Computer simulation of metal-amyloid interaction and its role in plaque formation

阿尔茨海默病是21世纪社会面临的最大的医疗挑战之一。这种疾病的特点是大脑功能，特别是记忆功能的逐渐丧失，它给人类、社会和经济造成的代价已经是巨大的，预计在未来几十年里，这种代价将会更大。阿尔茨海默病与脑组织中原纤维和斑块的形成有关，这些斑块是神经元外部和周围致密的、大部分不溶性的蛋白质和细胞物质沉积，损害神经元的正常功能。斑块是由淀粉样肽聚集形成的，淀粉样肽在分离时可溶，但在相互结合时不溶。金属的存在，特别是铜、锌和铁，是淀粉样β肽聚集和随后毒性的重要组成部分：在患病大脑的斑块区域发现Cu和Zn水平升高，而那些不含金属离子的斑块被发现是无毒的。此外，不同的金属，如铂和钌已被证明可以抑制聚集，为治疗和诊断开辟了新的途径。确定金属如何与淀粉样β肽结合的实验是困难和昂贵的：肽本身具有固有的高度柔韧性，并且倾向于聚集成不可溶的团块，无法使用光谱学等传统手段进行研究。此外，纯合成它们既昂贵又有问题。从这个角度来看，利用计算机模拟金属如何与淀粉样肽结合并影响其结构和聚集是一个有吸引力的提议。计算机模型用于现代生活的各个领域，计算机辅助分子设计在新药、农用化学品、催化剂、染料和材料的发现中起着至关重要的作用，仅举几例。该项目将使用现代模拟方法详细描述金属如何与导致阿尔茨海默氏症的肽结合，以及不同金属对其结构和聚集特性的影响。为了以可靠的方式做到这一点，我们需要能够使用超级计算机来描述数百或数千个原子的运动的方法，同时也要适当地描述金属原子在不同环境中的特定化学性质。我们已经确定配体场分子力学（LFMM）作为这项任务的理想候选者，因为它有效且可转移地捕获金属的行为，并且先前已用于检查含铜蛋白质的动力学和光谱以及铂基药物与DNA的结合等过程。由于实验结构很少，我们将通过比较较慢但更严格的量子力学和混合量子分子力学（QM/MM）方法来测试这种方法对金属-淀粉样蛋白相互作用的具体情况。这样做之后，我们将在分子动力学模拟中使用LFMM来明确地允许肽改变其形状以响应不同的金属。至关重要的是，LFMM的速度加上我们可用的超级计算资源意味着我们可以同时模拟两个或多个肽的行为，从而检查金属对聚合初始阶段的影响。

Alzheimer's disease is one of the greatest healthcare challenges facing 21st century society. Characterised by progressive loss of brain function, especially memory, the human, social and financial costs of this disease are already huge, and are forecast to become even more so in the coming decades. AD is associated with formation of fibrils and plaques (dense, mostly insoluble deposits of protein and cellular material outside and around neurons) in brain tissue that impair proper functioning of neurons. Plaques are formed by aggregation of amyloid-beta peptides that are soluble in isolation, but insoluble when bound to one another. The presence of metals, notably copper, zinc and iron, is a vital part of the aggregation and subsequent toxicity of amyloid beta peptides: increased levels of Cu and Zn are found in plaque regions of diseased brain, and those plaques which do not contain metal ions have been found to be non-toxic. Moreover, different metals such as platinum and ruthenium have been shown to inhibit aggregation, opening new avenues for treatment and diagnosis.Experiments to determine how metals might bind to amyloid beta peptides are difficult and costly to perform: the peptides themselves are inherently highly flexible, and tend to aggregate into an insoluble mass that cannot be studied using conventional means such as spectroscopy. Moreover, they can be expensive and problematic to synthesise in pure form. In this light, using computers to simulate how metals bind to amyloid beta peptides and affect their structure and aggregation is an attractive proposition. Computer models are used in all walks of modern life, and computer-aided molecular design plays a vital role in the discovery of new drugs, agrochemicals, catalysts, dyes and materials, to name but a few.This project will use modern simulation methods to describe in detail how metals can bind to the peptides that cause Alzheimer's, and the effect different metals have on their structure and aggregation characteristics. To do this in a reliable manner, we need methods that are capable of using supercomputers to describe the motions of hundreds or thousands of atoms, while also properly describing the particular chemistry of metal atoms in different environments. We have identified ligand field molecular mechanics (LFMM) as the ideal candidate for this task, as it efficiently and transferably captures the behaviour of metals, and has been used previously to examine processes such as the dynamics and spectroscropy of copper-containing proteins and the binding of platinum- based drugs to DNA. We will test this method for the specific case of metal-amyloid interactions by comparing against slower but more rigorous quantum mechanical and hybrid quantum-molecular mechanical (QM/MM) methods, since experimental structures are scarce. Having done so, we will use LFMM within molecular dynamics simulations to explicitly allow the peptide to change its shape in response to different metals. Crucially, the speed of LFMM coupled with the supercomputing resources available to us means that we can simulate the behaviour of two or more peptides together, and hence to examine the effect of metal on the initial stages of aggregation.