PREDBL6: a system for predicting C57BL/6 mouse T-cell epitopes

The MHC class I antigen processing pathway plays a critical role in the adaptive immune system by presenting peptides for recognition by CD8+ T cells. While most prediction tools focus on MHC binding, accurately identifying immunogenic T-cell epitopes requires accounting for additional factors such as antigen processing and peptide-MHC binding thermostability. We developed a bioinformatics tool that integrates MHC binding predictions with thermostability assessment and antigen processing steps to enhance T cell epitope identification accuracy for C57BL/6 mice. Our machine learning models, trained on a comprehensive dataset of eluted H2-K<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sup> and H2-D<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sup> ligands and thermostability data across a range of physiologically relevant temperatures (37°C, 50°C, 70°C), were rigorously validated. These models showed improved overall accuracy on an external validation dataset compared to the widely used NetMHCPan-4.1. We consolidated the models into a user-friendly web-based application named PREDBL6 to facilitate accurate predictions of immunogenic peptides that stably bind H2b molecules and stimulate immune responses in C57BL/6 mice. PREDBL6 is accessible at http://met-hilab.org:3001/.