Non-Tryptic Terminus Simulation for Generalizable De Novo Peptide Sequencing

Tandem mass spectrometry (MS/MS)-based de novo peptide sequencing provides a high-throughput approach for identifying peptide sequences without relying on a predefined protein sequence database, making it valuable for discovering novel protein sequences. The performance of de novo peptide sequencing AI model is fundamentally dependent on the scale and diversity of training data. However, existing training datasets are dominated by tryptic peptides, whose C termini are strongly enriched for lysine (K) or arginine (R) due to the cleavage specificity of trypsin. This constrained cleavage pattern substantially limits the diversity of peptide sequences and fragmentation patterns represented in training data. Here, we propose Non-Tryptic Terminus Simulation (NTS), an interpretable and general data augmentation method that generates MS/MS spectra with non-tryptic C termini directly from existing tryptic peptide-spectrum matches. NTS removes the C-terminal residue from a tryptic peptide and transforms its corresponding spectrum by selectively removing or mass-shifting fragment ions dependent on the removed residue, while preserving their original intensities. This targeted and interpretable transformation substantially expands the diversity of training data without requiring additional wet-lab experiments or spectrum-generation models. Across multiple widely used datasets and three representative de novo sequencing models, Casanovo, π-HelixNovo, and π-HelixNovo2, NTS significantly improves model performance and generalization, yielding average relative peptide recall gains of 50.9%, 12.4%, and 17.2%, respectively. These results demonstrate that NTS provides an interpretable and broadly applicable solution to the training-data bottleneck in de novo peptide sequencing.