CDS&E: New Algorithms to Enable Faster, More Accurate Analysis of Hydrogen Exchange Mass Spectrometry Data

在化学系化学测量和成像计划的支持下，堪萨斯大学劳伦斯分校的大卫·韦斯教授正在开发改进蛋白质分析的新工具。蛋白质是健康和疾病生化过程的关键参与者和指示器。具体地说，韦斯教授和他的团队正在研究蛋白质在长期储存过程中如何降解。该团队正在努力提高数据分析的速度，并提高该技术的预测能力。这个跨学科的项目为本科生和研究生提供化学、数学和生物方面的高级培训。更好地了解蛋白质的降解和预测蛋白质长期稳定性的能力对于基础研究、商业和人类健康都很重要。例如，基于蛋白质的药物是增长最快、最昂贵的一类药物。本研究中开发的工具和方法最终可能有助于以较低成本提供更快的药物开发。氢气交换质谱仪(HX-MS)现在被广泛用于从基础生物学研究到药物开发的各个领域探索蛋白质的结构和动力学。需要更好的信息学工具来分析和解释HX-MS。这项研究的总体目标是开发新的算法来分析和解释从蛋白质HX-MS测量获得的数据。该项目寻求开发一种改进的全球数据分析算法和使用HX-MS测量预测蛋白质稳定性的方法。全球数据分析方法迅速将计算和人力资源直接集中在比较的蛋白质状态之间的关键差异上，大大加快了数据分析速度。这项研究建立了蛋白质的结构指纹，有助于建立可比性或生物相似性，缓解了制药发展的一个主要瓶颈。这项研究正在评估HX-MS测量是否可以用于快速预测储存条件下的稳定性，使稳定性评估更快、更准确。最终，这项研究可能会导致一种方法，能够快速预测蛋白质的长期储存稳定性，并高分辨率地绘制它们的故障点。

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.