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.

A Whole Exon Screening-Based Score Model Predicts Prognosis and Immune Checkpoint Inhibitor Therapy Effects in Low-Grade Glioma.

Objective: This study aims to identify prognostic factors for low-grade glioma (LGG) via different machine learning methods in the whole genome and to predict patient prognoses based on these factors. We verified the results through in vitro experiments to further screen new potential therapeutic targets. Method: A total of 940 glioma patients from The Cancer Genome Atlas (TCGA) and The Chinese Glioma Genome Atlas (CGGA) were included in this study. Two different feature extraction algorithms - LASSO and Random Forest (RF) - were used to jointly screen genes significantly related to the prognosis of patients. The risk signature was constructed based on these screening genes, and the K-M curve and ROC curve evaluated it. Furthermore, we discussed the differences between the high- and low-risk groups distinguished by the signature in detail, including differential gene expression (DEG), single-nucleotide polymorphism (SNP), copy number variation (CNV), immune infiltration, and immune checkpoint. Finally, we identified the function of a novel molecule, METTL7B, which was highly correlated with PD-L1 expression on tumor cell, as verified by in vitro experiments. Results: We constructed an accurate prediction model based on seven genes (AUC at 1, 3, 5 years= 0.91, 0.85, 0.74). Further analysis showed that extracellular matrix remodeling and cytokine and chemokine release were activated in the high-risk group. The proportion of multiple immune cell infiltration was upregulated, especially macrophages, accompanied by the high expression of most immune checkpoints. According to the in vitro experiment, we preliminarily speculate that METTL7B affects the stability of PD-L1 mRNA by participating in the modification of m6A. Conclusion: The seven gene signatures we constructed can predict the prognosis of patients and identify the potential benefits of immune checkpoint inhibitors (ICI) therapy for LGG. More importantly, METTL7B, one of the risk genes, is a crucial molecule that regulates PD-L1 and could be used as a new potential therapeutic target.