biostudies-literatureZong YMinistry of Science and Technology of the People’s Republic of ChinaNational Natural Science Foundation of China2269https://www.ebi.ac.uk/biostudies/studies/S-EPMC10119288biostudies-literatureUnknown14(1)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 loNature communicationsDeepFLR facilitates false localization rate control in phosphoproteomics.PMC101192882193400122022401220740222022YFC2704300Yang YZong YShen CZhao DWang XWang YQiao LfalseDeepFLR facilitates false localization rate control in phosphoproteomics.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 lo2023-01-01T00:00:00Z2023 Apr2026-05-28T16:50:18.973Z2025-04-04T03:42:36.879ZS-EPMC101192883708098410.1038/s41467-023-38035-1