Proteoform characterization based on top-down mass spectrometry. Issue 2 (2nd March 2020)

• Record Type: Journal Article
• Authors: Zhong, Jiancheng; Sun, Yusui; Xie, Minzhu; Peng, Wei; Zhang, Chushu; Wu, Fang-Xiang; Wang, Jianxin
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 2(2021)
• Page Start: 1729
• Page End: 1750
• Publication Date: 2020-03-02
• Subjects: top-down mass spectrometry -- deconvolution -- proteoform identification -- proteoform localization -- posttranslational modification; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa015
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Proteins are dominant executors of living processes. Compared to genetic variations, changes in the molecular structure and state of a protein (i.e. proteoforms) are more directly related to pathological changes in diseases. Characterizing proteoforms involves identifying and locating primary structure alterations (PSAs) in proteoforms, which is of practical importance for the advancement of the medical profession. With the development of mass spectrometry (MS) technology, the characterization of proteoforms based on top-down MS technology has become possible. This type of method is relatively new and faces many challenges. Since the proteoform identification is the most important process in characterizing proteoforms, we comprehensively review the existing proteoform identification methods in this study. Before identifying proteoforms, the spectra need to be preprocessed, and protein sequence databases can be filtered to speed up the identification. Therefore, we also summarize some popular deconvolution algorithms, various filtering algorithms for improving the proteoform identification performance and various scoring methods for localizing proteoforms. Moreover, commonly used methods were evaluated and compared in this review. We believe our review could help researchers better understand the current state of the development in this field and design new efficient algorithms for the proteoform characterization.