Developing FFLUX to predictively model short peptide fibrillation

- 该项目试图解决的研究问题/该项目的目标;在过去的十年中，CoI的小组已经开发了一个平台，用于基于具有交替的亲水和疏水残基的短的两亲性肽家族（通常为8至12个氨基酸长，每个氨基酸在下面由字母代码表示，例如V=缬氨酸）设计肽水凝胶。总体目标是系统地测试和进一步开发PI小组的新FFLUX-REG方法，用于解释和预测β折叠原纤化。然后，这种计算工具将指导肽设计。具体而言，我们将：1.用FFLUX-REG研究八肽FEFKFEFK和AEAKAEAK，并正确和深入地理解为什么前者是强β折叠形成剂而后者不是。2.理解和控制构象空间时，交换A与F：例如AEFKAEFK，FEAKFEAK仅举几例. 3.了解疏水性（用L，E，I和V替换F）和亲水性（用D替换E，用R替换K）氨基酸的作用。将采取的方法来回答这些问题（学生实际上会做什么）;相关的科学协议和方法被称为FFLUX，这是一个全新的力场，由新颖的原理设计并编码为软件包。这使得该项目真正具有开创性和雄心勃勃的，因为它远远超出了使用标准力场，如琥珀。下一代内部力场FFLUX比基于点电荷的力场（如AMBER）更真实。此外，FFLUX“看到了电子”，因此更接近最终控制所有物质行为的基本量子力学。FFLUX还引入了多极矩，这对精确的静电学至关重要。 有一种现代而精确的能量划分方法称为相互作用量子原子（IQA），它提供了原子能量分析严格性的一个步骤。IQA是一种直观的无参数方法，但同时非常接近原子本身的量子力学特性。 学生有三种类型的活动：-方法和编码的开发：（i）高斯过程回归机器学习（ML）的改进，目标是训练水簇，（ii）所谓的“编织”过程，其中较小系统的ML模型预测大型系统中的原子性质，以及（iii）在色散能量方面将后Hartree-Fock波函数纳入FFLUX。在水溶液中对八肽运行DL_FFLUX并分析轨迹。运行内部REG.py代码，以便对个人能源贡献进行排名，并着眼于解释总能源概况的行为。研究的新工程和/或物理科学内容（将其置于EPSRC职权范围内的科学）。总的来说，该项目属于主题物理科学的两个子组合，即“计算和理论”和“凝聚态物质：电子结构”。该项目也属于化学科学大挑战，即定向组装具有目标性质的扩展结构（DAESTP）。最后，这个项目有一个强大的机器学习组件，因此与人工智能重叠。考虑到对肽的长期影响，该提案将适合化学生物学和生物化学的优先级。此外，从长远来看，该提案与医疗保健技术的重大挑战有关。这个项目有一个强大的机器学习组件，因此与人工智能重叠。

- the research questions the project is trying to address/the objectives of the project; In past decade the CoI's group has developed a platform for the design of peptide hydrogels based on a family of short amphipathic peptides (typically 8 to 12 amino acids long, each amino acid expressed below by a letter code, e.g. V=valine) with alternating hydrophilic and hydrophobic residues. The overall objective is to systematically test and further develop the novel FFLUX-REG method of the PI's group in both the interpretation and prediction of beta-sheet fibrillation. This computational tool will then guide peptide design. In particular, we will: 1. Investigate with FFLUX-REG the octapeptides FEFKFEFK and AEAKAEAK, and obtain a correct and deep understanding as to why the former is a strong beta-sheet former while the latter is not.2. Understand and control conformational space when swapping A with F: e.g. AEFKAEFK, FEAKFEAK to name a few.3. Understand the role of hydrophobic (replacement of F with L, E, I and V) and hydrophilic (replacement of E by D, and K by R) amino acids.- the approach that will be taken to answer these questions (what the student will actually be doing); The associated scientific protocol and methodology is called FFLUX, which is a completely new force field, designed by novel principles and encoded as a software package. This makes the project truly ground breaking and ambitious because it goes far beyond using standard force fields such as AMBER. The next-generation in-house force field FFLUX is much more realistic than a point-charge based force field such as AMBER. Moreover, FFLUX "sees the electrons" and is hence closer to the underlying quantum mechanics that ultimately governs the behaviour of all matter. FFLUX also introduces multipole moments, which is essential for accurate electrostatics. There is a modern and accurate energy partitioning method called Interacting Quantum Atoms (IQA), which offers a step change in the rigour of atomistic energy analysis. IQA is a parameter-free method that is intuitive but, at the same time, very close to the quantum mechanical character of atoms themselves. There are three types of activities for the student:- development of methodology and coding: (i) improvement of Gaussian Process Regression machine learning (ML) with the goal of training for water clusters, (ii) so-called "knitting" procedure where ML models of smaller systems make predictions for atomic properties in large systems, and (iii) incorporation into FFLUX of post-Hartree-Fock wavefunctions in terms of dispersion energies.- Running of DL_FFLUX on octapeptides in aqueous solution and analysing trajectories.- Running of the in-house REG.py code in order to rank individual energy contributions with an eye on explaining the behaviour of total energy profiles.- the novel engineering and/or physical sciences content of the research (the science that places it within EPSRC's remit). Overall the project fits under two sub-portfolios of the Theme Physical Sciences, namely "Computational and Theoretical", and "Condensed Matter: Electronic Structure". This project also resorts under the Chemical Sciences Grand Challenge of Directed Assembly of Extended Structures with Targeted Properties (DAESTP). Finally, there is a strong Machine Learning component to this project, and thus overlap with Artificial Intelligence. Given the longer term impact on peptides, this proposal will fit the priority Chemical Biology and Biological Chemistry. Furthermore, in the very long term, this proposal is relevant for the grand challenge Healthcare Technologies. There is a strong Machine Learning component to this project, and thus overlap with Artificial Intelligence.