Seeing the Molecule Behind the Map

Biomolecular structure predictors can generate plausible conformations, but these may not correspond to the state observed in a particular sample. Cryo-electron microscopy (cryo-EM) density provides spatial constraints from the target experiment; using them to guide both global structural relationships and local atomic placement remains a central challenge. We introduce EviBoltz, which uses global density interactions to update the single and pair representations of the Boltz-2 Pairformer, and atom-centered, multiscale density queries continually guide diffusion-based coordinate generation. Among evaluable cases in an independent benchmark, EviBoltz raises acceptable protein–protein interface success from 27.8% to 70.4% and joint ligand-pocket success from 7.0% to 34.9% relative to Boltz-2. The protein–peptide and antibody–antigen tasks show the same direction of change, with success rates rising from 0.0% to 75.0% and from 0.0% to 66.7%, respectively. In addition, long-loop analysis shows improved backbone agreement with the input density. In a controlled, supervised experiment on two states of the same URAT1 sequence, representations induced by different target maps guide a frozen generator toward the corresponding states. These results connect improved interaction recovery and local geometry with the ability of map-conditioned representations to carry information about the observed state.