DeepFLR facilitates false localization rate control in phosphoproteomics

Yu Zong, Yuxin Wang, Yi Yang, Dan Zhao, Xiaoqing Wang, Chengpin Shen and Liang Qiao ()
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Yu Zong: Fudan University
Yuxin Wang: Fudan University
Yi Yang: Fudan University
Dan Zhao: Fudan University
Xiaoqing Wang: Shanghai Omicsolution Co., Ltd
Chengpin Shen: Shanghai Omicsolution Co., Ltd
Liang Qiao: Fudan University

Nature Communications, 2023, vol. 14, issue 1, 1-16

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.

Date: 2023
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DOI: 10.1038/s41467-023-38035-1

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