Trapoxin A Analogue as a Selective Nanomolar Inhibitor of HDAC11

HDAC11 Inhibition as a Potential Therapeutic Strategy for AML: Target Identification, Lead Discovery, Antitumor Potency, and Mechanism Investigation

Herein, we identified that HDAC11 is involved in the pathogenesis of AML and is a potential therapeutic target for AML. Considering the scarcity of HDAC11 inhibitors, structurally novel HDAC11 inhibitors were developed, identifying A9 as the most potent one, which phenocopied the apoptosis induction, cell cycle arrest, and differentiation promotion effects of HDAC11 knockdown in AML cells. Moreover, A9 not only promoted iron uptake by upregulating TF and TFRC but also promoted iron release by upregulating HMOX1, which cooperatively led to iron homeostasis disruption and the consequent ferroptosis in AML cells. Mechanism investigation indicated that A9-induced HMOX1 upregulation was due to the activation of the p62-Keap1-Nrf2 pathway. Notably, the combination of A9 with chemotherapy drugs synergistically reduced AML cell viability in vitro. The robust in vivo anti-AML efficacy of A9, alone and combined with cytarabine, was also validated. Collectively, our study revealed pharmacological inhibition of HDAC11 as a potential therapeutic strategy for AML.

Targeting Histone Deacetylase 11 with a Highly Selective Inhibitor for the Treatment of MASLD

Metabolic dysfunction-associated steatotic liver disease (MASLD) represents the most prevalent chronic liver disorder globally. Due to its intricate pathogenesis and the current lack of efficacious pharmacological interventions, there is a pressing need to discover novel therapeutic targets and agents for MASLD treatment. Herein, it is found that histone deacetylase 11 (HDAC11), a subtype of HDAC family, is markedly overexpressed in both in vitro and in vivo models of MASLD. Furthermore, the knockdown of HDAC11 is observed to mitigate lipid accumulation in hepatic cells. A highly selective HDAC11 inhibitor, B6, which exhibits favorable pharmacokinetic property and liver distribution, is further designed and synthesized. Integrating RNA-seq data with in vivo and in vitro experiments, B6 is found to inhibit de novo lipogenesis (DNL) and promote fatty acid oxidation, thus mitigating hepatic lipid accumulation and pathological symptoms in MASLD mice. Further omics analysis and experiments reveal that B6 enhances the phosphorylation of AMPKα1 at Thr172 through the inhibition of HDAC11, consequently modulating DNL and fatty acid oxidation in the liver. In summary, this study identifies HDAC11 as a potential therapeutic target in MASLD and reports the discovery of a highly selective HDAC11 inhibitor with favorable drug-like properties for the treatment of MASLD.

Discovery of De Novo Macrocycle Inhibitors of Histone Deacetylase 11

Histone deacetylase (HDAC) enzymes are epigenetic regulators that affect diverse protein function by removing acyl groups from lysine side chains throughout the proteome. The most recently discovered human isozyme, HDAC11, differs from other HDACs in substrate preference and tissue expression profile. Elucidation of the biological function of this enzyme has been scarce and only a few chemical probes to help advance this insight have been developed thus far. Here we discovered macrocyclic inhibitors that exhibit selectivity for HDAC11 and penetrate the cytoplasmic membrane in cultured cells as determined by the chloroalkane penetration assay. Our work establishes the combination of de novo macrocycle synthesis with incorporation of N-alkylated hydroxamic acid moieties as a viable strategy for targeting HDAC11. Further, this study demonstrates the potential of applying macrocyclic peptide-based library synthesis to directly furnish high-affinity, cell-permeating ligands. The discovered inhibitors comprise tool compounds for the investigation of the biological function of HDAC11.

