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Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis
Fabrizio Fierro1, Eda Suku2, Mercedes Alfonso-Prieto1,3
1Computational Biomedicine, Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum JülichJülich, Germany.
Frontiers in Molecular Biosciences
|September 22, 2017
Summary
Bioinformatics and multi-scale simulations improve predictions of ligand interactions with human G-protein coupled receptors (hGPCRs). This study enhances understanding of chemosensory receptor activation, crucial for drug discovery.
Area of Science:
- Biochemistry
- Pharmacology
- Computational Biology
Background:
- Human G-protein coupled receptors (hGPCRs) are a large, pharmaceutically important membrane receptor superfamily.
- Chemosensory receptors (bitter taste and olfaction) are key hGPCRs involved in vital physiological processes.
- Molecular modeling is crucial for understanding hGPCR agonist binding and activation.
Purpose of the Study:
- To investigate ligand/receptor interactions and activation mechanisms of bitter taste and odorant receptors using bioinformatics.
- To evaluate the accuracy of traditional homology modeling and docking procedures for hGPCRs.
- To explore advanced computational methods, such as multi-scale simulations, for improved prediction accuracy.
Main Methods:
- Bioinformatics-based predictions across bitter taste and odorant receptors with available site-directed mutagenesis data.
- Comparative analysis of homology modeling and docking against experimental data.
- Review of multi-scale simulations for enhanced prediction of ligand/receptor complexes.
Main Results:
- State-of-the-art homology modeling and docking reproduced only a limited fraction of experimental ligand/receptor interactions.
- Low sequence identity with structural templates limits the accuracy of traditional modeling methods.
- Multi-scale simulations significantly enhanced the predictive power for studied hGPCR complexes.
Conclusions:
- Advanced computational methods are necessary to overcome limitations of traditional modeling for hGPCRs.
- Specific residues (e.g., 1.50, 2.50, 7.52) are implicated in hGPCR activation.
- Improved computational approaches can advance drug discovery targeting hGPCRs.