Construction of databases in metaproteomics is challenging, particularly as large databases increase computational demands, extend search times, and risk missed identifications or false positives . Additionally, identifications are constrained to peptides present in the database, meaning that most sequence variants or novel peptides will be missed. These limitations have driven interest in de novo sequencing, which altogether eliminates the need for a protein database during the analysis of the acquired fragmentation (MS/MS) spectra. In a typical de novo workflow, these MS/MS spectra are analyzed using a de novo sequencing algorithm to infer peptide sequences. Candidate peptides then undergo validation and post-processing before downstream analyses, which can follow either a peptide-centric, or a protein-centric approach. In the peptide-centric approach the obtained peptide sequences are matched exactly to reference databases by tools like Unipept , which use precomputed indices to perform taxonomic profiling using the lowest common ancestor algorithm , and which provide functional annotations based on the matched proteins. The peptides can also be mapped to KEGG pathways using the PathwayPilot . In the protein-centric approach, however, the obtained peptide sequences are mapped to reference databases using a similarity-based homology search instead, thus allowing the peptides to deviate somewhat from those found in the reference protein sequences. Traditionally, this homology search is carried out using BLAST+ , and the functions and taxa associated with the thusidentified proteins are then grouped and quantified from the mapped peptides . Even though several authors in the metaproteomics field have recommended de novo sequencing , its application has remained limited. However, with recent technological advancements, we believe its use may gain renewed attention in the field. In this section, we provide a comprehensive overview of the progress and applications of de novo sequencing in metaproteomics.