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Two-dimensional geometric template diffusion for boosting single-sequence protein structure prediction

delete2026-04-01
delete0
PRE
AI
X
Xudong Wang
Z
Zhang, Tong
C
Cui, Zhen *
G
Guo, Xu
W
Wang, Fuyun
Y
Yuanzhi Wang
C
Cai, Xing
Z
Zheng, Wenming
DOI：10.1038/s42256-026-01210-2delete
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摘要

摘要

En 中文
基于单序列的蛋白质结构预测因获取同源信息的高计算成本而日益受到关注。本文提出一种二维几何模板扩散方法TDFold，用于生成高质量的配对几何结构（包括配对距离和方向），随后用于准确且高效的蛋白质三维结构预测。给定蛋白质序列，TDFold通过包含两个阶段（二维几何模板生成和序列-几何协同学习）的网络架构推断三维结构。与现有的蛋白质语言模型（如ESMFold和OmegaFold）及同源基方法（如AlphaFold2、AlphaFold3和RoseTTAFold）相比，TDFold具有三大优势：更优的单序列预测性能、更低的资源消耗以及更高的推理效率。本研究在Orphan、Orphan25等同源信息不足的数据集和CASP基准测试上验证了模型的有效性，为单序列蛋白质结构预测提供了一种替代方案，同时加速了蛋白质相关研究，尤其有利于资源受限的大学和学术机构。
Keyword:
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期刊

Nature Machine Intelligence 封面图
Nature Machine Intelligence
IF:
23.9
论文数:
1.3K
被引数:
1.5W

机构

N
nanjing university of science & technology
学者数:
1.6K
论文数: 544
被引数: 0
S
southeast university - china
学者数:
5.3W
论文数: 4.9W
被引数: 57
B
beijing normal university
学者数:
5.6K
论文数: 2.2K
被引数: 0
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