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Target deconvolution of HDAC pharmacopoeia reveals MBLAC2 as common off-target

An Author Correction to this article was published on 15 July 2022

This article has been updated


Drugs that target histone deacetylase (HDAC) entered the pharmacopoeia in the 2000s. However, some enigmatic phenotypes suggest off-target engagement. Here, we developed a quantitative chemical proteomics assay using immobilized HDAC inhibitors and mass spectrometry that we deployed to establish the target landscape of 53 drugs. The assay covers 9 of the 11 human zinc-dependent HDACs, questions the reported selectivity of some widely-used molecules (notably for HDAC6) and delineates how the composition of HDAC complexes influences drug potency. Unexpectedly, metallo-β-lactamase domain-containing protein 2 (MBLAC2) featured as a frequent off-target of hydroxamate drugs. This poorly characterized palmitoyl-CoA hydrolase is inhibited by 24 HDAC inhibitors at low nanomolar potency. MBLAC2 enzymatic inhibition and knockdown led to the accumulation of extracellular vesicles. Given the importance of extracellular vesicle biology in neurological diseases and cancer, this HDAC-independent drug effect may qualify MBLAC2 as a target for drug discovery.

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Fig. 1: Chemical proteomics assay for HDACis.
Fig. 2: Target landscape of HDAC and metallohydrolase drugs.
Fig. 3: Complex selectivity of class I HDACis.
Fig. 4: HDACis abrogate MBLAC2 acyl-CoA hydrolase activity at nanomolar concentrations.
Fig. 5: MBLAC2 knockdown or inhibition lead to vesicle accumulation in supernatants of HEK293 cells.

Data availability

Chemoproteomics data are freely accessible in ProteomicsDB ( The mass spectrometry proteomics data, including the used Swiss-Prot reference database, have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD026657. The untargeted metabolomics data for lipidomics are made available on MetaboLights repository with the identifier MTBLS3557 and can further be accessed on Zenodo (

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We thank W. Greentree (Cornell University) and L. Wanat (Technical University of Munich) for technical support. We gratefully acknowledge funding by the Deutsche Forschungsgemeinschaft (DFG) (SFB 1309, project 401883058 and project 407391208). M.I.P.M. and M.E.L. acknowledge support from the National Institutes of Health (grant GM121540). Contributions by V.W. and J.K.P. are funded by the Bavarian State Ministry of Science and the Arts within the framework of the Bavarian Research Institute for Digital Transformation (bidt). A.K.M. acknowledges the Helmholtz Drug Initiative for financial support. We also thank the CHDI foundation for generously providing CHDI00465983 and CHDI00390576.

Author information

Authors and Affiliations



G.M. conceived and directed the project. S.L., B.K. and G.M. wrote the manuscript. S.L. profiled the drugs and performed the knockdown, western blots, purification of extracellular vesicles, proteomics experiments and lipidomics experiments. S.L. and B.K. measured the proteomics samples. M.I.P.M. and M.E.L. performed the MBLAC2 enzyme activity assays and C12-ceramidase assay. C.G. and M.W.P. performed the vesicle tracking experiments. R.R.S. and A.K.M. performed the HDAC enzyme activity assays, FRET and NanoBRET binding assays. A.B., P.R., S.N., C.S., X.K., P.P., T.H., W.S. and G.M. performed the synthetic chemistry work. S.L, A.B., P.R., S.N., C.S., C.L., X.K. and P.P. performed the affinity matrix evaluation experiments. L.L. and M.W. made the data available in ProteomicsDB. K. Kleigrewe measured the lipidomics samples. S.L., V.W., C.M., K. Kleigrewe and J.K.P. analyzed the lipidomics data. K. Kramer performed the cell imaging experiments.

Corresponding author

Correspondence to Guillaume Médard.

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Competing interests

M.W. and B.K. are cofounders and shareholders of OmicScouts and msAId. They have no operational role in either company. The remaining authors declare no competing interests.

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Nature Chemical Biology thanks Christian Olsen, Hongyan Sun, Zheng-Jiang Zhu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Evaluation of affinity probes for capturing HDACs in cancer cell lysates.

