While the potential of de novo sequencing is high, it comes with notable limitations. One of the primary challenges is spectral quality. Historically, de novo sequencing has been more errorprone than database searches, especially with poor-quality MS/MS spectra. Reliable de novo sequencing heavily depends on complete ion fragmentation and minimal noise, as missing fragment ions quickly leads to considerable sequencing ambiguity . This might lead to errors such as substitution errors, mass ambiguities in which a set of residues has the exact or similar combined mass as another residue set; or to inversion errors which are caused by mixing up the b-type fragment ion ladder with the y-type counterpart . In cases of low spectral quality, database searches avoid this ambiguity issue by restricting the allowed sequence space to known peptides, whereas de novo methods must contend with a much larger search space. Approaches such as the masking of low-confidence sequence parts with mass deltas, or applying transfer learning to incorporate species-specific data have been proposed to mitigate these issues . However, ambiguity-based sequence errors from the de novo process can still propagate, complicating the mapping of peptides to proteins, and further downstream to taxa and functions.