- Research article
- Open Access
- Open Peer Review
Virtual screening and experimental validation of novel histone deacetylase inhibitors
- Yan-xin Huang1Email authorView ORCID ID profile,
- Jian Zhao†1,
- Qiu-hang Song†1,
- Li-hua Zheng1,
- Cong Fan1,
- Ting-ting Liu1,
- Yong-li Bao1,
- Lu-guo Sun1,
- Li-biao Zhang2Email author and
- Yu-xin Li3Email author
© The Author(s). 2016
- Received: 2 December 2015
- Accepted: 12 July 2016
- Published: 21 July 2016
Histone deacetylases (HDACs) are promising therapeutic targets for the treatment of cancer, diabetes and other human diseases. HDAC inhibitors, as a new class of potential therapeutic agents, have attracted a great deal of interest for both research and clinical applications. Increasing efforts have been focused on the discovery of HDAC inhibitors and some HDAC inhibitors have been approved for use in cancer therapy. However, most HDAC inhibitors, including the clinically approved agents, do not selectively inhibit the deacetylase activity of class I and II HDAC isforms, and many suffer from metabolic instability. This study aims to identify new HDAC inhibitors by using a high-throughput virtual screening approach.
An integration of in silico virtual screening and in vitro experimental validation was used to identify novel HDAC inhibitors from a chemical database.
A virtual screening workflow for HDAC inhibitors were created by integrating ligand- and receptor- based virtual screening methods. Using the virtual screening workflow, 22 hit compounds were selected and further tested via in vitro assays. Enzyme inhibition assays showed that three of the 22 compounds had HDAC inhibitory properties. Among these three compounds, ZINC12555961 significantly inhibited HDAC activity. Further in vitro experiments indicated that ZINC12555961 can selectively inhibit proliferation and promote apoptosis of cancer cells.
In summary, our study presents three new and potent HDAC inhibitors and one of these HDAC inhibitors shows anti-proliferative and apoptosis-inducing activity against various cancer cell lines. These results suggest that the developed virtual screening workflow can provide a useful source of information for the screening and validation of new HDAC inhibitors. The new-found HDAC inhibitors are worthy to further and more comprehensive investigations.
- HDAC inhibitors
- Virtual screening
The dynamic post-translational modification of nucleosomal histones plays a critical role in transcriptional regulation. Hyperacetylation of nucleosomal core histones results in transcriptional activation, while their hypoacetylation leads to transcriptional repression. Modifications of nucleosomal histone acetylation and deacetylation affect the chromatin structure and related gene expression, and thus regulate various cellular processes, including DNA synthesis, cell division and differentiation, apoptosis, and others [1, 2]. The level of histone acetylation is determined by histone acetyltransferase (HAT) and histone deacetylase (HDAC) activities [3, 4]. Impaired HDAC activity could interfere with the balance between HATs and HDACs and thus alter the transcriptional status of many genes, in particular those related to disease. Therefore, HDACs have become promising therapeutic targets for the treatment of cancer, diabetes, and other human diseases [5, 6]. HDACs are classified into four classes (Classes I–IV) depending on their sequence identity and domain organization. Classes I (HDACs 1–3 and 8), II (HDACs 4–7, 9, and 10), and IV (HDAC 11) are referred to as classical HDACs and are generally simultaneously targeted by most HDAC inhibitors . Class III HDACs include Sirt1–7; they are nicotinamide (NAD)-dependent and are insensitive to HDAC inhibitors . To date, a number of HDAC inhibitors have been reported and they can be divided into several structural categories: hydroxamic acids, aliphatic acids, benzamides, cyclic peptides and others [9–11]. HDAC inhibitors have emerged as a new class of therapeutic agents and have generated much interest among pharmacologists, and cancer and diabetes researchers [5, 12, 13]. Three HDAC inhibitors, vorinostat (SAHA) , cyclic peptide FK228 (romidepsin) [15, 16] and belinostat , have been approved by the U.S. Food and Drug Administration (FDA) for the treatment of cutaneous and peripheral T cell lymphoma. However, most HDAC inhibitors, including the clinically approved agents, non-selectively inhibit the deacetylase activity of class