Proteomics-driven identification of short open reading frame-encoded peptides

被引:9
作者
Zhang, Zheng
Li, Yujie
Yuan, Wenqian
Wang, Zhiwei
Wan, Cuihong [1 ,2 ]
机构
[1] Cent China Normal Univ, Sch Life Sci, Wuhan, Hubei, Peoples R China
[2] Cent China Normal Univ, Hubei Key Lab Genet Regulat & Integrat Biol, Wuhan, Hubei, Peoples R China
关键词
LC; MS; method development; peptidomics; sample preparation; short open reading frame; sORF-encode peptides; HIDDEN MARKOV MODEL; PROTEIN INTERACTIONS; SAMPLE PREPARATION; LIVING CELLS; SMALL ORFS; BOTTOM-UP; TRANSLATION; RNA; DISCOVERY; REVEALS;
D O I
10.1002/pmic.202100312
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Accumulating evidence has shown that a large number of short open reading frames (sORFs) also have the ability to encode proteins. The discovery of sORFs opens up a new research area, leading to the identification and functional study of sORF encoded peptides (SEPs) at the omics level. Besides bioinformatics prediction and ribosomal profiling, mass spectrometry (MS) has become a significant tool as it directly detects the sequence of SEPs. Though MS-based proteomics methods have proved to be effective for qualitative and quantitative analysis of SEPs, the detection of SEPs is still a great challenge due to their low abundance and short sequence. To illustrate the progress in method development, we described and discussed the main steps of large-scale proteomics identification of SEPs, including SEP extraction and enrichment, MS detection, data processing and quality control, quantification, and function prediction and validation methods.
引用
收藏
页数:17
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