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 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 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. 5a also showed promising activity with an EC50 of 3.6 µM on neuroblastoma cells. Furthermore, we supported our study by comparative docking and MD simulations.