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.

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.

3,4-Diaryl-11-methyl-7-[(aryl)methylidene]-4,5,11-triazatricyclo[6.2.1.0*2,6*]undec-5-enes 14a–s were synthesized through reaction of 2,4-bis[(aryl)methylidene]-8-methyl-8-azabicyclo[3.2.1]octan-3-ones 12a–f with aryl hydrazines in the presence of catalytic amount of thiamine hydrochloride. Meanwhile, the 4-acetyl analogs 16a,b were obtained through reaction of 12b,e and hydrazine hydrate in acetic acid. Good support for the structure was received from single crystal X-ray studies of 14a. Some of the synthesized tropane containing-compounds showed promising antitumor properties during the in vitro MTT bio-assay against HepG2 (hepatocellular) and MCF7 (breast) human tumor carcinoma cell lines, with potency higher than that of doxorubicin (DNA intercalating agent, standard reference). Statistically significant 2D-QSAR model describes the antitumor properties against MCF7.

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.

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.

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.