Zhang Haotian, Sun Yundong, Wang Yansong, Luo Xiaoling, Liu Yumeng, Chen Bin, Jin Xiaopeng, Zhu Dongjie
School of Computer Science and Technology, Harbin Institute of Technology, Weihai 264209, China.
Department of Electronic Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
Int J Mol Sci. 2025 Apr 25;26(9):4088. doi: 10.3390/ijms26094088.
Currently, protein-protein interaction (PPI) networks have become an essential data source for protein function prediction. However, methods utilizing graph neural networks (GNNs) face significant challenges in modeling PPI networks. A primary issue is over-smoothing, which occurs when multiple GNN layers are stacked to capture global information. This architectural limitation inherently impairs the integration of local and global information within PPI networks, thereby limiting the accuracy of protein function prediction. To effectively utilize information within PPI networks, we propose GTPLM-GO, a protein function prediction method based on a dual-branch Graph Transformer and protein language model. The dual-branch Graph Transformer achieves the collaborative modeling of local and global information in PPI networks through two branches: a graph neural network and a linear attention-based Transformer encoder. GTPLM-GO integrates local-global PPI information with the functional semantic encoding constructed by the protein language model, overcoming the issue of inadequate information extraction in existing methods. Experimental results demonstrate that GTPLM-GO outperforms advanced network-based and sequence-based methods on PPI network datasets of varying scales.
目前，蛋白质-蛋白质相互作用（PPI）网络已成为蛋白质功能预测的重要数据源。然而，利用图神经网络（GNN）的方法在对PPI网络进行建模时面临重大挑战。一个主要问题是过平滑，当堆叠多个GNN层以捕获全局信息时就会出现这种情况。这种架构限制本质上损害了PPI网络中局部和全局信息的整合，从而限制了蛋白质功能预测的准确性。为了有效利用PPI网络中的信息，我们提出了GTPLM-GO，一种基于双分支图变换器和蛋白质语言模型的蛋白质功能预测方法。双分支图变换器通过两个分支实现了PPI网络中局部和全局信息的协同建模：一个图神经网络和一个基于线性注意力的变换器编码器。GTPLM-GO将局部-全局PPI信息与由蛋白质语言模型构建的功能语义编码相结合，克服了现有方法中信息提取不足的问题。实验结果表明，在不同规模的PPI网络数据集上，GTPLM-GO优于基于网络和基于序列的先进方法。
