Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis

Fierro, Fabrizio; Giorgetti, Alejandro; Carloni, Paolo; Cichon, Sven; Alfonso-Prieto, Mercedes; Suku, Eda

doi:10.3389/fmolb.2017.00063

Journal Article

FZJ-2017-06445

Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis

Fierro, F. (First author)FZJ* ; Suku, E. (First author) ; Alfonso-Prieto, M. (Corresponding author)FZJ* ; Giorgetti, A. (Corresponding author)FZJ* ; Cichon, S.FZJ* ; Carloni, P. (Last author)FZJ*

2017
Frontiers Lausanne

Frontiers in molecular biosciences 4, 63 (2017) [10.3389/fmolb.2017.00063]

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Please use a persistent id in citations: http://hdl.handle.net/2128/15282 doi:10.3389/fmolb.2017.00063

Abstract: 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.

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