ProtSolM: Protein Solubility Prediction with Multi-modal Features

Understand

Understanding protein solubility is essential for their functional applications.

• Computational methods for predicting protein solubility are crucial for reducing experimental costs and enhancing the efficiency and success rates of protein engineering.
• Existing methods either construct a supervised learning scheme on small-scale datasets with manually processed physicochemical properties, or blindly apply pre-trained protein language models to extract amino acid interaction information.
• The scale and quality of available training datasets leave significant room for improvement in terms of accuracy and generalization.