School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China.
Key Laboratory of Parallel, Distributed and Intelligent Computing in Guangxi Universities and Colleges, Guangxi University, Nanning 530004, China.
Brief Bioinform. 2024 Jul 25;25(5). doi: 10.1093/bib/bbae365.
The annotation of enzyme function is a fundamental challenge in industrial biotechnology and pathologies. Numerous computational methods have been proposed to predict enzyme function by annotating enzyme labels with Enzyme Commission number. However, the existing methods face difficulties in modelling the hierarchical structure of enzyme label in a global view. Moreover, they haven't gone entirely to leverage the mutual interactions between different levels of enzyme label. In this paper, we formulate the hierarchy of enzyme label as a directed enzyme graph and propose a hierarchy-GCN (Graph Convolutional Network) encoder to globally model enzyme label dependency on the enzyme graph. Based on the enzyme hierarchy encoder, we develop an end-to-end hierarchical-aware global model named GloEC to predict enzyme function. GloEC learns hierarchical-aware enzyme label embeddings via the hierarchy-GCN encoder and conducts deductive fusion of label-aware enzyme features to predict enzyme labels. Meanwhile, our hierarchy-GCN encoder is designed to bidirectionally compute to investigate the enzyme label correlation information in both bottom-up and top-down manners, which has not been explored in enzyme function prediction. Comparative experiments on three benchmark datasets show that GloEC achieves better predictive performance as compared to the existing methods. The case studies also demonstrate that GloEC is capable of effectively predicting the function of isoenzyme. GloEC is available at: https://github.com/hyr0771/GloEC.
酶功能注释是工业生物技术和病理学的一个基本挑战。已经提出了许多计算方法，通过用酶委员会编号注释酶标签来预测酶功能。然而，现有的方法在全局视角下对酶标签的层次结构进行建模时存在困难。此外，它们还没有完全利用酶标签不同层次之间的相互作用。在本文中，我们将酶标签的层次结构表示为有向酶图，并提出了一种层次图卷积网络（Graph Convolutional Network，GCN）编码器，以全局建模酶图上的酶标签依赖关系。基于酶层次编码器，我们开发了一种端到端的层次感知全局模型，命名为 GloEC，用于预测酶功能。GloEC 通过层次 GCN 编码器学习层次感知的酶标签嵌入，并对标签感知的酶特征进行演绎融合，以预测酶标签。同时，我们的层次 GCN 编码器被设计为双向计算，以从自下而上和自上而下的方式研究酶标签的相关性信息，这在酶功能预测中尚未被探索。在三个基准数据集上的对比实验表明，GloEC 与现有方法相比，具有更好的预测性能。案例研究还表明，GloEC 能够有效地预测同工酶的功能。GloEC 可在：https://github.com/hyr0771/GloEC。
