Informatics Strategies for Large-Scale Novel Cross-Linking Analysis

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• 作者：Gordon A. Anderson ; Nikola Tolic ; Xiaoting Tang ; Chunxiang Zheng ; James E. Bruce
• 刊名：Journal of Proteome Research
• 出版年：2007
• 出版时间：September 2007
• 年：2007
• 卷：6
• 期：9
• 页码：3412 - 3421
• 全文大小：496K
• 年卷期：v.6,no.9(September 2007)
• ISSN：1535-3907

文摘

The detection of protein interactions in biological systems represents a significant challenge for today'stechnology. Chemical cross-linking provides the potential to impart new chemical bonds in a complexsystem that result in mass changes in a set of tryptic peptides detected by mass spectrometry. However,system complexity and cross-linking product heterogeneity have precluded widespread chemical cross-linking use for large-scale identification of protein-protein interactions. The development of massspectrometry identifiable cross-linkers called protein interaction reporters (PIRs) has enabled on-cellchemical cross-linking experiments with product type differentiation. However, the complex datasetsresultant from PIR experiments demand new informatics capabilities to allow interpretation. Thismanuscript details our efforts to develop such capabilities and describes the program X-links, whichallows PIR product type differentiation. Furthermore, we also present the results from Monte Carlosimulation of PIR-type experiments to provide false discovery rate estimates for the PIR product typeidentification through observed precursor and released peptide masses. Our simulations also providepeptide identification calculations based on accurate masses and database complexity that can providean estimation of false discovery rates for peptide identification. Overall, the calculations show a lowrate of false discovery of PIR product types due to random mass matching of approximately 12% with10 ppm mass measurement accuracy and spectral complexity resulting from 100 peptides. In addition,consideration of a reduced database resulting from stage 1 analysis of Shewanella oneidensis MR-1containing 367 proteins resulted in a significant reduction of expected identification false discoveryrate estimation compared to that from the entire Shewanella oneidensis MR-1 proteome.