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<p>Cell-penetrating peptides (CPPs) are short peptides consisting of 5 to 50 amino acids and useful for drug delivery and intracellular localization. Laboratory-based techniques are often lengthy and resource-intensive, whereas computational approaches offer a rapid and cost-effective solution. To address these limitations, this research introduces a predictive model called pCPPs-sADNN leveraging feature fusion, integrating embeddings from the protein pre-trained language models Protein Text-to-Text Transfer Transformer and Evolutionary Scale Modeling, along with Conjoint Triad Features. By combining the distinct derived feature sets, generates an enhanced and robust features vector. Furthermore, we employed Random Forest-based Recursive Feature Elimination for feature selection and used the Adaptive Synthetic Sampling Approach to address class imbalance by generating synthetic minority samples. The hybrid feature set was subsequently utilized to train a deep neural network enhanced with an attention mechanism. The proposed pCPPs-sADNN model achieved a high training accuracy of 98.58% and an AUC of 0.99. In evaluation on test dataset, pCPPs-sADNN demonstrated strong performance with an accuracy of 96.84% and an AUC of 0.99.</p>

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pCPPs-sADNN: predicting cell-penetrating peptides using self-attention based deep neural network

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月2日

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  • Naif Almusallam,
  • Shahid,
  • Maqsood Hayat,
  • Fawaz Khaled Alarfaj

摘要

Cell-penetrating peptides (CPPs) are short peptides consisting of 5 to 50 amino acids and useful for drug delivery and intracellular localization. Laboratory-based techniques are often lengthy and resource-intensive, whereas computational approaches offer a rapid and cost-effective solution. To address these limitations, this research introduces a predictive model called pCPPs-sADNN leveraging feature fusion, integrating embeddings from the protein pre-trained language models Protein Text-to-Text Transfer Transformer and Evolutionary Scale Modeling, along with Conjoint Triad Features. By combining the distinct derived feature sets, generates an enhanced and robust features vector. Furthermore, we employed Random Forest-based Recursive Feature Elimination for feature selection and used the Adaptive Synthetic Sampling Approach to address class imbalance by generating synthetic minority samples. The hybrid feature set was subsequently utilized to train a deep neural network enhanced with an attention mechanism. The proposed pCPPs-sADNN model achieved a high training accuracy of 98.58% and an AUC of 0.99. In evaluation on test dataset, pCPPs-sADNN demonstrated strong performance with an accuracy of 96.84% and an AUC of 0.99.