AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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FoldKit is introduced, a Python package for efficient storage and analysis of large-scale AF3 co-folding results that reduces storage requirements and facilitating programmatic access to relevant outputs, which facilitates large-scale computational studies of biomolecular interactions.
An ensemble approach (enFoldX) that leverages structure prediction models such as AlphaFold3 to build sensitive binding predictors and outperforms the current co-folding methods which rely on predictions from the single top ranked structure.
An ensemble approach (enFoldX) that leverages structure prediction models such as AlphaFold3 to build sensitive binding predictors and outperforms the current co-folding methods which rely on predictions from the single top ranked structure.
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.
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