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

Abstract

<abstract> <p>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 (<a href="https://bioinfo.wugenqiang.top/~smw/DeepDN_iGlu/" target="_blank">https://bioinfo.wugenqiang.top/~smw/DeepDN_iGlu/</a>) that is available to make glutarylation site prediction data more accessible.</p> </abstract>

GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier

“…Hence, we compared GBDT_KgluSite with four available models on the independent test data (Table 5 and Fig. 8 ), namely GlutPred [ 7 ], iGlu_Lys [ 8 ], BiPepGlut [ 11 ], and DeepDN_iGlu [ 17 ].…”

“…ProtTrans-Glutar [ 16 ] incorporated the XGBoost and pre-trained features by Transformer. DeepDN_iGlu [ 17 ] was proposed by employing binary encoding as feature representation, using DenseNet as the classification model, and utilizing the focal loss function to address the imbalance issue. Deepro-Glu [ 18 ], as the latest Kglu prediction model, used the combination of pre-trained features obtained by ProtBert as well as four other manual features and introduced the attention mechanism in the MLP model.…”

iPro2L-DG: Hybrid network based on improved densenet and global attention mechanism for identifying promoter sequences

“…Assessing the performance of a model requires the use of scientific evaluation criteria. These evaluation criteria include sensitivity (Sn), specificity (Sp), accuracy (Acc), and Matthew correlation coefficient (MCC) [ 51 ]. These metrics allow a comprehensive assessment of model performance and can guide model optimization and improvement.…”

i5mC-DCGA: an improved hybrid network framework based on the CBAM attention mechanism for identifying promoter 5mC sites

“…We used scientific evaluation metrics to comprehensively assess the performance of the i5mC-DCGA model, which included sensitivity (Sn), specificity (Sp), accuracy (Acc), and matthews correlation coefficient (MCC) [ 43 ]. These evaluation metrics can comprehensively reflect the performance of the model and help guide the optimization and improvement of the model.…”