DeepPROTECTNeo: A Context-aware Personalized and Reverse Vaccinology-guided Deep Learning Framework for Immunogenicity Prediction
Das, D.; Bhaduri, S.; Mitra, P. · bioinformatics · 2026-09-06 · 原文
DOI：10.1101/2025.01.04.631301作者：3 位
Background: The development of personalized cancer vaccines relies on accurately identifying neoepitopes capable of eliciting strong immune responses. T cell receptor (TCR)-epitope interactions are fundamental to cancer immunotherapy. Traditional computational approaches focus primarily on epitope-major histocompatibility complex (MHC) binding, often overlooking the critical contribution of TCR binding. Furthermore, the clinical applicability of existing methods is constrained by fragmented pipelines that require separate workflows for variant calling, HLA typing, and independent peptide-MHC (pMHC) or peptide-TCR (pTCR) evaluation stages. Results: We present DeepPROTECTNeo, a unified deep learning framework that integrates genomic variant detection, HLA typing, high-affinity pMHC binding prediction, variant-driven TCR repertoire mining, followed by a hybrid transformer-Convolutional Neural Network dual-branch feature extractor with an explicit cross-attention-based deep learning model for TCR-epitope binding prediction. Our reverse vaccinology-inspired biologically informed architecture integrates Bidirectional Long short-term memory (Bi-LSTM) sequence features, convolutional-atten
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1. 人话版
Background: The development of personalized cancer vaccines relies on accurately identifying neoepitopes capable of eliciting strong immune responses.
T cell receptor (TCR)-epitope interactions are fundamental to cancer immunotherapy.
2. 领域脉络
本文类目：bioinformatics，属于其所在研究脉络的最新进展。
3. 机制拆解
Furthermore, the clinical applicability of existing methods is constrained by fragmented pipelines that require separate workflows for variant calling, HLA typing, and independent peptide-MHC (pMHC) or peptide-TCR (pTCR) evaluation stages.
Results: We present DeepPROTECTNeo, a unified deep learning framework that integrates genomic variant detection, HLA typing, high-affinity pMHC binding prediction, variant-driven TCR repertoire mining, followed by a hybrid transformer-Convolutional Neural Network dual-branch feature extractor with an explicit cross-attention-based deep learning model for TCR-epitope binding prediction.
4. 证据与数字
摘要未给出量化结果——留意原文的实验与数据。
5. 反例与边界
Traditional computational approaches focus primarily on epitope-major histocompatibility complex (MHC) binding, often overlooking the critical contribution of TCR binding.
6. 跨领域连接与意外收获
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7. 可复用方法
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