With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Professor David Weis at the University of Kansas, Lawrence, is developing new tools for improved analysis of proteins. Proteins are key participants and indicators of healthy and diseased biochemical processes. Specifically, Professor Weis and his team are studying how proteins degrade during long-term storage. The team is working to make data analysis faster and to improve the predictive power of the technique. This cross-disciplinary project provides advanced training to undergraduate and graduate students at the interface of chemistry, mathematics, and biology. Better understanding of degradation and the ability to predict long-term stability of proteins is important for basic research, in commerce, and in human health. For example, protein-based pharmaceuticals are the fastest growing and most expensive class of drugs. The tools and methods developed in this research may ultimately contribute to faster development of drugs delivered at lower cost.
Hydrogen exchange-mass spectrometry (HX-MS) is now widely used to probe the structure and dynamics of proteins in fields ranging from basic biological research to the development of pharmaceuticals. Better informatics tools are needed to analyze and interpret HX-MS. The overall goal of this research is to develop new algorithms to analyze and interpret data obtained from HX-MS measurements made on proteins. The project seeks to develop an improved algorithm for global data analysis and methods to predict protein stability using HX-MS measurements. The global data analysis approach rapidly focuses computational and human resources directly on key differences between the protein states compared, substantially accelerating data analysis. The research establishes structural fingerprints of proteins that can help establish comparability or biosimiarlity, relieving a major bottleneck in the development of pharmaceuticals. This research is assessing whether HX-MS measurements can be used to rapidly predict stability under storage conditions, making stability assessment faster and more accurate. Ultimately, this research may lead to an approach that can enable the rapid prediction of long-term storage stability of proteins and the high-resolution mapping of their failure points.
