Machine learning-based rescoring with MS²Rescore boosts peptide identification and taxonomic specificity in metaproteomics

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 identification sensitivity of current identification algorithms, which were primarily designed for single-species analyses. Addressing this, we evaluated the machine learning-driven MS²Rescore post-processing tool on multiple metaproteomics datasets from diverse microbial environments, including highly complex environments such as soil, which represent the most analytically demanding conditions in metaproteomics. Results We demonstrate that machine learning-driven rescoring outperforms state-of-the-art metaproteomics identification workflows. It significantly increases peptide identification rates compared to Sage, which itself already implements basic rescoring. Moreover, it enables lowering the false discovery rate (FDR) to 0.1% with minimal to no sensitivity loss, a substantial improvement over the 1% or 5% FDR thresholds commonly used in metaproteomics, in turn leading to greater confidence in downstream taxonomic annotation when paired with downstream statistical tools like Peptonizer2000. The performance gains are most pronounced in the most complex settings, making it particularly well-suited for environmental metaproteomics. Conclusions Our findings show that MS²Rescore substantially improves peptide identification sensitivity as well as specificity in metaproteomics, and delivers improved confidence in taxonomic annotation when paired with downstream taxonomic analysis tools. This advancement results in a more reliable downstream taxonomic analysis, reinforcing the potential of machine learning-based rescoring in metaproteomics research.