Database selection for shotgun metaproteomic of low-diversity dairy microbiomes

The metaproteomics field has recently gained more and more interest as a valuable tool for studying both the taxonomy and function of microbiomes, including those used in food fermentations. One crucial step in the metaproteomics pipeline is selecting a database to obtain high-quality taxonomical and functional information from microbial communities. One of the best strategies described for building protein databases is using sample-specific or study-specific protein databases obtained from metagenomic sequencing. While this is true for high-diversity microbiomes (such as gut and soil), there is still a lack of validation for different database construction strategies in low-diversity microbiomes, such as those found in fermented dairy products where starter cultures containing few species are used. In this study, we assessed the performance of various database construction strategies applied to metaproteomics on two low-diversity microbiomes obtained from cheese production using commercial starter cultures and analyzed by LC-MS/MS. Substantial differences were detected between the strategies, and the best performance in terms of the number of peptides and proteins identified from the spectra was achieved by metagenomic-derived databases. However, extensive databases constructed from a high number of available online genomes obtained a similar taxonomical and functional annotation of the metaproteome compared to the metagenomic-derived databases. Our results indicate that, in the case of low-diversity dairy microbiomes, the use of publically available genomes to construct protein databases can be considered as an alternative to metagenome-derived databases.