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DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation

Author

Listed:
  • Ronghui Lou

    (iHuman Institute, ShanghaiTech University
    ShanghaiTech University
    University of Chinese Academy of Sciences)

  • Weizhen Liu

    (ShanghaiTech University)

  • Rongjie Li

    (ShanghaiTech University)

  • Shanshan Li

    (iHuman Institute, ShanghaiTech University)

  • Xuming He

    (ShanghaiTech University
    Shanghai Engineering Research Center of Intelligent Vision and Imaging)

  • Wenqing Shui

    (iHuman Institute, ShanghaiTech University
    ShanghaiTech University)

Abstract

Phosphoproteomics integrating data-independent acquisition (DIA) enables deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining heavily relies on a spectral library that in most cases is built on DDA analysis of the same sample. Construction of this project-specific DDA library impairs the analytical throughput, limits the proteome coverage, and increases the sample size for DIA phosphoproteomics. Herein we introduce a deep neural network, DeepPhospho, which conceptually differs from previous deep learning models to achieve accurate predictions of LC-MS/MS data for phosphopeptides. By leveraging in silico libraries generated by DeepPhospho, we establish a DIA workflow for phosphoproteome profiling which involves DIA data acquisition and data mining with DeepPhospho predicted libraries, thus circumventing the need of DDA library construction. Our DeepPhospho-empowered workflow substantially expands the phosphoproteome coverage while maintaining high quantification performance, which leads to the discovery of more signaling pathways and regulated kinases in an EGF signaling study than the DDA library-based approach. DeepPhospho is provided as a web server as well as an offline app to facilitate user access to model training, predictions and library generation.

Suggested Citation

  • Ronghui Lou & Weizhen Liu & Rongjie Li & Shanshan Li & Xuming He & Wenqing Shui, 2021. "DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26979-1
    DOI: 10.1038/s41467-021-26979-1
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    References listed on IDEAS

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    1. Brian C. Searle & Kristian E. Swearingen & Christopher A. Barnes & Tobias Schmidt & Siegfried Gessulat & Bernhard Küster & Mathias Wilhelm, 2020. "Generating high quality libraries for DIA MS with empirically corrected peptide predictions," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    2. 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.
    2. Michael A. Skinnider & Mopelola O. Akinlaja & Leonard J. Foster, 2023. "Mapping protein states and interactions across the tree of life with co-fractionation mass spectrometry," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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