SoluProtMut: Siamese Deep Learning for Predicting Solubility Effects of Protein Mutations with Experimental Validation

Protein solubility is an attractive engineering target because it is a critical property influencing the scalability of protein production and the success of therapeutic proteins in biomedical applications. However, predicting in silico solubility changes upon mutation is challenging due to data heterogeneity and protein bias. Here, we explore how different sources of solubility data can be used for machine learning and present SoluProtMut, a Siamese deep geometric neural network trained to predict the impact of mutations on protein solubility. Our final model was trained exclusively on deep mutational scanning data derived from recombinant protein expression assays in bacterial and yeast systems. We compare our model with five established solubility prediction methods. On an independent set of 44 proteins, the model achieves the highest informedness among the methods compared, with the largest margin for multipoint mutations (a ternary informedness of 26.5%, 8.8 percentage points above the next-best method). Our findings also reaffirm that the scarcity of solubility data continues to hamper progress in this field. To address this limitation, we experimentally quantified solubility changes for 277 single-point and multipoint mutants of haloalkane dehalogenase. Complemented by recent deep-mutational-scanning data on myoglobin, we employed both datasets for external validation. We identify protein-level generalization, rather than model capacity or training-set size, as the main limiting factor in the domain. Integrating high-throughput assays with deep learning nonetheless offers a route to improving the accuracy and scope of solubility prediction.