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DeepFLR facilitates false localization rate control in phosphoproteomics.

Yu Zong1, Yuxin Wang1,2, Yi Yang1

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DeepFLR, a new deep learning framework, enhances phosphoproteomics by accurately controlling false localization rates (FLR) for protein phosphosites. This tool improves phosphopeptide identification and quantification in complex biological samples.

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Area of Science:

  • Proteomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Protein phosphorylation is a critical post-translational modification regulating cellular functions.
  • Accurate identification and quantification of protein phosphosites are essential but challenging.
  • Existing tools lack efficient methods for controlling false localization rates (FLR) in phosphoproteomics.

Purpose of the Study:

  • To develop DeepFLR, a deep learning framework for robust FLR control in phosphoproteomics.
  • To enhance the accuracy of phosphopeptide identification and quantification.
  • To provide a versatile tool for analyzing diverse phosphoproteomics datasets.

Main Methods:

  • Deep learning-based tandem mass spectrum (MS/MS) prediction module.
  • FLR assessment module utilizing a target-decoy approach.
  • Framework compatibility with various organisms, instrument types, and acquisition methods (DDA/DIA).

Main Results:

  • DeepFLR demonstrates improved accuracy in phosphopeptide MS/MS prediction over existing methods.
  • Accurate FLR estimation for both synthetic and biological phosphoproteomics datasets.
  • Localization of a greater number of phosphosites compared to probability-based approaches.

Conclusions:

  • DeepFLR offers a powerful deep learning solution for FLR control in phosphoproteomics.
  • The framework enhances the reliability and scope of phosphosite identification and quantification.
  • DeepFLR is broadly applicable across diverse phosphoproteomics experimental designs.