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DC Field Value Language Huh, Sunghyun ko Hwang, Daehee ko Kim, Min-Sik ko 2021-01-22T07:04:24Z - 2021-01-22T07:04:24Z - 2020-11-26 - 2020-10 -
dc.identifier.citation Analytical Chemistry, v.92, no.19, pp.12975 - 12986 -
dc.identifier.issn 0003-2700 -
dc.identifier.uri -
dc.description.abstract Citrullination is a post-translational modification implicated in various human diseases including rheumatoid arthritis, Alzheimer's disease, multiple sclerosis, and cancers. Due to a relatively low concentration of citrullinated proteins in the total proteome, confident identification of citrullinated proteome is challenging in mass spectrometry (MS)-based proteomic analysis. From these MS-based analyses, MS features that characterize citrullination, such as immonium ions (IMs) and neutral losses (NLs), called diagnostic ions, have been reported. However, there has been a lack of systematic approaches to comprehensively search for diagnostic ions and no statistical methods for the identification of citrullinated proteome based on these diagnostic ions. Here, we present a systematic approach to identify diagnostic IMs, internal ions (INTs), and NLs for citrullination from tandem mass (MS/MS) spectra. Diagnostic INTs mainly consisted of internal fragment ions for di- and tripeptides that contained two and three amino acids with at least one citrullinated arginine, respectively. A statistical logistic regression model was built for a confident assessment of citrullinated peptides that database searches identified (true positives) and prediction of citrullinated peptides that database searches failed to identify (false negatives) using the diagnostic IMs, INTs, and NLs. Applications of our model to complex global proteome data sets demonstrated the increased accuracy in the identification of citrullinated peptides, thereby enhancing the size and functional interpretation of citrullinated proteomes. Copyright © 2020 American Chemical Society. -
dc.language English -
dc.publisher American Chemical Society -
dc.title Statistical Modeling for Enhancing the Discovery Power of Citrullination from Tandem Mass Spectrometry Data -
dc.type Article -
dc.identifier.doi 10.1021/acs.analchem.0c01687 -
dc.identifier.wosid 000580426800031 -
dc.identifier.scopusid 2-s2.0-85095979063 -
dc.type.local Article(Overseas) -
dc.type.rims ART -
dc.description.journalClass 1 -
dc.identifier.citationVolume 92 -
dc.identifier.citationNumber 19 -
dc.identifier.citationStartPage 12975 -
dc.identifier.citationEndPage 12986 -
dc.identifier.citationTitle Analytical Chemistry -
dc.type.journalArticle Article -
dc.description.isOpenAccess N -
dc.subject.keywordPlus NEUTRAL LOSS -
dc.subject.keywordPlus IDENTIFICATION -
dc.subject.keywordPlus FRAGMENTATION -
dc.subject.keywordPlus PROTEINS -
dc.subject.keywordPlus SPECTRA -
dc.subject.keywordPlus PROBE -
dc.contributor.affiliatedAuthor Hwang, Daehee -
dc.contributor.affiliatedAuthor Kim, Min-Sik -


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