Fourier-transform ion cyclotron resonance mass spectrometry for characterizing proteoforms.
Fourier-transform ion cyclotron resonance mass spectrometry for characterizing proteoforms.
复制标题
傅立叶变换离子回旋共振质谱法用于蛋白质型的表征。
DOI：
10.1002/mas.21653
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发表时间：
2022-03
影响因子：
6.6
通讯作者：
Ge Y
中科院分区：
文献类型：
--
作者：
Tucholski T;Ge Y
Proteoforms contribute functional diversity to the proteome and aberrant proteoforms levels have been implicated in biological dysfunction and disease. Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS), with its ultrahigh mass-resolving power, mass accuracy, and versatile tandem mass spectrometry capabilities, has empowered top-down, middle-down, and native MS-based approaches for characterizing proteoforms and their complexes in biological systems. Herein, we review the features which make FT-ICR MS uniquely suited for measuring proteoform mass with ultrahigh resolution and mass accuracy; obtaining in-depth proteoform sequence coverage with expansive tandem MS capabilities; and unambiguously identifying and localizing post-translational and non-covalent modifications. We highlight examples from our body of work in which we have quantified and comprehensively characterized proteoforms from cardiac and skeletal muscle to better understand conditions such as chronic heart failure, acute myocardial infarction, and sarcopenia. Structural characterization of monoclonal antibodies (mAbs) and their proteoforms by FT-ICR MS and emerging applications, such as native top-down FT-ICR MS and high-throughput top-down FT-ICR MS-based proteomics at 21 tesla, are also covered. Historically, the information gleaned from FT-ICR MS analyses have helped provide biological insights. We predict FT-ICR MS will continue to enable the study of proteoforms of increasing size from increasingly complex endogenous mixtures and facilitate the benchmarking of sensitive and specific assays for clinical diagnostics.
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影响因子：
7.4
作者：
Chen B;Brown KA;Lin Z;Ge Y
通讯作者：
Ge Y
影响因子：
56.9
作者：
Chamot-Rooke, Julia;Mikaty, Guillain;Dumenil, Guillaume
通讯作者：
Dumenil, Guillaume
影响因子：
3.5
作者：
Chakouri, Nourdine;Reboul, Cyril;Cazorla, Olivier
通讯作者：
Cazorla, Olivier
影响因子：
7.4
作者：
Chen, Bifan;Lin, Ziqing;Ge, Ying
通讯作者：
Ge, Ying
影响因子：
4.4
作者：
Cannon J;Lohnes K;Wynne C;Wang Y;Edwards N;Fenselau C
通讯作者：
Fenselau C