I and II HDACs, and many suffer from metabolic instability. These characteristics have been associated with reduced potency and toxic side effects in vivo . Significant efforts are ongoing to address these and other deficiencies of HDAC inhibitors to improve their HDAC inhibitory potential for the treatment of cancer and other diseases [19–21]. In addition, substantial efforts have been made to develop new HDAC inhibitors with potential therapeutic applications . In the present study, we present a hierarchical virtual screening protocol with SYBYL-X2.0  and Gold 5.2  software suites for the identification of compounds as potential HDAC inhibitors. It provides a stable and reliable solution for virtual screening of HDAC inhibitors based on commercial software’s of drug discovery. A pharmacophore model was built and used for virtual screening to identify candidate compounds from the enamine dataset in the ZINC database . Then, the remaining compounds were docked into the active site of HDAC8. Finally, 22 compounds were identified as the final hit compounds. Enzyme inhibition assays with the HDAC inhibitor drug screening kit showed that three of the 22 compounds had HDAC inhibitory properties. Among these three compounds, ZINC12555961 was confirmed to have significant inhibitory activity against HDACs. Further in vitro cell experiments demonstrated that ZINC12555961 can selectively inhibit proliferation and promote apoptosis of cancer cells.
GOLD 5.2 was adopted for molecular docking screening. HDAC8 (PDB id: 1 T69) was selected as the docking target. All the water molecules in HDAC8 were removed and hydrogen atoms were added. The binding site of HDAC8 was defined as those residues within 10 Å from the ligand in the X-ray structures. The parameters of the genetic algorithm (GA) were used in default values (such as the population size of 100, the selection pressure of 1.1, etc.) except that ligands were subjected to 30 GA runs, the number of operations was set to 300,000, and the early termination option was turned off. The three top scoring conformations of every ligand were retained at the end of the calculation. Two of the fitness functions implemented in GOLD 5.2, ChemPLP and ChemScore were used in our experiments.
HDAC inhibitory activity assay
The HDAC inhibitor drug screening kit (k340-100, BioVision, CA, USA) was used to measure HDAC inhibitory activities of the candidate compounds according to the manufacturer’s instructions. The candidate compounds, assay buffer, and HDAC fluorometric substrate, which comprises an acetylated lysine side chain, were added to HeLa nuclear extracts in a 96-well plate and incubated at 37 °C for 30 min. The reaction was stopped by adding lysine developer, and the mixture was incubated for another 30 min at 37 °C. An additional positive control included incubation with double-distilled water, and the inhibitor control consisted of incubation with Trichostatin A (TSA) at 20 μM. HDAC activities were quantified by a fluorescence plate reader (POLARstar OPTIMA, BMG, BRD) with excitation at 370 nm and emission at 450 nm.
Four cell lines, namely HepG2 (human hepatocellular carcinoma cell line), L02 (human normal liver cell line), MDA-MB-231 (human breast cancer cell line), and MCF-10A (human normal breast cell line), were obtained from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). Cells were cultured in an appropriate medium supplemented with 10 % fetal bovine serum (TBD Science, Tianjin, China), 100U/ml penicillin and 100 mg/mL streptomycin (Ameresco, US) at 37 °C and 5 % CO2.
The MTT (3-(4, 5-dimethylthia-zol-2-yl)-2, 5-diphenyl tetrazolium bromide) assay was used to examine the effects of the candidate compounds on cell viability. The candidate compounds were dissolved in DMSO (dimethyl sulfoxide) as 10 mM/L stock solutions. Cells were plated in 96-well plates (1 × 104 cells/well) in 100 μL of growth medium and allowed to grow for 24 h. The cells were then treated with 0, 1, 10, 50 and 100 μM of each candidate compound in the presence of 3 % serum. After 44 h of treatment, 20 μL of MTT [5 mg/mL in phosphate-buffered saline (PBS); Sigma Chemical Co.] were added to each well for an additional 4 h of incubation. The blue MTT formazan precipitate was dissolved in 100 μL of DMSO. The optical density of samples was measured at 570 nm using a micro ELISA reader (Bio-Rad, Hercules, CA). Cell viability was expressed as a percentage relative to the untreated control cells.
