TCellPredX: A Novel Approach for Accurate Prediction of Hepatitis C Virus Linear T Cell Epitopes

Hepatitis C Virus (HCV) is a bloodborne RNA virus that leads to severe liver diseases, and currently, no effective prophylactic biologics are available to prevent its transmission. The prevention of HCV is closely related to the major histocompatibility complex (MHC). Linear antigenic peptides of HCV, known as T cell epitopes (TCEs), are crucial in the presentation process by MHC molecules to T cells, playing a key role in immune responses. Therefore, the rapid and accurate identification of these TCE-HCVs is essential for advancing vaccine development. Herein, we propose TCellPredX, a novel integrated predictor for TCE-HCV identification. TCellPredX leverages five distinct feature encoding schemes, including local and global sequence encodings, composition-transition-distribution descriptors, physicochemical properties, and embeddings from two protein language models, which are processed through 12 machine learning algorithms. Our results indicate that feature fusion significantly enhances predictive accuracy. Moreover, the maximal relevance minimal redundancy feature selection method is particularly effective in isolating informative features, ensuring the model's use of the most informative data. Additionally, ensemble models, especially when combined with an averaged voting strategy, demonstrate superior stability and accuracy compared to individual classifiers, effectively reducing noise and enhancing model robustness. TCellPredX achieves notable accuracies of 0.900 and 0.897 in 10-fold cross-validation and independent test, respectively. Furthermore, TCellPredX's high accuracy is validated on experimentally verified peptide sequences documented for their potential benefits in vaccine development. Overall, TCellPredX can offer a robust tool for the precise identification of TCE-HCV, potentially serving as a cornerstone for future epitope research and advancing HCV vaccines development.