FAIRsharing record for: Mutational data for protein solubility

This FAIRsharing record describes: SoluProtMutDB is a comprehensive, manually curated database of the protein solubility data. Low protein solubility presents major challenges to industrial applications and is often reported to be behind many human diseases. Understanding how mutations affect protein solubility can therefore help elucidate the mechanisms associated with the development of human diseases and better utilize protein engineering in industrial applications. Multiple factors may play a role here: the presence of a chaperone or co-factor required for correct protein folding, unnatural physiological conditions such as high temperature, pH, or protein concentration, tendency of a protein to aggregate due to aggregation-prone regions, etc. The predictive power of the existing protein engineering tools is often compromised by limited experimental data available for rigorous training and testing of solubility predictions. The published data used for solubility prediction upon mutation are usually scattered in the literature and had to be collected manually. The goal of the SoluProtMut database is to collect the reported evidence of solubility changes upon mutations from published sources to guide future protein engineering effort in producing soluble protein variants. The database currently contains data previously used for training Machine Learning-based predictors, such as PON-Sol, CamSol, AGGRESCAN3D, OptSolMut, as well as recently published datasets. We are providing manually curated and reliable data in the standardized format which are pre-processed for machine learning applications.