The Peptonizer2000: graphical model based taxonomic identifications of metaproteomic samples

Abstract

AbstractMetaproteomics, the large-scale study of proteins from microbial communities, presents complex challenges in taxonomic inference due to sequence homologies between proteins within and across taxa. Commonly, taxonomic inference relies on heuristics, and few more advanced methods are available. We introduce the Peptonizer2000, a novel graphical model-based workflow designed to provide high-resolution taxonomic identifications of metaproteomic samples with associated confidence scores. This tool integrates peptide scores from any proteomic search engine with peptide-taxon map-pings from the Unipept database, using advanced statistical modeling to enhance tax-onomic resolution. We demonstrate the Peptonizer2000’s accuracy and robustness through the analysis of various publicly available metaproteomic samples, showcas-ing its ability to deliver reliable probabilistic taxonomic identifications. Our results highlight the Peptonizer2000’s potential to improve the specificity and confidence of taxonomic assignments in metaproteomics, providing a valuable resource for the study of complex microbial communities.