DeepNovo , for example, uses a convolutional neural network (CNN) to encode the spectrum and a recurrent neural network (RNN) to decode peptides, predicting one amino acid at a time. Newer architectures, such as transformer models, further enhance precision by accurately mapping even incomplete MS/MS fragmentation patterns to peptide sequences. Tools like DeepNovo , Graphnovo , Casanovo , and PointNovo now achieve amino acid recall rates exceeding 70% on single-organism datasets, compared to only 40% with earlier methods like PepNovo . This improved accuracy of de novo sequencing with DeepNovo inspired the development of deep learning workflows for metaproteomics. In 2021, Kleikamp et al. developed NovoBridge, a pipeline that starts with DeepNovo or PEAKS results combined with Unipept queries , enabling the identification of bacteria absent from existing databases.