Utilization of an optimized AlphaFold protein model for structure-based design of a selective HDAC11 inhibitor with anti-neuroblastoma activity.

AlphaFold is an artificial intelligence approach for predicting the three-dimensional (3D) structures of proteins with atomic accuracy. One challenge that limits the use of AlphaFold models for drug discovery is the correct prediction of folding in the absence of ligands and cofactors, which compromises their direct use. We have previously described the optimization and use of the histone deacetylase 11 (HDAC11) AlphaFold model for the docking of selective inhibitors such as FT895 and SIS17. Based on the predicted binding mode of FT895 in the optimized HDAC11 AlphaFold model, a new scaffold for HDAC11 inhibitors was designed, and the resulting compounds were tested in vitro against various HDAC isoforms. Compound 5a proved to be the most active compound with an IC50 of 365 nM and was able to selectively inhibit HDAC11. Furthermore, docking of 5a showed a binding mode comparable to FT895 but could not adopt any reasonable poses in other HDAC isoforms. We further supported the docking results with molecular dynamics simulations that confirmed the predicted binding mode. 5a also showed promising activity with an EC50 of 3.6 µM on neuroblastoma cells.

Design, synthesis, and biological evaluation of dinaciclib and CAN508 hybrids as CDK inhibitors.

The scaffolds of two known CDK inhibitors (CAN508 and dinaciclib) were the starting point for synthesizing two series of pyarazolo[1,5-a]pyrimidines to obtain potent inhibitors with proper selectivity. The study presented four promising compounds; 10d, 10e, 16a, and 16c based on cytotoxic studies. Compound 16a revealed superior activity in the preliminary anticancer screening with GI % = 79.02-99.13 against 15 cancer cell lines at 10 µM from NCI full panel 60 cancer cell lines and was then selected for further investigation. Furthermore, the four compounds revealed good safety profile toward the normal cell lines WI-38. These four compounds were subjected to CDK inhibitory activity against four different isoforms. All of them showed potent inhibition against CDK5/P25 and CDK9/CYCLINT. Compound 10d revealed the best activity against CDK5/P25 (IC50 = 0.063 µM) with proper selectivity index against CDK1 and CDK2. Compound 16c exhibited the highest inhibitory activity against CDK9/CYCLINT (IC50 = 0.074 µM) with good selectivity index against other isoforms. Finally, docking simulations were performed for compounds 10e and 16c accompanied by molecular dynamic simulations to understand their behavior in the active site of the two CDKs with respect to both CAN508 and dinaciclib.

Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor.

HDAC11 is a class IV histone deacylase with no crystal structure reported so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms, which makes conventional homology modeling less reliable. AlphaFold is a machine learning approach that can predict the 3D structure of proteins with high accuracy even in absence of similar structures. However, the fact that AlphaFold models are predicted in the absence of small molecules and ions/cofactors complicates their utilization for drug design. Previously, we optimized an HDAC11 AlphaFold model by adding the catalytic zinc ion and minimization in the presence of reported HDAC11 inhibitors. In the current study, we implement a comparative structure-based virtual screening approach utilizing the previously optimized HDAC11 AlphaFold model to identify novel and selective HDAC11 inhibitors. The stepwise virtual screening approach was successful in identifying a hit that was subsequently tested using an in vitro enzymatic assay. The hit compound showed an IC50 value of 3.5 µM for HDAC11 and could selectively inhibit HDAC11 over other HDAC subtypes at 10 µM concentration. In addition, we carried out molecular dynamics simulations to further confirm the binding hypothesis obtained by the docking study. These results reinforce the previously presented AlphaFold optimization approach and confirm the applicability of AlphaFold models in the search for novel inhibitors for drug discovery.