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Reliable computational prediction of molecular assembly

分子组装的可靠计算预测

基本信息

批准号：
EP/K005472/1
负责人：
Paul Lode Albert Popelier
金额：
$ 159.25万
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2013
资助国家：
英国
起止时间：
2013 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FK005472%2F1
关键词：
Reliable computational prediction molecular assembly

项目摘要

An important modern frontier of research in the physical sciences is the proper understanding and control over molecular assembly. The Grand Challenge of "Directed Assembly of Extended Structures with Targeted Properties" tackles this frontier from various angles. I focus on the angle of chemical computing, which has established itself as an independent source of information, complementary to experiment. There is a need, of ever increasing urgency, for accurate and hence more reliable prediction of interaction energies between molecules. The structure and dynamics of molecular assemblies sensitively depend on most subtle energy changes. This is why the scientific challenge of accurate energy prediction is still as acute as ever. If energy is correctly predicted then everything else follows: realistic structures, dynamics and properties. I propose a novel approach, drastically different to the current paradigm. Matter at ambient conditions is governed by a master equation called the Schrödinger equation, which returns the interaction energy of a molecular assembly. Solving this master equation accurately for sizeable molecular aggregates is very expensive or even impossible with current computer power. Force fields, however, can provide this interaction energy, and do so many orders of magnitude faster. A force field is a formula that delivers the energy of a molecular system as a direct function of the system's atomic coordinates. This formula contains many parameters, specific to the system at hand. The challenge is to design a force field that is reliable. The best and only long term strategy is to map the force field as faithfully as possible onto the solution of the Schrödinger equation, both in terms of energy and the wave function. With exascale computers around the corner and GPU technology recently overtaking CPUs, it is pivotal and timely to invest into more realistic force fields. Here I aim to offer biomolecular modelling a completely new route to designing a force field, with much more truthful electrostatics. We propose the completed construction of the first ever (high-rank) multipolar force field for flexible molecules, with both intra- and intermolecular polarisation. This is crucial for molecular assembly and recognition, as well as the realistic modelling of hydrogen bonding. Molecular systems, in the presence of the strong and inhomogeneous electric fields caused by ions, will also be modelled realistically for the first time. The true predictive power of a force field depends on the reliability of the information transfer of small molecules (or molecular clusters) to large molecules. Only if this transferability is high, a force field will make reliable predictions. The main idea behind our force field, called QCTFF, is to construct "knowledgeable" atoms. These atoms are drawn from small molecules and made to interact in order to predict properties of large molecules. They are 3D fragments of electron density, with a finite volume. These atoms have sharp boundaries, which endows them with a "malleable" character. Their precise shape responds to the immediate environment of the molecule they are part of. A machine learning method then captures how these atoms change their multipole moments in response to the positions of their neighbours. We have successfully reached the proof-of-concept stage of this novel idea and now I intend to fully exploit it.Although QCTFF is generic, its application is biased towards proteins, ions and water. Only a fellowship can deliver the ambitious but feasible goal of creating this transformative enabling technology towards life science applications. This technology will also serve as a robust platform from which to develop an innovative novel force field, to study reactions in solution and in enzymes, as well as crystal nucleation. The radical and innovating decisions taken at the outset of QCTFF's design are the best guarantee for its long lasting success.

现代物理科学研究的一个重要前沿是对分子组装的正确理解和控制。“具有目标特性的扩展结构的定向组装”的大挑战从各个角度解决了这一前沿问题。我关注的是化学计算的角度，它已经确立了自己作为一个独立的信息来源，补充实验。人们越来越迫切地需要精确地并因此更可靠地预测分子之间的相互作用能。分子组装体的结构和动力学敏感地依赖于最细微的能量变化。这就是为什么准确的能源预测的科学挑战仍然像以往一样尖锐。如果能量被正确预测，那么其他一切都将随之而来：现实的结构，动力学和属性。我提出了一种新颖的方法，与当前的范式截然不同。物质在环境条件下由一个称为薛定谔方程的主方程控制，该方程返回分子组装的相互作用能。精确地求解这个主方程对于相当大的分子聚集体是非常昂贵的，甚至是不可能的与当前的计算机能力。然而，力场可以提供这种相互作用能，并且速度快了许多数量级。力场是一个公式，它传递分子系统的能量，作为系统原子坐标的直接函数。该公式包含许多特定于当前系统的参数。挑战在于设计一个可靠的力场。最好的和唯一的长期策略是尽可能忠实地将力场映射到薛定谔方程的解上，无论是能量还是波函数。随着亿级计算机即将到来，GPU技术最近超过了CPU，投资于更现实的力场是关键和及时的。在这里，我的目标是提供生物分子建模一个全新的路线来设计一个力场，更真实的静电。我们建议完成建设的第一次（高阶）多极力场的灵活的分子，与内部和分子间的极化。这对于分子组装和识别以及氢键的真实建模至关重要。分子系统，在由离子引起的强和不均匀的电场的存在下，也将第一次逼真地建模。力场的真正预测能力取决于小分子（或分子簇）向大分子传递信息的可靠性。只有当这种可转移性很高时，力场才能做出可靠的预测。我们的力场背后的主要思想，称为QCTFF，是构建“知识”原子。这些原子是从小分子中提取出来的，并进行相互作用以预测大分子的性质。它们是电子密度的3D碎片，具有有限的体积。这些原子具有尖锐的边界，这赋予它们“可塑性”。它们的精确形状响应于它们所处的分子的直接环境。然后，一种机器学习方法捕捉这些原子如何改变其多极矩以响应其邻居的位置。我们已经成功地达到了这个新想法的概念验证阶段，现在我打算充分利用它。虽然QCTFF是通用的，但它的应用偏向于蛋白质，离子和水。只有奖学金才能实现雄心勃勃但可行的目标，即为生命科学应用创造这种变革性的使能技术。该技术还将作为一个强大的平台，从中开发创新的新力场，研究溶液和酶中的反应以及晶体成核。QCTFF在设计之初所采取的激进和创新的决定是其长期成功的最佳保证。