In (meta)proteomics, standard protein identification involves compiling a protein FASTA database from established repositories such as UniProtKB or MGnify, or sample-specific metagenomes. This step is followed by in-silico digestion and fragmentation to generate theoretical spectra, which are then compared to the experimentally measured spectra. Peptide and protein identifications are validated by calculating the false discovery rate (FDR). To enhance the number of valid peptide identifications in metaproteomics, applying artificial errorbased rescoring of the FDR is particularly beneficial . This approach refines the accuracy of peptide detection and reduces false positives. Once valid peptides and their associated proteins are identified, taxonomic and functional annotations are assigned from the original databases or sequence-based comparison against other repositories. Bioinformatic analysis for metaproteomics is challenging due to the usage of metagenomes for protein identification, the grouping of redundant protein identification from homologous proteins, and a comprehensive taxonomic and functional annotation. Therefore, several specific tools for metaproteomics, like Unipept , MetaProteomeAnalyzer , and Metalab , were developed. These tools facilitate a better understanding of how microbial species contribute to resistance mechanisms across human, animal, and environmental health domains.