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.