a, Structures of synthesized HDAC affinity probes 1-15. Positions for on-bead immobilisation are marked with red triangles. Probes 2 and 3 are respectively para and meta linkable analogues of reported class IIa inhibitor MC15681. Probe 4 is an analogue of a HDAC8 inhibitor2,3. Probe 6 is a linkable analogue of Vorinostat4. 8-15 feature alternative chelating groups to hydroxamate, grafted on hybrid structures of Vorinostat, Santacruzamate and LMK235: methoxymethyl ketone (8, 10, 11) found in a bicyclic tetrapeptide5, ethyl carbamate (12, 13, 14) found in Santacruzamate6 and 4-hydroxymethylimidazole (15) computationally suggested as superior to hydroxamic acid7. b, Strategy and retro-synthesis scheme for the design and preparation of class IIa affinity probe iC (1). The analogation of lead oxazole 318 to a triazole allows for immobilisation on Sepharose beads. The matrix is conveniently obtained by on-bead Copper-catalysed Azide-Alkyne Cycloaddition (CuAAC) of a hydroxamate-protected alkyne precursor with azide-functionalised beads, followed by deprotection. The alkyne precursor was prepared by converting Bürli’s bromo ester intermediate in 3 steps: Sonogashira coupling with TMS-acetylene, simultaneous deprotection/saponification and conversion to protected hydroxamic acid. An almost equimolar mixture of 3 enantiomer pairs (only all-cis isomers are not obtained during the Johnson-Corey-Chaykovsky synthetic step) constitute iC. (See Supporting Note) c, Screening cancer cell lines for class IIa HDAC expression using iC affinity probe. The heatmap depicts the average protein intensity of class IIa HDACs pulled down by iC (MS raw intensity). d, Intensities of HDACs pulled down by iC from three most promising cell lines from a different lysis batch. e, Evaluating HDAC affinity matrices for HDAC pulldown capabilities. The heatmap shows protein intensities of pulled down HDACs by 14 distinct affinity probes using cell lysate mixes of MV4-11 and SW620. For probes 2-15 a 1:1 mix of SW620 and MV4-11 cell lysate was used, while the heatmap for probe 1 represents an average of detected protein intensities from 2 single pulldowns in SW620 and MV4-11 cell lysate. All pulldowns were performed in three technical replicates.

Extended Data Fig. 2 Evaluation of metalloprotein affinity matrix iA for pulling down metalloproteins.

a and b, enrichment of proteins from PATU-8998-S lysate by iA relative to control beads or iQ is plotted against the intensity of the corresponding proteins in iA pulldown. iBAQ intensity values were employed as proxy for the protein amount. Asterisks on the x-axis mark proteins that have been identified in each replicate of the iA pulldown but in none of control or iQ replicates and thus represent highly significantly enriched proteins. a, enriched HDACs and proposed off-targets are highlighted (two sided t-test, p value without multiple testing correction). b, differential chemoproteomic enrichment of FeS-cluster proteins (pink circles) as an example for iA-enriched metalloprotein classes compared to control beads and iQ (two sided t-test, p-value without multiple testing correction). c, iBAQ intensities of selected significantly enriched proteins from the single pulldown experiments. All pulldowns were performed in technical replicates (duplicates for control and iQ, triplicates for iA) and data are shown as mean +/− SEM. As control beads we used the same sepharose beads for affinity matrix generation, but, instead of affinity probes, we coupled 2-aminoethanol to the NHS-activated Sepharose.

Extended Data Fig. 3 Evaluation of binding kinetics at different incubation temperatures for Entinostat and CHDI00465983.

A 1:1 mix of SW620 and MV4-11 cell lysate was incubated with excess drug (30 μM) for different periods of time at either room temperature (22 °C) or 4 °C before pulling down HDACs with iQ (for Entinostat) or iC (for CHDI00465983). HDAC1 and HDAC2 competition depends on the incubation temperature and Entinostat shows relatively slow binding kinetics compared to CHDI00465983.

Extended Data Fig. 4 Comparison of acquired affinity data to literature data.

pKdapp values determined in this study were plotted against pKd values from three former HDACi profiling studies4,9,10 and in case of Panobinostat to another optimized chemoproteomics study using immobilized Panobinostat as probe9. For data sets without missing values, regression lines were plotted (s = slope of regression line). In general good correlation is observable between recombinant enzyme activity assay data (Bradner et al.)10 and our study as well as for the comparison to the optimized chemoproteomics assay using immobilized Panobinostat as affinity matrix (Becher et al.)9.

Extended Data Fig. 5 2-Dose cell viability and morphology screen with HDACi library on SW620 cells.

