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rPTMDetermine: A Fully Automated Methodology for Endogenous Tyrosine Nitration Validation, Site-Localization, and Beyond

化学 去酰胺 单同位素质量 计算生物学 生物信息学 串联质谱法 蛋白质组 组合化学 质谱法 色谱法 生物化学 基因 生物 酶
作者
Naiping Dong,D.M. Spencer,Quan Quan,J. C. Yves Le Blanc,Jinwen Feng,Mengzhu Li,K. W. Michael Siu,Ivan K. Chu
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:92 (15): 10768-10776 被引量:4
标识
DOI:10.1021/acs.analchem.0c02148
摘要

We present herein rPTMDetermine, an adaptive and fully automated methodology for validation of the identification of rarely occurring post-translational modifications (PTMs), using a semisupervised approach with a linear discriminant analysis (LDA) algorithm. With this strategy, verification is enhanced through similarity scoring of tandem mass spectrometry (MS/MS) comparisons between modified peptides and their unmodified analogues. We applied rPTMDetermine to (1) perform fully automated validation steps for modified peptides identified from an in silico database and (2) retrieve potential yet-to-be-identified modified peptides from raw data (that had been missed through conventional database searches). In part (1), 99 of 125 3-nitrotyrosyl-containing (nitrated) peptides obtained from a ProteinPilot search were validated and localized. Twenty nitrated peptides were falsely assigned because of incorrect monoisotopic peak assignments, leading to erroneous identification of deamidation and nitration. Five additional nitrated peptides were, however, validated after performing nonmonoisotopic peak correction. In part (2), an additional 236 unique nitrated peptides were retrieved and localized, containing 113 previously unreported nitration sites; 25 endogenous nitrated peptides with novel sites were selected and verified by comparison with synthetic analogues. In summary, we identified and confidently validated 296 unique nitrated peptides—collectively representing the largest number of endogenously identified 3-nitrotyrosyl-containing peptides from the cerebral cortex proteome of a Macaca fascicularis model of stroke. Furthermore, we harnessed the rPTMDetermine strategy to complement conventional database searching and enhance the confidence of assigning rarely occurring PTMs, while recovering many missed peptides. In a final demonstration, we successfully extended the application of rPTMDetermine to peptides featuring tryptophan oxidation.

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