DeepDN_iGlu: prediction of lysine glutarylation sites based on attention residual learning method and DenseNet
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 group...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-01-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023132?viewType=HTML |
| _version_ | 1828051460988862464 |
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| author | Jianhua Jia Mingwei Sun Genqiang Wu Wangren Qiu |
| author_facet | Jianhua Jia Mingwei Sun Genqiang Wu Wangren Qiu |
| author_sort | Jianhua Jia |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T19:38:04Z |
| format | Article |
| id | doaj.art-c7883806cb8c432ca6b97aaaa364900e |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-10T19:38:04Z |
| publishDate | 2023-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-c7883806cb8c432ca6b97aaaa364900e2023-01-30T01:32:20ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-01-012022815283010.3934/mbe.2023132DeepDN_iGlu: prediction of lysine glutarylation sites based on attention residual learning method and DenseNetJianhua Jia 0Mingwei Sun 1Genqiang Wu 2Wangren Qiu 3School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333403, ChinaSchool of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333403, ChinaSchool of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333403, ChinaSchool of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333403, ChinaAs 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.https://www.aimspress.com/article/doi/10.3934/mbe.2023132?viewType=HTMLpost-translational modificationglutarylation site predictiondensenetattention residual learningfocal loss function |
| spellingShingle | Jianhua Jia Mingwei Sun Genqiang Wu Wangren Qiu DeepDN_iGlu: prediction of lysine glutarylation sites based on attention residual learning method and DenseNet Mathematical Biosciences and Engineering post-translational modification glutarylation site prediction densenet attention residual learning focal loss function |
| title | DeepDN_iGlu: prediction of lysine glutarylation sites based on attention residual learning method and DenseNet |
| title_full | DeepDN_iGlu: prediction of lysine glutarylation sites based on attention residual learning method and DenseNet |
| title_fullStr | DeepDN_iGlu: prediction of lysine glutarylation sites based on attention residual learning method and DenseNet |
| title_full_unstemmed | DeepDN_iGlu: prediction of lysine glutarylation sites based on attention residual learning method and DenseNet |
| title_short | DeepDN_iGlu: prediction of lysine glutarylation sites based on attention residual learning method and DenseNet |
| title_sort | deepdn iglu prediction of lysine glutarylation sites based on attention residual learning method and densenet |
| topic | post-translational modification glutarylation site prediction densenet attention residual learning focal loss function |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023132?viewType=HTML |
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