Predictive modelling of ligand binding to flexible proteins

配体与柔性蛋白质结合的预测模型

• 批准号: EP/K002082/1
• 负责人: Julien Michel
• 金额: $ 12.02万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FK002082%2F1
• 关键词: Predictive; modelling; ligand; binding; flexible

It is now recognised that a large fraction of the human proteome is made of extremely flexible proteins whose structure cannot be characterized by a unique fold. These intrinsically disordered proteins (IDPs) play key roles in cells and have been implicated in a striking range of diseases such as cancers, neurodegenerative disorders and diabetes. These diseases will become increasingly prevalent in an aging UK population. It is therefore highly desirable to develop small drug-like molecules that could bind to IDPs and modulate their function. Yet it is very difficult to determine the range and nature of conformations adopted by an IDP using experimental techniques. IDPs are therefore generally considered "undruggable" by the pharmaceutical industry. It is important to develop new techniques and technologies to address the national and global health challenges caused by IDPs. Computer simulations have the potential to provide detailed structural models of IDP/small molecule interactions to guide rational structure-based drug design efforts. However standard simulation techniques are unable to perform this task. Their description of intermolecular interactions is too approximate. The exploration of the complicated energy landscape of IDPs is too time consuming. It is therefore essential to develop novel simulation methodologies that can handle the high flexibility of IDPs.We propose the development of new molecular simulation algorithms that will enable the rapid computation of the structural, thermodynamic and kinetic properties of IDPs in complex with drug-like molecules. Our research is structured around three objectives:1. We will develop a force-field optimisation method that iterates biased molecular dynamics simulations and energy reweighting to minimize systematic errors in the prediction of molecular observables for IDP/small molecule complexes (e.g. NMR chemical shifts). 2. We will develop a simulation method to steer "on-the-fly" computational efforts towards the exploration of molecular conformations that contribute the most to overall uncertainties in predicting the dynamics of IDP/small molecule complexes.3. We will perform simulation studies to elucidate the mechanisms of small molecule binding to selected IDPs. Such interactions currently challenge our understanding of molecular recognition. Test systems will include for instance the IDPs c-myc and p53 that play key roles in the progression of several cancers. The primary goal of this research is therefore to develop new computational methodologies that will enable preclinical drug discovery efforts to target intrinsically disordered proteins with structure-based approaches. In addition the algorithms and software developed during this research will be widely applicable and this research will also enable applications in a broad range of soft-condensed matter research areas.

现在人们认识到，人类蛋白质组的很大一部分是由非常灵活的蛋白质组成的，其结构不能以独特的折叠为特征。这些内在无序蛋白（IDP）在细胞中起着关键作用，并与癌症、神经退行性疾病和糖尿病等一系列疾病有关。这些疾病将在英国老龄化人口中变得越来越普遍。因此，非常需要开发可以结合IDP并调节其功能的小药物样分子。然而，它是非常困难的，以确定的范围和性质的构象所采用的IDP使用实验技术。因此，制药业一般认为国内流离失所者是“不能用药的”。必须开发新的技巧和技术，以应对国内流离失所者造成的国家和全球健康挑战。计算机模拟有可能提供详细的IDP/小分子相互作用的结构模型，以指导合理的基于结构的药物设计工作。然而，标准的模拟技术无法执行此任务。他们对分子间相互作用的描述过于近似。探索国内流离失所者复杂的能源前景太费时间。因此，它是必不可少的，以开发新的模拟方法，可以处理的IDPs的高灵活性。我们提出了新的分子模拟算法的发展，这将使快速计算的结构，热力学和动力学性质的IDPs在复杂的药物样分子。我们的研究围绕三个目标：1。我们将开发一种力场优化方法，该方法迭代有偏的分子动力学模拟和能量重新加权，以最大限度地减少IDP/小分子复合物（例如NMR化学位移）分子可观测量预测的系统误差。2.我们将开发一种模拟方法，以引导“在飞”的计算工作，对分子构象的探索，贡献最大的整体不确定性，在预测IDP/小分子复合物的动力学。我们将进行模拟研究，以阐明小分子结合到选定的IDP的机制。这种相互作用目前挑战了我们对分子识别的理解。测试系统将包括例如在几种癌症的进展中起关键作用的IDP c-myc和p53。因此，这项研究的主要目标是开发新的计算方法，使临床前药物发现工作能够以基于结构的方法靶向内在无序的蛋白质。此外，在这项研究中开发的算法和软件将被广泛应用，这项研究也将使软凝聚态物质的研究领域的广泛应用。

项目成果

Elucidation of Ligand-Dependent Modulation of Disorder-Order Transitions in the Oncoprotein MDM2.

• DOI: 10.1371/journal.pcbi.1004282
• 发表时间: 2015-06
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Bueren-Calabuig JA;Michel J
• 通讯作者: Michel J