ProG-SOL: Predicting Protein Solubility Using Protein Embeddings and Dual-Graph Convolutional Networks

Solubility is a key biophysical property of proteins and is essential for evaluating the effectiveness of proteins in biochemical engineering. In recent years, the prediction method of protein solubility has received extensive attention in the protein engineering research community. Many methods have been developed to predict protein solubility, but the generalization performance of existing prediction methods on independent test sets must be improved. In addition, solubility prediction methods do not work well when they are used for regression tasks. To address these issues, we developed a new method, ProG-SOL, an innovative sequence-based dual-graph convolutional network that simultaneously exploits the protein pretrained graph and the protein evolutionary graph for assessing solubility. Compared with other methods, ProG-SOL achieves better classification and regression results for different independent test sets at the same time. The model framework of our method may also be used to predict other properties of proteins such as protein function, protein-protein interaction, protein folding, and drug design, which provide broad application prospects in protein engineering.