ArticleNature communications2025
π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- InstaNovo-P: a de novo peptide sequencing model for phosphoproteomics.Nature communications · 2026Article
- Zero-shot de novo peptide sequencing with open posttranslational modification discovery.Nature biotechnology · 2026Article
- PowerNovo2: A generative flow-based approach to non-autoregressive de novo peptide sequencing.PLoS computational biology · 2026Article
- A Framework for Database Search with AI Models in Mass Spectrometry-Based Proteomics.Journal of proteome research · 2026Review
- DDA-BERT: end-to-end training for data-dependent acquisition mass spectrometry-based proteomics.Nature communications · 2026Article
- Generalizable Direct Protein Sequencing With InstaNexus.Molecular & cellular proteomics : MCP · 2026Article
- DiNovo enables high-coverage and high-confidence de novo peptide sequencing via mirror proteases and deep learning.Nature communications · 2026Article
- Modanovo: A Unified Model for Post-translational Modification-Aware De Novo Sequencing Using Experimental Spectra From In Vivo and Synthetic Peptides.Molecular & cellular proteomics : MCP · 2026Article
- An artificial intelligence-driven synthesis planning platform (PhotoCat) for photocatalysis.Communications chemistry · 2026Article
- AI-driven discovery in protein science for immunology and infectious disease research.Frontiers in bioinformatics · 2026Review
- Supervised Contrastive Learning Leads to More Reasonable Spectral Embeddings.Analytical chemistry · 2025Article
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Peptide sequencing via tandem mass spectrometry (MS/MS) is essential in proteomics. Unlike traditional database searches, deep learning excels at de novo peptide sequencing, even for peptides missing from existing databases. Current deep learning models often rely on autoregressive generation, which suffers from error accumulation and slow inference speeds. In this work, we introduce π-PrimeNovo, a non-autoregressive Transformer-based model for peptide sequencing. With our architecture design and a CUDA-enhanced decoding module for precise mass control, π-PrimeNovo achieves significantly higher accuracy and up to 89x faster inference than state-of-the-art methods, making it ideal for large-scale applications like metaproteomics. Additionally, it excels in phosphopeptide mining and detecting low-abundance post-translational modifications (PTMs), marking a substantial advance in peptide sequencing with broad potential in biological research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.