A new computational strategy for the modelling of self-assembly biomolecules

自组装生物分子建模的新计算策略

• 批准号: 2104698
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2104698
• 关键词: new; computational; strategy; modelling; self

Metal ions play a vital role in many biological and chemical processes. Besides being of fundamental interest in chemistry, ions play a critical role in the assembly and function of biomolecules. Examples include protein cages,1, 2 multi-enzyme complexes,3 and RNA. Moreover, in combination with organic molecules, they also serve as building blocks for the assembly of synthetic systems metallocages. Metal-mediated self-assembly has emerged as a promising approach to develop complex biomimetic systems from simpler building blocks.4, 5 However, even for naturally occurring self-assemblies, understanding the fundamental rules driving self-organisation, structure and evolution remains a challenge, which substantially limits our ability to re-purpose the properties of these systems. The overall aim of this project is to . This aim will be met by addressing the following aspects: 1. Development of accurate force fields for metal centres (1-9 months). 2. Implementation of efficient modelling techniques to explore of self-assembly pathways (8-18 months). 3. Study of protein self-assembly in protein cages (18-36 months). 1. Development of accurate force fields for metal centres. While a plethora of automated force-field (FF) topology builders exist for organic molecules, systematic extensions to model transition metals (TMs) remain challenging. Here, we will utilise machine learning (ML) to develop potentials describing the behaviour of metal centres in aqueous solvent. Specifically, we will use the Gaussian Approximation Potentials (GAP) framework,6 in combination with ab initio and density functional theory calculations, to develop ML force field potentials that can describe metal centres in aqueous phase. To test the accuracy of these models, we will evaluate the properties of aquo-complexes for which experimental data are available. The metals initially considered will include: Mg (II), Fe (II), Fe (III), Cu(II), Co(II), Zn(II), and Ni(II). 2. Implementation of efficient modelling techniques to explore of self-assembly pathway. The student will be introduced to Markov state models (MSM) to explore the dynamics and kinetic of metal-driven self-assembly employing small peptide systems. NMR and calorimetry experimental data will be use to validate our results. The software package PyEMMA will be initially tested for this purpose.7 3. Study of protein self-assembly in protein cages. In a final stage, we aim to apply the force field models and the sampling techniques mentioned above to study the self-assembly in members of the ferritin superfamily. This knowledge will facilitate efforts towards controlled assembly/disassembly for new industrially relevant applications. We aim to address open questions regarding their assembly pathways, formation and stability of metastable species, metal-dependence and the potential for re-engineering. To make tangible steps towards this goal we will first study lower order species, which are predicted to be metastable states along the assembly process before moving into higher order aggregates or atomic resolution. This will be done in collaboration with Dr David Clarke (Edinburgh) who has carried out characterisation of this system and has the appropriate protocols for testing computationally designed constructs.Area: Theoretical Chemistry

金属离子在许多生物和化学过程中起着至关重要的作用。除了在化学中的基本兴趣，离子在生物分子的组装和功能中起着关键作用。实例包括蛋白笼、1、2多酶复合物、3和RNA。此外，与有机分子结合，它们还充当合成系统组装的构建块。金属介导的自组装已经成为一种很有前途的方法，可以从更简单的构建块开发复杂的仿生系统。然而，即使对于自然发生的自组装，理解驱动自组织，结构和进化的基本规则仍然是一个挑战，这大大限制了我们重新利用这些系统的能力。该项目的总体目标是。这一目标将通过解决以下几个方面来实现：1。为金属中心建立精确的力场（1-9个月）。2.实施有效的建模技术，探索自组装途径（8-18个月）。3.研究蛋白质笼中的蛋白质自组装（18-36个月）。1.为金属中心开发精确的力场。虽然有机分子存在过多的自动力场（FF）拓扑结构构建器，但过渡金属（TM）模型的系统扩展仍然具有挑战性。在这里，我们将利用机器学习（ML）来开发描述金属中心在水性溶剂中的行为的潜力。具体来说，我们将使用高斯近似势（GAP）框架，6结合从头算和密度泛函理论计算，开发可以描述水相中金属中心的ML力场势。为了测试这些模型的准确性，我们将评估实验数据可用的水性复合物的性质。最初考虑的金属将包括：Mg（II）、Fe（II）、Fe（III）、Cu（II）、Co（II）、Zn（II）和Ni（II）。2.实施有效的建模技术，探索自组装途径。学生将被引入马尔可夫状态模型（MSM），以探索采用小肽系统的金属驱动自组装的动力学和动力学。核磁共振和量热实验数据将被用来验证我们的结果。将为此目的对PyEMMA软件包进行初步测试。蛋白质笼中蛋白质自组装的研究。在最后一个阶段，我们的目标是应用力场模型和上述采样技术来研究铁蛋白超家族成员的自组装。这方面的知识将有助于努力控制新的工业相关应用程序的组装/拆卸。我们的目标是解决有关其组装途径，亚稳态物种的形成和稳定性，金属依赖性和重新设计的潜力的开放性问题。为了实现这一目标，我们将首先研究低阶物种，这是预测到亚稳态沿着组装过程中移动到更高阶的聚集体或原子分辨率。这将与大卫克拉克博士（爱丁堡）合作完成，他已经对该系统进行了表征，并具有用于测试计算设计的结构的适当协议。