化学
质谱法
离子
离解(化学)
分析化学(期刊)
电荷(物理)
电离
化学物理
原子物理学
色谱法
物理化学
物理
量子力学
有机化学
作者
Qingrong Chen,Rongrong Dai,Xiaopeng Yao,Lingxiao Chaihu,Wenjun Tong,Yanyi Huang,Guanbo Wang
标识
DOI:10.1021/acs.analchem.2c02586
摘要
In mass analysis of proteins, mass spectrometry directly measures the mass to charge ratios of ionized proteins and promises higher accuracy than that of indirect approaches measuring other physicochemical properties, provided that the charge states of detected ions are determined. Accurate mass determination of heterogeneously glycosylated proteins is often hindered by unreliable charge determination due to the insufficient resolution of signals from different charge states and inconsistency among mass profiles of ions in individual charge states. Limited charge reduction of a subpopulation of proteoforms using electron transfer/capture reactions (ETnoD/ETnoD) solves this problem by narrowing the mass distribution of examined proteoforms and preserving the mass profile of the precursor charge state in the reduced charge states. However, the limited availability of ETnoD/ETnoD function in commercial instruments limits the application of this approach. Here, utilizing a range of charge-dependent and accuracy-affecting spectral features revealed by a systematic evaluation at levels of both the ensemble and subpopulation of proteoforms based on theoretical models and experiments, we developed a limited charge reduction workflow that enables using collision-induced dissociation and higher energy collisional dissociation, two widely available reactions, as alternatives to ETnoD/ETnoD while providing adequate accuracy. Alternatively, substituting proton transfer charge reduction for ETnoD/ETnoD provides higher accuracy of mass determination. Performing mass selection in a window-sliding manner improves the accuracy and allows profiling of the whole proteoform distribution. The proposed workflow may facilitate the development of universal characterization strategies for more complex and heterogeneous protein systems.
科研通智能强力驱动
Strongly Powered by AbleSci AI