## 3 Results
At the same time, we calculated the area under the precision-recall curve at the amino-acid level and the peptide level, respectively. Figure 7 shows the AUC comparison of DeepNovo and Denovo-GCN on nine species datasets. The Denovo-GCN showed a mean improvement of 0.095 and 0.05 relative to the DeepNovo in peptide level and amino-acid level. Figure 5: (**a**) DeepNovo- and Denovo-GCN-identified number distributions of spectra of different types of peptide. (**b**) The Venn diagram of correct peptides identified by DeepNovo and Denovo-GCN. (**c**) The proportion of correct peptides identified by DeepNovo and Denovo-GCN, respectively. Figure 6: (a) Precision-recall curves for amino acids on nine species data sets. (b) Precision-recall curves for peptides on nine species. Figure 8 shows four specific examples of fragment ions and peptide-sequence-matching results. In these four examples, Denovo-GCN identified the correct peptide sequence, while DeepNovo identified the false peptide sequence. We matched the fragment ions and peptide sequences. The more fragment ions were labeled, the better the result of the peptide-spectrum matching. We matched the fragment ions for all the peptide sequences. It can be seen from Figure 8a,b that the incorrect peptide sequence was very similar to the correct peptide sequence. This was due to the similar masses of some of the amino  
Figure 8: Specific examples of fragment ions and peptide-sequence-matching results. **(a)** DeepNovo predicted an incorrect peptide due to isobaric combination of amino acids and Denovo-GCN predicted correct peptide. **(b)** DeepNovo predicted an incorrect peptide due to interchange of two amino acids and similar amino acid mass, and Denovo-GCN predicted correct peptide. **(c)** DeepNovo predicted an almost completely incorrect peptide and Denovo-GCN predicted correct peptide with continuous fragment ions. **(d)** The peptide sequence predicted by Denovo-GCN has better peptide spectrum matching results and DeepNovo predicted incorrect peptide. Figure 7: The areas under precision-recall curves of DeepNovo and Denovo-GCN on nine high-resolution datasets. (a) AUCs at amino-acid level. **(b)** AUCs at peptide level. acids. DeepNovo cannot distinguish these amino acids with similar masses, but Denovo-GCN can, as well as recalling the correct peptide sequence. Because Denovo-GCN takes into account the relationship between the peaks and the more abundant ion types, it captures the characteristics of y4 ions. As shown in Figure 8c,d, the peptide sequence predicted by DeepNovo was almost completely different from that predicted by Denovo-GCN. The correct peptide sequence had more complete and continuous fragment ions, but the fragment ions matched by DeepNovo were discontinuous and the number was lower than that of Denovo-GCN. It can be seen from the matching of the fragment ions and peptide sequences that Denovo-GCN is better than DeepNovo. In addition, in order to prove the effectiveness of the GCN model and the CNN model, we conducted ablation experiments on one of the nine species of Bacillus dataset, and calculated the recall at the peptide level using different models. As shown in Figure 9a, in the case of only using the GCN model, the Denovo-GCN model improved the peptide recall by 2.3% compared with DeepNovo. After adding the CNN model, the peptide recall improved by 6.5%. At the same time, we compared the use of different models in different peptide lengths. Figure 9b shows that there were increases in peptide length of 11-15 and 16-20, and the recall improved by 9.6% and 10.5%, respectively.