MetaLab 2.0 Enables Accurate Post-Translational Modifications Profiling in Metaproteomics
Author(s) -
Kai Cheng,
Zhibin Ning,
Xu Zhang,
Leyuan Li,
Bo Liao,
Janice Mayne,
Daniel Figeys
Publication year - 2020
Publication title -
journal of the american society for mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.961
H-Index - 127
eISSN - 1879-1123
pISSN - 1044-0305
DOI - 10.1021/jasms.0c00083
Subject(s) - metaproteomics , microbiome , computational biology , workflow , proteomics , profiling (computer programming) , posttranslational modification , human microbiome , identification (biology) , human microbiome project , function (biology) , metagenomics , bioinformatics , chemistry , computer science , biology , biochemistry , genetics , ecology , gene , database , enzyme , operating system
Studying the structure and function of microbiomes is an emerging research field. Metaproteomic approaches focusing on the characterization of expressed proteins and post-translational modifications (PTMs) provide a deeper understanding of microbial communities. Previous research has highlighted the value of examining microbiome-wide protein expression in studying the roles of the microbiome in human diseases. Nevertheless, the regulation of protein functions in complex microbiomes remains underexplored. This is mainly due to the lack of efficient bioinformatics tools to identify and quantify PTMs in the microbiome. We have developed comprehensive software termed MetaLab for the data analysis of metaproteomic data sets. Here, we build an open search workflow within MetaLab for unbiased identification and quantification of unmodified peptides as well as peptides with various PTMs from microbiome samples. This bioinformatics platform provides information about proteins, PTMs, taxa, functions, and pathways of microbial communities. The performance of the workflow was evaluated using conventional proteomics, metaproteomics from mouse and human gut microbiomes, and modification-specific enriched data sets. Superior accuracy and sensitivity were obtained simultaneously by using our method compared with the traditional closed search strategy.
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