a, Representative images of vehicle (DMSO) and drug treated cells (100 nM Entinostat) after 60 h of incubation. Bright light images (upper panel) were recorded using an Incucyte S3 and processed by ilastik11, a supervised machine learning toolkit for automated image analysis. Vital round shaped (V), flattened/pancake-like shaped (P) and apoptotic cell morphologies (A) were identified and labelled accordingly (lower panel). Scale bar corresponds to 100 µm length. b, Relative areas covered by the the three cell morphologies after drug treatment, performed in quadruplicates. Pixels of the ilastic software output classification were counted and divided by the sum of all cells pixels: bar chart displays the means and standard deviations of quadruplicate experiments in 96-well plates. Treatments were ranked from highest to lowest cytotoxicity (that is high proportion of apoptotic cells (A) for the 100 nM and 1 µM treatment). Control DMSO treatments are listed as CTRL1-6. Drugs are assigned to groups according to their target spectrum (see Fig. 2c). c, Correlation between drug affinities (pKdapp of HDAC1,2,3,6 and MBLAC2) and proportion of apoptotic cells after 1 µM drug treatment. Representative clinical drugs Romidepsin (depsipeptide), aminoanilides (Mocetinostat, Entinostat, Tucidinostat) and hydroxamates (Vorinostat, AR-42, Abexinostat) as well as some particularly interesting tool compounds (ACY-738, Nexturastat A, CHDI00465983) are highlighted in the plots for orientation. In accordance to literature, class I inhibition correlated with cytotoxicity, while HDAC6 and MBLAC2 inhibition does not correlate with cytotoxicity but clusters into two clouds, defined by the drugs additional class I target affinity (cytotoxicity mainly mediated by class I inhibition). As expected, drugs of similar class I target affinity show distinct cytotoxicity, most probably reflecting distinct intracellular drug concentrations (for example via cellular uptake, metabolism, active export). Chemically similar drugs (Mocetinostat, Entinostat, and Tucidinostat) that share the same target space and most probably similar intracellular distributions according to their similar physicochemical properties cluster together.

Extended Data Fig. 6 Confirming selectivity of ACY-738 (HDAC6i) and TH65 (HDAC10i) with orthogonal biochemical assays.

a, Dose-dependent in vitro HDAC activity assays were performed using the HDAC-Glo assay for HDAC1/2/3/6/8. In vitro HDAC10 target engagement was determined by the TR-FRET assay (mean of triplicates with SD). b and c, HDAC6 and HDAC10 nano-BRET assays for in-cellulo target engagement measurement were performed with the selective compounds TH65 and ACY738 as well as with Vorinostat and Quisinostat for comparison. Of note, the HDAC6 nano-BRET only determines in-cell binding affinity to HDAC6 catalytic domain 2 (CD2) and not to CD1. d, Dose-dependent in vitro activity assay data for ACY-738 as determined by the HDAC-Glo assay shows the selectivity of ACY-738 over class I HDACs 1/2/3/8. a, d: HDAC-Glo assay was performed in three replicates; b, c: TR-FRET assay was performed in six replicates. Data are presented as mean values + /- SD.

Extended Data Fig. 7 Complex selectivity of class I HDACis.

Schematic representation of acquiring HDAC complex selectivity data. Different interaction partners of the same HDAC (Interactor A and B) can impact the binding affinity of drugs to the HDAC target enzyme, for instance by inducing conformational changes. Since HDACs are bound by competing drugs or affinity probes when in complex with their interactors, all the complex partners show dose-response curves. These curves allow to infer the affinity of drugs to HDACs in complex with the respective interactor (A/B). As a result, the observed curve for the corresponding HDAC is an aggregation of all the potential complex variants and single HDACs, which are not part of complexes. b, Complex selectivity map for class I HDACis. The colours of the heatmap indicate differences in affinity to HDAC3 (∆pEC50) depending on its interaction partners of the NCoR complex. Colour shades of blue show that HDACi affinity is increased upon interaction between HDAC3 and the corresponding interaction partner, red shades indicate that HDACi loses binding affinity to HDACs interacting with the corresponding interaction partner (Blank space: lack of robust protein quantification prevents precise EC50 value determination).

Extended Data Fig. 8 Dose dependent inhibition of MBLAC2 hydrolase activity by HDAC inhibitors.

a, Clinical drugs Abexinostat, Pracinostat and AR-42 all show EC50 inhibition values below 10 nM (assay threshold). b, Tool compounds and pre-clinical drugs also engage with MBLAC2. While potent MBLAC2 binders from the chemoproteomic profiling show inhibition with nanomolar affinities, PCI-34051 (non-binder chemoproteomic assay) shows over 100-fold lower activity against MBLAC2 (pEC50 = 6.0).

Extended Data Fig. 9 MBLAC2 inhibition and knockdown effects on proteome and lipidome.

