Exploration of Novel Mono Hydroxamic Acid Derivatives as Inhibitors for Histone Deacetylase Like Protein (HDLP) by Molecular Dynamics Studies

Gunasingham Parthiban; Ramachandren Dushanan; Samantha Weerasinghe; Dhammike Dissanayake; Rajendram Senthilnithy

doi:10.22146/ijc.74167

Exploration of Novel Mono Hydroxamic Acid Derivatives as Inhibitors for Histone Deacetylase Like Protein (HDLP) by Molecular Dynamics Studies

https://doi.org/10.22146/ijc.74167

Gunasingham Parthiban⁽¹⁾, Ramachandren Dushanan⁽²⁾, Samantha Weerasinghe⁽³⁾, Dhammike Dissanayake⁽⁴⁾, Rajendram Senthilnithy^(5*)

(1) Department of Chemistry, Eastern University, Vantharumoolai 30376, Sri Lanka
(2) Department of Chemistry, The Open University of Sri Lanka, Nugegoda 10250, Sri Lanka
(3) Department of Chemistry, University of Colombo, Colombo 00300, Sri Lanka
(4) Department of Chemistry, University of Colombo, Colombo 00300, Sri Lanka
(5) Department of Chemistry, The Open University of Sri Lanka, Nugegoda 10250, Sri Lanka
(*) Corresponding Author

Abstract

The acetylation modification process of histone has an essential role in the epigenetic regulation of gene expression. This process is controlled by the balance between histone deacetylases (HDAC) and histone acetyltransferases (HAT). HDACs are thought to be vital for cell function. Particularly, higher HDAC expression is frequent in various cancers, resulting in the dysregulation of several target genes involved in cell proliferation, differentiation, and survival. In this study, the inhibitory feasibility of several HDAC inhibitors was investigated, including vorinostat (SAHA), N-hydroxy-3-phenylprop-2-enamide (CPD1), N-hydroxy-3-(pyridine-4-yl)prop-2-enamide (CPD2), N-hydroxy-3-(pyridine-2-yl)prop-2-enamide (CPD3), 4-(diphenylamino)-N-(5-(hydroxyamino)-5-oxopentyl)benzamide (CPD4), 2-(6-(((6-fluoronaphthalen-2-yl)methyl)amino)-3-azabicyclo[3.1.0]hex-3-yl)-N-hydroxypirimidine-5-carboxamide (CPD5), and N-(3-aminopropyl)-N-hydroxy-2-((naphthalene-1-yloxy)methyl)oct-2-enediamide (CPD6). By examining the stability of the enzyme, positional stability of the individual amino acids, and binding energies of HDLP-inhibitor complexes, the inhibitory feasibility was assessed. The complexes of the HDLP enzyme with SAHA, CPD4, CPD5, and CPD6 had higher stability than the other studied complexes, according to the results of trajectory analysis and the Ramachandran plot. Based on the calculated MM-PBSA binding free energies, the stability of the HDLP enzyme followed this order CPD4 > CPD5 > SAHA > CPD6 > CPD2 > CPD3 > CPD1. The drugability values followed the same trend as the previous ones. Based on the obtained in silico results, CPD4, CPD5, and CPD6 were discovered to be possible lead compounds as reference inhibitors of SAHA.

Keywords

epigenetic regulation; HDAC; MM-PBSA; Ramachandran plot; hydroxamic acid derivatives

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References

[1] Jacobs, L.A., and Shulman, L.N., 2017, Follow-up care of cancer survivors: Challenges and solutions, Lancet Oncol., 18 (1), e19–e29.

[2] Leeman, J.E., Romesser, P.B., Zhou, Y., McBride, S., Riaz, N., Sherman, E., Cohen, M.A., Cahlon, O., and Lee, N., 2017, Proton therapy for head and neck cancer: Expanding the therapeutic window, Lancet Oncol., 18 (5), e254–e265.