Distribution and diversity of classical deacylases in bacteria

Classical Zn2+-dependent deac(et)ylases play fundamental regulatory roles in life and are well characterized in eukaryotes regarding their structures, substrates and physiological roles. In bacteria, however, classical deacylases are less well understood. We construct a Generalized Profile (GP) and identify thousands of uncharacterized classical deacylases in bacteria, which are grouped into five clusters. Systematic structural and functional characterization of representative enzymes from each cluster reveal high functional diversity, including polyamine deacylases and protein deacylases with various acyl-chain type preferences. These data are supported by multiple crystal structures of enzymes from different clusters. Through this extensive analysis, we define the structural requirements of substrate selectivity, and discovered bacterial de-D-/L-lactylases and long-chain deacylases. Importantly, bacterial deacylases are inhibited by archetypal HDAC inhibitors, as supported by co-crystal structures with the inhibitors SAHA and TSA, and setting the ground for drug repurposing strategies to fight bacterial infections. Thus, we provide a systematic structure-function analysis of classical deacylases in bacteria and reveal the basis of substrate specificity, acyl-chain preference and inhibition.

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.

Engineering novel scaffolds for specific HDAC11 inhibitors against metabolic diseases exploiting deep learning, virtual screening, and molecular dynamics simulations

The prevalence of metabolic diseases is increasing at a frightening rate year by year. The burgeoning development of deep learning enables drug design to be more efficient, selective, and structurally novel. The critical relevance of Histone deacetylase 11 (HDAC11) to the pathogenesis of several metabolic diseases makes it a promising drug target for curbing metabolic disorders. The present study aims to design new specific HDAC11 inhibitors for the treatment of metabolic diseases. Deep learning was performed to learn the properties of existing HDAC11 inhibitors and yield a novel compound library containing 23,122 molecules. Subsequently, the compound library was screened by ADMET properties, Lipinski & Veber rules, traditional machine classification models, and molecular docking, and 10 compounds were screened as candidate HDAC11 inhibitors. The stability of the 10 new molecules was further evaluated by deploying RMSD, RMSF, MM/GBSA, free energy landscape mapping, and PCA analysis in molecular dynamics simulations. As a result, ten compounds, Cpd_17556, Cpd_2184, Cpd_8907, Cpd_7771, Cpd_14959, Cpd_7108, Cpd_12383, Cpd_13153, Cpd_14500and Cpd_21811, were characterized as good HDAC11 inhibitors and are expected to be promising drug candidates for metabolic disorders, and further in vitro, in vivo and clinical trials to demonstrate in the future.

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.

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

Histone deacetylase 11 (HDAC11), an enzyme that cleaves acyl groups from acylated lysine residues, is the sole member of class IV of HDAC family with no reported crystal structure so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms which complicates the conventional template-based homology modeling. AlphaFold is a neural network machine learning approach for predicting the 3D structures of proteins with atomic accuracy even in absence of similar structures. However, the structures predicted by AlphaFold are missing small molecules as ligands and cofactors. In our study, we first optimized the HDAC11 AlphaFold model by adding the catalytic zinc ion followed by assessment of the usability of the model by docking of the selective inhibitor FT895. Minimization of the optimized model in presence of transplanted inhibitors, which have been described as HDAC11 inhibitors, was performed. Four complexes were generated and proved to be stable using three replicas of 50 ns MD simulations and were successfully utilized for docking of the selective inhibitors FT895, MIR002 and SIS17. For SIS17, The most reasonable pose was selected based on structural comparison between HDAC6, HDAC8 and the HDAC11 optimized AlphaFold model. The manually optimized HDAC11 model is thus able to explain the binding behavior of known HDAC11 inhibitors and can be used for further structure-based optimization.

HDAC11: A novel target for improved cancer therapy

Histone deacetylase 11 (HDAC11) is a unique member of the histone deacetylase family that plays an important role in the regulation of gene expression and protein function. In recent years, research on the role of HDAC11 in tumors has attracted increasing attention. This review summarizes the current knowledge on the subcellular localization, structure, expression, and functions of HDAC11 in tumors, as well as the regulatory mechanisms involved in its network, including ncRNA and substrates. Moreover, we focus on the progress made in targeting HDAC11 to overcome tumor therapy resistance, and the development of HDAC11 inhibitors for cancer treatment. Collectively, this review provides comprehensive insights into the potential clinical implications of HDAC11 for cancer therapy.