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MetaLab 2.0 enables accurate post-translational modifications profiling in metaproteomics

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Abstract 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 under-explored. This is mainly due to the lack of efficient bioinformatics tools to identify and quantify PTMs in the microbiome. We have developed a comprehensive software termed MetaLab for the data analysis of metaproteomic datasets. Here we build an open search workflow within MetaLab for unbiased identification and quantification of 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 datasets. Superior accuracy and sensitivity were obtained simultaneously by using our method comparing with the traditional closed search strategy.
Title: MetaLab 2.0 enables accurate post-translational modifications profiling in metaproteomics
Description:
Abstract 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 under-explored.
This is mainly due to the lack of efficient bioinformatics tools to identify and quantify PTMs in the microbiome.
We have developed a comprehensive software termed MetaLab for the data analysis of metaproteomic datasets.
Here we build an open search workflow within MetaLab for unbiased identification and quantification of 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 datasets.
Superior accuracy and sensitivity were obtained simultaneously by using our method comparing with the traditional closed search strategy.

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