ArticleNature communications2023
Accurate de novo peptide sequencing using fully convolutional neural networks.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- MassNet: billion-scale AI-ready mass spectral corpus empowering scalable deep learning in proteomics.Nature methods · 2026Article
- TIPs: a deep learning-guided proteogenomic framework to expand the landscape of transposable element-derived antigens with immunopeptidomics.Genome biology · 2026Article
- Identification of Species-Specific Peptide Markers in Highly Processed Meat Products Using De Novo Sequencing.Foods (Basel, Switzerland) · 2026Article
- PowerNovo2: A generative flow-based approach to non-autoregressive de novo peptide sequencing.PLoS computational biology · 2026Article
- End-to-end deep attention-based multitask pipeline for predicting uncertainty-quantified peptide properties from mass spectrometry data.Scientific reports · 2026Article
- DiNovo enables high-coverage and high-confidence de novo peptide sequencing via mirror proteases and deep learning.Nature communications · 2026Article
- Predominant mutated non-canonical tumor-specific antigens identified by proteogenomics demonstrate immunogenicity and tumor suppression in CRC.Cell genomics · 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
- FIDDLE: a deep learning method for chemical formulas prediction from tandem mass spectra.Nature communications · 2025Article
- Artificial Intelligence and the Evolving Landscape of Immunopeptidomics.Proteomics. Clinical applications · 2025Review
- Identification of two metallothioneins in Agaricus crocodilinus reveals gene duplication and domain expansion, a pattern conserved across fungal species.Biometals : an international journal on the role of metal ions in biology, biochemistry, and medicine · 2025Article
- Immunologic specificity in glioblastoma: Antigen discovery and translational implications.Neuro-oncology advances · 2025Review
- De novo peptide databases enable protein-based stable isotope probing of microbial communities with up to species-level resolution.Environmental microbiome · 2025Article
- Unveiling the Bioactive Potential of the Invasive JellyfishBiomolecules · 2025Article
- A transformer model for de novo sequencing of data-independent acquisition mass spectrometry data.Nature methods · 2025Article
- Progress in the Identification and Design of Novel Antimicrobial Peptides Against Pathogenic Microorganisms.Probiotics and antimicrobial proteins · 2025Review
- In silico Screening of Duhaldea nervosa-derived Endogenous Peptides to Predict the Potential Anticancer Mechanisms.Plant foods for human nutrition (Dordrecht, Netherlands) · 2025Article
- A transformer-based semi-autoregressive framework for high-speed and accurate de novo peptide sequencing.Communications biology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
De novo peptide sequencing, which does not rely on a comprehensive target sequence database, provides us with a way to identify novel peptides from tandem mass spectra. However, current de novo sequencing algorithms suffer from low accuracy and coverage, which hinders their application in proteomics. In this paper, we present PepNet, a fully convolutional neural network for high accuracy de novo peptide sequencing. PepNet takes an MS/MS spectrum (represented as a high-dimensional vector) as input, and outputs the optimal peptide sequence along with its confidence score. The PepNet model is trained using a total of 3 million high-energy collisional dissociation MS/MS spectra from multiple human peptide spectral libraries. Evaluation results show that PepNet significantly outperforms current best-performing de novo sequencing algorithms (e.g. PointNovo and DeepNovo) in both peptide-level accuracy and positional-level accuracy. PepNet can sequence a large fraction of spectra that were not identified by database search engines, and thus could be used as a complementary tool to database search engines for peptide identification in proteomics. In addition, PepNet runs around 3x and 7x faster than PointNovo and DeepNovo on GPUs, respectively, thus being more suitable for the analysis of large-scale proteomics data.
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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.