Identification of Hydroxamic Acid Based Selective HDAC1 Inhibitors: Computer Aided Drug Design Studies

Author(s): Preeti Patel, Vijay K. Patel, Avineesh Singh, Talha Jawaid, Mehnaz Kamal, Harish Rajak*.

Journal Name: Current Computer-Aided Drug Design

Volume 15 , Issue 2 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


Background: Overexpression of Histone deacetylase 1 (HDAC1) is responsible for carcinogenesis by promoting epigenetic silence of tumour suppressor genes. Thus, HDAC1 inhibitors have emerged as the potential therapeutic leads against multiple human cancers, as they can block the activity of particular HDACs, renovate the expression of several tumour suppressor genes and bring about cell differentiation, cell cycle arrest and apoptosis.

Methods: The present research work comprises atom-based 3D-QSAR, docking, molecular dynamic simulations and DFT (density functional theory) studies on a diverse series of hydroxamic acid derivatives as selective HDAC1 inhibitors. Two pharmacophoric models were generated and validated by calculating the enrichment factors with the help of the decoy set. The Four different 3D-QSAR models i.e., PLS (partial least square) model, MLR (multiple linear regression) model, Field-based model and GFA (Genetic function approximation) model were developed using ‘PHASE’ v3.4 (Schrödinger) and Discovery Studio (DS) 4.1 software and validated using different statistical parameters like internal and external validation.

Results and Discussion: The results showed that the best PLS model has R2=0.991 and Q2=0.787, the best MLR model has R2= 0.993 and Q2= 0.893, the best Field-based model has R2= 0.974 and Q2= 0.782 and the best GFA model has R2= 0.868 and Q2= 0.782. Cross-validated coefficients, (rcv 2) of 0.967, 0.926, 0.966 and 0.829 was found for PLS model, MLR, Field based and GFA model, respectively, indicated the satisfactory correlativity and prediction. The docking studies were accomplished to find out the conformations of the molecules and their essential binding interactions with the target protein. The trustworthiness of the docking results was further confirmed by molecular dynamics (MD) simulations studies. Density Functional Theory (DFT) study was performed which promptly optimizes the geometry, stability and reactivity of the molecule during receptor-ligand interaction.

Conclusion: Thus, the present research work provides spatial fingerprints which would be beneficial for the development of potent HDAC1 inhibitors.

Keywords: HDAC1, 3D-QSAR, docking, molecular dynamic simulations, MM-GBSA, DFT.

