Docking-enabled pharmacophore model for histone deacetylase 8 inhibitors and its application in anti-cancer drug discovery

Thangapandian, Sundarapandian; Strouboulis, John; Sakkiah, Sugunadevi; Woo, Lee Keun

doi:10.1016/j.jmgm.2010.07.007

Cited by 59 publications

(33 citation statements)

References 59 publications

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“…Our pharmacophore model ADDRR.20 was also in alignment with the previous developed models toward histone de-acetylases (HDAC's) with known hydroxamic acids and cyclic peptides having four pharmacophore features i.e., one hydrogen bond acceptor, one hydrophobic group, and two aromatic rings (Vyas et al, 2016) and also with known hydroxamic acids, benzamides, and biphenyl derivatives on HDAC8 (Vadivelan et al, 2008) having three pharmacophore features i.e., hydrogen bond acceptors, hydrogen bond donors, and hydrophobic aromatic ring. Thus, our developed pharmacophore model with known hydroxamide derivatives toward HDAC4 having five pharmacophore features i.e., one hydrogen acceptor (HBA), two hydrogen donor (HBD), and two aromatic rings (RA) correlates with the previous studies (Thangapandian et al, 2010a). The pharmacophore model ADDRR.20 comprises of features for inhibitor-protein interaction, crucial for its binding and biological activity which is evident through its binding mode and pharmacophore overlay of the selected molecules (SEI and ACI), validating the significance of its features and the model.…”

Section: Discussionmentioning

confidence: 63%

Mechanistic Insights into the Binding of Class IIa HDAC Inhibitors toward Spinocerebellar Ataxia Type-2: A 3D-QSAR and Pharmacophore Modeling Approach

et al. 2017

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Spinocerebellar ataxia (SCA-2) type-2 is a rare neurological disorder among the nine polyglutamine disorders, mainly caused by polyQ (CAG) trinucleotide repeats expansion within gene coding ataxin-2 protein. The expanded trinucleotide repeats within the ataxin-2 protein sequesters transcriptional cofactors i.e., CREB-binding protein (CBP), Ataxin-2 binding protein 1 (A2BP1) leading to a state of hypo-acetylation and transcriptional repression. Histone de-acetylases inhibitors (HDACi) have been reported to restore transcriptional balance through inhibition of class IIa HDAC's, that leads to an increased acetylation and transcription as demonstrated through in-vivo studies on mouse models of Huntington's. In this study, 61 di-aryl cyclo-propanehydroxamic acid derivatives were used for developing three dimensional (3D) QSAR and pharmacophore models. These models were then employed for screening and selection of anti-ataxia compounds. The chosen QSAR model was observed to be statistically robust with correlation coefficient (r2) value of 0.6774, cross-validated correlation coefficient (q2) of 0.6157 and co-relation coefficient for external test set (pred_r2) of 0.7570. A high F-test value of 77.7093 signified the robustness of the model. Two potential drug leads ZINC 00608101 (SEI) and ZINC 00329110 (ACI) were selected after a coalesce procedure of pharmacophore based screening using the pharmacophore model ADDRR.20 and structural analysis using molecular docking and dynamics simulations. The pharmacophore and the 3D-QSAR model generated were further validated for their screening and prediction ability using the enrichment factor (EF), goodness of hit (GH), and receiver operating characteristics (ROC) curve analysis. The compounds SEI and ACI exhibited a docking score of −10.097 and −9.182 kcal/mol, respectively. An evaluation of binding conformation of ligand-bound protein complexes was performed with MD simulations for a time period of 30 ns along with free energy binding calculations using the g_mmpbsa technique. Prediction of inhibitory activities of the two lead compounds SEI (7.53) and ACI (6.84) using the 3D-QSAR model reaffirmed their inhibitory characteristics as potential anti-ataxia compounds.

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Section: Discussionmentioning

confidence: 63%

Mechanistic Insights into the Binding of Class IIa HDAC Inhibitors toward Spinocerebellar Ataxia Type-2: A 3D-QSAR and Pharmacophore Modeling Approach

et al. 2017

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“…We performed atom-based 3D-QSAR, as it takes into account the entire molecular structure [23][24][25][26] . We evaluated the best scoring hypothesis [27,28] by generating training and test sets using K-means clustering [29,30] . The Canvas 1.2 module of Schrödinger was used for this purpose.…”

Section: Computational Detailsmentioning

confidence: 99%

Discovery of Some Potent PDHK Inhibitors Using Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies

Azad

Bhandari

Kakkar

2016

Journal of Biochemistry and Molecular Biology Research

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“…The mechanism behind controlling the gene transcription is the extent of histone-DNA binding which is the effect of exposure of the positive charge of lysine residues on deacetylation. In contrast, HATs loosen the histone-DNA binding by acetylating the positively charged lysine residues back to their acetylated form and leads to the transcriptional activation [7]. The transcriptional repression of various pre-programmed set of genes including tumor suppressor gene leads to cancer.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Structure-Based Pharmacophore Model Development: A New and Effective Addition in the Histone Deacetylase 8 (HDAC8) Inhibitor Discovery

Thangapandian

Strouboulis

Lee

et al. 2011

IJMS

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Histone deacetylase 8 (HDAC8) is an enzyme involved in deacetylating the amino groups of terminal lysine residues, thereby repressing the transcription of various genes including tumor suppressor gene. The over expression of HDAC8 was observed in many cancers and thus inhibition of this enzyme has emerged as an efficient cancer therapeutic strategy. In an effort to facilitate the future discovery of HDAC8 inhibitors, we developed two pharmacophore models containing six and five pharmacophoric features, respectively, using the representative structures from two molecular dynamic (MD) simulations performed in Gromacs 4.0.5 package. Various analyses of trajectories obtained from MD simulations have displayed the changes upon inhibitor binding. Thus utilization of the dynamically-responded protein structures in pharmacophore development has the added advantage of considering the conformational flexibility of protein. The MD trajectories were clustered based on single-linkage method and representative structures were taken to be used in the pharmacophore model development. Active site complimenting structure-based pharmacophore models were developed using Discovery Studio 2.5 program and validated using a dataset of known HDAC8 inhibitors. Virtual screening of chemical database coupled with drug-like filter has identified drug-like hit compounds that match the pharmacophore models. Molecular docking of these hits reduced the false positives and identified two potential compounds to be used in future HDAC8 inhibitor design.

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Docking-enabled pharmacophore model for histone deacetylase 8 inhibitors and its application in anti-cancer drug discovery

Cited by 59 publications

References 59 publications

Mechanistic Insights into the Binding of Class IIa HDAC Inhibitors toward Spinocerebellar Ataxia Type-2: A 3D-QSAR and Pharmacophore Modeling Approach

Mechanistic Insights into the Binding of Class IIa HDAC Inhibitors toward Spinocerebellar Ataxia Type-2: A 3D-QSAR and Pharmacophore Modeling Approach

Discovery of Some Potent PDHK Inhibitors Using Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies

Dynamic Structure-Based Pharmacophore Model Development: A New and Effective Addition in the Histone Deacetylase 8 (HDAC8) Inhibitor Discovery

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