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<p>Small ubiquitin-like modifiers (SUMOs) are crucial protein regulators influencing diverse biological processes through covalent modifications or non-covalent interactions. SUMOylation, a key post-translational modification (PTM), plays a vital role in cellular regulation. This study presents Hybrid-Sumo, a deep learning-based model integrating protein structural and sequence features to predict SUMOylation sites. Hybrid-Sumo combines three advanced feature extraction techniques: Half-Sphere Exposure (HSE), Position-Specific Scoring Matrix with Discrete Wavelet Transform (PSSM-DWT), and Bidirectional Encoder Representations from Transformers (BERT). The SHapley Additive exPlanations (SHAP) algorithm is employed for optimal feature selection, while a Deep Neural Network (DNN) serves as the classification model. Extensive 10-fold cross-validation confirms the effectiveness of Hybrid-Sumo, achieving 99.74% accuracy on benchmark datasets and 96.15% and 95.83% on balanced and imbalanced independent datasets, respectively. These results surpass existing models, improving training accuracy by 1.45% and testing accuracy (both balanced and imbalanced) by 1.90% and 0.25%, respectively. These findings highlight Hybrid-Sumo as a robust computational tool for accurate prediction of SUMOylation sites, accelerating research on protein function and modification analysis.</p>

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Deep learning framework with interpretable feature selection for accurate SUMOylation site prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月25日

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  • Aasem N. Alyahya,
  • Salman Khan,
  • Naqqash Dilshad,
  • Nijad Ahmad

摘要

Small ubiquitin-like modifiers (SUMOs) are crucial protein regulators influencing diverse biological processes through covalent modifications or non-covalent interactions. SUMOylation, a key post-translational modification (PTM), plays a vital role in cellular regulation. This study presents Hybrid-Sumo, a deep learning-based model integrating protein structural and sequence features to predict SUMOylation sites. Hybrid-Sumo combines three advanced feature extraction techniques: Half-Sphere Exposure (HSE), Position-Specific Scoring Matrix with Discrete Wavelet Transform (PSSM-DWT), and Bidirectional Encoder Representations from Transformers (BERT). The SHapley Additive exPlanations (SHAP) algorithm is employed for optimal feature selection, while a Deep Neural Network (DNN) serves as the classification model. Extensive 10-fold cross-validation confirms the effectiveness of Hybrid-Sumo, achieving 99.74% accuracy on benchmark datasets and 96.15% and 95.83% on balanced and imbalanced independent datasets, respectively. These results surpass existing models, improving training accuracy by 1.45% and testing accuracy (both balanced and imbalanced) by 1.90% and 0.25%, respectively. These findings highlight Hybrid-Sumo as a robust computational tool for accurate prediction of SUMOylation sites, accelerating research on protein function and modification analysis.