ArticleNature communications2024
Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing.
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 17 papers.
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Who cites it
17 citing papers in PubMed.
- Foundation model enables interpretable open and error-tolerant searching for mass spectrometry-based proteomics.Bioinformatics (Oxford, England) · 2026Article
- AI and Machine Learning for Proteomics-Driven Drug Discovery: Methods, Tools, and Best Practices.Current issues in molecular biology · 2026Review
- Mass spectrometry-based de novo sequencing reveals non-canonical neoantigens with antitumor efficacy in hepatocellular carcinoma.JHEP reports : innovation in hepatology · 2026Article
- CAMPER: mechanistic artificial intelligence for designing peptides that target MRSA persisters.Nature communications · 2026Article
- Peptidomics-based identification of endogenous free peptides fromFrontiers in pharmacology · 2026Article
- Applications and Prospects of Artificial Intelligence in ProteomicsCurrent protein & peptide science · 2026Review
- De novo peptide databases enable protein-based stable isotope probing of microbial communities with up to species-level resolution.Environmental microbiome · 2025Article
- A transformer model for de novo sequencing of data-independent acquisition mass spectrometry data.Nature methods · 2025Article
- Quantification of Peptides in Food Hydrolysate fromFoods (Basel, Switzerland) · 2025Article
- Integration of proteomics profiling data to facilitate discovery of cancer neoantigens: a survey.Briefings in bioinformatics · 2025Review
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing.Nature communications · 2025Article
- A multi-species benchmark for training and validating mass spectrometry proteomics machine learning models.Scientific data · 2024Article
- Assessment of Data-Independent Acquisition Mass Spectrometry (DIA-MS) for the Identification of Single Amino Acid Variants.Proteomes · 2024Article
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model.Nature communications · 2024Article
- Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification.Molecular & cellular proteomics : MCP · 2024Review
- A learned score function improves the power of mass spectrometry database search.Bioinformatics (Oxford, England) · 2024Article
- Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing.Nature communications · 2024Article
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7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unlike for DNA and RNA, accurate and high-throughput sequencing methods for proteins are lacking, hindering the utility of proteomics in applications where the sequences are unknown including variant calling, neoepitope identification, and metaproteomics. We introduce Spectralis, a de novo peptide sequencing method for tandem mass spectrometry. Spectralis leverages several innovations including a convolutional neural network layer connecting peaks in spectra spaced by amino acid masses, proposing fragment ion series classification as a pivotal task for de novo peptide sequencing, and a peptide-spectrum confidence score. On spectra for which database search provided a ground truth, Spectralis surpassed 40% sensitivity at 90% precision, nearly doubling state-of-the-art sensitivity. Application to unidentified spectra confirmed its superiority and showcased its applicability to variant calling. Altogether, these algorithmic innovations and the substantial sensitivity increase in the high-precision range constitute an important step toward broadly applicable peptide sequencing.
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