ArticleNature communications2024
Sequence-to-sequence translation from mass spectra to peptides with a transformer model.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed.
- MARLOWE: taxonomic characterization of unknown samples for forensics using de novo peptide identification.Scientific reports · 2026Article
- Beyond Sequence: Posttranslational Remodeling of Antigens in Autoimmunity.Immunological reviews · 2026Review
- Protein hydrolysates derived from poultry by-products as nutraceuticals: Bioactive potential, limitations, safety considerations, and commercialization perspectives.Poultry science · 2026Review
- usiGrabber: automating the curation of proteomics spectra data at scale, making large datasets ready for use in machine learning systems.Bioinformatics (Oxford, England) · 2026Article
- Polymorphic IGLV6-57 AL amyloid fibrils and features of a shared folding pathway.Nature communications · 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
- Foundation model enables interpretable open and error-tolerant searching for mass spectrometry-based proteomics.Bioinformatics (Oxford, England) · 2026Article
- WGTMM: WGAN with Transformer Feature Matching for Generating fMRI Data in MCI Patients.Brain sciences · 2026Article
- Self-assembling proteins compose the chemically resistant shell biomaterial of planktonic tintinnid ciliates.Nature communications · 2026Article
- A Viral Mutation Profiling and Discovery Strategy for Sensitive Multiplex Detection of Viruses and Variants in Saliva by Proteomics.bioRxiv : the preprint server for biology · 2026Article
- MegaPX: fast and space-efficient peptide assignment method using IBF-based multi-indexing.Bioinformatics (Oxford, England) · 2026Article
- A Framework for Database Search with AI Models in Mass Spectrometry-Based Proteomics.Journal of proteome research · 2026Review
- PowerNovo2: A generative flow-based approach to non-autoregressive de novo peptide sequencing.PLoS computational biology · 2026Article
- DDA-BERT: end-to-end training for data-dependent acquisition mass spectrometry-based proteomics.Nature communications · 2026Article
- Clinical peptidomics for respiratory diseases: matrices, workflows, and translation towards treatable traits, with a focus on COPD.Clinical proteomics · 2026Review
- Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS.Nature biotechnology · 2026Article
- Generalizable Direct Protein Sequencing With InstaNexus.Molecular & cellular proteomics : MCP · 2026Article
- XA-Novo: high-throughput mass spectrometry-based de novo sequencing technology for monoclonal antibodies and antibody mixtures.Nature communications · 2026Article
- DiNovo enables high-coverage and high-confidence de novo peptide sequencing via mirror proteases and deep learning.Nature communications · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
A fundamental challenge in mass spectrometry-based proteomics is the identification of the peptide that generated each acquired tandem mass spectrum. Approaches that leverage known peptide sequence databases cannot detect unexpected peptides and can be impractical or impossible to apply in some settings. Thus, the ability to assign peptide sequences to tandem mass spectra without prior information-de novo peptide sequencing-is valuable for tasks including antibody sequencing, immunopeptidomics, and metaproteomics. Although many methods have been developed to address this problem, it remains an outstanding challenge in part due to the difficulty of modeling the irregular data structure of tandem mass spectra. Here, we describe Casanovo, a machine learning model that uses a transformer neural network architecture to translate the sequence of peaks in a tandem mass spectrum into the sequence of amino acids that comprise the generating peptide. We train a Casanovo model from 30 million labeled spectra and demonstrate that the model outperforms several state-of-the-art methods on a cross-species benchmark dataset. We also develop a version of Casanovo that is fine-tuned for non-enzymatic peptides. Finally, we demonstrate that Casanovo's superior performance improves the analysis of immunopeptidomics and metaproteomics experiments and allows us to delve deeper into the dark proteome.
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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.