Statistical Modeling for Enhancing the Discovery Power
of Citrullination from Tandem Mass Spectrometry Data
Posted on 2020-09-16 - 14:05
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.
CITE THIS COLLECTION
DataCite
3 Biotech
3D Printing in Medicine
3D Research
3D-Printed Materials and Systems
4OR
AAPG Bulletin
AAPS Open
AAPS PharmSciTech
Abhandlungen aus dem Mathematischen Seminar der Universität Hamburg
ABI Technik (German)
Academic Medicine
Academic Pediatrics
Academic Psychiatry
Academic Questions
Academy of Management Discoveries
Academy of Management Journal
Academy of Management Learning and Education
Academy of Management Perspectives
Academy of Management Proceedings
Academy of Management Review
Huh, Sunghyun; Hwang, Daehee; Kim, Min-Sik (2020). Statistical Modeling for Enhancing the Discovery Power
of Citrullination from Tandem Mass Spectrometry Data. ACS Publications. Collection. https://doi.org/10.1021/acs.analchem.0c01687
or
Select your citation style and then place your mouse over the citation text to select it.