DAQplugin: Interactive Deep Learning-Based Validation of Cryo-EM Protein Models in ChimeraX

Abstract

Although an increasing number of protein structures are determined by cryogenic electron microscopy (cryo-EM), structure modeling frequently suffers from residue misassignments and sequence register shifts, particularly in regions with ambiguous density. Here, we present DAQplugin, a ChimeraX plugin for real-time evaluation of protein models against cryo-EM density maps using the deep-learning-based residue-wise model quality (DAQ) score. Unlike existing validation tools that are typically applied after model construction, DAQplugin enables interactive validation during model building and refinement. DAQ has been shown to accurately identify residue assignment errors, including sequence register shifts, as well as local conformational modeling errors. DAQplugin also provides guidance for correcting sequence register shifts by suggesting alternative residue placements along the backbone. The plugin is computationally efficient and runs on standard CPUs without requiring GPU hardware, enabling deep-learning-based validation on ordinary laptops during interactive model building, model-map fitting, and refinement. DAQplugin facilitates more accurate interpretation of cryo-EM density maps and improve the reliability assessment of protein structure models.