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

通过分子动力学模拟了解 G 蛋白偶联受体的偏向激动机制

• 批准号: 1653834
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1653834
• 关键词: Molecular; Dynamics; simulations; understand; mechanism

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 conformationsUse the MD models to screen other ligands to predict biased/unbiased ligand phenotype and predict the effect of mutationsFurthermore 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.

G蛋白偶联受体（GPCR）是高度动态的蛋白质，在信号传导中表现出复杂的行为模式，例如，相同的GPCR可以偶联到细胞中的多种信号通路，而作用于相同GPCR的不同配体可以导致不同的信号输出（配体偏置）。这是制药行业特别感兴趣的，因为它可以导致开发具有增强治疗效果和更少副作用的新药。也许令人惊讶的是，gpcr配体偏倚等基本概念的计算模型并不先进。因此，本项目的目的是使用分子动力学模拟（MDs）作为了解配体偏倚在生物学上重要的GPCR的工具。Eamonn Kelly教授对配体与阿片受体（MOPr）的相互作用和分子信号传导进行了详细的研究，阿片受体是一种极其重要的GPCR，对哺乳动物的疼痛和奖励通路至关重要。埃蒙·凯利教授在这种受体的配体偏倚方面也有丰富的经验。Richard Sessions博士是一位经验丰富的蛋白质建模师，包括使用MDs对膜蛋白进行建模。他们希望通过计算机建模和受体细胞信号的测量相结合，了解mu阿片受体配体偏倚的分子基础。基于已发表的mu阿片受体和相关gpcr的晶体结构，学生将建立用于MDs的mu阿片受体模型，使用这些模拟来确定偏置/无偏置配体与MOPr结合囊相互作用的性质，以及由偏置和无偏置配体诱导/稳定的受体构象变化，以产生活性；使用MD模型来筛选其他配体，以预测有偏/无偏配体表型，并预测突变的影响。此外，学生将通过在哺乳动物细胞系中表达mu阿片受体和相关突变体，并确定该受体的有偏配体的结合和信号传导，来测试和确认这些建模结果。