National Resource for Translational and Developmental Proteomics, Departments of Chemistry and Molecular Biosciences , Northwestern University , Evanston , Illinois 60208 , United States.
Morgridge Institute for Research , Madison , Wisconsin 53706 , United States.
Anal Chem. 2018 Jul 17;90(14):8553-8560. doi: 10.1021/acs.analchem.8b01638. Epub 2018 Jul 5.
High-throughput top-down proteomic experiments directly identify proteoforms in complex mixtures, making high quality tandem mass spectra necessary to deeply characterize proteins with many sources of variation. Collision-based dissociation methods offer expedient data acquisition but often fail to extensively fragment proteoforms for thorough analysis. Electron-driven dissociation methods are a popular alternative approach, especially for precursor ions with high charge density. Combining infrared photoactivation concurrent with electron transfer dissociation (ETD) reactions, i.e., activated ion ETD (AI-ETD), can significantly improve ETD characterization of intact proteins, but benefits of AI-ETD have yet to be quantified in high-throughput top-down proteomics. Here, we report the first application of AI-ETD to LC-MS/MS characterization of intact proteins (<20 kDa), highlighting improved proteoform identification the method offers over higher energy-collisional dissociation (HCD), standard ETD, and ETD followed by supplemental HCD activation (EThcD). We identified 935 proteoforms from 295 proteins from human colorectal cancer cell line HCT116 using AI-ETD compared to 1014 proteoforms, 915 proteoforms, and 871 proteoforms with HCD, ETD, and EThcD, respectively. Importantly, AI-ETD outperformed each of the three other methods in MS/MS success rates and spectral quality metrics (e.g., sequence coverage achieved and proteoform characterization scores). In all, this four-method analysis offers the most extensive comparisons to date and demonstrates that AI-ETD both increases identifications over other ETD methods and improves proteoform characterization via higher sequence coverage, positioning it as a premier method for high-throughput top-down proteomics.
高通量自上而下的蛋白质组学实验可直接鉴定复杂混合物中的蛋白质异构体，因此需要高质量的串联质谱来深入分析具有多种变异来源的蛋白质。基于碰撞的解离方法提供了便捷的数据采集，但通常无法广泛地对蛋白质异构体进行片段化处理，从而无法进行彻底的分析。电子驱动的解离方法是一种很受欢迎的替代方法，特别是对于电荷密度较高的前体离子。将红外光解与电子转移解离（ETD）反应相结合，即激活离子 ETD（AI-ETD），可以显著改善完整蛋白质的 ETD 特征，但 AI-ETD 在高通量自上而下蛋白质组学中的优势尚未得到量化。在这里，我们首次将 AI-ETD 应用于 LC-MS/MS 对完整蛋白质（<20 kDa）的特征分析，突出了该方法在蛋白质异构体鉴定方面的优势，优于更高能量碰撞解离（HCD）、标准 ETD 和 ETD 后补充 HCD 激活（EThcD）。与 HCD、ETD 和 EThcD 相比，我们使用 AI-ETD 从人结直肠癌细胞系 HCT116 中鉴定到 295 种蛋白质的 935 种蛋白质异构体，而分别使用 HCD、ETD 和 EThcD 鉴定到 1014 种、915 种和 871 种蛋白质异构体。重要的是，AI-ETD 在 MS/MS 成功率和谱质量指标（例如，达到的序列覆盖率和蛋白质异构体特征评分）方面均优于其他三种方法。总的来说，这种四方法分析提供了迄今为止最广泛的比较，并证明 AI-ETD 不仅在 ETD 方法中增加了鉴定结果，而且通过更高的序列覆盖率提高了蛋白质异构体的特征，将其定位为高通量自上而下蛋白质组学的首选方法。
