Boosting metaproteomics identification rates and taxonomic specificity with MS 2 Rescore
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Background
Metaproteomics, the study of the collective proteome within microbial ecosystems, has gained increasing interest over the past decade. However, peptide identification rates in metaproteomics remain low compared to single-species proteomics. A key challenge is the limited sequence resolution of current identification algorithms, which were primarily designed for single-species analyses. To address this, we applied the machine learning-driven MS 2 Rescore tool to multiple metaproteomics datasets from diverse microbial environments and benchmark studies.
Results
We demonstrate that MS 2 Rescore outperforms typical metaproteomics identification workflows. It significantly increases peptide identification rates compared to standard database searches, even when compared to Percolator rescoring. Moreover, it enables lowering the false discovery rate (FDR) to 0.1% with minimal sensitivity loss, a substantial improvement over the 1% or 5% FDR thresholds commonly used in metaproteomics, in turn leading to greater specificity in downstream taxonomic annotation with Unipept.
Conclusions
Our findings show that MS 2 Rescore substantially improves peptide identification sensitivity as well as specificity in metaproteomics, and delivers improved taxonomic specificity. This advancement results in a more reliable downstream taxonomic analysis, reinforcing the potential of machine learning-based rescoring in metaproteomics research.