[3] Eckschlager, T., Plch, J., Stiborova, M., and Hrabeta, J., 2017, Histone deacetylase inhibitors as anticancer drugs, Int. J. Mol. Sci., 18 (7), 1414.

[4] Chrun, E.S., Modolo, F., and Daniel, F.I., 2017, Histone modifications: A review about the presence of this epigenetic phenomenon in carcinogenesis, Pathol., Res. Pract., 213 (11), 1329–1339.

[5] Cavalieri, V., 2021, The expanding constellation of histone post-translational modifications in the epigenetic landscape, Genes, 12 (10), 1596.

[6] Dushanan, R., Weerasinghe, S., Dissanayake, D., and Senthilnithy, R., 2022, Driving the new generation histone deacetylase inhibitors in cancer therapy; Manipulation of the histone abbreviation at the epigenetic level: An in-silico approach, Can. J. Chem., e-First.

[7] Dushanan, R., Weerasinghe, S., Dissanayake, D.P., and Senthilnithy, R., 2021, An in-silico approach to evaluate the inhibitory potency of selected hydroxamic acid derivatives on zinc-dependent histone deacetylase enzyme, J. Comput. Biophys. Chem., 20 (6), 603–618.

[8] Dushanan, R., Weerasinghe, S., Dissanayake, D.P., and Senthilnithy, R., 2022, Cracking a cancer code histone deacetylation in epigenetic: The implication from molecular dynamics simulations on efficacy assessment of histone deacetylase inhibitors, J. Biomol. Struct. Dyn., 40 (5), 2352–2368.

[9] Cappellacci, L., Perinelli, D.R., Maggi, F., Grifantini, M., and Petrelli, R., 2018, Recent progress in histone deacetylase inhibitors as anticancer agents, Curr. Med. Chem., 27 (15), 2449–2493.

[10] Seto, E., and Yoshida, M., 2014, Erasers of histone acetylation: The histone deacetylase enzymes, Cold Spring Harbor Perspect. Biol., 6 (4), a018713.

[11] Ganai, S.A., Shanmugam, K., and Mahadevan, V., 2015, Energy-optimised pharmacophore approach to identify potential hotspots during inhibition of class II HDAC isoforms, J. Biomol. Struct. Dyn., 33 (2), 374–387.

[12] Stiborová, M., Eckschlager, T., Poljaková, J., Hraběta, J., Adam, V., Kizek, R., and Frei, E., 2012, The synergistic effects of DNA-targeted chemotherapeutics and histone deacetylase inhibitors as therapeutic strategies for cancer treatment, Curr. Med. Chem., 19 (25), 4218–4238.

[13] Ibrahim Uba, A., and Yelekçi, K., 2019, Homology modeling of human histone deacetylase 10 and design of potential selective inhibitors, J. Biomol. Struct. Dyn., 37 (14), 3627–3636.

[14] Senthilnithy, R., Gunawardhana, H.D., De Costa, M.D.P., and Dissanayake, D.P., 2006, Absolute pK_a determination for N-phenylbenzohydroxamic acid derivatives, J. Mol. Struct.: THEOCHEM, 761 (1-3), 21–26.

[15] Senthilnithy, R., de Costa, M.D.P., and Gunawardhana, H.D., 2008, The pK_a values of ligands and stability constants of the complexes of Fe(III), Cu(II) and Ni(II) with some hydroxamic acids: A comparative study of three different potentiometric methods, J. Natl. Sci. Found. Sri Lanka, 36 (3), 191–198.

[16] Nielsen, T.K., Hildmann, C., Dickmanns, A., Schwienhorst, A., and Ficner, R., 2005, Crystal structure of a bacterial class 2 histone deacetylase homologue, J. Mol. Biol., 354 (1), 107–120.

[17] Yan, C., Xiu, Z., Li, X., Li, S., Hao, C., and Teng, H., 2008, Comparative molecular dynamics simulations of histone deacetylase-like protein: Binding modes and free energy analysis to hydroxamic acid inhibitors, Proteins: Struct., Funct., Bioinf., 73 (1), 134–149.

