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Deciphering signaling specificity with interpretable deep neural networks

View ORCID ProfileYunan Luo, View ORCID ProfileJianzhu Ma, Yang Liu, Qing Ye, Trey Ideker, Jian Peng
doi: https://doi.org/10.1101/288647
Yunan Luo
University of Illinois at Urbana-Champaign;
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Jianzhu Ma
University of California San Diego
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Yang Liu
University of Illinois at Urbana-Champaign;
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Qing Ye
University of Illinois at Urbana-Champaign;
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Trey Ideker
University of California San Diego
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Jian Peng
University of Illinois at Urbana-Champaign;
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  • For correspondence: jianpeng@illinois.edu
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Abstract

Protein kinase phosphorylation is a prevalent post-translational modification (PTM) regulating protein function and transmitting signals throughout the cell. Defective signal transductions, which are associated with protein phosphorylation, have been revealed to link to many human diseases, such as cancer. Defining the organization of the phosphorylation-based signaling network and, in particular, identifying kinase-specific substrates can help reveal the molecular mechanism of the signaling network. Here, we present DeepSignal, a deep learning framework for predicting the substrate specificity for kinase/SH2 sequences with or without mutations. Empowered by the memory and selection mechanism of recurrent neural network, DeepSignal can identify important specificity-defining residues to predict kinase specificity and changes upon mutations. Evaluated on several public benchmark datasets, DeepSignal significantly outperforms current methods on predicting substrate specificity on both kinase and SH2 domains. Further analysis in The Cancer Genome Atlas (TCGA) demonstrated that DeepSignal is able to aggregate mutations on both kinase/SH2 domains and substrates to quantify binding specificity changes, predict cancer genes related to signaling transduction, and identify novel perturbed pathways.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 25, 2018.
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Deciphering signaling specificity with interpretable deep neural networks
Yunan Luo, Jianzhu Ma, Yang Liu, Qing Ye, Trey Ideker, Jian Peng
bioRxiv 288647; doi: https://doi.org/10.1101/288647
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Deciphering signaling specificity with interpretable deep neural networks
Yunan Luo, Jianzhu Ma, Yang Liu, Qing Ye, Trey Ideker, Jian Peng
bioRxiv 288647; doi: https://doi.org/10.1101/288647

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