DAPI staining assay
A DAPI staining assay was performed to reveal the presence of condensing nuclei and apoptotic bodies in compound-treated cells. HepG2 and MDA-MB-231 cells were treated with the candidate compounds (60 and 90 μM) for 48 h, and then harvested, fixed with 4 % paraformaldehyde for 30 min, washed with PBS, and stained with DAPI at a final concentration of 0.5 μg/mL for 15 min at room temperature. The cells were then analyzed using a fluorescence microscope. Three independent experiments were performed, and at least four different fields with a minimum of 100 cells/field were scored.
Annexin V-FITC/PI (propidium iodide) assay was performed to evaluate apoptosis of cancer cells induced by the hit compound ZINC12555961. HepG2 and MDA-MB-231 cells were seeded on 6-well plates at a density of 1 × 106 cells/well, and incubated with 90 μM of ZINC12555961 for 48 h. Then, the cells were harvested by trypsinization, washed in ice-cold PBS, and resuspended in 190 μL binding buffer containing 5 μL Annexin V and 10 μL PI (Beyotime, China). The cells were incubated in the dark for 10 min and then analyzed by flow cytometry (BD FACSCanto™).
Cell cycle analysis
DNA staining with PI (Beyotime, China) was used to determine the cell cycle distribution of compound-treated cells. The number of cells at specific phases of the cell cycle was analyzed and sorted using flow cytometry. HepG2 and MDA-MB-231 cells were seeded at a density of 1 × 106 cells/well. After treatment, the cells were collected, washed with PBS, fixed with 50 % alcohol and stained with PI at a final concentration of 1 mg/mL for 30 min. The percentages of cells in different phases of the cell cycle were measured with a flow cytometer (BD FACSCantoTM) and analyzed with the Modfit software (Verity Software House, Topsham, USA).
Pharmacophore-based virtual screening
Pharmacophore models generated by GALAHAD
Where D indicates the total number of compounds in the test datasets; A means the total number of known inhibitors in the test datasets; Ht is the hit number of compounds retrieved from the test datasets; and Ha represents the number of known inhibitors in the hit compounds.
The EF values of the pharmacophore models for the test and decoy datebases
Molecular docking-based virtual screening
The EF values and functional thresholds for the top 1, 5, 10 and 20 % of the decoy database in individual docking function test
The EF values and functional thresholds for the top 1, 5, 10 and 20 % of the decoy database in combined docking function test
Structure of the 22 final compounds
Different binding patterns of 22 hit compounds
Pharmacophore interaction regions
ZINC01895726, ZINC02639234, ZINC06178852, ZINC23140995, ZINC23141716, ZINC23143331, ZINC23886004, ZINC58161863, ZINC60063267, ZINC67907864, ZINC84111476
metal chelate bonds and hydrogen bonds
ZINC03307410, ZINC06415107, ZINC06497704, ZINC09350495, ZINC12555961, ZINC23141899
metal chelate bonds and hydrogen bonds
ZBG domain and linker domain
metal chelate bonds and hydrogen bonds
ZBG domain and cap group domain
metal chelate bonds and hydrogen bonds
ZBG domain, linker domain and cap group domain
ZBG domain or linker domain
Inhibitory enzymatic activity evaluation
Anti-proliferative activity and apoptosis-inducing mechanism
Comparison of the IC50 values of SAHA, ZINC12555961, ZINC02639234 and ZINC09715944 against the HepG2, L02, MDA-MB-231 and MCF-10A cell lines
166 ± 9
85 ± 2
178 ± 13
59 ± 10
87 ± 10
85 ± 15
57 ± 7
142 ± 17
157 ± 12
65 ± 3
47 ± 7
