ArticleNature machine intelligence2025
InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments.
Article in Nature machine intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Beyond Sequence: Posttranslational Remodeling of Antigens in Autoimmunity.Immunological reviews · 2026Review
- Comprehensive Characterization and Antioxidant Function Prediction of Endogenous and Exogenous Peptides fromCurrent issues in molecular biology · 2026Article
- TIPs: a deep learning-guided proteogenomic framework to expand the landscape of transposable element-derived antigens with immunopeptidomics.Genome biology · 2026Article
- InstaNovo-P: a de novo peptide sequencing model for phosphoproteomics.Nature communications · 2026Article
- Degradomics for large-scale mechanistic insights on proteases and proteolysis in human health.The FEBS journal · 2026Review
- Zero-shot de novo peptide sequencing with open posttranslational modification discovery.Nature biotechnology · 2026Article
- Contemporary data-driven innovations in peptide-based therapeutic design.Briefings in bioinformatics · 2026Review
- A Framework for Database Search with AI Models in Mass Spectrometry-Based Proteomics.Journal of proteome research · 2026Review
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 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
- Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS.Nature biotechnology · 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
- 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
19 authors.
Funding
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Abstract
Mass spectrometry-based proteomics focuses on identifying the peptide that generates a tandem mass spectrum. Traditional methods rely on protein databases but are often limited or inapplicable in certain contexts. De novo peptide sequencing, which assigns peptide sequences to spectra without prior information, is valuable for diverse biological applications; however, owing to a lack of accuracy, it remains challenging to apply. Here we introduce InstaNovo, a transformer model that translates fragment ion peaks into peptide sequences. We demonstrate that InstaNovo outperforms state-of-the-art methods and showcase its utility in several applications. We also introduce InstaNovo+, a diffusion model that improves performance through iterative refinement of predicted sequences. Using these models, we achieve improved therapeutic sequencing coverage, discover novel peptides and detect unreported organisms in diverse datasets, thereby expanding the scope and detection rate of proteomics searches. Our models unlock opportunities across domains such as direct protein sequencing, immunopeptidomics and exploration of the dark proteome.
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Registered trials
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