EPIC: Multi-objective Guided Diffusion for Epitope Design in TCR-pMHC Complexes

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ID: 315868
2026
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Abstract
Abstract Motivation T cell receptor (TCR) recognition of peptide-MHC (pMHC) complexes is central to adaptive immunity, yet rational design of immunogenic epitopes remains elusive due to complex triplet binding constraints and data scarcity. No existing method can generate epitopes satisfying simultaneous requirements for antigenicity, MHC presentation, and TCR specificity. Results We present EPIC, a multi-objective diffusion framework that decomposes TCR-pMHC binding into three biologically grounded sub-tasks, enabling training-free gradient guidance without end-to-end retraining. By integrating ESM-based classifiers with a peptide diffusion generator, EPIC leverages heterogeneous immunological interaction datasets to generate diverse, context-aware epitopes. EPIC-designed top 3 epitopes achieve lower predicted interface energies compared to ground-truth epitopes in 78.31% of test cases, while maintaining 80.1% sequence novelty and comparable structural confidence. Generated epitopes exhibit 100% uniqueness, high diversity (64.05%), and high antigenicity scores (0.4723). To our knowledge, EPIC is the first computational framework capable of de novo epitope design while explicitly integrating the triplet constraints of TCR-pMHC binding. This paradigm shift from discovery to design unlocks new potential for personalized cancer vaccines, precision adoptive T cell therapy, and rapid response to emerging infectious diseases. Availability and Implementation The source code of EPIC is available at https://github.com/Octopus125/EPIC and archived on Zenodo (DOI: 10.5281/zenodo.18537646). Supplementary Information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7163546660 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yueshan Huang, Gufeng Yu, Letian Chen, Haoyang Luan, Yang Yang
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag358
URL
https://doi.org/10.1093/bioinformatics/btag358
Keywords Keywords not found

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