siRNA-mediated MBLAC2 knockdown does not lead to major changes of intracellular protein levels. Volcano plots display the difference in protein abundances of HEK293 cells after DMSO treatment of transfection with MBLAC2-directed siPOOL or control siPOOL (biological triplicates). HEK293 were lysed and submitted to a deep bottom-up proteomics workflow, including SP3-bead protein digestion protocol and TMT-labelling for robust quantification. While MBLAC2 knockdown is still efficient 3.5 d after transfection with siRNA POOL (13% compared to control siRNA), the whole proteome is generally unaffected. Intracellular levels of proteins involved in extracellular vesicle biogenesis and associated to MBLAC2 do not change significantly (labelled in grey are potential interaction partners or co-regulated proteins TSG101, ATP11C, SLC9A6, VAMP4, ZHHDC20, CD63, ANXA7, VPS28). We attribute the apparent LRP5 upregulation and LAMA5 downregulation (orange) to effects caused by transfection with control siRNA, since they are only affected in control siRNA pool treated cells. In analogy, proteins marked in blue are only regulated in DMSO treated cells and therefor attributed to DMSO-related effects (for example oxidative mechanisms). b, Western blot against human MBLAC2 (and beta-Actin as loading control), showing knockdown efficiencies in dependence of transfection amount and duration. Bands were quantified with the Licor software (see Methods) and normalized to the loading control. This normalized MBLAC2 expression was then again normalized to control siRNA treated cells of corresponding knockdown duration to give a relative and time-dependent knockdown efficiency of MBLAC2. In concordance with the full proteome data, knockdown is still sufficient three days after transfection. c, MBLAC2 knockdown leads to abundance changes of intracellular lipid families. MBLAC2 knockdown leads to global intracellular downregulation of monogalactosylceramides (general structure scaffold indicated) in HEK293 cells. Volcano plot shows the ratio and statistical significance (q-value) of lipid quantities between MBLAC2 knockdown (MBLAC2-KD - siRNA POOL in triplicate) and control (control siRNA in triplicate), as measured by mass-spectrometry based lipidomics, after MeOH/CHCl3 lipid extraction. d, Schematic of Ceramidase activity assay. A fluorescently labelled C12-NBD-Ceramide was incubated for different time periods with varying concentrations of WT MBLAC2 or native cell lysates containing endogenously expressed ceramidases. The reaction was terminated by boiling and solvent evaporated. The reaction products were resuspended in CHCl3/MeOH run on a TLC plate and developed with CHCl3/MeOH/NH4OH. Fluorescent substrates and products were read out at 488 nm. e, Ceramidase assay evaluation. Incubation of the fluorescently labelled ceramide with native cell lysates from MD-231 and HEK293 cells at 37 °C shows turnover of the substrate after 4 h of incubation and confirms the functional setup. f, Incubation of fluorescently labelled Ceramide with increasing concentration of MBLAC2 for 2 h at 37 °C does not show notable turnover compared to negative control (-), while incubation with cell lysate reduces substantially the signal of intact ceramide substrate. g, Extended incubation of ceramide substrate with 1 µM WT MBLAC2 at 37 °C for increasing periods of time does not lead to notable substrate turnover even after 6 h incubation.

Extended Data Fig. 10 ACY-738 and ACY-775 induce similar HDAC6 substrate overacetylation.

SW620 cells were treated for 6 h with ACY-738, ACY-775 or DMSO control and the lysates were used for western blots against acetylated alpha-Tubulin (Lys40). The intensities of analyte bands were normalized to beta-actin intensities of the same lane to control for loading amount biases. ACY-775 as well as ACY-738 induced significant overacetylation of alpha-Tubulin Lys40 (***: p < 0.0001, n.s: p = 0.4147). Similar overacetylation of alpha-Tubulin indicates comparable target engagement of HDAC6 inhibitors. Data of bar chart are represented as mean +/− SD of three biological replicates (2-sided t-test).

Supplementary information

Supplementary Information

Supplementary Tables 1–2. Supplementary Data 1–4 (captions). Supplementary Figs. 1–3. and Supplementary Note.

Reporting Summary

Supplementary Data 1

The drug-target landscape of HDACis. Excel-based table that lists all the numeric values of calculated pKdapp for HDACis and their targets.

Supplementary Data 2

MBLAC2 interactors, gene ontologies and coregulations. Excel-based table that lists the interactors of MBLAC2 according to BioPlex and the corresponding GO terms of the interaction network. Moreover, MBLAC2-coregulated proteins as derived from the ProteomeHD platform are listed with the corresponding GO terms of the coregulated proteome.

Supplementary Data 3

Proteomic data of the MBLAC2 knockdown experiments. Excel-based table that lists all protein intensities (log2) of the proteins detected in all replicates and conditions, as well as results of Student’s t-tests that are the basis for the volcano plots in Extended Data Fig. 9a (conditions: 1, MBLAC2 knockdown; 2, control siRNA transfection; 5, DMSO treated cells).

Supplementary Data 4

Lipidomics data set from MBLAC2 knockdown experiment. Excel-based tables that list the identified lipids and their regulation as the basis for the volcano plot in Fig. 5c and Extended Data Fig. 9c. Moreover, MS-DIAL4 output files from lipidomics data analysis are included.

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Lechner, S., Malgapo, M.I.P., Grätz, C. et al. Target deconvolution of HDAC pharmacopoeia reveals MBLAC2 as common off-target. Nat Chem Biol 18, 812–820 (2022).

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