<p>Lysine crotonylation (Kcr), as an emerging post-translational modification, plays a crucial role in core life activities such as chromatin dynamics and gene expression. To address the current limitations of Kcr site detection techniques, including high experimental costs, complex procedures, and high false-positive rates, as well as the poor generalization performance of existing computational models caused by limited training data and class imbalance, this study proposes an innovative intelligent recognition framework named MVFAN-Kcr. The system integrates multi-view feature fusion and attention mechanisms to synergistically enhance the prediction accuracy and robustness of Kcr site identification. Physicochemical property features are combined with global sequence semantic information derived from the ESM-2 protein language model to construct a fused feature representation that captures both local physicochemical information and global contextual information. To optimize computational efficiency, feature selection is performed using analysis of variance. To effectively address the problem of imbalanced data distribution, a stratified undersampling strategy based on the chi-square test is developed. In addition, a convolutional neural network combined with attention is designed to efficiently extract local sequence patterns and enhance the representation of key features. Under a rigorous evaluation framework based on five-fold cross-validation and an independent test set, MVFAN-Kcr demonstrates excellent predictive performance. The method significantly outperforms baseline approaches in terms of accuracy, achieving an ACC value of 79.18%, while the area under the ROC curve reaches 0.8618. SHAP analysis and gradient-based analysis reveal a strong dependence on pKa-related properties, charge characteristics, and locally enriched small side-chain residues, and indicate that the model can nonlinearly integrate high-order sequence semantic information for effective identification of Kcr sites. Overall, MVFAN-Kcr combines data balancing strategies, multi-view feature fusion, and attention mechanisms to achieve high accuracy, robustness, and interpretability, providing an effective tool for protein Kcr site prediction. The data and code are available on <a href="https://github.com/Lilyjoys/MVFAN-Kcr">https://github.com/Lilyjoys/MVFAN-Kcr</a>, and a free web platform is provided at <a href="http://www.mvfan-kcr.com">http://www.mvfan-kcr.com</a>.</p> Graphical Abstract <p></p>

MVFAN-Kcr: A Multi-View Feature Fusion and Attention-Based Network for Lysine Crotonylation Site Identification

期刊论文
发表日期：

2026年6月3日

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Yun Zuo,
Li Zhou,
Wenjie Gong,
Qiao Ning,
Xiangrong Liu,
Sisi Yuan,
Zhaohong Deng

摘要

Lysine crotonylation (Kcr), as an emerging post-translational modification, plays a crucial role in core life activities such as chromatin dynamics and gene expression. To address the current limitations of Kcr site detection techniques, including high experimental costs, complex procedures, and high false-positive rates, as well as the poor generalization performance of existing computational models caused by limited training data and class imbalance, this study proposes an innovative intelligent recognition framework named MVFAN-Kcr. The system integrates multi-view feature fusion and attention mechanisms to synergistically enhance the prediction accuracy and robustness of Kcr site identification. Physicochemical property features are combined with global sequence semantic information derived from the ESM-2 protein language model to construct a fused feature representation that captures both local physicochemical information and global contextual information. To optimize computational efficiency, feature selection is performed using analysis of variance. To effectively address the problem of imbalanced data distribution, a stratified undersampling strategy based on the chi-square test is developed. In addition, a convolutional neural network combined with attention is designed to efficiently extract local sequence patterns and enhance the representation of key features. Under a rigorous evaluation framework based on five-fold cross-validation and an independent test set, MVFAN-Kcr demonstrates excellent predictive performance. The method significantly outperforms baseline approaches in terms of accuracy, achieving an ACC value of 79.18%, while the area under the ROC curve reaches 0.8618. SHAP analysis and gradient-based analysis reveal a strong dependence on pKa-related properties, charge characteristics, and locally enriched small side-chain residues, and indicate that the model can nonlinearly integrate high-order sequence semantic information for effective identification of Kcr sites. Overall, MVFAN-Kcr combines data balancing strategies, multi-view feature fusion, and attention mechanisms to achieve high accuracy, robustness, and interpretability, providing an effective tool for protein Kcr site prediction. The data and code are available on https://github.com/Lilyjoys/MVFAN-Kcr, and a free web platform is provided at http://www.mvfan-kcr.com.

Graphical Abstract