Kinzler, K.W.; Vogelstein, B. Cancer-susceptibility genes. Gatekeepers and caretakers. Nature, 1997, 386(6627), 761-763.
Suzuki, T.; Miyata, N. Non-hydroxamate histone deacetylase inhibitors. Curr. Med. Chem., 2005, 12(24), 2867-2880.
Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer satistics, 2017. CA Cancer J. Clin., 2017, 67(1), 7-30.
Strahl, B.D.; Allis, C.D. The language of covalent histone modifications. Nature, 2000, 403(6765), 41-45.
Rajak, H.; Singh, A.; Raghuwanshi, K.; Kumar, R.; Dewangan, P.K.; Veerasamy, R.; Sharma, P.C.; Dixit, A.; Mishra, P. A structural insight into hydroxamic acid based histone deacetylase inhibitors for the presence of anticancer activity. Curr. Med. Chem., 2014, 21(23), 2642-2664.
Singh, A.; Patel, P.; Patel, V.K.; Jain, D.K.; Veerasamy, R.; Sharma, P.C.; Rajak, H. Histone deacetylase inhibitors for the treatment of colorectal cancer: Recent progress and future prospects. Curr. Cancer Drug Targets, 2017, 17(5), 456-466.
Roth, S.Y.; Denu, J.M.; Allis, C.D. Histone acetyltransferases. Annu. Rev. Biochem., 2001, 70, 81-120.
Thiagalingam, S.; Cheng, K.H.; Lee, H.J.; Mineva, N.; Thiagalingam, A.; Ponte, J.F. Histone deacetylases: Unique players in shaping the epigenetic histone code. Ann. N. Y. Acad. Sci., 2003, 983, 84-100.
Stimson, L.; La Thangue, N.B. Biomarkers for predicting clinical responses to HDAC inhibitors. Cancer Lett., 2009, 280(2), 177-183.
Gray, S.G.; Ekstrom, T.J. The human histone deacetylase family. Exp. Cell Res., 2001, 262(2), 75-83.
Minucci, S.; Pelicci, P.G. Histone deacetylase inhibitors and the promise of epigenetic (and more) treatments for cancer. Nat. Rev. Cancer, 2006, 6(1), 38-51.
Brunmeir, R.; Lagger, S.; Seiser, C. Histone deacetylase HDAC1/HDAC2-controlled embryonic development and cell differentiation. Int. J. Dev. Biol., 2009, 53(2-3), 275-289.
Gallinari, P.; Di Marco, S.; Jones, P.; Pallaoro, M.; Steinkuhler, C. HDACs, histone deacetylation and gene transcription: From molecular biology to cancer therapeutics. Cell Res., 2007, 17(3), 195-211.
Spiegel, S.; Milstien, S.; Grant, S. Endogenous modulators and pharmacological inhibitors of histone deacetylases in cancer therapy. Oncogene, 2012, 31(5), 537-551.
Frikeche, J.; Peric, Z.; Brissot, E.; Gregoire, M.; Gaugler, B.; Mohty, M. Impact of HDAC inhibitors on dendritic cell functions. Exp. Hematol., 2012, 40(10), 783-791.
Muller, B.M.; Jana, L.; Kasajima, A.; Lehmann, A.; Prinzler, J.; Budczies, J.; Winzer, K.J.; Dietel, M.; Weichert, W.; Denkert, C. Differential expression of histone deacetylases HDAC1, 2 and 3 in human breast cancer overexpression of HDAC2 and HDAC3 is associated with clinicopathological indicators of disease progression. BMC Cancer, 2013, 13, 215.
Barneda-Zahonero, B.; Parra, M. Histone deacetylases and cancer. Mol. Oncol., 2012, 6(6), 579-589.
Dokmanovic, M.; Clarke, C.; Marks, P.A. Histone deacetylase inhibitors: Overview and perspectives. Mol. Cancer Res., 2007, 5(10), 981-989.
Kozikowski, A.P.; Chen, Y.; Gaysin, A.; Chen, B.; D’Annibale, M.A.; Suto, C.M.; Langley, B.C. Functional differences in epigenetic modulators-superiority of mercaptoacetamide-based histone deacetylase inhibitors relative to hydroxamates in cortical neuron neuroprotection studies. J. Med. Chem., 2007, 50(13), 3054-3061.
Schafer, S.; Saunders, L.; Eliseeva, E.; Velena, A.; Jung, M.; Schwienhorst, A.; Strasser, A.; Dickmanns, A.; Ficner, R.; Schlimme, S.; Sippl, W.; Verdin, E.; Jung, M. Phenylalanine-containing hydroxamic acids as selective inhibitors of class IIb histone deacetylases (HDACs). Bioorg. Med. Chem., 2008, 16(4), 2011-2033.
Gryder, B.E.; Sodji, Q.H.; Oyelere, A.K. Targeted cancer therapy: Giving histone deacetylase inhibitors all they need to succeed. Future Med. Chem., 2012, 4(4), 505-524.
Lindsley, C.W. Novel drug approvals in 2015 and thus far in 2016. ACS Chem. Neurosci., 2016, 7(9), 1175-1176.
