Advancing immunogenic peptide identification using explainable machine learning framework for rational vaccine design

Abstract

Abstract The identification of immunogenic peptides is essential for the development of effective vaccines and immunotherapies. However, accurately identifying these peptides remains a significant challenge, as it involves a complex interplay of multiple biological factors. In this study, we present a comprehensive framework for immunogenic peptide identification using a dataset of 492 peptides collected from different sources. To capture diverse peptide characteristics, we apply feature engineering based on sequence composition and physicochemical properties. Our results show that the modified $$\nu$$ -SVM model, combined with L1 feature selection, achieves the best performance on an independent test set, with an AUC of 0.991 and an accuracy of 0.949. Further statistical analysis reveals that leucine-rich motifs are significantly enriched in immunogenic peptides. To enhance transparency, we integrate explainable AI techniques, including SHAP and LIME, to identify and interpret the most influential features contributing to model predictions. Overall, this study highlights the importance of combining robust feature extraction, effective feature selection, and interpretable machine learning to accurately predict peptide immunogenicity. The proposed framework provides biologically meaningful insights and offers a practical approach to support rational vaccine design.