Multi-scale simulations of membrane proteins: The case of bitter taste receptors

Suku, Eda; Fierro, Fabrizio; Giorgetti, Alejandro; Alfonso‐Prieto, Mercedes; Carloni, Paolo

doi:10.1016/j.jsamd.2017.03.001

Cited by 9 publications

(14 citation statements)

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“…Structural predictions of human GPCRs are a challenge for computational biologists (Michino et al, 2009 ; Katritch et al, 2010 ; Kufareva et al, 2011 , 2014 ; Cavasotto and Palomba, 2015 ). Integration of experimental and computational information is fundamental to understand ligand binding to these proteins (Thomas et al, 2014 ; Munk et al, 2016 ), and in particular to hChem-GPCRs (Charlier et al, 2013 ; Di Pizio and Niv, 2014 ; Suku et al, 2017 ). The reliability of the structural predictions must be validated not only by comparison against previously published experimental data, but also by performing additional site-directed mutagenesis and functional experiments.…”

Section: Discussionmentioning

confidence: 99%

“…Understanding agonist binding of hChem-GPCRs at the molecular level can provide complementary insights on chemical sensing (Charlier et al, 2013 ; Di Pizio and Niv, 2014 ; Suku et al, 2017 ), as well as offer exciting and unexplored opportunities for drug design (Foster et al, 2014 ; Ferrer et al, 2016 ; Shaik et al, 2016 ; Lu et al, 2017 ). In addition, it may provide hints on receptors' agonist binding site architecture (Sandal et al, 2015 ) and activation mechanisms (Lai et al, 2005 , 2014 ; Biarnes et al, 2010 ; Dai et al, 2011 ; Singh et al, 2011a ; Pydi et al, 2014a ; de March et al, 2015b ).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, it may provide hints on receptors' agonist binding site architecture (Sandal et al, 2015 ) and activation mechanisms (Lai et al, 2005 , 2014 ; Biarnes et al, 2010 ; Dai et al, 2011 ; Singh et al, 2011a ; Pydi et al, 2014a ; de March et al, 2015b ). Because of the lack of experimental structural information, structural insights rely on computations (reviewed in Charlier et al, 2013 ; Di Pizio and Niv, 2014 ; Suku et al, 2017 ). The predictions may be validated against site-directed mutagenesis and functional (agonist dose–response curves) assays (Table 2 ).…”

Section: Introductionmentioning

confidence: 99%

“…Computational approaches aimed at structural predictions of hChem-GPCR/ligand complexes (reviewed in Charlier et al, 2013 ; Di Pizio and Niv, 2014 ; Suku et al, 2017 ) include homology modeling, based on GPCR X-ray structures as templates, along with molecular docking, often guided by information about the putative binding site, as done for other GPCRs (Michino et al, 2009 ; Kufareva et al, 2011 , 2014 ). Unfortunately, on one hand the sequence identity of hChem-GPCRs with GPCRs for which experimental structural information is available, is <20% (Charlier et al, 2013 ; Di Pizio and Niv, 2014 ; Suku et al, 2017 ). Hence, the resulting homology models have low statistical confidence.…”

Section: Introductionmentioning

confidence: 99%

“…Refinement of the hChem-GPCR/ligand complex models have been carried out by molecular dynamics (MD) simulation (Gelis et al, 2012 ; Lai and Crasto, 2012 ; Charlier et al, 2013 ; Marchiori et al, 2013 ; Lai et al, 2014 ; Sandal et al, 2015 ). This approach may alleviate some of the limitations of the bioinformatics procedure (Charlier et al, 2013 ; Di Pizio and Niv, 2014 ; Suku et al, 2017 ). In particular, it allows a more extensive exploration of the conformational space in the presence of the solvent (Spyrakis et al, 2011 ; Chen, 2015 ; Spyrakis and Cavasotto, 2015 ; Broomhead and Soliman, 2017 ), though at the expense of a higher computational cost.…”

Section: Introductionmentioning

confidence: 99%

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Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis

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et al. 2017

Front. Mol. Biosci.

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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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