CryoRes: Local Resolution Estimation of Cryo-EM Density Maps by Deep Learning

J Mol Biol. 2023 May 1;435(9):168059. doi: 10.1016/j.jmb.2023.168059. Epub 2023 Mar 24.


Recent progress in cryo-EM research has ignited a revolution in biological macromolecule structure determination. Resolution is an essential parameter for quality assessment of a cryo-EM density map, and it is known that resolution varies in different regions of a map. Currently available methods for local resolution estimation require manual adjustment of parameters and in some cases necessitate acquisition or de novo generation of so-called "half maps". Here, we developed CryoRes, a deep-learning algorithm to estimate local resolution directly from a single final cryo-EM density map, specifically by learning resolution-aware patterns of density map voxels through supervised training on a large dataset comprising 1,174 experimental cryo-EM density maps. CryoRes significantly outperforms all of the state-of-the-art competing resolution estimation methods, achieving an average RMSE of 2.26 Å for local resolution estimation relative to the currently most reliable FSC-based method blocres, yet requiring only the single final map as input. Further, CryoRes is able to generate a molecular mask for each map, with accuracy 12.12% higher than the masks generated by ResMap. CryoRes is ultra-fast, fully automatic, parameter-free, applicable to cryo-EM subtomogram data, and freely available at

Keywords: cryo-EM density maps; cryo-ET; deep learning; local resolution; single-particle analysis.

MeSH terms

  • Algorithms
  • Cryoelectron Microscopy / methods
  • Deep Learning*
  • Macromolecular Substances
  • Models, Molecular
  • Protein Conformation


  • Macromolecular Substances