MetaLab 2.0 enables accurate post-translational modifications profiling in metaproteomics

Abstract

<jats:title>Abstract</jats:title><jats:p>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.</jats:p>

Pairing Metagenomics and Metaproteomics to Characterize Ecological Niches and Metabolic Essentiality of gut microbiomes

“…Proteomics database searches used FASTA databases of the individual strains downloaded from NCBI and MaxQuant 42 1.6.17.0 for analysis, without the label-free quantification. Metaproteomic database searches of cultured microbiome samples were performed using MetaLab V2.2 43 , MaxQuant option was used to search the TMT dataset against the IGC database of the human gut microbiome. The resulting data table was normalized using R package MSstatsTMT , and missing values were imputed using R package DreamAI 45 .…”