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• W4366550125 abstract "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." @default.
• W4366550125 created "2023-04-22" @default.
• W4366550125 creator A5000095970 @default.
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• W4366550125 date "2023-04-20" @default.
• W4366550125 modified "2023-10-14" @default.
• W4366550125 title "DeepFLR facilitates false localization rate control in phosphoproteomics" @default.
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• W4366550125 doi "https://doi.org/10.1038/s41467-023-38035-1" @default.
• W4366550125 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37080984" @default.
• W4366550125 hasPublicationYear "2023" @default.
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