DeepPROTECTNeo: A Deep learning-based Personalized and RV-guided Optimization tool leveraging TCR Epitope interaction using Context-aware Transformers

The development of personalized cancer vaccines relies heavily on accurately identifying neoepitopes capable of eliciting strong immune responses. T cell receptor (TCR)-epitope interactions are fundamental to cancer immunotherapy. Despite significant advances, computational approaches primarily focus on epitope-major histocompatibility complex (MHC) binding, often overlooking the critical contribution of T-cell receptor (TCR) binding particularly in the context of diverse affinity determinants crucial for binding groove of the peptide, amino acid permutations at binding sites, anchor motifs and hydrophobicity determining the quality of anti-tumour immune responses. Here, we present DeepPROTECTNeo, a unified deep learning framework to transform raw patient-specific whole-exome or whole-genome sequencing data into prioritized neoepitope candidates, within a single seamless pipeline. DeepPROTECTNeo integrates genomic variant detection, HLA typing, high-affinity peptide-MHC binding prediction, variant-driven TCR repertoire mining followed by a hybrid transformer-Convolutional Neural Network dual-branch feature extractor with explicit cross-attention based Deep Learning (DL) model for TCR-epitope binding prediction. Our biologically informed architecture inspired by reverse vaccinology (RV) pipeline fuses bidirectional (Long Short-Term Memory) LSTM sequence features, deep convolutional attention over evolutionary and physicochemical descriptors, advanced gated fusion of the physicochemical features and TCR numbered contextual embedding enabling precise modelling of TCR-epitope interactions and yielding interpretable representations at residue level. DeepPROTECTNeo substantially outperforms state-of-the-art methods, achieving a mean AUROC of 0.7234 and mean AUPRC of 0.7659 in zero-shot TCR-peptide binding scenarios on our held-out five-fold cross validation dataset. While benchmarked against challenging viral and mutational independent datasets, our model captures critical clinical features and demonstrates high binding scores across the datasets. DeepPROTECTNeo also identified 18 out of 34 validated neo-epitopes exhibiting high TCR affinity from a patient-specific cancer cohort, establishing itself as a platform to reliably prioritize actionable neoepitopes directly from clinical sequencing data and escalate the process of personalized cancer immunotherapy.