School of Tropical Agriculture and Forestry, Hainan University, 58 Renmin Avenue, Haikou 570228, Hainan, China.
School of Computer Science and Technology, Hainan University, 58 Renmin Avenue, Haikou 570228, Hainan, China.
Brief Bioinform. 2024 Jul 25;25(5). doi: 10.1093/bib/bbae397.
The prediction of metabolite-protein interactions (MPIs) plays an important role in plant basic life functions. Compared with the traditional experimental methods and the high-throughput genomics methods using statistical correlation, applying heterogeneous graph neural networks to the prediction of MPIs in plants can reduce the cost of manpower, resources, and time. However, to the best of our knowledge, applying heterogeneous graph neural networks to the prediction of MPIs in plants still remains under-explored. In this work, we propose a novel model named heterogeneous neighbor contrastive graph attention network (HNCGAT), for the prediction of MPIs in Arabidopsis. The HNCGAT employs the type-specific attention-based neighborhood aggregation mechanism to learn node embeddings of proteins, metabolites, and functional-annotations, and designs a novel heterogeneous neighbor contrastive learning framework to preserve heterogeneous network topological structures. Extensive experimental results and ablation study demonstrate the effectiveness of the HNCGAT model for MPI prediction. In addition, a case study on our MPI prediction results supports that the HNCGAT model can effectively predict the potential MPIs in plant.
代谢物-蛋白质相互作用（MPIs）的预测在植物基本生命功能中起着重要作用。与传统的实验方法和使用统计相关性的高通量基因组学方法相比，将异质图神经网络应用于植物中 MPIs 的预测可以降低人力、资源和时间的成本。然而，据我们所知，将异质图神经网络应用于植物中 MPIs 的预测仍未得到充分探索。在这项工作中，我们提出了一种名为异质邻居对比图注意网络（HNCGAT）的新模型，用于预测拟南芥中的 MPIs。HNCGAT 采用基于类型的注意力邻居聚合机制来学习蛋白质、代谢物和功能注释的节点嵌入，并设计了一种新的异质邻居对比学习框架来保留异质网络拓扑结构。广泛的实验结果和消融研究证明了 HNCGAT 模型在 MPI 预测方面的有效性。此外，对我们 MPI 预测结果的案例研究支持 HNCGAT 模型可以有效地预测植物中潜在的 MPIs。
