美国普渡大学Daisuke Kihara团队利用深度学习和结构预测实现冷冻电镜蛋白质结构建模集成协议。相关论文于2023年12月8日在线发表于国际学术期刊《自然—方法学》。

研究人员开发了一种蛋白质结构建模方法——DeepMainmast，该方法采用深度学习捕捉氨基酸和原子的局部图谱特征，以帮助进行主链追踪。此外，研究人员还将AlphaFold2与从头密度追踪协议整合在一起，将二者的互补优势结合起来，实现了比单独使用每种方法更高的精确度。此外，该协议还能为同源多聚物的结构模型准确地分配链身份，这对现有方法来说并非易事。

据介绍，根据图谱建立三维结构模型是利用冷冻电镜研究蛋白质及其复合物不可或缺的一步。虽然冷冻电镜图谱的分辨率已普遍提高，但仍有许多情况下难以追踪蛋白质主链，即使是以接近原子分辨率绘制的图谱也是如此。

附：英文原文

Title: DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction

Author: Terashi, Genki, Wang, Xiao, Prasad, Devashish, Nakamura, Tsukasa, Kihara, Daisuke

Issue&Volume: 2023-12-08

Abstract: 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.

DOI: 10.1038/s41592-023-02099-0

Source: https://www.nature.com/articles/s41592-023-02099-0