WikiSysop: Created page with "== Citation == Chen, J., Li, T., He, J. and Huang, S.-Y. 2026. Protein model building for intermediate-resolution cryo-EM maps by integrating evolutionary and experimental information. Structure. 34, (2026), 375–384. == Abstract == Accurate model building in intermediate-resolution cryo-EM maps normally requires flexible fitting of reliable initial structures. However, while deep learning-based methods such as AlphaFold2 can predict highly accurate structures, the p..."

2026-02-17T04:51:20Z

Created page with "== Citation == Chen, J., Li, T., He, J. and Huang, S.-Y. 2026. Protein model building for intermediate-resolution cryo-EM maps by integrating evolutionary and experimental information. Structure. 34, (2026), 375–384. == Abstract == Accurate model building in intermediate-resolution cryo-EM maps normally requires flexible fitting of reliable initial structures. However, while deep learning-based methods such as AlphaFold2 can predict highly accurate structures, the p..."

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== Citation ==

Chen, J., Li, T., He, J. and Huang, S.-Y. 2026. Protein model building for intermediate-resolution cryo-EM maps by integrating evolutionary and experimental information. Structure. 34, (2026), 375–384.

== Abstract ==

Accurate model building in intermediate-resolution cryo-EM maps normally requires flexible fitting of reliable
initial structures. However, while deep learning-based methods such as AlphaFold2 can predict highly accurate
structures, the predicted structures often differ from experimental EM maps on both global and local
scales, which poses a great challenge to accurate model building in intermediate-resolution EM maps with
such initial structures. Addressing the challenge, we propose CryoEvoBuild, an automated method for
improved protein model building from intermediate-resolution EM maps through the effective integration
of evolutionary and experimental information. CryoEvoBuild implements a novel domain-wise fitting, refinement,
assembly, and rebuilding pipeline with a recycling framework guided by AlphaFold2. Extensive benchmarking
on a diverse test set of 117 maps at 4.0–10.0 A˚ resolutions demonstrates that CryoEvoBuild significantly
improves the accuracy of AF2-predicted structures and outperforms state-of-the-art approaches,
including EMBuild and phenix.dock_and_rebuild.

== Keywords ==

== Links ==

https://www.cell.com/structure/fulltext/S0969-2126(25)00438-1

== Related software ==

== Related methods ==

== Comments ==

2026Chen CryoEvoBuild - Revision history