DeepFLR 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 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. Protein phosphorylation is a critical modification in many cellular processes. Here, the authors present DeepFLR, a deep learning-based framework to accurately predict phosphopeptide tandem mass spectra and effectively control false localization rates in phosphoproteomics.

Automated summary

DeepFLR is a deep learning-based framework that accurately predicts phosphopeptide tandem mass spectra and effectively controls false localization rates in phosphoproteomics.