2024Wang DiffModeller

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Revision as of 10:16, 8 November 2024 by WikiSysop (talk | contribs) (Created page with "== Citation == X. Wang, H. Zhu, G. Terashi, M. Taluja, and D. Kihara, “DiffModeler: large macromolecular structure modeling for cryo-EM maps using a diffusion model,” Nature Methods, pp. 1–11, 2024. == Abstract == Cryogenic electron microscopy (cryo-EM) has now been widely used for determining multichain protein complexes. However, modeling a large complex structure, such as those with more than ten chains, is challenging, particularly when the map resolution de...")
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Citation

X. Wang, H. Zhu, G. Terashi, M. Taluja, and D. Kihara, “DiffModeler: large macromolecular structure modeling for cryo-EM maps using a diffusion model,” Nature Methods, pp. 1–11, 2024.

Abstract

Cryogenic electron microscopy (cryo-EM) has now been widely used for determining multichain protein complexes. However, modeling a large complex structure, such as those with more than ten chains, is challenging, particularly when the map resolution decreases. Here we present DiffModeler, a fully automated method for modeling large protein complex structures. DiffModeler employs a diffusion model for backbone tracing and integrates AlphaFold2-predicted single-chain structures for structure fitting. DiffModeler showed an average template modeling score of 0.88 and 0.91 for two datasets of cryo-EM maps of 0–5 Å resolution and 0.92 for intermediate resolution maps (5–10 Å), substantially outperforming existing methodologies. Further benchmarking at low resolutions (10–20 Å) confirms its versatility, demonstrating plausible performance.

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https://www.nature.com/articles/s41592-024-02479-0

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