WikiSysop: Created page with "== Citation == J. Wang, C. Tan, Z. Gao, G. Zhang, Y. Zhang, and S. Z. Li, “End-to-end Cryo-EM complex structure determination with high accuracy and ultra-fast speed,” Nature Machine Intelligence, pp. 1–13, 2025. == Abstract == While cryogenic-electron microscopy yields high-resolution density maps for complex structures, accurate determination of the corresponding atomic structures still necessitates significant expertise and labour-intensive manual interpretat..."

2025-07-11T07:54:36Z

Created page with "== Citation == J. Wang, C. Tan, Z. Gao, G. Zhang, Y. Zhang, and S. Z. Li, “End-to-end Cryo-EM complex structure determination with high accuracy and ultra-fast speed,” Nature Machine Intelligence, pp. 1–13, 2025. == Abstract == While cryogenic-electron microscopy yields high-resolution density maps for complex structures, accurate determination of the corresponding atomic structures still necessitates significant expertise and labour-intensive manual interpretat..."

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== Citation ==

J. Wang, C. Tan, Z. Gao, G. Zhang, Y. Zhang, and S. Z. Li, “End-to-end Cryo-EM complex structure determination with high accuracy and ultra-fast speed,” Nature Machine Intelligence, pp. 1–13, 2025.

== Abstract ==

While cryogenic-electron microscopy yields high-resolution density maps
for complex structures, accurate determination of the corresponding
atomic structures still necessitates significant expertise and labour-intensive
manual interpretation. Recently, artificial intelligence-based methods have
emerged to streamline this process; however, several challenges persist.
First, existing methods typically require multi-stage training and inference,
causing inefficiencies and inconsistency. Second, these approaches often
encounter bias and incur substantial computational costs in aligning predicted
atomic coordinates with sequence. Last, due to the limitations of available
datasets, previous studies struggle to generalize effectively to complicated
and unseen test data. Here, in response to these challenges, we introduce
end-to-end and efficient CryoFold (E3-CryoFold), a deep learning method
that enables end-to-end training and one-shot inference. E3-CryoFold uses
three-dimensional and sequence transformers to extract features from density
maps and sequences, using cross-attention modules to integrate the two
modalities. Additionally, it uses an SE(3) graph neural network to construct
atomic structures based on extracted features. E3-CryoFold incorporates a
pretraining stage, during which models are trained on simulated density maps
derived from Protein Data Bank structures. Empirical results demonstrate
that E3-CryoFold improves the average template modelling score of the
generated structures by 400% as compared to Cryo2Struct and significantly
outperforms ModelAngelo, while achieving this huge improvement using
merely one-thousandth of the inference time required by these methods. Thus,
E3-CryoFold represents a robust, streamlined and cohesive framework for
cryogenic-electron microscopy structure determination.

== Keywords ==

== Links ==

https://www.nature.com/articles/s42256-025-01056-0

== Related software ==

== Related methods ==

== Comments ==

2025Wang E3CryoFold - Revision history