Protein solubility is an attractive engineering target primarily due to its relation to yields in protein production and manufacturing. Moreover, better knowledge of the mutational effects on protein solubility could connect several serious human diseases with protein aggregation. However, we have limited understanding of the protein structural determinants of solubility, and the available data have mostly been scattered in the literature. Here, we present SoluProtMutDB - the first database containing data on protein solubility changes upon mutations. Our database accommodates 33 000 measurements of 17 000 protein variants in 103 different proteins. The database can serve as an essential source of information for the researchers designing improved protein variants or those developing machine learning tools to predict the effects of mutations on solubility. The database comprises all the previously published solubility datasets and thousands of new data points from recent publications, including deep mutational scanning experiments. Moreover, it features many available experimental conditions known to affect protein solubility. The datasets have been manually curated with substantial corrections, improving suitability for machine learning applications. The database is available at loschmidt.chemi.muni.cz/soluprotmutdb.

Department of Information Systems Faculty of Information Technology Brno University of Technology Bozetechova 2 Brno 61200 Czech Republic

International Clinical Research Center St Anne's University Hospital Brno Pekarska 53 Brno 65691 Czech Republic

Loschmidt Laboratories Department of Experimental Biology and RECETOX Faculty of Science Masaryk University Kotlarska 2 Brno 61137 Czech Republic

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AggreProt: a web server for predicting and engineering aggregation prone regions in proteins

Machine Learning-Guided Protein Engineering