Adaptive immunity relies on T-cell receptor (TCR) recognition of non-self epitopes, short peptides presented by the Major Histocompatibility Complex (MHC) on the cell surface. Accurate computational prediction of TCR-epitope binding would unlock the development of targeted immunotherapies, such as cancer vaccines and TCR T cell therapies, while simultaneously deepening our fundamental understanding of self/nonself discrimination, pathogen recognition, and autoimmunity. We created an ensemble approach (enFoldX) that leverages structure prediction models such as AlphaFold3 to build sensitive binding predictors. enFoldX can distinguish T cell reactivity between peptides that differ by a single amino acid substitution, as needed for cancer neoantigens. enFoldX utilizes a customized highly parallelized workflow which allows us to produce ensembles of predicted protein structures at scale and train classifiers to infer reactivity based on distributions of engineered structure features and alignment confidence metrics. While state-of-the-art sequence-based approaches we evaluated could predict well for observed TCRs and epitopes close in sequence to training data, their applicability to novel sequences was limited. Conversely, our ensemble approach is the only model that showed true generalizability to novel datasets and even across species. Moreover, our ensemble approach outperforms the current co-folding methods which rely on predictions from the single top ranked structure. By leveraging the entire protein universe at scale, structure ensembles therefore enable classifiers that reflect physical free energies, providing a tractable path towards TCR T therapy design at the sensitivity required for cancer neoantigen discrimination and imparting lessons for a wide array of complex binding problems.
Olga Lyudovyk, Jonathan A. Levine, Melissa Pathil, Stephen Martis, Yuval Elhanati, Vinod P. Balachandran, Quaid Morris, Benjamin D. Greenbaum. enFoldX: AI classification of AlphaFold3-derived structural ensembles enables T cell specificity prediction [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr PR004.
O. Lyudovyk, Jonathan A. Levine, M. Pathil et al.· Clinical Cancer Research· 0 citations
Adaptive immunity relies on T-cell receptor (TCR) recognition of non-self epitopes, short peptides presented by the Major Histocompatibility Complex (MHC) on the cell surface. Accurate computational prediction of TCR-epitope binding would unlock the development of targeted immunotherapies, such as cancer vaccines and TCR T cell therapies, while simultaneously deepening our fundamental understanding of self/nonself discrimination, pathogen recognition, and autoimmunity. We created an ensemble approach (enFoldX) that leverages structure prediction models such as AlphaFold3 to build sensitive binding predictors. enFoldX can distinguish T cell reactivity between peptides that differ by a single amino acid substitution, as needed for cancer neoantigens. enFoldX utilizes a customized highly parallelized workflow which allows us to produce ensembles of predicted protein structures at scale and train classifiers to infer reactivity based on distributions of engineered structure features and alignment confidence metrics. While state-of-the-art sequence-based approaches we evaluated could predict well for observed TCRs and epitopes close in sequence to training data, their applicability to novel sequences was limited. Conversely, our ensemble approach is the only model that showed true generalizability to novel datasets and even across species. Moreover, our ensemble approach outperforms the current co-folding methods which rely on predictions from the single top ranked structure. By leveraging the entire protein universe at scale, structure ensembles therefore enable classifiers that reflect physical free energies, providing a tractable path towards TCR T therapy design at the sensitivity required for cancer neoantigen discrimination and imparting lessons for a wide array of complex binding problems.
Olga Lyudovyk, Jonathan A. Levine, Melissa Pathil, Stephen Martis, Yuval Elhanati, Vinod P. Balachandran, Quaid Morris, Benjamin D. Greenbaum. enFoldX: AI classification of AlphaFold3-derived structural ensembles enables T cell specificity prediction [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr A028.
O. Lyudovyk, Jonathan A. Levine, M. Pathil et al.· Clinical Cancer Research· 0 citations
Adaptive immunity relies on T-cell receptor (TCR) recognition of peptides presented by the major histocompatibility complex (pMHC). Accurate prediction of TCR:pMHC binding pairs from sequence data remains a longstanding challenge in computational immunology, limiting the development of precision immunotherapies like cancer vaccines and adoptive cell therapies. Here, we present enFoldX (ensemble of Folded compleXes), a structure-based approach leveraging biophysical characterization of AlphaFold3-generated ensembles to classify TCR:pMHC sequence pairs as cognate versus non-cognate. Unlike previous methods reliant on only sequence data or a single, static predicted structure, enFoldX extracts features from an entire generated ensemble with a custom focus on the biophysical binding interface. Our model distinguishes T cell reactivity between peptides differing by a single amino acid substitution, the resolution required for cancer neoantigens, and generalizes to unseen peptides, MHCs, and TCRs, a major objective for artificial intelligence (AI) in immunology. Our performance on these crucial tasks demonstrates that diverse, structural sampling of biophysical interactions over an ensemble is fundamental for accurate AI-driven binding predictions and offers lessons for efficient future data generation to improve models. Our findings therefore offer a scalable framework to accelerate therapeutic binder design, and we provide access to a publicly available code repository.
O. Lyudovyk, JA Levine, M. Pathil et al.· bioRxiv· 1 citation