WikiSysop: Created page with "== Citation == S. Chen, S. Zhang, X. Fang, L. Lin, H. Zhao, and Y. Yang, “Protein complex structure modeling by cross-modal alignment between cryo-EM maps and protein sequences,” Nature Communications, vol. 15, no. 1, p. 8808, 2024. == Abstract == Cryo-electron microscopy (cryo-EM) technique is widely used for protein structure determination. Current automatic cryo-EM protein complex modeling methods mostly rely on prior chain separation. However, chain separation..."

2024-11-08T10:06:47Z

Created page with "== Citation == S. Chen, S. Zhang, X. Fang, L. Lin, H. Zhao, and Y. Yang, “Protein complex structure modeling by cross-modal alignment between cryo-EM maps and protein sequences,” Nature Communications, vol. 15, no. 1, p. 8808, 2024. == Abstract == Cryo-electron microscopy (cryo-EM) technique is widely used for protein structure determination. Current automatic cryo-EM protein complex modeling methods mostly rely on prior chain separation. However, chain separation..."

New page

== Citation ==

S. Chen, S. Zhang, X. Fang, L. Lin, H. Zhao, and Y. Yang, “Protein complex structure modeling by cross-modal alignment between cryo-EM maps and protein sequences,” Nature Communications, vol. 15, no. 1, p. 8808, 2024.

== Abstract ==

Cryo-electron microscopy (cryo-EM) technique is widely used for protein
structure determination. Current automatic cryo-EM protein complex modeling
methods mostly rely on prior chain separation. However, chain separation
without sequence guidance often suffers from errors caused by crosschain
interaction or noise densities, which would accumulate and mislead the
subsequent steps. Here, we present EModelX, a fully automated cryo-EM
protein complex structure modeling method, which achieves sequenceguiding
modeling through cross-modal alignments between cryo-EM maps
and protein sequences. EModelX first employs multi-task deep learning to
predict Cα atoms, backbone atoms, and amino acid types from cryo-EMmaps,
which is subsequently used to sample Cα traces with amino acid profiles. The
profiles are then aligned with protein sequences to obtain initial structural
models, which yielded an average RMSD of 1.17 Å in our test set, approaching
atomic-level precision in recovering PDB-deposited structures. After filling
unmodeled gaps through sequence-guiding Cα threading, the final models
achieved an average TM-score of 0.808, outperforming the state-of-the-art
method. The further combination with AlphaFold can improve the average
TM-score to 0.911. Analyzes conducted by comparing some EModelX-built
models and PDB structures highlight its potential to improve PDB structures.
EModelX is accessible at https://bio-web1.nscc-gz.cn/app/EModelX.

== Keywords ==

== Links ==

https://www.nature.com/articles/s41467-024-53116-5

== Related software ==

== Related methods ==

== Comments ==

2024Chen EModelX - Revision history