Terashi, G., Wang, X., Prasad, D. et al. (2023). DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction. Nature Methods, 21: 122-131 (2024),


Wang, X.,Terashi, G., & Kihara, D. (2023). CryoREAD: de novo structure modeling for nucleic acids in cryo-EM maps using deep learning Nature Methods, 20(11), 1739–1747

DAQ Score

Terashi, G., Wang, X., Maddhuri Venkata Subramaniya, S. R., Tesmer, J. J., & Kihara, D. (2022). Residue-wise local quality estimation for protein models from cryo-EM maps. Nature Methods, 19(9), 1116-1125.


Terashi, G., Wang, X., Kihara, D. (2023). Protein model refinement for cryo-EM maps using AlphaFold2 and the DAQ score. Acta Crystallographica Section D Structural Biology, 79, 10–21.


Maddhuri Venkata Subramaniya, S. R., Terashi, G., & Kihara, D. (2019). Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning. Nature Methods, 16(9), 911-917.


Wang, X., Alnabati, E., Aderinwale, T. W., Subramaniya, S. R. M. V., Terashi, G., & Kihara, D. (2021). Detecting protein and DNA/RNA structures in cryo-EM maps of intermediate resolution using deep learning. Nature Communications, 12(1), 2302


Terashi, G., & Kihara, D. (2018). De novo main-chain modeling for EM maps using MAINMAST. Nature Communications, 9(1), 1618.


Han, X., Terashi, G., Christoffer, C., Chen, S., & Kihara, D. (2021). VESPER: global and local cryo-EM map alignment using local density vectors. Nature Communications, 12(1), 2090.