DeepFLR facilitates false localization rate control in phosphoproteomics
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 fa...
Main Authors: | , , , , , , |
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Format: | Article |
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Nature Portfolio
2023-04-01
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Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/s41467-023-38035-1 |
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author | Yu Zong Yuxin Wang Yi Yang Dan Zhao Xiaoqing Wang Chengpin Shen Liang Qiao |
author_facet | Yu Zong Yuxin Wang Yi Yang Dan Zhao Xiaoqing Wang Chengpin Shen Liang Qiao |
author_sort | Yu Zong |
collection | DOAJ |
description | 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. |
first_indexed | 2024-04-09T16:22:40Z |
format | Article |
id | doaj.art-4700cd83d821403aa913ca6656447e0f |
institution | Directory Open Access Journal |
issn | 2041-1723 |
language | English |
last_indexed | 2024-04-09T16:22:40Z |
publishDate | 2023-04-01 |
publisher | Nature Portfolio |
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series | Nature Communications |
spelling | doaj.art-4700cd83d821403aa913ca6656447e0f2023-04-23T11:22:00ZengNature PortfolioNature Communications2041-17232023-04-0114111610.1038/s41467-023-38035-1DeepFLR facilitates false localization rate control in phosphoproteomicsYu Zong0Yuxin Wang1Yi Yang2Dan Zhao3Xiaoqing Wang4Chengpin Shen5Liang Qiao6Department of Chemistry, and Shanghai Stomatological Hospital, Fudan UniversityDepartment of Chemistry, and Shanghai Stomatological Hospital, Fudan UniversityDepartment of Chemistry, and Shanghai Stomatological Hospital, Fudan UniversityDepartment of Chemistry, and Shanghai Stomatological Hospital, Fudan UniversityShanghai Omicsolution Co., LtdShanghai Omicsolution Co., LtdDepartment of Chemistry, and Shanghai Stomatological Hospital, Fudan UniversityAbstract 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.https://doi.org/10.1038/s41467-023-38035-1 |
spellingShingle | Yu Zong Yuxin Wang Yi Yang Dan Zhao Xiaoqing Wang Chengpin Shen Liang Qiao DeepFLR facilitates false localization rate control in phosphoproteomics Nature Communications |
title | DeepFLR facilitates false localization rate control in phosphoproteomics |
title_full | DeepFLR facilitates false localization rate control in phosphoproteomics |
title_fullStr | DeepFLR facilitates false localization rate control in phosphoproteomics |
title_full_unstemmed | DeepFLR facilitates false localization rate control in phosphoproteomics |
title_short | DeepFLR facilitates false localization rate control in phosphoproteomics |
title_sort | deepflr facilitates false localization rate control in phosphoproteomics |
url | https://doi.org/10.1038/s41467-023-38035-1 |
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