Molecular Dynamics simulations to understand the mechanism of biased agonism at a G protein coupled receptor

G protein-coupled receptors (GPCRs) are highly dynamic proteins that display complex patterns of behaviour in signalling, for example the same GPCR can couple to multiple signalling pathways in a cell, whilst different ligands acting at the same GPCR can lead to distinct signalling outputs (ligand bias). This is of particular interest to the pharmaceutical industry as it can lead to the development of novel drugs with enhanced therapeutic efficacy and fewer adverse effects. Perhaps surprisingly, the computational modelling of fundamental concepts such as ligand bias at GPCRs is not well advanced. The aim of this project therefore is to use Molecular dynamics simulations (MDs) as a tool to understand ligand bias at a biologically important GPCR.

Prof Eamonn Kelly has made detailed studies of ligand interaction with, and molecular signalling of, the opioid receptor (MOPr), an extremely important GPCR which is crucial for pain and reward pathways in mammals. Prof Eamonn Kelly also has wide experience of ligand bias at this receptor. Dr Richard Sessions is a highly experienced protein modeller, including the use of MDs to model membrane proteins. Together they wish to understand the molecular basis of ligand bias at the mu opioid receptor, using a combination of computer modelling and measures of receptor cell signalling. Based upon the published crystal structure of the mu opioid receptor and related GPCRs, the student would build a model of the mu opioid receptor for MDs, using these simulations to:

Determine the nature of the interaction of biased/unbiased ligands with the MOPr binding pocket, as well as receptor conformational changes induced/stabilised by biased and unbiased ligands to produce active, presumably distinct receptor conformations
Use the MD models to screen other ligands to predict biased/unbiased ligand phenotype and predict the effect of mutations
Furthermore the student will test and confirm these modelling outcomes by expressing mu opioid receptor and relevant mutants in mammalian cell lines and determining the binding and signalling of biased ligands at this receptor.