Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis

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.

GABA A Receptor Family: Overview on Structural Characterization

“…Por un lado se estudió la unión de los ligandos GABA, muscimol, bicuculina y gabazine Una vez clasificados los aminoácidos se computaron dosíndices estadísticos: precisión y sensibilidad, que han sido empleados en trabajos para evaluar la calidad de modos de unión obtenidos computacionalmente [237][238][239][240][241].…”

Predicting ligand binding poses for low-resolution membrane protein models: Perspectives from multiscale simulations

“…Unfortunately, the average sequence identity between these chemosensory hGPCR targets and the hGPCR templates with available experimental structural information is invariably 20% or lower [23,109], making bioinformatics/docking-based structural predictions far from trivial [9,10,12,13,[123][124][125][126][127]. Indeed, predictions of agonist binding to bitter taste and olfactory receptors using a variety of docking approaches showed that their predictive power is very limited [23], as they are not able to capture the residues shown experimentally to be important for binding. These initial binding poses can be further refined with MD simulations, in particular using our MM/CG approach, resulting in a dramatic improvement in the predictions [23,128,129].…”

“…Indeed, predictions of agonist binding to bitter taste and olfactory receptors using a variety of docking approaches showed that their predictive power is very limited [23], as they are not able to capture the residues shown experimentally to be important for binding. These initial binding poses can be further refined with MD simulations, in particular using our MM/CG approach, resulting in a dramatic improvement in the predictions [23,128,129].…”

“…Moreover, they pointed out key interactions between hTAS2R38 and its agonists, which were impossible to capture by using the standard bioinformatics/docking approach only [23,128].…”

“…The predictions may be improved by combining docking with molecular dynamics (MD) simulations [21][22][23]. Unfortunately, these methods are still highly demanding in terms of resources, storage, and trajectory analysis, despite recent advances in hardware (such as the Anton massively parallel supercomputer [24,25]) and software (such as the GPU-based algorithms [26][27][28] and MD-based enhanced sampling methods [29,30]).…”