关键词: 从头测序, Transformer, 门控循环单元, 谱峰连接图, 磷酸化肽

Abstract: Tandem mass spectrometry-based peptide sequencing, commonly known as peptide identification, is a cornerstone technology in proteomics research. However, current de novo sequencing algorithms face challenges in accurately identifying phosphopeptides, which are of significant biological importance. The primary challenges arise from the complex fragmentation patterns induced by phosphorylation, the frequent occurrence of neutral loss peaks, and the generally less abundance of phosphopeptides in conventional mass spectrometry data. To address these issues, a de novo sequencing algorithm, TGNovo, based on Transformer and Gated Recurrent Unit (GRU), was proposed. A spectrum graph was introduced in TGNovo, which explicitly modeled the mass differences between peaks, guiding the Transformer encoder to capture spectral features. The decoder was designed to associate these features with amino acid sequences, while the relationships among peaks and between peaks and amino acids were modeled by the GRU, collaboratively enabling peptide reconstruction. Compared to the fully Transformer-based de novo sequencing algorithm Casanovo, TGNovo fully utilizes prior spectral information through the spectrum graph and GRU module, enhancing the model's ability to capture spectral structures. In evaluations of phosphopeptides across species, TGNovo outperforms Casanovo, with an average improvement of 16.5 percentage points in peptide-level recall and 37.1 percentage points in amino acid-level recall. Additionally, experiments on an immunopeptide dataset show that TGNovo-identified high-confidence antigenic peptides cover 86% of the database search results.

Key words: de novo peptide sequencing, Transformer, Gated Recurrent Unit &#40, GRU&#41