DeepDN_iGlu: prediction of lysine glutarylation sites based on attention residual learning method and DenseNet

Jianhua Jia; Mingwei Sun; Genqiang Wu; Wangren Qiu; Jianhua Jia; Mingwei Sun; Genqiang Wu; Wangren Qiu

doi:10.3934/mbe.2023132

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 2: 2815-2830. doi: 10.3934/mbe.2023132

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Research article

DeepDN_iGlu: prediction of lysine glutarylation sites based on attention residual learning method and DenseNet

School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333403, China

Academic Editor: Geng Dong

Received: 15 October 2022 Revised: 14 November 2022 Accepted: 17 November 2022 Published: 01 December 2022

As a key issue in orchestrating various biological processes and functions, protein post-translational modification (PTM) occurs widely in the mechanism of protein's function of animals and plants. Glutarylation is a type of protein-translational modification that occurs at active ε-amino groups of specific lysine residues in proteins, which is associated with various human diseases, including diabetes, cancer, and glutaric aciduria type I. Therefore, the issue of prediction for glutarylation sites is particularly important. This study developed a brand-new deep learning-based prediction model for glutarylation sites named DeepDN_iGlu via adopting attention residual learning method and DenseNet. The focal loss function is utilized in this study in place of the traditional cross-entropy loss function to address the issue of a substantial imbalance in the number of positive and negative samples. It can be noted that DeepDN_iGlu based on the deep learning model offers a greater potential for the glutarylation site prediction after employing the straightforward one hot encoding method, with Sensitivity (Sn), Specificity (Sp), Accuracy (ACC), Mathews Correlation Coefficient (MCC), and Area Under Curve (AUC) of 89.29%, 61.97%, 65.15%, 0.33 and 0.80 accordingly on the independent test set. To the best of the authors' knowledge, this is the first time that DenseNet has been used for the prediction of glutarylation sites. DeepDN_iGlu has been deployed as a web server (https://bioinfo.wugenqiang.top/~smw/DeepDN_iGlu/) that is available to make glutarylation site prediction data more accessible.
- post-translational modification,
- glutarylation site prediction,
- DenseNet,
- attention residual learning,
- focal loss function
Citation: Jianhua Jia, Mingwei Sun, Genqiang Wu, Wangren Qiu. DeepDN_iGlu: prediction of lysine glutarylation sites based on attention residual learning method and DenseNet[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 2815-2830. doi: 10.3934/mbe.2023132

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Abstract

As a key issue in orchestrating various biological processes and functions, protein post-translational modification (PTM) occurs widely in the mechanism of protein's function of animals and plants. Glutarylation is a type of protein-translational modification that occurs at active ε-amino groups of specific lysine residues in proteins, which is associated with various human diseases, including diabetes, cancer, and glutaric aciduria type I. Therefore, the issue of prediction for glutarylation sites is particularly important. This study developed a brand-new deep learning-based prediction model for glutarylation sites named DeepDN_iGlu via adopting attention residual learning method and DenseNet. The focal loss function is utilized in this study in place of the traditional cross-entropy loss function to address the issue of a substantial imbalance in the number of positive and negative samples. It can be noted that DeepDN_iGlu based on the deep learning model offers a greater potential for the glutarylation site prediction after employing the straightforward one hot encoding method, with Sensitivity (Sn), Specificity (Sp), Accuracy (ACC), Mathews Correlation Coefficient (MCC), and Area Under Curve (AUC) of 89.29%, 61.97%, 65.15%, 0.33 and 0.80 accordingly on the independent test set. To the best of the authors' knowledge, this is the first time that DenseNet has been used for the prediction of glutarylation sites. DeepDN_iGlu has been deployed as a web server (https://bioinfo.wugenqiang.top/~smw/DeepDN_iGlu/) that is available to make glutarylation site prediction data more accessible.

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