AI-Driven Structural Mapping of Thyroid Autoantigens Reveals High-Confidence Conformational Epitopes for Precision Immunotherapy

Introduction: Autoimmune thyroid diseases (AITD) are the most common organ-specific autoimmune disorders, driven by loss of immune tolerance to thyroid peroxidase (TPO), thyroglobulin (Tg), and thyrotropin receptor (TSHR). Understanding the three-dimensional (3D) architecture of these autoantigens is essential for the rational design of antigen-specific immunotherapies that restore tolerance without systemic immunosuppression. Objective: To predict the 3D structures of thyroid autoantigens using AI-Driven and to identify structurally and immunologically validated epitopes suitable for therapeutic peptide development. Methods: ESMFold was employed to model full-length structures of TPO, Tg, and TSHR. Predicted epitopes were characterized using integrated bioinformatic tools for major histocompatibility complex (MHC) class II binding, surface accessibility, and glycosylation proximity. Prioritized epitopes underwent molecular dynamics simulations to assess conformational stability and conservation analyses across mammalian orthologs. Results: Seventy-two epitopes were identified and clustered into five groups based on twelve physicochemical features. Fifteen high-priority epitopes demonstrated high structural confidence (mean pLDDT > 90), broad MHC disorder (IC₅₀ < 1000 nM), and strong concordance with experimentally validated domains. Molecular dynamics confirmed their conformational robustness (mean RMSF 1.4 Å), and cross-species analysis revealed >80% sequence conservation, supporting translational applicability. Conclusion: AI-Driven modeling provides a precise structural framework for identifying therapeutic epitopes in AITD. The prioritized epitopes offer promising candidates for tolerance-inducing peptide immunotherapies targeting thyroid autoimmunity.