# nanobody-antigen complex structure prediction

This study evaluates state-of-the-art machine-learning-based methods for nanobody structure prediction and benchmarks various HADDOCK workflows for modeling nanobody-antigen interactions across different input nanobody ensembles and information scenarios, proposing an ensemble docking pipeline that starts from nanobody structural models predicted by AlphaFold2 and ImmuneBuilder and, provided some epitope information is available, achieves higher success rates than the AlphaFold baseline on all generated models.

This study evaluates state-of-the-art machine-learning-based methods for nanobody structure prediction and benchmarks various HADDOCK workflows for modeling nanobody-antigen interactions across different input nanobody ensembles and information scenarios, proposing an ensemble docking pipeline that starts from nanobody structural models predicted by AlphaFold2 and ImmuneBuilder and, provided some epitope information is available, achieves higher success rates than the AlphaFold baseline on all generated models.