Deep learning on protein language model embeddings unlocks accurate prediction of protein solubility

Introduction Protein misfolding is a major limitation in prokaryotic expression systems, which lack post-translational modifications and exhibit distinct intracellular environments. This severely hinders the functional expression of many heterologous proteins, especially in Escherichia coli . Accurate prediction of protein solubility is crucial for synthetic biology and protein engineering but remains a challenging task. Methods Here, we present DeepSolNet, a deep learning model that leverages advanced protein language models to enhance solubility prediction. DeepSolNet adopts a multi-module architecture, integrating contextual embeddings from ESM Cambrian with bidirectional long short-term memory networks, convolutional neural networks, and attention mechanisms. Results On the validation set, DeepSolNet achieved an accuracy of 0.75 and a Matthews correlation coefficient of 0.50 for soluble/insoluble protein classification. On an independently constructed test set containing gammabody, transglutaminase, and aldehyde dehydrogenase sequences, the model maintained high performance with an accuracy of 0.53, achieving state-of-the-art performance. Visualization analyses further showed that DeepSolNet is sensitive to key residues influencing protein solubility. Discussion These results demonstrate that DeepSolNet serves as a powerful and generalizable tool for large-scale protein design and expression optimization. The tool is freely available at https://github.com/wangxinglong1990/DeepSolNet .