Prediction of Post-Translational Modification Sites of Paeonia lactiflora Proteins Based on Attention Mechanism

The precise prediction of post-translational modification sites of proteins was an important aspect of elucidating the mechanisms of traditional Chinese medicine. Existing computational models struggled to meet the needs of herbal pharmacological research due to their neglect of the specificity of herbal materials and the complexity of modification sites. This study focused on the traditional Chinese medicinal material Paeonia lactiflora and constructed a deep learning model based on convolutional neural networks and attention mechanisms by integrating multi-dimensional features of amino acid sequences. A dataset for Paeonia lactiflora was built using the TCMSP database, which included 1080 positive samples and 1976 negative samples, and the input space was optimized through feature normalization and dimensionality reduction. Experimental results indicated that the model effectively captured the modification patterns of proteins from different herbal materials, and SHAP feature selection significantly improved the prediction accuracy of post-translational modification sites. Compared to traditional single algorithm models, the proposed integrated architecture demonstrated significant advantages in balancing sequence conservation and functional specificity, providing a new computational tool for elucidating the mechanisms of action of active components in traditional Chinese medicinal materials and guiding rational clinical medication.