ArticleProceedings of the National Academy of Sciences of the United States of America2017
De novo peptide sequencing by deep learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 177 papers.
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Who cites it
177 citing papers in PubMed.
- Vib2Mol: from vibrational spectra to molecular structures-a unified deep learning framework.Chemical science · 2026Article
- Beyond Sequence: Posttranslational Remodeling of Antigens in Autoimmunity.Immunological reviews · 2026Review
- Early maturation of host antimicrobial peptide defences is associated with host-pathogen coexistence.Nature chemical biology · 2026Article
- InstaNovo-P: a de novo peptide sequencing model for phosphoproteomics.Nature communications · 2026Article
- Advancing proteomic discovery through optimized multi-stage scoring and deep learning-enhanced open search.Bioinformatics (Oxford, England) · 2026Article
- Foundation model enables interpretable open and error-tolerant searching for mass spectrometry-based proteomics.Bioinformatics (Oxford, England) · 2026Article
- Immunopeptidomics-guided identification of functional neoantigens in non-small cell lung cancer.NPJ precision oncology · 2026Article
- Enhancing the Recovery of Antioxidant Compounds from Microalgae-Cyanobacteria Consortia Through Alcalase Hydrolysis: A Focus on Bioactive Peptides.Marine drugs · 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
- Deep coverage and extended sequence reads obtained with a single archaeal protease expedite de novo protein sequencing by mass spectrometry.Cell systems · 2026Article
- Clinical peptidomics for respiratory diseases: matrices, workflows, and translation towards treatable traits, with a focus on COPD.Clinical proteomics · 2026Review
- What Does Next-Generation Mass Spectrometry Offer for Proteomics? A Comprehensive Platform Comparison.Journal of proteome research · 2026Article
- End-to-end deep attention-based multitask pipeline for predicting uncertainty-quantified peptide properties from mass spectrometry data.Scientific reports · 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
- Hybrid Deep Learning Model for EI-MS Spectra Prediction.International journal of molecular sciences · 2026Article
- Integrative Genomic and AI Approaches to Lung Cancer and Implications for Disease Prevention in Former Smokers.International journal of molecular sciences · 2026Review
- Applications and Prospects of Artificial Intelligence in ProteomicsCurrent protein & peptide science · 2026Review
- C/EBPβ-induced alternative splicing of RCAN1 generates a potent TCR-T target in mesenchymal glioblastoma.Cellular & molecular immunology · 2026Article
117 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization of proteins, especially for new sequences, such as mAbs. In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing. DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides. The networks are further integrated with local dynamic programming to solve the complex optimization task of de novo sequencing. We evaluated the method on a wide variety of species and found that DeepNovo considerably outperformed state of the art methods, achieving 7.7-22.9% higher accuracy at the amino acid level and 38.1-64.0% higher accuracy at the peptide level. We further used DeepNovo to automatically reconstruct the complete sequences of antibody light and heavy chains of mouse, achieving 97.5-100% coverage and 97.2-99.5% accuracy, without assisting databases. Moreover, DeepNovo is retrainable to adapt to any sources of data and provides a complete end-to-end training and prediction solution to the de novo sequencing problem. Not only does our study extend the deep learning revolution to a new field, but it also shows an innovative approach in solving optimization problems by using deep learning and dynamic programming.
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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.