Tofacitinib and analogs as inhibitors of the histone kinase PRK1 (PKN1).

AIM: The histone kinase PRK1 has been identified as a potential target to combat prostate cancer but selective PRK1 inhibitors are lacking. The US FDA -approved JAK1-3 inhibitor tofacitinib also potently inhibits PRK1 in vitro. RESULTS: We show that tofacitinib also inhibits PRK1 in a cellular setting. Using tofacitinib as a starting point for structure-activity relationship studies, we identified a more potent and another more selective PRK1 inhibitor compared with tofacitinib. Furthermore, we found two potential PRK1/JAK3-selectivity hotspots. CONCLUSION: The identified inhibitors and the selectivity hotspots lay the basis for the development of selective PRK1 inhibitors. The identification of PRK1, but also of other cellular tofacitinib targets, has implications on its clinical use and on future development of tofacitinib-like JAK inhibitors. [Formula: see text].

Virtual screening of PRK1 inhibitors: ensemble docking, rescoring using binding free energy calculation and QSAR model development.

Protein kinase C Related Kinase 1 (PRK1) has been shown to be involved in the regulation of androgen receptor signaling and has been identified as a novel potential drug target for prostate cancer therapy. Since there is no PRK1 crystal structure available to date, multiple PRK1 homology models were generated in order to address the protein flexibility. An in-house library of compounds tested on PRK1 was docked into the ATP binding site of the generated models. In most cases a correct pose of the inhibitors could be identified by ensemble docking, while there is still a challenge of finding a reasonable scoring function that is able to rank compounds according to their biological activity. We estimated the binding free energy for our data set of structurally diverse PRK1 inhibitors using the MM-PB(GB)SA and QM/MM-GBSA methods. The obtained results demonstrate that a correlation between calculated binding free energies and experimental IC50 values was found to be usually higher than using docking scores. Furthermore, the developed approach was tested on a set of diverse PRK1 inhibitors taken from literature, which resulted in a significant correlation. The developed method is computationally inexpensive and can be applied as a postdocking filter in virtual screening as well as for optimization of PRK1 inhibitors in order to prioritize compounds for further biological characterization.