Predicting chiral crystallization

犹他州大学的Michael Gruenwald获得了化学部化学理论、模型和计算方法（CTMC）项目的奖项，以进行手性分子结晶的理论和计算研究。如果物体不能与其镜像叠加，则称为手征性物体--就像人类的左手和右手一样。 许多分子，如氨基酸（在DNA中）和糖，对人体功能很重要，是手性的。它们以右手和左手成对存在，称为对映体。 值得注意的是，当涉及到药物时，在许多药物应用中仅需要一种对映异构体。 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。药物的实验室合成通常只产生混合物，并且分离对映体是困难和昂贵的。对于一小部分药物分子，结晶可用于从仅含有一种对映体的溶液中产生固体。 结晶，当它工作时，便宜而有效地分离对映体。Gruenwald研究小组正在探索使用结晶的分离方法，以了解它们如何受到分子形状和相互作用力等方面的指导。计算模型正在开发中，以帮助预测结晶形成纯对映体的时间;这种理解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。正在创建一个教育研讨会，利用手性分子，其晶体结构和M.C.艺术品之间的强大视觉联系。该奖项支持的研究旨在了解为什么手性分子的外消旋或其他混合物在溶液中自发形成对映体纯晶体。分子模型和计算方法正在发展，以揭示形成对映体纯和外消旋晶体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，包括轨迹采样和粗粒化的特定方法。创建计算结晶实验的大数据集，并根据其形成对映体纯或外消旋晶体的倾向来表征模型。特别感兴趣的是计算方法的发展，可以列举所有的低能量晶体结构的这些模型，从而确定手性结晶的热力学景观。通过确定小分子团簇的分布，可以确定对映体纯和外消旋晶体的成核动力学的根本差异。正在开发的统计模型，使人们能够预测的可能性，对映体纯结晶的外消旋和对映体纯晶体结构的知识。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，从而允许分子的合理合成修饰以增强分离。另一个是分子模型和晶体结构计数的方法，这里开发的是有用的手性和非手性构建块在不同的长度尺度上，包括蛋白质和无机nanostructures.This奖项反映了NSF的法定使命，并已被认为是值得通过使用该基金会的智力价值和更广泛的影响审查标准进行评估的支持未来的研究。

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.