Dataset Information

DeepFLR Facilitates False Localization Rate Control in Phosphoproteomics

ABSTRACT: Site-specific phosphorylation events affect nearly all the cellular processes and correct phosphosite localization plays an important role in biological or medical health studies. However, direct false localization rate (FLR) control remains challenging in phosphoproteomics. Here, we propose DeepFLR, a deep learning-based framework utilizing spectrum prediction and the target-decoy method for FLR estimation. We demonstrate that the similarity between predicted and experimental phosphopeptide spectra is comparable to the measurement reproducibility. We further benchmark our method with four synthetic datasets and three real biological sample datasets, showcasing its ability for sensitive phosphosite localization with accurate FLR estimation.

ORGANISM(S): Homo Sapiens

SUBMITTER: Liang Qiao

PROVIDER: PXD037580 | iProX | Tue Apr 04 00:00:00 BST 2023

REPOSITORIES: iProX

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Publications

DeepFLR facilitates false localization rate control in phosphoproteomics.

Zong Yu Y Wang Yuxin Y Yang Yi Y Zhao Dan D Wang Xiaoqing X Shen Chengpin C Qiao Liang L

Nature communications 20230420 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 ( ...[more]

PMID: 37080984

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Dataset Information

DeepFLR Facilitates False Localization Rate Control in Phosphoproteomics

Publications

DeepFLR facilitates false localization rate control in phosphoproteomics.

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