Selectivity Mechanism of ATP-Competitive Inhibitors for PKB and PKA

The novel AKT inhibitor afuresertib suppresses human Merkel cell carcinoma MKL-1 cell growth

Merkel cell carcinoma (MCC) is a highly aggressive neuroendocrine neoplasm of the skin, which has an exceedingly poor prognosis. The AKT/mammalian target of rapamycin (mTOR) signalling pathway, which plays a pivotal role in the modulation of protein synthesis and cell survival, has been shown to be extremely important for Merkel cell carcinogenesis. In the current study, we found that AKT has important regulatory functions in MCC cells and that inhibition of AKT with the novel ATP-competitive AKT inhibitor, afuresertib, has widespread effects on proliferative pathways. In particular, we found that treatment of MCC cells with afuresertib led to deactivation of mTOR and glycogen synthase kinase 3 pathway proteins while increasing activation of proapoptotic pathways through the upregulation of p16 expression and phosphomodulation of the B-cell lymphoma-2-associated death promoter. Overall, afuresertib treatment led to significant and robust inhibition of MCC cell proliferation, thus raising intriguing questions regarding the potential efficacy of AKT inhibition for the future clinical management of MCC.

Predicting subtype selectivity of dopamine receptor ligands with three-dimensional biologically relevant spectrum

We applied a novel molecular descriptor, three-dimensional biologically relevant spectrum (BRS-3D), in subtype selectivity prediction of dopamine receptor (DR) ligands. BRS-3D is a shape similarity profile calculated by superimposing the objective compounds against 300 template ligands from sc-PDB. First, we constructed five subtype selectivity regression models between DR subtypes D1-D2, D1-D3, D2-D3, D2-D4, and D3-D4. The models' 10-fold cross-validation-squared correlation coefficient (Q² , for training sets) and determination coefficient (R² , for test sets) were in the range of 0.5-0.7 and 0.6-0.8, respectively. Then, four pair-wise (D1-D2, D2-D3, D2-D4, and D3-D4) and a multitype (D2, D3, and D4) classification models were developed with the prediction accuracies around or over 90% (for test sets). Lastly, we compared the performances of the models developed on BRS-3D and classical descriptors. The results showed that BRS-3D performed similarly to classical 2D descriptors and better than other 3D descriptors. Combining BRS-3D and 2D descriptors can further improve the prediction performance. These results confirmed the capacity of BRS-3D in the prediction of DR subtype-selective ligands.