pCPPs-sADNN: predicting cell-penetrating peptides using self-attention based deep neural network
Year: 2025
Type: article
Abstract: 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.
Source: Scientific Reports
Authors: Naif Almusallam, Shahid, Maqsood Hayat, Fawaz Khaled Alarfaj
Institutions: King Faisal University, Abdul Wali Khan University Mardan
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,690
Funder: King Faisal University
OpenAlex ID: W4416901561