Mailankody, S.; Prasad, V. Five years of cancer drug approvals: Innovation, efficacy, and costs. JAMA Oncol., 2015, 1(4), 539-540.
Singh, A.; Patel, V.K.; Jain, D.K.; Patel, P.; Rajak, H. Panobinostat as Pan-deacetylase inhibitor for the treatment of pancreatic cancer: Recent progress and future prospects. Oncol. Ther., 2016, 4(1), 73-89.
Mottamal, M.; Zheng, S.; Huang, T.L.; Wang, G. Histone deacetylase inhibitors in clinical studies as templates for new anticancer agents. Molecules, 2015, 20(3), 3898-3941.
Wu, S.; Qi, W.; Su, R.; Li, T.; Lu, D.; He, Z. CoMFA and CoMSIA analysis of ACE-inhibitory, antimicrobial and bitter-tasting peptides. Eur. J. Med. Chem., 2014, 84, 100-106.
Nair, S.B.; Teli, M.K.; Pradeep, H.; Rajanikant, G.K. Computational identification of novel histone deacetylase inhibitors by docking based QSAR. Comput. Biol. Med., 2012, 42(6), 697-705.
Cheng, J.; Qin, J.; Guo, S.; Qiu, H.; Zhong, Y. Design, synthesis and evaluation of novel HDAC inhibitors as potential antitumor agents. Bioorg. Med. Chem. Lett., 2014, 24(19), 4768-4772.
Yang, W.; Li, L.; Ji, X.; Wu, X.; Su, M.; Sheng, L.; Zang, Y.; Li, J.; Liu, H. Design, synthesis and biological evaluation of 4-anilinothieno [2,3-d]pyrimidine-based hydroxamic acid derivatives as novel histone deacetylase inhibitors. Bioorg. Med. Chem. Lett., 2014, 22(21), 6146-6155.
Yao, Y.; Liao, C.; Li, Z.; Wang, Z.; Sun, Q.; Liu, C.; Yang, Y.; Tu, Z.; Jiang, S. Design, synthesis, and biological evaluation of 1, 3-disubstituted-pyrazole derivatives as new class I and IIb histone deacetylase inhibitors. Eur. J. Med. Chem., 2014, 86, 639-652.
Su, H.; Nebbioso, A.; Carafa, V.; Chen, Y.; Yang, B.; Altucci, L.; You, Q. Design, synthesis and biological evaluation of novel compounds with conjugated structure as anti-tumor agents. Bioorg. Med. Chem., 2008, 16(17), 7992-8002.
Patel, V.K.; Singh, A.; Jain, D.K.; Patel, P.; Veerasamy, R.; Sharma, P.C.; Rajak, H. Combretastatin A-4 based thiophene derivatives as antitumor agent: Development of structure activity correlation model using 3D-QSAR, pharmacophore and docking studies. Future. J. Pharm. Sci., 2017, 3(2), 71-78.
Jin, Y.; Qi, P.; Wang, Z.; Shen, Q.; Wang, J.; Zhang, W.; Song, H. 3D-QSAR study of combretastatin A-4 analogs based on molecular docking. Molecules, 2011, 16(8), 6684-6700.
Watts, K.S.; Dalal, P.; Murphy, R.B.; Sherman, W.; Friesner, R.A.; Shelley, J.C. ConfGen: A conformational search method for efficient generation of bioactive conformers. J. Chem. Inf. Model., 2010, 50(4), 534-546.
Sallam, A.A.; Houssen, W.E.; Gissendanner, C.R.; Orabi, K.Y.; Foudah, A.I.; El Sayed, K.A. Bioguided discovery and pharmacophore modeling of the mycotoxic indole diterpene alkaloids penitrems as breast cancer proliferation, migration, and invasion inhibitors. MedChemComm, 2013, 4(10)
Teli, M.K.; Rajanikant, G.K. Pharmacophore generation and atom-based 3D-QSAR of novel quinoline-3-carbonitrile derivatives as Tpl2 kinase inhibitors. J. Enzyme Inhib. Med. Chem., 2012, 27(4), 558-570.
Dixon, S.L.; Duan, J.; Smith, E.; Von Bargen, C.D.; Sherman, W.; Repasky, M.P. AutoQSAR: An automated machine learning tool for best-practice quantitative structure-activity relationship modeling. Future Med. Chem., 2016, 8(15), 1825-1839.
Berk, R. The formalities of multiple regression; SAGE Publications Ltd: London, 2003, pp. 103-110.
Rogers, D.; Hopfinger, A.J. Application of genetic function approximation to quantitative structure-activity relationships and quantitative structure-property relationships. J. Chem. Inf. Comput. Sci., 1994, 34(4), 854-866.
Tropsha, A.; Gramatica, P.; Gombar, V.K. The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb. Sci., 2003, 22(1), 69-77.
Roy, P.P.; Roy, K. On some aspects of variable selection for partial least squares regression models. QSAR Comb. Sci., 2008, 27(3), 302-313.
Veerasamy, R.; Rajak, H.; Jain, A.; Sivadasan, S.; Varghese, C.P.; Agrawal, R.K. Validation of QSAR models-strategies and importance. Int. J. Drug Des. Discov., 2011, 3, 511-519.
