ArticleNature communications2023
Functional annotation of proteins for signaling network inference in non-model species.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Article
- Towards a kingdom of reproductive life - the core sperm proteome.Reproduction (Cambridge, England) · 2025Article
- Lineage-specific microbial protein prediction enables large-scale exploration of protein ecology within the human gut.Nature communications · 2025Article
- A computational framework for extracting biological insights from SRA cancer data.Scientific reports · 2025Article
- Learning maximally spanning representations improves protein function annotation.bioRxiv : the preprint server for biology · 2025Article
- Advancing plant biology through deep learning-powered natural language processing.Plant cell reports · 2024Review
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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