DEMO-EMol cryo-EM based protein-RNA/DNA complex structures assembly

DEMO-EMol is a hierarchical method for assembling protein-nucleic acid complex structures from cryo-EM density maps. The process begins by segmenting the density map into protein and nucleic acid regions using deep learning. Subsequently, each chain is independently fitted to its corresponding region (protein or nucleic acid) through a quasi-Newton based pose search, with protein chains undergoing additional domain-level fitting and optimization. Finally, the complex structure is constructed by identifying the optimal combination of all chain poses, followed by a global domain-level optimization. (more about DEMO-EMol...)

[About] [Download] [Example Input] [Example Output] [Domain Assembly]

DEMO-EMol On-line Server [View an example output]

Upload PDB format structures for assembly as a single compressed file (*.tar.gz, *.zip, *.tar, or *.tar.bz2) (Example, Explanation)

Upload cryo-EM density map file in MRC or CCP4 format (Example, Explanation):

Input the resolution of density map (e.g. 3.7 Å): Å

Email: (not required but recommended, where results will be sent to)

ID: (optional, your given name to this job)

Voxel size (default: 2Å): Å

Contour level (default: 0):

Option I: Use domain-level assembly and optimization (default: Yes)

Option II: Use iterative assembly (default: Yes)

DEMO-EMol Resource:

References:

Ziying Zhang, Liang Xu, Shuai Zhang, Chunxiang Peng, Guijun Zhang, Xiaogen Zhou. DEMO-EMol: Modeling protein-nucleic acid complex structures from cryo-EM maps by coupling chain assembly with map segmentation. Nucleic Acids Research, gkaf416 (2025).
Ziying Zhang, Yaxian Cai, Biao Zhang, Wei Zheng, Lydia Freddolino, Guijun Zhang, Xiaogen Zhou. DEMO-EM2: Assembling protein complex structures from cryo-EM maps through intertwined chain and domain fitting. Briefings in Bioinformatics, 25(2): bbae113 (2024).
Xiaogen Zhou, Yang Li, Chengxin Zhang, Wei Zheng, Guijun Zhang, Yang Zhang. Progressive assembly of multi-domain protein structures from cryo-EM density maps. Nature Computational Science, 2: 265-275 (2022).

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