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 solubility changes upon mutation in silico 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. We compare our model with five established solubility prediction methods. The model achieves state-of-the-art performance on an independent dataset of various proteins, especially in predicting the effects of multipoint mutations (informedness of 26.5 %). 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 hundreds of single-point and multipoint mutants of haloalkane dehalogenase. Complemented with recent deep-mutational-scanning data on myoglobin, we employed both these data for external validation. Although the generalization to unseen proteins remains limited, our findings demonstrate the potential of integrating high-throughput assays with deep learning to improve the accuracy and scope of solubility prediction.