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Functional annotation of proteins for signaling network inference in non-model species

Author

Listed:
  • Lisa Van den Broeck

    (North Carolina State University)

  • Dinesh Kiran Bhosale

    (North Carolina State University)

  • Kuncheng Song

    (North Carolina State University)

  • Cássio Flavio Fonseca de Lima

    (Ghent University
    VIB Center for Plant Systems Biology)

  • Michael Ashley

    (North Carolina State University)

  • Tingting Zhu

    (Ghent University
    VIB Center for Plant Systems Biology)

  • Shanshuo Zhu

    (Ghent University
    VIB Center for Plant Systems Biology)

  • Brigitte Van De Cotte

    (Ghent University
    VIB Center for Plant Systems Biology)

  • Pia Neyt

    (Ghent University
    VIB Center for Plant Systems Biology)

  • Anna C. Ortiz

    (USDA-ARS Soybean & Nitrogen Fixation Research Unit)

  • Tiffany R. Sikes

    (USDA-ARS Soybean & Nitrogen Fixation Research Unit)

  • Jonas Aper

    (Protealis NV)

  • Peter Lootens

    (Fisheries and Food (ILVO))

  • Anna M. Locke

    (USDA-ARS Soybean & Nitrogen Fixation Research Unit
    North Carolina State University)

  • Ive De Smet

    (Ghent University
    VIB Center for Plant Systems Biology)

  • Rosangela Sozzani

    (North Carolina State University)

Abstract

Molecular biology aims to understand cellular responses and regulatory dynamics in complex biological systems. However, these studies remain challenging in non-model species due to poor functional annotation of regulatory proteins. To overcome this limitation, we develop a multi-layer neural network that determines protein functionality directly from the protein sequence. We annotate kinases and phosphatases in Glycine max. We use the functional annotations from our neural network, Bayesian inference principles, and high resolution phosphoproteomics to infer phosphorylation signaling cascades in soybean exposed to cold, and identify Glyma.10G173000 (TOI5) and Glyma.19G007300 (TOT3) as key temperature regulators. Importantly, the signaling cascade inference does not rely upon known kinase motifs or interaction data, enabling de novo identification of kinase-substrate interactions. Conclusively, our neural network shows generalization and scalability, as such we extend our predictions to Oryza sativa, Zea mays, Sorghum bicolor, and Triticum aestivum. Taken together, we develop a signaling inference approach for non-model species leveraging our predicted kinases and phosphatases.

Suggested Citation

  • Lisa Van den Broeck & Dinesh Kiran Bhosale & Kuncheng Song & Cássio Flavio Fonseca de Lima & Michael Ashley & Tingting Zhu & Shanshuo Zhu & Brigitte Van De Cotte & Pia Neyt & Anna C. Ortiz & Tiffany R, 2023. "Functional annotation of proteins for signaling network inference in non-model species," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40365-z
    DOI: 10.1038/s41467-023-40365-z
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    References listed on IDEAS

    as
    1. Siquan Hu & Ruixiong Ma & Haiou Wang, 2019. "An improved deep learning method for predicting DNA-binding proteins based on contextual features in amino acid sequences," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-21, November.
    2. Andrew W. Senior & Richard Evans & John Jumper & James Kirkpatrick & Laurent Sifre & Tim Green & Chongli Qin & Augustin Žídek & Alexander W. R. Nelson & Alex Bridgland & Hugo Penedones & Stig Petersen, 2020. "Improved protein structure prediction using potentials from deep learning," Nature, Nature, vol. 577(7792), pages 706-710, January.
    3. Lam Dai Vu & Xiangyu Xu & Tingting Zhu & Lixia Pan & Martijn van Zanten & Dorrit de Jong & Yaowei Wang & Tim Vanremoortele & Anna M. Locke & Brigitte van de Cotte & Nancy De Winne & Elisabeth Stes & E, 2021. "The membrane-localized protein kinase MAP4K4/TOT3 regulates thermomorphogenesis," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
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    Cited by:

    1. Matthias A. Schmitz & Nicholas J. Dimonaco & Thomas Clavel & Thomas C. A. Hitch, 2025. "Lineage-specific microbial protein prediction enables large-scale exploration of protein ecology within the human gut," Nature Communications, Nature, vol. 16(1), pages 1-12, December.

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