Mass Spectrometry and Enzyme Processes

描述（由申请人提供）：本次竞争性更新的长期目标是开发使用质谱法分析各种酶促过程的方法。主要研究方向包括：1)磷酸己糖异构酶的动力学及催化机理的研究。2)酪氨酸蛋白硫转移酶的动力学、底物测定和机制测定。这也将包括一个新的程序，用于明确测定天然底物上硫酸酸化的位置。3)研究底物和抑制剂与新发现的人硫酸酯内酯酶Hsulf-2的相互作用。每个区域都采用电喷雾电离（ESI）质谱（MS），使用LTQ离子阱或FTICR仪器测量各种动力学参数，如Km， Vmax, Ki， kcat和特异性常数。本文提出可以使用ESI-MS计算动力学参数，从而避免了传统分光光度法对发色团的需要。同样重要的是，动力学信息，无论是以前难以处理或难以测量，可以使用本文概述的基于质谱的方法收集。分析硫酸盐生物分子的问题在质谱分析中得到了很好的认识。所提出的研究将缓解电喷雾电离和正离子模式下质谱联用所带来的硫酸盐损失问题。同样，也提出了获取磷异构酶系统生物学数据的方法；其中一种很难从动力学和力学上进行分类。

DESCRIPTION (provided by applicant): The long term goal of this competing renewal is the development of methods that allow for the analysis of various enzymatic processes using mass spectrometry. The major areas proposed herein include: 1) Kinetics and catalytic mechanism determination for phosphohexose-isomerases. 2) Kinetics, substrate determination, and mechanism determination of tyrosylprotein sulfotransferases. This will also include a new procedure for unambiguous determination of sites of sulfation on natural substrates. 3) Investigation of substrate and inhibitor interactions with a newly discovered human endosulfatase, Hsulf-2. Each of these areas employs electrospray ionization (ESI) mass spectrometry (MS) using either an LTQ ion trap or an FTICR instrument to measure various kinetic parameters such as Km, Vmax, Ki, kcat and specificity constants. It is proposed that kinetic parameters can be calculated using ESI-MS thus obviating the need for chromophores as required by traditional spectrophotometric techniques. Equally important is the fact that kinetic information, either previously intractable or difficult to measure, can be gleaned using the MS based methods outlined herein. The problem with analyzing sulfated biomolecules is well recognized in mass spectrometry. The proposed research will alleviate those problems associated with loss of sulfate upon electrospray ionization and MS/MS in the positive ion mode. Similarly, methods are proposed for obtaining biological data on phosphoisomerase systems; one of which is highly difficult to classify kinetically and mechanistically.