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Time for a Step Change in Force Field Design

是时候对力场设计进行一步改变了

基本信息

批准号：
EP/X024393/1
负责人：
Paul Lode Albert Popelier
金额：
$ 274.53万
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FX024393%2F1
关键词：
Time Step Change Force Field

项目摘要

The literature shows that there is a serious problem at the heart of biomolecular simulation: it cannot reliably predict the structure and dynamics of proteins in aqueous solution. Thus, molecular dynamics simulation cannot usefully complement experiment while studying the onset of Alzheimer's disease. Popular force fields do not reliably reproduce the misfolding and aggregation of the intrinsically disordered amyloid beta peptide. Remedies usually add or take away ill-defined energy terms or repeatedly re-parameterise. Because this strategy has not solved the problem, force field architecture needs to beoverhauled. We propose FFLUX, a truly novel force field, that is much closer to the underlying quantum reality and one that "sees the electrons". FFLUX exploits a parameter-free definition of an atom inside a system. Using machine learning FFLUX learns how atomic energies, charges and multipole moments vary with the surrounding atoms' geometry. As such it captures all polarisation and many-body effects, as well as charge transfer, in one streamlined scheme. The approach avoids perturbation theory and thus benefits from a clear treatment of short-range interactions. Moreover, FFLUX breaks free from the rigid-body constraints of advanced polarisable force fields. The well-defined atom at the heart of FFLUX enables physics-based machine learning. It uses kriging instead of neural nets, thereby reducing the training data size. Our careful work plan is rooted in amino acids and water clusters, and scaled up to the solvated amyloid beta peptide via a sequence of increasingly relevant systems, both in gas-phase and in water. We willintroduce more sophisticated machine learning and implement state-of-art parallellisation on CPUs, GPUs and FPGAs, thereby offering a new user community the program DL_FFLUX. Bringing about a step change in biomolecular simulation is a huge task, even for this type of grant, but feasible as evidenced by proofs-of-concept from our group.

文献表明，生物分子模拟的核心存在一个严重的问题：它不能可靠地预测水溶液中蛋白质的结构和动力学。因此，在研究阿尔茨海默病的发病过程中，分子动力学模拟不能有效地补充实验。大众力场不能可靠地再现本质上无序的淀粉样β肽的错误折叠和聚集。补救措施通常是增加或删除定义不清的能量项或反复重新参数化。由于这一策略并没有解决问题，力场架构需要彻底改革。我们提出FFLUX，这是一种真正新颖的力场，它更接近潜在的量子现实，并且可以“看到电子”。FFLUX利用了系统内部原子的无参数定义。通过机器学习，FFLUX学习原子的能量、电荷和多极矩如何随周围原子的几何形状而变化。因此，它在一个流线型方案中捕获了所有的极化和多体效应，以及电荷转移。该方法避免了摄动理论，因此受益于对短程相互作用的清晰处理。此外，FFLUX摆脱了高级极化力场的刚体约束。在FFLUX的核心，定义良好的原子使基于物理的机器学习成为可能。它使用克里格而不是神经网络，从而减少了训练数据的大小。我们细致的工作计划植根于氨基酸和水簇，并通过一系列日益相关的系统（气相和水中）扩展到溶剂化淀粉样蛋白肽。我们将引入更复杂的机器学习，并在cpu、gpu和fpga上实现最先进的并行化，从而为新的用户社区提供DL_FFLUX程序。即使对于这种类型的拨款，在生物分子模拟方面带来一个阶段性的变化是一项艰巨的任务，但我们小组的概念证明是可行的。