4.5 Article

Standard procedures for native CZE-MS of proteins and protein complexes up to 800 kDa

期刊

ELECTROPHORESIS
卷 42, 期 9-10, 页码 1050-1059

出版社

WILEY
DOI: 10.1002/elps.202000317

关键词

CESI; Nativemass spectrometry; Native top-down; Sheathless ionization; Standard operating procedure

资金

  1. National Institute of General Medical Sciences [P41 GM108569]
  2. NIH Office of Director [S10OD025194]
  3. SCIEX

向作者/读者索取更多资源

This study provides standard operating procedures for acquiring high-quality data using capillary electrophoresis in native mode coupled online to various Orbitrap mass spectrometers. The evaluation of various CZE method parameters on data quality is discussed, along with a universal approach for optimization in the context of protein subunit and metalloenzyme characterization.
Native mass spectrometry (nMS) is a rapidly growing method for the characterization of large proteins and protein complexes, preserving native non-covalent inter- and intramolecular interactions. Direct infusion of purified analytes into a mass spectrometer represents the standard approach for conducting nMS experiments. Alternatively, CZE can be performed under native conditions, providing high separation performance while consuming trace amounts of sample material. Here, we provide standard operating procedures for acquiring high-quality data using CZE in native mode coupled online to various Orbitrap mass spectrometers via a commercial sheathless interface, covering a wide range of analytes from 30-800 kDa. Using a standard protein mix, the influence of various CZE method parameters were evaluated, such as BGE/conductive liquid composition and separation voltage. Additionally, a universal approach for the optimization of fragmentation settings in the context of protein subunit and metalloenzyme characterization is discussed in detail for model analytes. A short section is dedicated to troubleshooting of the nCZE-MS setup. This study is aimed to help normalize nCZE-MS practices to enhance the CE community and provide a resource for the production of reproducible and high-quality data.

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