Utilization of AlphaFold models for drug discovery: Feasibility and challenges. Histone deacetylase 11 as a case study

Development of new pyrazoles as class I HDAC inhibitors: Synthesis, molecular modeling, and biological characterization in leukemia cells

Class I histone deacetylases (HDACs) are considered promising targets in current cancer research. To obtain subtype-selective and potent HDAC inhibitors, we used the aminobenzamide scaffold as the zinc-binding group and prepared new derivatives with a pyrazole ring as the linking group. The synthesized compounds were analyzed in vitro using an enzymatic assay against HDAC1, -2, and -3. Compounds 12b, 15b, and 15i were found to be potent HDAC1 inhibitors, also in comparison to the reference compounds entinostat and tacedinaline, with IC50 values of 0.93, 0.22, and 0.68 μM, respectively. The best compounds were measured for their cellular effect and target engagement in acute myeloid leukemia (AML) cells. In addition, we studied the interaction of the compounds with HDAC subtypes using docking and molecular dynamic simulations. In summary, we have developed a new chemotype of HDAC1 inhibitors that can be used for further structure-based optimization.

Biological function and small molecule inhibitors of histone deacetylase 11

HDAC11, as a rising star in the histone deacetylase (HDAC) family, has attracted widespread interest in the biomedical field in recent years specially owing to its high defatty-acylase activity compared its innate deacetylase activity. Numerous studies have provided evidence indicating the crucial involvement of HDAC11 in cancers, immune responses, and metabolic processes. Several potent and selective HDAC11 inhibitors have been discovered and identified, which is crucial for exploring the function of HDAC11 and its potential therapeutic applications. Herein, we present a critical overview of the current advances in the biological function of HDAC11 and its inhibitors. We initially discuss the physiological functions of HDAC11 and its pathological roles in relevant diseases. Subsequently, our main focus centers on the design strategy and development process of HDAC11 inhibitors. Additionally, we address significant challenges and outline future directions in this field. This perspective may provide guidance for the further development of HDAC11 inhibitors and their prospects in disease treatment.

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.

Unraveling HDAC11: Epigenetic orchestra in different diseases and structural insights for inhibitor design

Histone deacetylase 11 (HDAC11), a member of the HDAC family, has emerged as a critical regulator in numerous physiological as well as pathological processes. Due to its diverse roles, HDAC11 has been a focal point of research in recent times. Different non-selective inhibitors are already approved, and research is going on to find selective HDAC11 inhibitors. The objective of this review is to comprehensively explore the role of HDAC11 as a pivotal regulator in a multitude of physiological and pathological processes. It aims to delve into the intricate details of HDAC11's structural and functional aspects, elucidating its molecular interactions and implications in different disease contexts. With a primary focus on elucidating the structure-activity relationships (SARs) of HDAC11 inhibitors, this review also aims to provide a holistic understanding of how its molecular architecture influences its inhibition. Additionally, by integrating both established knowledge and recent research, the review seeks to contribute novel insights into the potential therapeutic applications of HDAC11 inhibitors. Overall, the scope of this review spans from fundamental research elucidating the complexities of HDAC11 biology to the potential of targeting HDAC11 in therapeutic interventions.

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.