Sharma, M.K.; Murumkar, P.R.; Kuang, G.; Tang, Y.; Yadav, M.R. Identifying the structural features and diversifying the chemical domain of peripherally acting CB1 receptor antagonists using molecular modeling techniques. RSC Advances, 2016, 6(2), 1466-1483.
Ojha, P.K.; Mitra, I.; Das, R.N.; Roy, K. Further exploring r m 2 metrics for validation of QSPR models. Chemom. Intell. Lab. Syst., 2011, 107(1), 194-205.
Patel, P.; Singh, A.; Patel, V.K.; Jain, D.K.; Veerasamy, R.; Rajak, H. Pharmacophore Based 3D-QSAR, virtual screening and docking studies on novel series of HDAC inhibitors with thiophen linker as anticancer agents. Comb. Chem. High Throughput Screen., 2016, 19(9), 735-751.
Li, X.; Li, Y.; Cheng, T.; Liu, Z.; Wang, R. Evaluation of the performance of four molecular docking programs on a diverse set of protein-ligand complexes. J. Comput. Chem., 2010, 31(11), 2109-2125.
Wang, Z.; Sun, H.; Yao, X.; Li, D.; Xu, L.; Li, Y.; Tian, S.; Hou, T. Comprehensive evaluation of ten docking programs on a diverse set of protein-ligand complexes: The prediction accuracy of sampling power and scoring power. Phys. Chem. Chem. Phys., 2016, 18(18), 12964-12975.
Rajamanikandan, S.; Srinivasan, P. Pharmacophore modeling and structure-based virtual screening to identify potent inhibitors targeting LuxP of Vibrio harveyi. J. Recept. Signal Transduct. Res., 2016, 36(6), 617-632.
Govind, N.; Petersen, M.; Fitzgerald, G.; King-Smith, D.; Andzelm, J. A generalized synchronous transit method for transition state location. Comput. Mater. Sci., 2003, 28(2), 250-258.
Truchon, J.F.; Bayly, C.I. Evaluating virtual screening methods: Good and bad metrics for the “early recognition” problem. J. Chem. Inf. Model., 2007, 47(2), 488-508.
Xu, L.; Sun, H.; Li, Y.; Wang, J.; Hou, T. Assessing the performance of MM/PBSA and MM/GBSA methods. 3. The impact of force fields and ligand charge models. J. Phys. Chem. B, 2013, 117(28), 8408-8421.
Sun, H.; Li, Y.; Shen, M.; Tian, S.; Xu, L.; Pan, P.; Guan, Y.; Hou, T. Assessing the performance of MM/PBSA and MM/GBSA methods. 5. Improved docking performance using high solute dielectric constant MM/GBSA and MM/PBSA rescoring. Phys. Chem. Chem. Phys., 2014, 16(40), 22035-22045.
Sun, H.; Li, Y.; Tian, S.; Xu, L.; Hou, T. Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Phys. Chem. Chem. Phys., 2014, 16(31), 16719-16729.
Chen, F.; Liu, H.; Sun, H.; Pan, P.; Li, Y.; Li, D.; Hou, T. Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein-protein binding free energies and re-rank binding poses generated by protein-protein docking. Phys. Chem. Chem. Phys., 2016, 18(32), 22129-22139.
Tripathi, S.K.; Selvaraj, C.; Singh, S.K.; Reddy, K.K. Molecular docking, QPLD, and ADME prediction studies on HIV-1 integrase leads. Med. Chem. Res., 2012, 21(12), 4239-4251.
Kroemer, R.T.; Vulpetti, A.; McDonald, J.J.; Rohrer, D.C.; Trosset, J-Y.; Giordanetto, F.; Cotesta, S.; McMartin, C.; Kihlén, M.; Stouten, P.F. Assessment of docking poses: Interactions-based accuracy classification (IBAC) versus crystal structure deviations. J. Chem. Inf. Comput. Sci., 2004, 44(3), 871-881.
Sakkiah, S.; Arooj, M.; Kumar, M.R.; Eom, S.H.; Lee, K.W. Identification of inhibitor binding site in human sirtuin 2 using molecular docking and dynamics simulations. PLoS One, 2013, 8(1), e51429.
Kaufman, J.J. Quantum chemical and physicochemical influences on structure-activity relations and drug design. Int. J. Quantum Chem., 1979, 16(2), 221-241.
Luque, F.J.; López, J.M.; Orozco, M. Perspective on electrostatic interactions of a solute with a continuum. A direct utilization of ab initio molecular potentials for the prevision of solvent effects. In: Theoretical Chemistry Accounts; Springer, 2000; pp. 343-345.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2019
Page: [145 - 166]
Pages: 22
DOI: 10.2174/1573409914666180502113135
Price: $65

Article Metrics

PDF: 45