HDAC enzymes have emerged as exciting and promising novel targets for the treatment of cancer, diabetes and other human diseases. HDAC inhibitors, as a new class of potential therapeutic agents, have attracted a great deal of interest both for research and clinical applications. Computer aided drug design (CADD) and virtual screening have been applied in the development of new HDAC inhibitors. Many HDAC inhibitors were designed and synthesized based on CADD approaches [35–41]. Certain potent HDAC inhibitors with novel structures were identified by virtual screening approaches [31, 42, 43]. Vadivelan et al. developed a pharmacophore model based on common chemical features of HDAC inhibitors . Melagrakia et al. developed a linear five-parameter quantitative structure-activity relationship (QSAR) model of HDAC inhibitors . Xiang et al. developed a pharmacophore model and three QSAR models for a series of benzimidazole and imidazole inhibitors of HDAC2 . Zhao et al. used a two-step modeling approach to study the selectivity and activity of HDAC inhibitors . Thangapandian et al. used pharmacophore modeling and molecular docking approaches for the identification of potential HDAC8 inhibitors . More recently, Thangapandian et al. used a combined pharmacophore modeling, molecular docking and molecular dynamics (MD) simulation approach for the identification of potential HDAC8 inhibitors . Nair et al. used a combined pharmacophore modeling, flexible docking, and three-dimensional QSAR (3D–QSAR) approach for the identification of benzimidazole and imidazole derivatives . Although these studies did not experimentally validate the activities of their candidate compounds, their use of virtual screening approaches for HDAC inhibitors provides support for further computational and experimental research. Park et al. identified novel classes of HDAC inhibitors with new zinc-chelating groups using docking simulations, and experimentally validated the activities of their candidate compounds . Tang et al. identified three hit compounds using a combinatorial QSAR screening model based on support vector machine and k-Nearest Neighbors algorithms, and experimentally confirmed the inhibitory activities of the compounds against HDAC1 . Zhang et al. identified a potent HDAC inhibitor with a novel scaffold using ZBG (zinc-binding group)-based virtual screening, and experimentally confirmed the inhibitory activities of the compounds against HDAC8 . In the present study, we developed a hierarchical virtual screening protocol for the identification of potential HDAC inhibitor compounds. The multistage virtual screening workflow was used to screen and identify 22 final hit compounds, and the HDAC inhibitory activities of three of the 22 compounds, namely ZINC12555961, ZINC02639234 and ZINC09715944, were experimentally validated by in vitro enzyme inhibition assays. The results confirmed the efficacy and validity of our screening method. The three active compounds showed a novel structure that does not belong to the previously reported four classes of HDAC inhibitors. All three active hits showed different scaffolds, thereby providing wide opportunities for future HDAC inhibitor design. The novelty of the 22 final hit compounds was assessed using SciFinder scholar (https://scifinder.cas.org/). The SciFinder results confirmed that these compounds were not previously tested for HDAC inhibitory activity.
We further examined the cytotoxicity of the three hit compounds with HDAC inhibitory activities against the human normal liver cell line, L02, and the liver cancer cell line, HepG2, as well as the human breast cancer cell line, MDA-MB-231, and the human breast epithelial cell line, MCF-10A. The MTT assay results demonstrated that the active compound ZINC12555961 could selectively suppress the viability of human cancer cell lines (HepG2 and MDA-MB-231 cells). Staining with DAPI and Annexin V-FITC/PI flow cytometry assays revealed that the effect of ZINC12555961 on cancer cell death may be mediated by the induction of apoptosis and G2/M phase cell cycle arrest. These results indicate that ZINC12555961 is a promising HDAC inhibitor and has anti-tumor potential. Future studies will be aimed at elucidating the molecular mechanisms underlying ZINC12555961-induced selective cancer cell apoptosis and evaluating the isoform-selective HDAC inhibitory effects of ZINC12555961. ZINC12555961-focused virtual screening will also be further developed in the future.
In conclusion, the study identified three new HDAC inhibitors. The new-found HDAC inhibitors are worthy to further investigations.
