ArticleNature communications2023
DeepFLR facilitates false localization rate control in phosphoproteomics.
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 17 papers.
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Who cites it
17 citing papers in PubMed.
- Proteoform-specific enrichment of phosphopeptide isomers by polymer-based synthetic receptors.Nature chemical biology · 2026Article
- PhosSight: A Unified Deep Learning Framework Boosting and Accelerating Phosphoproteome Identification to Enable Biological Discoveries.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Deep Learning Prediction of O-Glycopeptide Tandem Mass Spectra Enhances O-Glycoproteomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Phosphoproteomics of WHO-Priority Fungal Pathogens: Conserved Signaling Architecture, Pathogen-Specific Outputs, and Therapeutic Vulnerabilities.Pathogens (Basel, Switzerland) · 2026Review
- The proteomics and phosphoproteomics landscape of melanoma under T cell attack.Cell reports. Medicine · 2026Article
- Clinical peptidomics for respiratory diseases: matrices, workflows, and translation towards treatable traits, with a focus on COPD.Clinical proteomics · 2026Review
- Zero-shot prediction of drug responses using biologically informed neural networks trained on phosphoproteomic timeseries.PLoS computational biology · 2026Article
- Applications and Prospects of Artificial Intelligence in ProteomicsCurrent protein & peptide science · 2026Review
- The pursuit of ultrasensitive phosphoproteomics to unravel signalling in rare cells.Communications biology · 2025Review
- Finding coexisting combinations of posttranslational modifications with HomMTM spectra.Briefings in bioinformatics · 2025Article
- N-Glycosylation Modification of Fzd4 Is Essential for the Fzd4-Wnt-β-Catenin Signalling Axis.Journal of cellular and molecular medicine · 2025Article
- Advancements in Global Phosphoproteomics Profiling: Overcoming Challenges in Sensitivity and Quantification.Proteomics · 2025Review
- Artificial Intelligence Transforming Post-Translational Modification Research.Bioengineering (Basel, Switzerland) · 2024Review
- Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification.Molecular & cellular proteomics : MCP · 2024Review
- Computational approaches to identify sites of phosphorylation.Proteomics · 2024Review
- Prediction of glycopeptide fragment mass spectra by deep learning.Nature communications · 2024Article
- Exploring the dynamic landscape of immunopeptidomics: Unravelling posttranslational modifications and navigating bioinformatics terrain.Mass spectrometry reviewsReview
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein phosphorylation is a post-translational modification crucial for many cellular processes and protein functions. Accurate identification and quantification of protein phosphosites at the proteome-wide level are challenging, not least because efficient tools for protein phosphosite false localization rate (FLR) control are lacking. Here, we propose DeepFLR, a deep learning-based framework for controlling the FLR in phosphoproteomics. DeepFLR includes a phosphopeptide tandem mass spectrum (MS/MS) prediction module based on deep learning and an FLR assessment module based on a target-decoy approach. DeepFLR improves the accuracy of phosphopeptide MS/MS prediction compared to existing tools. Furthermore, DeepFLR estimates FLR accurately for both synthetic and biological datasets, and localizes more phosphosites than probability-based methods. DeepFLR is compatible with data from different organisms, instruments types, and both data-dependent and data-independent acquisition approaches, thus enabling FLR estimation for a broad range of phosphoproteomics experiments.
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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.