美国伊利诺伊大学赵惠民小组近日取得一项新成果。经过不懈努力，他们研究出交叉注意图神经网络的酶特异性预测。相关论文于2025年10月8日发表于国际顶尖学术期刊《自然》杂志上。

在此，研究小组开发了一个名为EZSpecificity的交叉注意授权SE(3)-等变图神经网络架构，用于预测酶底物特异性，该架构是在一个全面定制的酶-底物相互作用的序列和结构水平数据库上进行训练的。正如未知底物和酶数据库以及七个概念验证蛋白家族所证明的那样，EZSpecificity在酶底物特异性预测方面优于现有的机器学习模型。8种卤化酶和78种底物的实验验证表明，EZSpecificity在识别单一潜在活性底物方面的准确率达到91.7%，显著高于最先进的模型ESP（58.3%）。EZSpecificity是一种通用的机器学习模型，用于准确预测与生物学和医学基础研究和应用研究相关的酶的底物特异性。

研究人员表示，酶是生命的分子机器，控制其功能的一个关键特性是底物特异性——酶识别和选择性作用于特定底物的能力。这种特异性源于酶活性位点的三维（3D）结构和反应的复杂过渡状态。许多酶可以混合催化反应或作用于它们最初进化的底物之外的底物。然而，数以百万计的已知酶仍然缺乏可靠的底物特异性信息，阻碍了它们的实际应用和对自然界生物催化多样性的全面理解。

附：英文原文

Title: Enzyme specificity prediction using cross attention graph neural networks

Author: Cui, Haiyang, Su, Yufeng, Dean, Tanner J., Yu, Tianhao, Zhang, Zhengyi, Peng, Jian, Shukla, Diwakar, Zhao, Huimin

Issue&Volume: 2025-10-08

Abstract: Enzymes are the molecular machines of life, and a key property that governs their function is substrate specificity—the ability of an enzyme to recognize and selectively act on particular substrates. This specificity originates from the three-dimensional (3D) structure of the enzyme active site and complicated transition state of the reaction1,2. Many enzymes can promiscuously catalyze reactions or act on substrates beyond those for which they were originally evolved1,3-5. However, millions of known enzymes still lack reliable substrate specificity information, impeding their practical applications and comprehensive understanding of the biocatalytic diversity in nature. Herein, we developed a cross-attention-empowered SE(3)-equivariant graph neural network architecture named EZSpecificity for predicting enzyme substrate specificity, which was trained on a comprehensive tailor-made database of enzyme-substrate interactions at sequence and structural levels. EZSpecificity outperformed the existing machine learning models for enzyme substrate specificity prediction, as demonstrated by both an unknown substrate and enzyme database and seven proof-of-concept protein families. Experimental validation with eight halogenases and 78 substrates revealed that EZSpecificity achieved a 91.7% accuracy in identifying the single potential reactive substrate, significantly higher than that of the state-of-the-art model ESP (58.3%). EZSpecificity represents a general machine learning model for accurate prediction of substrate specificity for enzymes related to fundamental and applied research in biology and medicine.

DOI: 10.1038/s41586-025-09697-2

Source: https://www.nature.com/articles/s41586-025-09697-2