AA, Hydrogen bond acceptors; CADD, Computer aided drug design; DMSO, Dimethyl sulfoxide; EF, Enrichment factor; ELISA, Enzyme-linked immunosorbnent assay; FITC, Fluorescin isothiocyanate; GA, Genetic algorithm; HB, Hydrogen bond donors; HB, Hydrogen bond; HDAC, Histone deacetylases; HepG2, Human hepatocellular carcinoma cell line; HY, Hydrophobes; L02, Human normal liver cell line; MCF-10A, Human normal breast cell line; MDA-MB-231, Human breast cancer cell line; MTT, 3-(4, 5-dimethylthia-zol-2-yl)-2, 5-diphenyl tetrazolium bromide; PBS, Phosphate-buffered saline; PDB, Protein data bank; PI, Propidium iodide; SAHA, Suberoylanilide hydroxamic acid; TSA, Trichostatin A; ZBG, Zinc-binding group
This work was supported by the National Natural Science Foundation of China (no. 61172183), the Natural Science Foundation of Jilin Province, China (no. 20130101148JC), the Changchun Science and Technology Bureau, China (no. 12ZX55), Science and Technology Development Program of Jilin Province (No. 20150309003YY), Key Science and Technology Project of Jilin Province (20150224038YY), the 2014 Industrial Technology Research and Development Special Project of Jilin Province, China (no. 2014Y100), and the 2015 Department of Education 12th Five-Year Science and Technology Research Planning Projects of Jilin Province, China (no. 2014B053).
Availability of data and materials
Data used in this study are given as tables and additional files. Details of materials used are available in the reference list.
YXH and YXL conceived and designed the research. JZ, QHS, LHZ, CF and TTL performed the research including data collection, experiments and analysis. YLB, LGS and LBZ suggested extension and modifications to the research. YXH supervised the whole research and revised the manuscript critically. All authors have read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
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- Hassig CA, Schreiber SL. Nuclear histone acetylases and deacetylases and transcriptional regulation: HATs off to HDACs. Curr Opin Chem Biol. 1997;1(3):300–8.View ArticlePubMedGoogle Scholar
- Moggs JG, Goodman JI, Trosko JE, Roberts RA. Epigenetics and cancer: implications for drug discovery and safety assessment. Toxicol Appl Pharmacol. 2004;196(3):422–30.View ArticlePubMedGoogle Scholar
- Kouzarides T. Histone acetylases and deacetylases in cell proliferation. Curr Opin Genet Dev. 1999;9(1):40–8.View ArticlePubMedGoogle Scholar
- Struhl K. Histone acetylation and transcriptional regulatory mechanisms. Genes Dev. 1998;12(5):599–606.View ArticlePubMedGoogle Scholar
- Christensen DP, Dahllof M, Lundh M, Rasmussen DN, Nielsen MD, Billestrup N, Grunnet LG, Mandrup-Poulsen T. Histone deacetylase (HDAC) inhibition as a novel treatment for diabetes mellitus. Mol Med. 2011;17(5–6):378–90.PubMedPubMed CentralGoogle Scholar
- Mork CN, Faller DV, Spanjaard RA. A mechanistic approach to anticancer therapy: targeting the cell cycle with histone deacetylase inhibitors. Curr Pharm Des. 2005;11(9):1091–104.View ArticlePubMedGoogle Scholar
- Gregoretti IV, Lee YM, Goodson HV. Molecular evolution of the histone deacetylase family: functional implications of phylogenetic analysis. J Mol Biol. 2004;338(1):17–31.View ArticlePubMedGoogle Scholar
