Integrated Protocol of Protein Structure Modeling for Cryo-EM with Deep Learning and Structure Prediction.
Integrated Protocol of Protein Structure Modeling for Cryo-EM with Deep Learning and Structure Prediction.
复制标题
具有深度学习和结构预测的冷冻电镜蛋白质结构建模集成协议。
DOI：
10.1101/2023.10.19.563151
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Kihara,Daisuke
中科院分区：
文献类型：
--
作者：
Terashi,Genki;Wang,Xiao;Prasad,Devashish;Nakamura,Tsukasa;Zhu,Han;Kihara,Daisuke
基于MAP的三维结构建模是用低温电子显微镜研究蛋白质及其复合体不可缺少的一步。尽管确定的低温电子显微镜图谱的分辨率普遍提高，但仍有许多情况下追踪蛋白质主链是困难的，即使在以近原子分辨率确定的图谱中也是如此。在这里，我们提出了一种蛋白质结构建模方法DeepMainmast，该方法利用深度学习来捕捉氨基酸和原子的局部地图特征，以辅助主链跟踪。此外，我们将AlphaFold2与从头密度跟踪协议相结合，将它们的互补优势结合在一起，获得了比每种方法单独使用更高的准确率。此外，该协议能够准确地将链单位分配给同系多聚体的结构模型，这对于现有的方法来说并不是一件容易的事情。
Three-dimensional structure modeling from maps is an indispensable step for studying proteins and their complexes with cryogenic electron microscopy. Although the resolution of determined cryogenic electron microscopy maps has generally improved, there are still many cases where tracing protein main chains is difficult, even in maps determined at a near-atomic resolution. Here we developed a protein structure modeling method, DeepMainmast, which employs deep learning to capture the local map features of amino acids and atoms to assist main-chain tracing. Moreover, we integrated AlphaFold2 with the de novo density tracing protocol to combine their complementary strengths and achieved even higher accuracy than each method alone. Additionally, the protocol is able to accurately assign the chain identity to the structure models of homo-multimers, which is not a trivial task for existing methods.