AI tools for cryo-EM: Protein particle picking, density map enhancement, and atomic model building

Single-particle cryo-electron microscopy (cryo-EM) has rapidly evolved from largely manual, stepwise workflows to AI-assisted, increasingly automated protein structure determination pipelines. This chapter presents an end-to-end view on our deep learning-based AI tools that address three major stages of cryo-EM data analysis: protein particle picking, density map enhancement, and atomic structural model building. First, we introduce two of our AI-based particle pickers: CryoTransformer, a transformer-residual architecture trained on the large-scale CryoPPP dataset, and CryoSegNet, which couples a cryo-EM-specialized attention-gated U-Net with SAM to segment particles across diverse proteins and imaging conditions. Together, they deliver high precision-recall performance and improve downstream 3D reconstructions, outperforming widely used pickers on independent benchmarks. Second, we describe CryoTEN, a 3D UNETR++-style transformer to enhance cryo-EM density maps. On a 150-map test set, CryoTEN robustly improves local interpretability and downstream protein structure modeling while running>10× faster than other deep learning methods. Third, we present our two atomic protein model building frameworks: Cryo2Struct and MICA. Cryo2Struct is a fully automated de novo modeling framework that identifies atoms and residue types from density maps alone via a 3D transformer and assembles them into chains with a Hidden Markov Model, producing more complete and accurate models than other ab initio tools. MICA, a multimodal framework that integrates cryo-EM density maps with AlphaFold3 structure predictions at both input and output levels to further refine model accuracy and completeness, achieving near-experimental TM-scores on recently released high resolution density maps. Finally, we conclude by outlining open challenges and potential future directions in cryo-EM data analysis.