Detection of the Arabidopsis Proteome and Its Post-translational Modifications and the Nature of the Unobserved (Dark) Proteome in PeptideAtlas

被引:10
|
作者
van Wijk, Klaas J. [3 ]
Leppert, Tami [1 ]
Sun, Zhi [1 ]
Kearly, Alyssa [2 ]
Li, Margaret [1 ]
Mendoza, Luis [1 ]
Guzchenko, Isabell [3 ]
Debley, Erica [3 ]
Sauermann, Georgia [3 ]
Routray, Pratyush [3 ]
Malhotra, Sagunya [1 ]
Nelson, Andrew [2 ]
Sun, Qi [4 ]
Deutsch, Eric W. [1 ]
机构
[1] Inst Syst Biol ISB, Seattle, WA 98109 USA
[2] Boyce Thompson Inst Plant Res, Ithaca, NY 14853 USA
[3] Cornell Univ, Sch Integrat Plant Sci SIPS, Sect Plant Biol, Ithaca, NY 14853 USA
[4] Cornell Univ, Computat Biol Serv Unit, Ithaca, NY 14853 USA
基金
美国国家卫生研究院; 美国国家科学基金会;
关键词
Arabidopsis; PeptideAtlas; post-translational modifications; ProteomeXchange; machine learning; signaling peptides; E3; ligases; SPECTROMETRY-BASED ANALYSIS; MASS-SPECTROMETRY; N-TERMINOME; THALIANA; PLANT; IDENTIFICATION; PROTEINS; PHOSPHORYLATION; DATABASE; REVEALS;
D O I
10.1021/acs.jproteome.3c00536
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource (build 2023-10) providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected post-translational modifications (PTMs), and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying similar to 0.6 million unique peptides and 18,267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins, and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome: the "dark" proteome. This dark proteome is highly enriched for E3 ligases, transcription factors, and for certain (e.g., CLE, IDA, PSY) but not other (e.g., THIONIN, CAP) signaling peptides families. A machine learning model trained on RNA expression data and protein properties predicts the probability that proteins will be detected. The model aids in discovery of proteins with short half-life (e.g., SIG1,3 and ERF-VII TFs) and for developing strategies to identify the missing proteins. PeptideAtlas is linked to TAIR, tracks in JBrowse, and several other community proteomics resources.
引用
收藏
页码:185 / 214
页数:30
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