Professor Xueyu Song, of Iowa State University, is supported by the Theoretical and Computational Chemistry Program to study phase behaviors and nucleation kinetics of protein solutions. Song develops computational models and machinery that allow for the understanding of protein crystallization. Models are based on parametrization of potentials which reproduce characteristics of twenty naturally occurring and understood amino acids. The resulting potentials are of greater complexity than many that in current use. These potentials are used in concert with classical density-functional theory to study crystallization of highly idealized models and then globular proteins. This theoretical work aids experimentalists in understanding how protein crystallization proceeds. This process is currently the bottleneck in obtaining complete structural information about the proteins defined from the human genome project. Computational skills gleaned by undergraduate, graduate and postdoctoral students aid in alleviating the current predictive void in the size regime relevant to simulation-based determination of the structure of biologically interesting molecules.
X-ray based structural determination of DNA and other biological molecules is the most reliable means for obtaining structural information. However such snapshots are only possible if researchers can create highly ordered crystals of these molecules and this experimental task is proving to be very difficult. Professor Song, of Iowa State University, uses knowledge gleaned from twenty amino acids to develop a means for predicting short-range interactions between neighboring molecules. These short-range interactions must be understood to determine the environmental conditions for which highly ordered crystals may be obtained. Success in the area of computationally informed protein crystallization would significantly speed the process of structural determination for the proteins associated with the human genome project. Educational efforts associated with this research will prepare the next generation of computational chemists for the challenges associated with computational biology and molecular-based simulation of fluids.
