Georges Mer

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.