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

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. Sikes, Jonas Aper, Peter Lootens, Anna M. Locke, Ive De Smet and Rosangela Sozzani ()
Additional contact information
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
Michael Ashley: North Carolina State University
Tingting Zhu: Ghent University
Shanshuo Zhu: Ghent University
Brigitte Van De Cotte: Ghent University
Pia Neyt: Ghent University
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
Ive De Smet: Ghent University
Rosangela Sozzani: North Carolina State University

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-40365-z

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