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Neutron-encoded Signatures Enable Product Ion Annotation From Tandem Mass Spectra

机译:中子编码签名可实现串联质谱的产物离子注释

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摘要

We report the use of neutron-encoded (NeuCode) stable isotope labeling of amino acids in cell culture for the purpose of C-terminal product ion annotation. Two NeuCode labeling isotopologues of lysine, 13C615N2 and 2H8, which differ by 36 mDa, were metabolically embedded in a sample proteome, and the resultant labeled proteins were combined, digested, and analyzed via liquid chromatography and mass spectrometry. With MS/MS scan resolving powers of ∼50,000 or higher, product ions containing the C terminus (i.e. lysine) appear as a doublet spaced by exactly 36 mDa, whereas N-terminal fragments exist as a single m/z peak. Through theory and experiment, we demonstrate that over 90% of all y-type product ions have detectable doublets. We report on an algorithm that can extract these neutron signatures with high sensitivity and specificity. In other words, of 15,503 y-type product ion peaks, the y-type ion identification algorithm correctly identified 14,552 (93.2%) based on detection of the NeuCode doublet; 6.8% were misclassified (i.e. other ion types that were assigned as y-type products). Searching NeuCode labeled yeast with PepNovo+ resulted in a 34% increase in correct de novo identifications relative to searching through MS/MS only. We use this tool to simplify spectra prior to database searching, to sort unmatched tandem mass spectra for spectral richness, for correlation of co-fragmented ions to their parent precursor, and for de novo sequence identification.
机译:我们报告了细胞培养中氨基酸的中子编码(NeuCode)稳定同位素标记的使用,目的是用于C末端产物离子注释。赖氨酸的两个NeuCode标记同位素,分别相差36 mDa的 13 C6 15 N2和 2 H8,被蛋白质组蛋白代谢嵌入,合并得到的标记蛋白,进行消化,并通过液相色谱和质谱分析。在MS / MS扫描解析度约为50,000或更高的条件下,含C末端的产物离子(即赖氨酸)以间隔为36 mDa的双峰形式出现,而N端碎片以单个m / z峰存在。通过理论和实验,我们证明了所有90%的y型产物离子都有可检测的双峰。我们报告了一种可以以高灵敏度和特异性提取这些中子特征的算法。换句话说,在15503个y型产物离子峰中,y型离子识别算法基于NeuCode doublet的检测正确地识别了14552个(93.2%)。 6.8%被错误分类(即被指定为y型产物的其他离子类型)。与仅通过MS / MS搜索相比,使用PepNovo + 搜索NeuCode标记的酵母可使正确的从头鉴定增加34%。我们使用此工具简化了数据库搜索之前的质谱图,对不匹配的串联质谱进行了分类,以求得光谱富集,共碎片离子与其母体前体的相关性以及从头序列识别。

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