<i>HLAPepBinder</i>: An Ensemble Model for The Prediction Of HLA-Peptide Binding.

Our study introduces a novel ensemble-learning model designed to enhance the accuracy and efficiency of HLA-peptide binding predictions. Due to the lack of reliable negative data and the typical assumption of unknown interactions being negative, we focus on analyzing the unknown HLA-peptide bindings in the test set that our model predicts with 100% certainty as positive bindings. Using <i>HLAPepBinder</i>, we identify 26 HLA-peptide pairs with absolute prediction confidence. These predictions are validated through a multi-step pipeline involving literature review, BLAST sequence similarity analysis, and molecular docking studies. This comprehensive validation process highlights <i>HLAPepBinder</i>'s ability to make accurate and reliable predictions, contributing significantly to advancements in immunotherapy and vaccine development.