Introducing π-HelixNovo for practical large-scale de novo peptide sequencing

De novo peptide sequencing is a promising approach for novel peptide discovery, highlighting the performance improvements for the state-of-the-art models. The quality of mass spectra often varies due to unexpected missing of certain ions, presenting a significant challenge in de novo peptide sequencing. Here, we use a novel concept of complementary spectra to enhance ion information of the experimental spectrum and demonstrate it through conceptual and practical analyses. Afterward, we design suitable encoders to encode the experimental spectrum and the corresponding complementary spectrum and propose a de novo sequencing model pi-HelixNovo based on the Transformer architecture. We first demonstrated that pi-HelixNovo outperforms other state-of-the-art models using a series of comparative experiments. Then, we utilized pi-HelixNovo to de novo gut metaproteome peptides for the first time. The results show pi-HelixNovo increases the identification coverage and accuracy of gut metaproteome and enhances the taxonomic resolution of gut metaproteome. We finally trained a powerful pi-HelixNovo utilizing a larger training dataset, and as expected, pi-HelixNovo achieves unprecedented performance, even for peptide-spectrum matches with never-before-seen peptide sequences. We also use the powerful pi-HelixNovo to identify antibody peptides and multi-enzyme cleavage peptides, and pi-HelixNovo is highly robust in these applications. Our results demonstrate the effectivity of the complementary spectrum and take a significant step forward in de novo peptide sequencing.