# AlphaFold2

Cryo-EM and cryo-ET often produce density maps whose molecular identity is unknown, and at intermediate resolution the missing side-chain features make it hard to pick the right candidate from thousands of structures. CryoAssign combines CoLoRes rigid-body docking with learning-to-rank prioritization, searching intact density maps without prior segmentation and reranking candidates using several measures of map-model agreement. On 14 held-out benchmark maps it outperformed the original CoLoRes cross-correlation score and any single engineered feature; for the radial spoke complex it placed 11 of 18 known proteins in the top 15, where the original score placed only six.

Cryo-EM and cryo-ET often produce density maps whose molecular identity is unknown, and at intermediate resolution the missing side-chain features make it hard to pick the right candidate from thousands of structures. CryoAssign combines CoLoRes rigid-body docking with learning-to-rank prioritization, searching intact density maps without prior segmentation and reranking candidates using several measures of map-model agreement. On 14 held-out benchmark maps it outperformed the original CoLoRes cross-correlation score and any single engineered feature; for the radial spoke complex it placed 11 of 18 known proteins in the top 15, where the original score placed only six.

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.