  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. To address these research gaps, we propose \sol, a
novel deep learning method that combines pre-training and fine-tuning schemes
for protein solubility prediction. ProtSolM integrates information from
multiple dimensions, including physicochemical properties, amino acid
sequences, and protein backbone structures. Our model is trained using \data,
the largest solubility dataset that we have constructed. PDBSol includes over
$60,000$ protein sequences and structures. We provide a comprehensive
leaderboard of existing statistical learning and deep learning methods on
independent datasets with computational and experimental labels. ProtSolM
achieved state-of-the-art performance across various evaluation metrics,
demonstrating its potential to significantly advance the accuracy of protein
solubility prediction.