- Landry J, Sutton A, Tafrov ST, Heller RC, Stebbins J, Pillus L, Sternglanz R. The silencing protein SIR2 and its homologs are NAD-dependent protein deacetylases. Proc Natl Acad Sci U S A. 2000;97(11):5807–11.View ArticlePubMedPubMed CentralGoogle Scholar
- Abend A, Kehat I. Histone deacetylases as therapeutic targets--from cancer to cardiac disease. Pharmacol Ther. 2015;147:55-62.Google Scholar
- Rodriquez M, Aquino M, Bruno I, De Martino G, Taddei M, Gomez-Paloma L. Chemistry and biology of chromatin remodeling agents: state of art and future perspectives of HDAC inhibitors. Curr Med Chem. 2006;13(10):1119–39.View ArticlePubMedGoogle Scholar
- Bolden JE, Peart MJ, Johnstone RW. Anticancer activities of histone deacetylase inhibitors. Nat Rev Drug Discov. 2006;5(9):769–84.View ArticlePubMedGoogle Scholar
- Johnstone RW. Histone-deacetylase inhibitors: novel drugs for the treatment of cancer. Nat Rev Drug Discov. 2002;1(4):287–99.View ArticlePubMedGoogle Scholar
- Marks PA, Richon VM, Breslow R, Rifkind RA. Histone deacetylase inhibitors as new cancer drugs. Curr Opin Oncol. 2001;13(6):477–83.View ArticlePubMedGoogle Scholar
- Grant S, Easley C, Kirkpatrick P. Vorinostat. Nat Rev Drug Discov. 2007;6(1):21–2.View ArticlePubMedGoogle Scholar
- Piekarz RL, Frye R, Prince HM, Kirschbaum MH, Zain J, Allen SL, Jaffe ES, Ling A, Turner M, Peer CJ, et al. Phase 2 trial of romidepsin in patients with peripheral T-cell lymphoma. Blood. 2011;117(22):5827–34.View ArticlePubMedPubMed CentralGoogle Scholar
- Grant C, Rahman F, Piekarz R, Peer C, Frye R, Robey RW, Gardner ER, Figg WD, Bates SE. Romidepsin: a new therapy for cutaneous T-cell lymphoma and a potential therapy for solid tumors. Expert Rev Anticancer Ther. 2010;10(7):997–1008.View ArticlePubMedGoogle Scholar
- Falkenberg KJ, Johnstone RW. Histone deacetylases and their inhibitors in cancer, neurological diseases and immune disorders. Nat Rev Drug Discov. 2014;13(9):673–91.View ArticlePubMedGoogle Scholar
- Gryder BE, Sodji QH, Oyelere AK. Targeted cancer therapy: giving histone deacetylase inhibitors all they need to succeed. Future Med Chem. 2012;4(4):505–24.View ArticlePubMedPubMed CentralGoogle Scholar
- Mendivil AA, Micha JP, Brown 3rd JV, Rettenmaier MA, Abaid LN, Lopez KL, Goldstein BH. Increased incidence of severe gastrointestinal events with first-line paclitaxel, carboplatin, and vorinostat chemotherapy for advanced-stage epithelial ovarian, primary peritoneal, and fallopian tube cancer. Int J Gynecol Cancer. 2013;23(3):533–9.View ArticlePubMedGoogle Scholar
- Dizon DS, Damstrup L, Finkler NJ, Lassen U, Celano P, Glasspool R, Crowley E, Lichenstein HS, Knoblach P, Penson RT. Phase II activity of belinostat (PXD-101), carboplatin, and paclitaxel in women with previously treated ovarian cancer. Int J Gynecol Cancer. 2012;22(6):979–86.View ArticlePubMedGoogle Scholar
- Dizon DS, Blessing JA, Penson RT, Drake RD, Walker JL, Johnston CM, Disilvestro PA, Fader AN. A phase II evaluation of belinostat and carboplatin in the treatment of recurrent or persistent platinum-resistant ovarian, fallopian tube, or primary peritoneal carcinoma: a Gynecologic Oncology Group study. Gynecol Oncol. 2012;125(2):367–71.View ArticlePubMedPubMed CentralGoogle Scholar
- McLaughlin F, La Thangue NB. Histone deacetylase inhibitors open new doors in cancer therapy. Biochem Pharmacol. 2004;68(6):1139–44.View ArticlePubMedGoogle Scholar
- SYBYL-X2.0: http://www.tripos.com.
