Nanomolar Benzothiazole-Based Inhibitors of 17ß-HSD10 with Cellular Bioactivity.

Multifunctional mitochondrial enzyme 17ß-hydroxysteroid dehydrogenase type 10 (17ß-HSD10) is a potential drug target for the treatment of various pathologies. The most discussed is the pathology associated with Alzheimer's disease (AD), where 17ß-HSD10 overexpression and its interaction with amyloid-ß peptide contribute to mitochondrial dysfunction and neuronal stress. In this work, a series of new benzothiazole-derived 17ß-HSD10 inhibitors were designed based on the structure-activity relationship analysis of formerly published inhibitors. A set of enzyme-based and cell-based methods were used to evaluate the inhibitory potency of new compounds, their interaction with the enzyme, and their cytotoxicity. Most compounds exhibited significantly a higher inhibitory potential compared to published benzothiazolyl ureas and good target engagement in a cellular environment accompanied by low cytotoxicity. The best hits displayed mixed-type inhibition with half maximal inhibitory concentration (IC50) values in the nanomolar range for the purified enzyme (3-7, 15) and/or low micromolar IC50 values in the cell-based assay (6, 13-16).

SoluProtMutDB: A manually curated database of protein solubility changes upon mutations.

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.