Benchmarking AlphaFold and related deep learning approaches for modeling antibody and TCR antigen recognition
Researchers benchmarked AlphaFold2, AlphaFold3, and related deep learning methods for modeling antibody-protein, antibody-peptide, and TCR-pMHC recognition. Results indicate that increased sampling and AlphaFold3 generally outperform default AlphaFold2 settings, though predictive accuracy varies significantly by interface class. The authors note that antibody-peptide complexes remain particularly difficult to model accurately and propose model pooling as a potential solution to leverage method complementarity.