- GOLD5.2: http://www.ccdc.cam.ac.uk/pages/Home.aspx.
- Enamine dataset in the ZINC database. http://zincdockingorg/catalogs/enamine, (accessed 13 June 2014).Google Scholar
- Lavoie R, Bouchain G, Frechette S, Woo SH, Abou-Khalil E, Leit S, Fournel M, Yan PT, Trachy-Bourget MC, Beaulieu C, et al. Design and synthesis of a novel class of histone deacetylase inhibitors. Bioorg Med Chem Lett. 2001;11(21):2847–50.View ArticlePubMedGoogle Scholar
- Woo SH, Frechette S, Abou Khalil E, Bouchain G, Vaisburg A, Bernstein N, Moradei O, Leit S, Allan M, Fournel M, et al. Structurally simple trichostatin A-like straight chain hydroxamates as potent histone deacetylase inhibitors. J Med Chem. 2002;45(13):2877–85.View ArticlePubMedGoogle Scholar
- Estiu G, Greenberg E, Harrison CB, Kwiatkowski NP, Mazitschek R, Bradner JE, Wiest O. Structural origin of selectivity in class II-selective histone deacetylase inhibitors. J Med Chem. 2008;51(10):2898–906.View ArticlePubMedGoogle Scholar
- Kozikowski AP, Tapadar S, Luchini DN, Kim KH, Billadeau DD. Use of the nitrile oxide cycloaddition (NOC) reaction for molecular probe generation: a new class of enzyme selective histone deacetylase inhibitors (HDACIs) showing picomolar activity at HDAC6. J Med Chem. 2008;51(15):4370–3.View ArticlePubMedPubMed CentralGoogle Scholar
- Chen Y, Li H, Tang W, Zhu C, Jiang Y, Zou J, Yu Q, You Q. 3D-QSAR studies of HDACs inhibitors using pharmacophore-based alignment. Eur J Med Chem. 2009;44(7):2868–76.View ArticlePubMedGoogle Scholar
- Tang H, Wang XS, Huang XP, Roth BL, Butler KV, Kozikowski AP, Jung M, Tropsha A. Novel inhibitors of human histone deacetylase (HDAC) identified by QSAR modeling of known inhibitors, virtual screening, and experimental validation. J Chem Inf Model. 2009;49(2):461–76.View ArticlePubMedGoogle Scholar
- Xia J, Tilahun EL, Kebede EH, Reid TE, Zhang L, Wang XS. Comparative modeling and benchmarking data sets for human histone deacetylases and sirtuin families. J Chem Inf Model. 2015;55(2):374–88.View ArticlePubMedPubMed CentralGoogle Scholar
- Caballero J. 3D-QSAR (CoMFA and CoMSIA) and pharmacophore (GALAHAD) studies on the differential inhibition of aldose reductase by flavonoid compounds. J Mol Graph Model. 2010;29(3):363–71.View ArticlePubMedGoogle Scholar
- Liebeschuetz JW, Cole JC, Korb O. Pose prediction and virtual screening performance of GOLD scoring functions in a standardized test. J Comput Aided Mol Des. 2012;26(6):737–48.View ArticlePubMedGoogle Scholar
- Guan P, Sun F, Hou X, Wang F, Yi F, Xu W, Fang H. Design, synthesis and preliminary bioactivity studies of 1,3,4-thiadiazole hydroxamic acid derivatives as novel histone deacetylase inhibitors. Bioorg Med Chem. 2012;20(12):3865–72.View ArticlePubMedGoogle Scholar
- Zhang L, Wang X, Li X, Xu W. Discovery of a series of small molecules as potent histone deacetylase inhibitors. J Enzyme Inhib Med Chem. 2014;29(3):333–7.View ArticlePubMedGoogle Scholar
- Wang S, Li X, Wei Y, Xiu Z, Nishino N. Discovery of potent HDAC inhibitors based on chlamydocin with inhibitory effects on cell migration. ChemMedChem. 2014;9(3):627–37.View ArticlePubMedGoogle Scholar
