# CryoID2

CryoEM can reconstruct a density map yet often cannot say which molecule the map contains, and matching hundreds or thousands of mass-spectrometry or genome-derived candidates by hand is a bottleneck. CryoID2 uses three 3D U-Nets to predict structure classes, key-atom positions and reduced residue or nucleotide types from the map, turns them into short sequence queries that rank candidates, then rigidly fits the candidates' PDB or AlphaFold models back into the density and ranks them by agreement. In a 166-protein mass-spectrometry pool it placed PF3D7_1446200/PfA-M17 first and selected PDB 4ZX8 as the best structural model; on the RNA side it ranked the Ca-raiA-containing interval fourth in a genome-derived non-coding pool, and structural evaluation then put PDB 9E73 first.

CryoEM can reconstruct a density map yet often cannot say which molecule the map contains, and matching hundreds or thousands of mass-spectrometry or genome-derived candidates by hand is a bottleneck. CryoID2 uses three 3D U-Nets to predict structure classes, key-atom positions and reduced residue or nucleotide types from the map, turns them into short sequence queries that rank candidates, then rigidly fits the candidates' PDB or AlphaFold models back into the density and ranks them by agreement. In a 166-protein mass-spectrometry pool it placed PF3D7_1446200/PfA-M17 first and selected PDB 4ZX8 as the best structural model; on the RNA side it ranked the Ca-raiA-containing interval fourth in a genome-derived non-coding pool, and structural evaluation then put PDB 9E73 first.