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DeepFLR facilitates false localization rate control in phosphoproteomics

Author

Listed:
  • Yu Zong

    (Fudan University)

  • Yuxin Wang

    (Fudan University
    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)

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.

Suggested Citation

  • Yu Zong & Yuxin Wang & Yi Yang & Dan Zhao & Xiaoqing Wang & Chengpin Shen & Liang Qiao, 2023. "DeepFLR facilitates false localization rate control in phosphoproteomics," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38035-1
    DOI: 10.1038/s41467-023-38035-1
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    References listed on IDEAS

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    1. Alicia Lundby & Anna Secher & Kasper Lage & Nikolai B. Nordsborg & Anatoliy Dmytriyev & Carsten Lundby & Jesper V. Olsen, 2012. "Quantitative maps of protein phosphorylation sites across 14 different rat organs and tissues," Nature Communications, Nature, vol. 3(1), pages 1-10, January.
    2. Martin Mehnert & Rodolfo Ciuffa & Fabian Frommelt & Federico Uliana & Audrey Drogen & Kilian Ruminski & Matthias Gstaiger & Ruedi Aebersold, 2020. "Multi-layered proteomic analyses decode compositional and functional effects of cancer mutations on kinase complexes," Nature Communications, Nature, vol. 11(1), pages 1-18, December.
    3. Nicolae Sapoval & Amirali Aghazadeh & Michael G. Nute & Dinler A. Antunes & Advait Balaji & Richard Baraniuk & C. J. Barberan & Ruth Dannenfelser & Chen Dun & Mohammadamin Edrisi & R. A. Leo Elworth &, 2022. "Current progress and open challenges for applying deep learning across the biosciences," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    4. Yi Yang & Xiaohui Liu & Chengpin Shen & Yu Lin & Pengyuan Yang & Liang Qiao, 2020. "In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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    Cited by:

    1. Yi Yang & Qun Fang, 2024. "Prediction of glycopeptide fragment mass spectra by deep learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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