For example, in an ocean metaproteomics dataset, NovoBridge detected Alphaproteobacteria in higher abundance than conventional 16S rRNA sequencing, revealing microorganisms lacking prior sequencing data. Moreover, Kleikamp et al. developed a taxonomy validation method using randomized peptide sequences as a control. By comparing correct and randomized sequences, they showed that while random sequences could occasionally match at higher taxonomic levels (like phylum), false positives dropped significantly at lower levels, such as genus or species, leading to more accurate community representation. NovoBridge further uses BLAST+ to homology-match high-quality sequences that could not be annotated by an exact match in Unipept, allowing them to be linked to closely related organisms or assigned to higher taxonomic ranks. Building on their findings with homology searches, they developed NovoLign , a pipeline that aligns de novo sequences from metaproteomics experiments using DIAMOND instead of BLAST+, with parameters optimized for short sequences and typical de novo sequencing errors. Applied to Orbitrap Astral MS/MS data, NovoLign significantly improved peptide-spectrum matches (PSMs) and identified additional taxa missed by conventional database searches. These studies highlight the potential of homology-based searches for metaproteomics while demonstrating how deep learning tools like DeepNovo provide de novo predictions that enable these methods, even though their models were not trained on metaproteomics data. Subsequently, Lee et al. demonstrated the utility of such metaproteomics-based training with the Kaiko deep learning model, trained on five million PSMs from 55 phylogenetically diverse bacteria using metaproteomics-specific datasets. Based on DeepNovo's architecture , Kaiko outperformed DeepNovo, which was trained on a less representative, nine-species dataset, in identifying organisms from soil and synthetic communities. Here, Kaiko was first used to annotate spectra, followed by taxonomic assignment through DIAMOND against the UniRef100 database, identifying the most abundant organisms in the sample. These identified taxa were used to generate a sample-specific protein database, albeit with the risk of increasing false positive matches . This database was then used for traditional database searches, where it uncovered previously unrecognized species like Candidatus Rokubacteria and Candidatus Tectomicrobia, both absent in 16S rRNA sequencing but critical for carbon and nutrient cycling in terrestrial ecosystems. Kaiko also identified key functional roles through Unipept , linking novel species to enzymes like thioredoxin-dependent peroxiredoxin, benzoate degradation enzymes, and streptomycin biosynthesis. Additionally, it revealed that abundant taxa such as Verrucomicrobia and Actinobacteria are well-represented in metaproteomic data, but missed by traditional sequencing methods, which showcases Kaiko's ability to detect abundant microbial taxa overlooked by other methods. Similarly, π-HelixNovo , a transformer-based de novo sequencing model, has advanced the detection of novel peptides by incorporating complementary spectra to enhance ion information. Applied to gut metaproteomes, π-HelixNovo identified significantly more taxon-specific and novel peptides compared to Casanovo, improving taxonomic resolution and peptide recall. For example, π-HelixNovo uncovered a larger number of bacterial-specific peptides (586 versus 350) and species-specific peptides (63 versus 15), demonstrating its capacity to identify previously unseen members of the microbiome. Recently, π-PrimeNovo showed similar improvements on the same dataset, while significantly speeding up the model by making the decoding process non-autoregressive .