ArticleNature methods2021
DeepLC can predict retention times for peptides that carry as-yet unseen modifications.
Article in Nature methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 121 papers.
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Who cites it
121 citing papers in PubMed.
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- Beyond Sequence: Posttranslational Remodeling of Antigens in Autoimmunity.Immunological reviews · 2026Review
- TIPs: a deep learning-guided proteogenomic framework to expand the landscape of transposable element-derived antigens with immunopeptidomics.Genome biology · 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
- AlphaDIA enables DIA transfer learning for feature-free proteomics.Nature biotechnology · 2026Article
- A One Health Framework for Proteomics Across the Tree of Life to Advance Food Security, Animal Health, and Ecosystem Resilience.Proteomes · 2026Review
- MLMarker: a machine learning framework for tissue inference and biomarker discovery.Genome biology · 2026Article
- iDeepLC: Chemical Structure Information Yields Improved Retention Time Prediction of Peptides with Unseen Modifications.Analytical chemistry · 2026Article
- Distinguishing Ile/Leu Variant Ligands in the Immunopeptidome Using Hybrid EAD + CID Fragmentation (ExCID).Analytical chemistry · 2026Article
- Article
- Prioritizing peptides for targeted mass spectrometry experiments using deep learning.bioRxiv : the preprint server for biology · 2026Article
- A Framework for Database Search with AI Models in Mass Spectrometry-Based Proteomics.Journal of proteome research · 2026Review
- DDA-BERT: end-to-end training for data-dependent acquisition mass spectrometry-based proteomics.Nature communications · 2026Article
- Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS.Nature biotechnology · 2026Article
- XL-MSDigger: a deep learning-based, versatile solution for cross-linking mass spectrometry.Nature communications · 2026Article
- Transfer learning in DeepLC improves LC retention time prediction across substantially different modifications and setups.Nature communications · 2026Article
- Sensitive detection of cancer antigens enabled by user-defined peptide libraries.Nature biotechnology · 2026Article
- Better Inputs, Better Learning: A Peptide Embedding Tutorial for Proteomic Mass Spectrometry.Journal of proteome research · 2026Article
61 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
The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex liquid chromatography-mass spectrometry identification workflows. However, due to the way peptides are encoded in current prediction models, accurate retention times cannot be predicted for modified peptides. This is especially problematic for fledgling open searches, which will benefit from accurate retention time prediction for modified peptides to reduce identification ambiguity. We present DeepLC, a deep learning peptide retention time predictor using peptide encoding based on atomic composition that allows the retention time of (previously unseen) modified peptides to be predicted accurately. We show that DeepLC performs similarly to current state-of-the-art approaches for unmodified peptides and, more importantly, accurately predicts retention times for modifications not seen during training. Moreover, we show that DeepLC's ability to predict retention times for any modification enables potentially incorrect identifications to be flagged in an open search of a wide variety of proteome data.
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