- Price S, Bordogna W, Bull RJ, Clark DE, Crackett PH, Dyke HJ, Gill M, Harris NV, Gorski J, Lloyd J, et al. Identification and optimisation of a series of substituted 5-(1H-pyrazol-3-yl)-thiophene-2-hydroxamic acids as potent histone deacetylase (HDAC) inhibitors. Bioorg Med Chem Lett. 2007;17(2):370–5.View ArticlePubMedGoogle Scholar
- Guan P, Wang L, Hou X, Wan Y, Xu W, Tang W, Fang H. Improved antiproliferative activity of 1,3,4-thiadiazole-containing histone deacetylase (HDAC) inhibitors by introduction of the heteroaromatic surface recognition motif. Bioorg Med Chem. 2014;22(21):5766–75.View ArticlePubMedGoogle Scholar
- Moradei OM, Mallais TC, Frechette S, Paquin I, Tessier PE, Leit SM, Fournel M, Bonfils C, Trachy-Bourget MC, Liu J, et al. Novel aminophenyl benzamide-type histone deacetylase inhibitors with enhanced potency and selectivity. J Med Chem. 2007;50(23):5543–6.View ArticlePubMedGoogle Scholar
- Shultz MD, Cao X, Chen CH, Cho YS, Davis NR, Eckman J, Fan J, Fekete A, Firestone B, Flynn J, et al. Optimization of the in vitro cardiac safety of hydroxamate-based histone deacetylase inhibitors. J Med Chem. 2011;54(13):4752–72.View ArticlePubMedGoogle Scholar
- Park H, Kim S, Kim YE, Lim SJ. A structure-based virtual screening approach toward the discovery of histone deacetylase inhibitors: identification of promising zinc-chelating groups. ChemMedChem. 2010;5(4):591–7.View ArticlePubMedGoogle Scholar
- Zhang L, Li M, Feng J, Fang H, Xu W. Discovery of a novel histone deacetylase 8 inhibitor by virtual screening. Med Chem Res. 2012;21:152–6.View ArticleGoogle Scholar
- Vadivelan S, Sinha BN, Rambabu G, Boppana K, Jagarlapudi SA. Pharmacophore modeling and virtual screening studies to design some potential histone deacetylase inhibitors as new leads. J Mol Graph Model. 2008;26(6):935–46.View ArticlePubMedGoogle Scholar
- Melagraki G, Afantitis A, Sarimveis H, Koutentis PA, Kollias G, Igglessi-Markopoulou O. Predictive QSAR workflow for the in silico identification and screening of novel HDAC inhibitors. Mol Divers. 2009;13(3):301–11.View ArticlePubMedGoogle Scholar
- Xiang Y, Hou Z, Zhang Z. Pharmacophore and QSAR studies to design novel histone deacetylase 2 inhibitors. Chem Biol Drug Des. 2012;79(5):760–70.View ArticlePubMedGoogle Scholar
- Zhao L, Xiang Y, Song J, Zhang Z. A novel two-step QSAR modeling work flow to predict selectivity and activity of HDAC inhibitors. Bioorg Med Chem Lett. 2013;23(4):929–33.View ArticlePubMedGoogle Scholar
- Thangapandian S, John S, Sakkiah S, Lee KW. Docking-enabled pharmacophore model for histone deacetylase 8 inhibitors and its application in anti-cancer drug discovery. J Mol Graph Model. 2010;29(3):382–95.View ArticlePubMedGoogle Scholar
- Thangapandian S, John S, Lee Y, Kim S, Lee KW. Dynamic structure-based pharmacophore model development: a new and effective addition in the histone deacetylase 8 (HDAC8) inhibitor discovery. Int J Mol Sci. 2011;12(12):9440–62.View ArticlePubMedPubMed CentralGoogle Scholar
- Nair SB, Teli MK, Pradeep H, Rajanikant GK. Computational identification of novel histone deacetylase inhibitors by docking based QSAR. Comput Biol Med. 2012;42(6):697–705.View ArticlePubMedGoogle Scholar