Novel force fields devised using machine learning

使用机器学习设计的新颖力场

• 批准号: BB/F003617/1
• 负责人: Paul Lode Albert Popelier
• 金额: $ 13.44万
• 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: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FF003617%2F1
• 关键词: Novel; force; fields; devised; using

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.

蛋白质因其在生命系统中的多功能性而被认为是一类非常重要的分子。该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。 Ponder 和 Case（流行计算机程序 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大幅提高，否则生物分子建模将陷入停滞。我们相信，最好的前进方式是重新开始，而不是调整现有的力场。 AMBER 等力场的设计是基于 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，这意味着可以采用新颖的力场设计理念，从一开始就避免使用 AMBER 等力场的近似值。在我们之前的工作中，我们引入了多极矩来代替点电荷。多极矩比点电荷更好地反映了原子的局部电子密度，点电荷错误地假设该密度是球形的。多极矩可以更准确地模拟一个原子与另一个原子之间的静电相互作用，尤其是在短距离内。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“装饰”蛋白质/肽主链，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，可以很好地可视化。这些原子也被广泛记录，深深扎根于量子力学并用于解释目的（例如电荷转移、氢键）。在沿着这些思路设计力场时，我们预先对关键的静电相互作用能量进行了建模，没有像构建经典力场那样进行拟合。我们的方法大大减少了拟合参数的数量。此外，安装的电荷不一定可以从小分子转移到较大分子。另一方面，量子拓扑原子在很大程度上是可转移的。然后，仅剩余的能量贡献需要适合训练分子的“从头开始”能量、力和振动频率。在这个提议中，我们关注电子密度的极化，即核位置变化时电子密度的变化。新颖的元素是使用先进的机器学习来捕获波动多极矩和核位置之间的关系。遗传编程算法的输入是给定中心原子的相邻原子的坐标，输出是给定中心原子的脉动多极矩。如果成功，所提出的方法预计也适用于其他重要类别的生化化合物，例如核苷酸（DNA、RNA）、碳水化合物和脂质，这些将在未来的项目中解决。鉴于未来十年计算机能力将增加至少两个数量级，我们的目标是保证大分子建模的未来更加安全。鉴于我们团队所建立的势头，我们处于理想的位置，可以将之前研究和发布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。