Novel force fields devised using machine learning

蛋白质被认为是一类非常重要的分子，因为它们在生命系统中具有多种功能。本提案从理论和计算的角度解决了寡肽（最终是蛋白质）结构预测的首要问题。庞德和凯斯（颇受欢迎的计算机程序AMBER的作者之一）最近发表的一篇权威评论认为，除非当前力场的准确性得到大幅提高，否则生物分子建模将会停滞不前。我们认为，最好的前进方式是重新开始，而不是调整现有的力场。力场（如AMBER）的设计是基于20世纪80年代可用的计算能力。从那时起，计算能力增加了10,000倍，这意味着可以采用一种新的力场设计理念，从一开始就避免了力场的近似，如AMBER。在我们之前的工作中，我们引入了多极矩来代替点电荷。多极矩比点电荷更能反映原子的局部电子密度，因为点电荷错误地认为原子的局部电子密度是球形的。多极矩更准确地模拟了原子与原子之间的静电相互作用，特别是在近距离内。我们使用现代量子化学拓扑理论（又名“分子中的原子”）将小分子的电子密度划分为原子碎片。然后，这些片段“装扮”蛋白质/肽骨架，并提供有关其电子密度的详细信息。量子拓扑原子具有可变形状的有限体积，这可以很好地可视化。这些原子也被广泛记录，深深植根于量子力学，并用于解释目的（例如电荷转移，氢键）。在沿着这些线设计力场时，我们预先模拟了关键静电相互作用能，而不是拟合，就像在构建经典力场时所做的那样。我们的方法大大减少了拟合参数的数量。此外，适合的电荷不一定能从小分子转移到大分子。另一方面，量子拓扑原子在很大程度上是可转移的。只有剩余的能量贡献，然后需要适应“从头开始”的能量，力和训练分子的振动频率。在这个建议中，我们把重点放在电子密度的极化上，也就是说，电子密度随着核位置的变化而变化。新颖的元素是使用先进的机器学习来捕捉波动多极矩和核位置之间的关系。遗传规划算法的输入是给定中心原子相邻原子的坐标，输出是给定中心原子的波动多极矩。如果成功，该方法有望应用于其他重要的生物化学化合物，如核苷酸（DNA、RNA）、碳水化合物和脂类，这些将在未来的项目中得到解决。在接下来的十年里，计算机的能力至少会增加两个数量级，我们的目标是保证大分子建模的未来更加安全。鉴于我们团队的发展势头，我们处于一个理想的位置，可以将之前研究和发表的设计的所有组成部分整合到一个连贯的软件包中，该软件包将免费提供给英国研究界。

Proteins are recognised as a very important class of molecules because of their versatile functionality in living systems. This proposal addresses the paramount problem of oligopeptide (and ultimately protein) structure prediction from a theoretical and computational point of view. A recent and authoritative review by Ponder and Case (one of the authors of the popular computer program AMBER) argues that biomolecular modelling will grind to a halt unless the accuracy of current force fields is substantially increased. We believe that the best way forward is by starting afresh rather than by tweaking existing force fields. The design of force fields such as AMBER was based on the computing power available in the 1980s. Since that time computing power has increased by a factor 10,000, which means that a novel force field design philosophy can be adopted, avoiding from the outset the approximations of force fields such as AMBER. In our previous work we introduced multipole moments to replace point charges. Multipole moments reflect better the local electron density of an atom than do point charges, which wrongly assume that this density is spherical. Multipole moments model the electrostatic interaction between one atom and another more accurately, especially at short range. We use the modern theory of Quantum Chemical Topology (aka 'Atoms in Molecules') to partition the electron density of small molecules into atomic fragments. These fragments then 'dress up' a protein/peptide backbone and provide detailed information on its electron density. Quantum topological atoms have a finite volume of variable shape, which can be nicely visualised. These atoms are also widely documented, strongly rooted in quantum mechanics and used for interpretative purposes (e.g. charge transfer, hydrogen bonding). While designing a force field along these lines we modeled the pivotal electrostatic interaction energy upfront, without fitting, as is done in constructing classical force fields. Our approach drastically reduces the number of fitted parameters. Moreover fitted charges are not necessarily transferable from small molecules to larger ones. On the other hand, quantum topological atoms are transferable to a very large extent. Only the remaining energy contributions then need to be fitted to 'ab initio' energies, forces and vibrational frequencies of training molecules. In this proposal we focus on the polarisation of the electron density, that is, the change in the electron density upon a change in the nuclear positions. The novel element is to use advanced machine learning to capture the relation between fluctuating multipole moments and nuclear positions. The input of the Genetic Programming algorithm are the coordinates of the neighbouring atoms of a given central atom and the output is a given fluctuating multipole moment of the central atom. If successful, the proposed methodology is expected to work for other important classes of biochemical compounds as well, such as nucleotides (DNA, RNA), carbohydrates and lipids, which will be tackled in future projects. Given an increase in computer power of at least two orders of magnitude occurring over the next decade we aim at guaranteeing a more secure future of macromolecular modeling. Given the momentum built up in our group we are in an ideal position to consolidate all the components of the design, previously researched and published, into a coherent software package that will be freely available to the UK research community.