biosciences Human G-protein coupled receptors (hGPCRs) constitute a large and highly pharmaceutically relevant membrane receptor superfamily. About half of the hGPCRs' family members are chemosensory receptors, involved in bitter taste and olfaction, along with a variety of other physiological processes. Hence these receptors constitute promising targets for pharmaceutical intervention. Molecular modeling has been so far the most important tool to get insights on agonist binding and receptor activation. Here we investigate both aspects by bioinformatics-based predictions across all bitter taste and odorant receptors for which site-directed mutagenesis data are available. First, we observe that state-of-the-art homology modeling combined with previously used docking procedures turned out to reproduce only a limited fraction of ligand/receptor interactions inferred by experiments. This is most probably caused by the low sequence identity with available structural templates, which limits the accuracy of the protein model and in particular of the side-chains' orientations. Methods which transcend the limited sampling of the conformational space of docking may improve the predictions. As an example corroborating this, we review here multi-scale simulations from our lab and show that, for the three complexes studied so far, they significantly enhance the predictive power of the computational approach. Second, our bioinformatics analysis provides support to previous claims that several residues, including those at positions 1.50, 2.50, and 7.52, are involved in receptor activation. Frontiers Media S.A. 2017-09-06 /pmc/articles/PMC5592726/ /pubmed/28932739 http://dx.doi.org/10.3389/fmolb.2017.00063 Text en Copyright © 2017 Fierro, Suku, Alfonso-Prieto, Giorgetti, Cichon and Carloni. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Molecular Biosciences Fierro, Fabrizio Suku, Eda Alfonso-Prieto, Mercedes Giorgetti, Alejandro Cichon, Sven Carloni, Paolo Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis |
title | Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis |
title_full | Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis |
title_fullStr | Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis |
title_full_unstemmed | Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis |
title_short | Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis |
title_sort | agonist binding to chemosensory receptors: a systematic bioinformatics analysis |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5592726/ https://www.ncbi.nlm.nih.gov/pubmed/28932739 http://dx.doi.org/10.3389/fmolb.2017.00063 |
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