Root-associated protein prediction using a protein large language model and hypergraph convolutional networks

Plant root-associated proteins promote plant growth and enhance stress tolerance. They participate in signaling and plant growth regulation. It is clear that they play key roles in plant growth, development and environmental adaptation. At present, the root-associated proteins have not been fully discovered. It is essential to identify latent root-associated proteins. Traditional methods (proteomic analysis, transcriptome and expression analysis) for determining root-associated proteins are highly relied on the data generated by biochemical experiments, which are always expensive and time-consuming. On the other hand, the current computational models show weak ability, providing great spaces for improvement. In this study, we propose a new computational model, Hypergraph-Root, for predicting root-associated proteins. The model employed several feature types to represent proteins, which were derived from proteins BLOSUM62 and position-specific scoring matrices as well as by a protein language model. These features were improved by hypergraph convolutional network and multi-head attention. The final predicted result was yielded by a fully connected layer. The model yielded high performance with AUC about 0.9 on training and independent datasets. It had evident advantages compared with existing models. Some additional tests were conducted to prove the rationality of the model's structure.