Predicting chiral crystallization

犹他大学的Michael Gruenwald获得了化学学部化学理论、模型和计算方法（CTMC）项目的奖励，对手性分子的结晶进行理论和计算研究。如果物体不能与其镜像重叠，就被称为手性物体——就像人类的左手和右手一样。许多分子，如氨基酸（DNA中的）和糖，对人体的功能很重要，是手性的。它们以左旋和右旋成对存在，称为对映体。值得注意的是，当涉及到医学时，在许多药物应用中只需要一种对映体。通常一种手性形式是有益的，而另一种可能没有活性或可能有害。实验室合成的药物常常只能得到混合物，分离对映体既困难又昂贵。对于一小部分药物分子，结晶可以用于从只含有一种对映体的溶液中产生固体。当结晶作用起作用时，可以廉价而高效地分离对映体。格伦瓦尔德研究小组正在探索利用结晶的分离，以了解它们是如何受到分子形状和相互作用力等方面的引导的。正在开发计算模型，以帮助预测何时结晶形成纯对映体；这种认识对药物开发和其他化学合成具有巨大的潜在价值。这项工作的核心模型用于广泛的教育推广，向年轻学生介绍化学原理。利用手性分子、它们的晶体结构和M.C. Escher的艺术作品之间强烈的视觉联系，正在创建一个教育研讨会。该奖项支持的研究旨在理解为什么溶液中的外消旋或其他手性分子的混合物会自发形成对映不纯晶体。正在开发分子模型和计算方法，以揭示对映纯晶体和外消旋晶体形成的驱动力和指导原则。计算效率高的模型用于考虑分子动力学计算机模拟中的广泛分子形状和相互作用，包括轨迹采样的特定方法以及粗粒化。计算结晶实验的大型数据集被创建，模型将根据它们形成对映纯或外消旋晶体的倾向来表征。特别令人感兴趣的是计算方法的发展，这些方法可以枚举这些模型的所有低能晶体结构，从而确定手性结晶的热力学景观。通过确定小分子簇的分布，可以确定对映纯晶体和外消旋晶体成核动力学的根本差异。统计模型正在发展，使人们能够根据外消旋和对映不纯晶体结构的知识预测对映不纯结晶的可能性。一个最终目的是告知计算筛选程序，以预先确定外消旋混合物分离的可能性，从而允许合理的分子合成修饰来增强分离。另一个是这里开发的分子模型和晶体结构枚举方法，对未来研究手性和非手性构建块在不同长度尺度上的自组装有用，包括蛋白质和无机纳米结构。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Michael Gruenwald at the University of Utah is supported by an award from the Chemical Theory, Models and Computational Methods (CTMC) Program in the Chemistry Division to perform theoretical and computational research on the crystallization of chiral molecules. Objects are called chiral if they cannot be superimposed with their mirror images -- just like the left and right hands of humans. Many molecules such as amino acids (in DNA) and sugars that are important for the functions of the human body are chiral. They exist in right-handed and left-handed pairs, called enantiomers. Notably, when it comes to medicine, only one enantiomer is wanted in many drug applications. Often one chiral form is beneficial and the other may have no activity or may be harmful. Laboratory syntheses of drugs frequently result only in mixtures, and it is difficult and costly to separate enantiomers. For a small fraction of drug molecules, crystallization can be used to produce solids from solution that contain only one of the enantiomers. Crystallization, when it works, separates entantiomers cheaply and efficiently. The Gruenwald research group is exploring separations that use crystallization to understand how they are guided by such aspects as molecular shape and interaction forces. Computational models are being developed to help predict when crystallization to form pure enantiomers will happen; This understanding has great potential value for drug development and other chemical syntheses. The models at the heart of this work are used in extensive educational outreach to introduce chemical principles to young students. An educational workshop is being created that leverages the strong visual connections between chiral molecules, their crystal structures, and artwork by M.C. Escher.Research supported by this award aims to understand why racemic or other mixtures of chiral molecules in solution spontaneously form enantiopure crystals. Molecular models and computational methods are being developed to reveal the driving forces and guiding principles for the formation of both enantiopure and racemic crystals. Computationally efficient models are used to consider a broad range of molecular shapes and interactions within molecular dynamics computer simulations, including specific methods of trajectory sampling as well as coarse-graining. Large data sets of computational crystallization experiments are created, and models will be characterized according to their propensity to form enantiopure or racemic crystals. Of particular interest is the development of computational methods that can enumerate all low-energy crystal structures of these models and thus determine the thermodynamic landscape for chiral crystallization. By determining distributions of small molecular clusters, fundamental differences in the nucleation dynamics of enantiopure and racemic crystals can be identified. Statistical models are being developed that allow one to predict the likelihood of enantiopure crystallization from knowledge of racemic and enantiopure crystal structures. One ultimate aim is to inform computational screening procedures to pre-determine the likelihood of a racemic mixture to separate and thus to allow the rational synthetic modification of molecules to enhance the separation. Another is for the molecular models and methods of crystal structure enumeration developed here to be useful for future studies of the self-assembly of chiral and non-chiral building blocks on different length scales, including proteins and inorganic nanostructures.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.