Monoisotopic Mass Determination Algorithm for Selenocysteine-Containing Polypeptides from Mass Spectrometric Data Based on Theoretical Modeling of Isotopic Peak Intensity Ratios

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• 作者：Jin Wook Kim ; Sunho Lee ; Kunsoo Park ; Seungjin Na ; Eunok Paek ; Hyung Seo Park ; Heejin Park ; Kong-Joo Lee ; Jaeho Jeong ; Hwa-Young Kim
• 刊名：Journal of Proteome Research
• 出版年：2012
• 出版时间：September 7, 2012
• 年：2012
• 卷：11
• 期：9
• 页码：4488-4498
• 全文大小：618K
• 年卷期：v.11,no.9(September 7, 2012)
• ISSN：1535-3907

文摘

Selenoproteins, containing selenocysteine (Sec, U) as the 21st amino acid in the genetic code, are well conserved from bacteria to human, except yeast and higher plants that miss the Sec insertion machinery. Determination of Sec association is important to find substrates and to understand redox action of selenoproteins. While mass spectrometry (MS) has become a common and powerful tool to determine an amino acid sequence of a protein, identification of a protein sequence containing Sec was not easy using MS because of the limited stability of Sec in selenoproteins. Se has six naturally occurring isotopes, ⁷⁴Se, ⁷⁶Se, ⁷⁷Se, ⁷⁸Se, ⁸⁰Se, and ⁸²Se, and ⁸⁰Se is the most abundant isotope. These characteristics provide a good indicator for selenopeptides but make it difficult to detect selenopeptides using software analysis tools developed for common peptides. Thus, previous reports verified MS scans of selenopeptides by manual inspection. None of the fully automated algorithms have taken into account the isotopes of Se, leading to the wrong interpretation for selenopeptides. In this paper, we present an algorithm to determine monoisotopic masses of selenocysteine-containing polypeptides. Our algorithm is based on a theoretical model for an isotopic distribution of a selenopeptide, which regards peak intensities in an isotopic distribution as the natural abundances of C, H, N, O, S, and Se. Our algorithm uses two kinds of isotopic peak intensity ratios: one for two adjacent peaks and another for two distant peaks. It is shown that our algorithm for selenopeptides performs accurately, which was demonstrated with two LC鈥揗S/MS data sets. Using this algorithm, we have successfully identified the Sec鈥揅ys and Sec鈥揝ec cross-linking of glutaredoxin 1 (GRX1) from mass spectra obtained by UPLC-ESI-q-TOF instrument.

Keywords:

mass spectrometry; selenocysteine; selenopeptide; selenium; isotopic distribution; intensity ratio