ArticleNature communications2021
Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 93 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
93 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mass Spectral Feature Analysis of Ubiquitylated Peptides Provides Insights into Probing the Dark Ubiquitylome.Journal of the American Society for Mass Spectrometry · 2024Pooled it
- Article
- Comparative Evaluation of DDA- and DIA-Based Proteomic Workflows in Beryllium-Related Lung Disease.bioRxiv : the preprint server for biology · 2026Article
- Noncanonical Transcription and Splicing Shape the Colorectal Cancer Immunopeptidome in MSI and MSS Tumors.Molecular & cellular proteomics : MCP · 2026Article
- Pipe dream to pipeline: Journey of cancer vaccines and the road ahead.Cell reports. Medicine · 2026Review
- Sensitive detection of cancer antigens enabled by user-defined peptide libraries.Nature biotechnology · 2026Article
- Community benchmarking and evaluation of human unannotated microprotein detection by mass spectrometry based proteomics.Nature communications · 2026Article
- Advances in Colorectal Cancer Cell Biology and Clonal Evolution.International journal of molecular sciences · 2026Review
- Comparative performance of Scribe and database search engines in metaproteomic profiling of a ground-truth microbiome dataset.Journal of proteomics · 2026Article
- A Novel Machine Learning Post-processing Filter for Mass Spectrometry-Based Proteogenomics Leveraging Retention Time Deviation and Peptide Physicochemical Properties.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Tempered signal strength via low-dose MEK inhibition optimizes therapeutic performance of engineered T cells.Journal for immunotherapy of cancer · 2025Article
- Koina: Democratizing machine learning for proteomics research.Nature communications · 2025Article
- From Identification to Insight: Making Full Use of the Diagnostic Potential of MS/MS Proteotyping in Clinical Microbiology Using Efficient Bioinformatics.Journal of proteome research · 2025Review
- Artificial Intelligence and the Evolving Landscape of Immunopeptidomics.Proteomics. Clinical applications · 2025Review
- Review
- Alternate RNA decoding results in stable and abundant proteins in mammals.bioRxiv : the preprint server for biology · 2025Article
- MHCquant2 refines immunopeptidomics tumor antigen discovery.Genome biology · 2025Article
- HLA Ligand Atlas DIA: extending the benign immunopeptidomics resource with increased sensitivity through data-independent acquisition mass spectrometry.Journal for immunotherapy of cancer · 2025Article
- Tumor antigens preferentially derive from unmutated genomic sequences in melanoma and non-small cell lung cancer.Nature cancer · 2025Article
- Prosit-XL: enhanced cross-linked peptide identification by fragment intensity prediction to study protein interactions and structures.Nature communications · 2025Article
33 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
24 authors.
Funding
Abstract
Characterizing the human leukocyte antigen (HLA) bound ligandome by mass spectrometry (MS) holds great promise for developing vaccines and drugs for immune-oncology. Still, the identification of non-tryptic peptides presents substantial computational challenges. To address these, we synthesized and analyzed >300,000 peptides by multi-modal LC-MS/MS within the ProteomeTools project representing HLA class I & II ligands and products of the proteases AspN and LysN. The resulting data enabled training of a single model using the deep learning framework Prosit, allowing the accurate prediction of fragment ion spectra for tryptic and non-tryptic peptides. Applying Prosit demonstrates that the identification of HLA peptides can be improved up to 7-fold, that 87% of the proposed proteasomally spliced HLA peptides may be incorrect and that dozens of additional immunogenic neo-epitopes can be identified from patient tumors in published data. Together, the provided peptides, spectra and computational tools substantially expand the analytical depth of immunopeptidomics workflows.
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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.