Fady Baselious

Class IV histone deacetylases (HDACs) are the least understood branch of the classical zinc-dependent HDAC family with HDAC11 standing out as the sole member of Class IV HDACs. Using a broad phylogenetic dataset spanning bacteria, archaea, and eukaryotes, we identified two deeply conserved HDAC11 lineages, clades A and B, that differ in evolutionary origin, predicted subcellular localization, and enzymatic properties. Clade A is enriched in phototrophic eukaryotes and targeted to mitochondria or plastids, whereas clade B predominates in heterotrophs and localizes mainly to the cytoplasm or nucleus. High-resolution crystal structures of selected representatives from each clade revealed a conserved catalytic core but distinct structural features—including electrostatic surface profiles, loop architectures, and foot pocket geometries—that clearly separate the two lineages and act as sequential “selectivity filters” shaping substrate specificity. Biochemical assays show robust long-chain fatty acid deacylase activity in clade B enzymes, but no detectable activity for any of clade A representatives against peptide substrates, suggesting adaptation to alternative, nonpeptidic targets. Together, these findings define a revised evolutionary framework for HDAC11 and provide structural and functional insights into the diversification of this ancient enzyme family.

Histone deacetylases (HDACs) play a pivotal role in various biological pathways and represent interesting drug targets. Therefore, HDAC inhibitors (HDACi) with high isoform selectivity and a zinc-binding group different from hydroxamic acid, because of its low metabolic stability, are required. HDAC11, as a highly potent defatty-acylase, differs from other HDACs in its substrate preference. Starting from this finding, we developed specific inhibitors for HDAC11 based on a peptide containing a fatty-acylated lysine side chain as the selectivity tail. The introduction of different heteroatoms at the fatty acyl residue was used to generate potent zinc-binding groups in combination with the scissile amide bond, as well as to suppress substrate properties of the resulting compounds. Further optimization resulted in a highly potent and selective HDAC11 inhibitor 31, which exhibits low nanomolar inhibition against HDAC11 without targeting other HDACs and is active in cells. The data presented here may help expand the range of zinc-binding groups utilized in HDAC inhibitors. Furthermore, the concept of the selectivity tail was demonstrated to facilitate straightforward access to selective defatty-acylase inhibitors.

The therapeutic potential of HDAC inhibitors containing a hydroxamic acid moiety as a zinc-binding group (ZBG) is limited in clinical use due to their potential mutagenicity. In addition, hydroxamic acids often exhibit off-target effects that can lead to undesirable toxicity. Therefore, the development of HDAC inhibitors with alternative ZBGs has proven to be a promising approach to overcome these drawbacks. HDAC inhibitors carrying alkyl hydrazide as ZBG have recently been published as selective inhibitors for different HDAC subtypes. In the present study, a ligand-based virtual screening workflow was developed and performed for a designed targeted chemical space. The identification of hit compounds was based on a categorical classification model of alkyl hydrazides as HDAC inhibitors. The two most promising hits of the screening were synthesized by docking against different HDACs, followed by in vitro enzyme inhibition assays. Both hits showed strong inhibition of HDAC11 with IC50 values in the nanomolar range. In addition, the compounds showed good selectivity towards HDAC11 at a concentration of 1 µM, only HDAC8 was also significantly inhibited. Finally, the binding mode of the selected candidates was further investigated through molecular dynamics simulations and metadynamics studies to provide insights for further chemical optimization.

In a fragment-based approach using NMR spectroscopy, benzyloxyacetohydroxamic acid-derived inhibitors of the bacterial deacetylase LpxC bearing a substituent to target the uridine diphosphate-binding site of the enzyme were developed. By appending privileged fragments via a suitable linker, potent LpxC inhibitors with promising antibacterial activities could be obtained, like the one-digit nanomolar LpxC inhibitor (S)-13j [K_i (EcLpxC C63A) = 9.5 nM; K_i (PaLpxC): 5.6 nM]. To rationalize the observed structure-activity relationships, molecular docking and molecular dynamics studies were performed. Initial in vitro absorption-distribution-metabolism-excretion-toxicity (ADMET) studies of the most potent compounds have paved the way for multiparameter optimization of our newly developed isoserine-based amides.

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.

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.

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.

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.

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 the conventional homology modeling less reliable. AlphaFold is a neural network 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 absence of small molecules and ions/cofactors complicate 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.

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.