Wang Xiao, Zhu Han, Terashi Genki, Taluja Manav, Kihara Daisuke
Department of Computer Science, Purdue University, West Lafayette, IN, USA.
Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
Nat Methods. 2024 Dec;21(12):2307-2317. doi: 10.1038/s41592-024-02479-0. Epub 2024 Oct 21.
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.
低温电子显微镜(cryo-EM)现已广泛用于确定多链蛋白质复合物。然而,对大型复合物结构进行建模,例如那些具有十条以上链的结构,具有挑战性,特别是当地图分辨率降低时。在这里,我们展示了DiffModeler,一种用于对大型蛋白质复合物结构进行建模的全自动方法。DiffModeler采用扩散模型进行主链追踪,并整合AlphaFold2预测的单链结构进行结构拟合。对于分辨率为0-5埃的两个低温电子显微镜图数据集,DiffModeler的平均模板建模得分分别为0.88和0.91,对于中等分辨率图(5-10埃)为0.92,大大优于现有方法。在低分辨率(10-20埃)下的进一步基准测试证实了其通用性,展示了合理的性能。