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通过离子淌度-质谱联用定量在线监测固定化酶网络。

Quantitative Online Monitoring of an Immobilized Enzymatic Network by Ion Mobility-Mass Spectrometry.

机构信息

Institute for Molecules and Materials, Radboud University, Heyendaalseweg 135, Nijmegen 6525 AJ, The Netherlands.

出版信息

J Am Chem Soc. 2024 Jul 31;146(30):20778-20787. doi: 10.1021/jacs.4c04218. Epub 2024 Jul 16.

Abstract

The forward design of in vitro enzymatic reaction networks (ERNs) requires a detailed analysis of network kinetics and potentially hidden interactions between the substrates and enzymes. Although flow chemistry allows for a systematic exploration of how the networks adapt to continuously changing conditions, the analysis of the reaction products is often a bottleneck. Here, we report on the interface between a continuous stirred-tank reactor, in which an immobilized enzymatic network made of 12 enzymes is compartmentalized, and an ion mobility-mass spectrometer. Feeding uniformly C-labeled inputs to the enzymatic network generates all isotopically labeled reaction intermediates and products, which are individually detected by ion mobility-mass spectrometry (IMS-MS) based on their mass-to-charge ratios and inverse ion mobilities. The metabolic flux can be continuously and quantitatively monitored by diluting the ERN output with nonlabeled standards of known concentrations. The real-time quantitative data obtained by IMS-MS are then harnessed to train a model of network kinetics, which proves sufficiently predictive to control the ERN output after a single optimally designed experiment. The high resolution of the time-course data provided by this approach is an important stepping stone to design and control sizable and intricate ERNs.

摘要

体外酶反应网络(ERNs)的正向设计需要对网络动力学和底物与酶之间潜在的隐藏相互作用进行详细分析。尽管流动化学允许系统地探索网络如何适应不断变化的条件,但反应产物的分析通常是一个瓶颈。在这里,我们报告了连续搅拌釜式反应器(其中固定化酶网络由 12 种酶组成)与离子淌度-质谱仪之间的接口。向酶网络中均匀地添加均带有 C 标记的输入物会生成所有同位素标记的反应中间产物和产物,这些产物通过基于质荷比和反离子淌度的离子淌度-质谱(IMS-MS)被单独检测。通过用具有已知浓度的非标记标准品稀释 ERN 输出,可以连续定量监测代谢通量。然后,利用 IMS-MS 获得的实时定量数据来训练网络动力学模型,该模型在经过单次最佳设计实验后,证明具有足够的预测能力,可以控制 ERN 的输出。该方法提供的时程数据的高分辨率是设计和控制复杂的大规模 ERN 的重要垫脚石。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/315e/11295183/999ee7d581d3/ja4c04218_0001.jpg

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