[18] Zhou, J., Xie, H., Liu, Z., Luo, H.B., and Wu, R., 2014, Structure-function analysis of the conserved tyrosine and diverse π-stacking among class I histone deacetylases: A QM (DFT)/MM MD study, J. Chem. Inf. Model., 54 (11), 3162–3171.

[19] Trejo-Muñoz, C.R., Vázquez-Ramírez, R., Mendoza, L., and Kubli-Garfias, C., 2018, Biophysical mechanism of the SAHA inhibition of Zn²⁺-histone deacetylase-like protein (FB188 HDAH) assessed via crystal structure analysis, Comput. Mol. Biosci., 8 (2), 91–114.

[20] Lu, W., Zhang, R., Jiang, H., Zhang, H., and Luo, C., 2018, Computer-aided drug design in epigenetics, Front. Chem., 6, 57.

[21] Kumari, R., Kumar, R., and Lynn, A., 2014, g-mmpbsa-A GROMACS tool for high-throughput MM-PBSA calculations, J. Chem. Inf. Model., 54 (7), 1951–1962.

[22] Goddard, T.D., Huang, C.C., and Ferrin, T.E., 2005, Software extensions to UCSF Chimera for interactive visualization of large molecular assemblies, Structure, 13 (3), 473–482.

[23] Frisch, M.J., Trucks, G.W., Schlegel, H.B., Scuseria, G.E., Robb, M.A., Cheeseman, J.R., Scalmani, G., Barone, V., Mennucci, B., Petersson, G.A., Nakatsuji, H., Caricato, M., Li, X., Hratchian, H.P., Izmaylov, A.F., Bloino, J., Zheng, G., Sonnenberg, J.L., Hada, M., Ehara, M., Toyota, K., Fukuda, R., Hasegawa, J., Ishida, M., Nakajima, T., Honda, Y., Kitao, O., Nakai, H., Vreven, T., Montgomery, Jr., J.A., Peralta, J.E., Ogliaro, F., Bearpark, M., Heyd, J.J., Brothers, E., Kudin, K.N., Staroverov, V.N., Kobayashi, R., Normand, J., Raghavachari, K., Rendell, A., Burant, J.C., Iyengar, S.S., Tomasi, J., Cossi, M., Rega, N., Millam, J.M., Klene, M., Knox, J.E., Cross, J.B., Bakken, V., Adamo, C., Jaramillo, J., Gomperts, R., Stratmann, R.E., Yazyev, O., Austin, A.J., Cammi, R., Pomelli, C., Ochterski, J.W., Martin, R.L., Morokuma, K., Zakrzewski, V.G., Voth, G.A., Salvador, P., Dannenberg, J.J., Dapprich, S., Daniels, A.D., Farkas, O., Foresman, J.B., Ortiz, J.V., Cioslowski, J., and Fox, D.J., 2009, Gaussian 09, Revision A.02, Gaussian, Inc., Wallingford CT.

[24] Hanwell, M.D., Curtis, D.E., Lonie, D.C., Vandermeersch, T., Zurek, E., and Hutchison, G.R., 2012, Avogadro: An advanced semantic chemical editor, visualization, and analysis platform, J. Cheminf., 4 (1), 17.

[25] Di Muzio, E., Toti, D., and Polticelli, F., 2017, DockingApp: A user friendly interface for facilitated docking simulations with AutoDock Vina, J. Comput.-Aided Mol. Des., 31 (2), 213–218.

[26] Therrien, E., Weill, N., Tomberg, A., Corbeil, C.R., Lee, D., and Moitessier, N., 2014, Docking ligands into flexible and solvated macromolecules. 7. Impact of protein flexibility and water molecules on docking-based virtual screening accuracy, J. Chem. Inf. Model., 54 (11), 3198–3210.

[27] Yuan, S., Chan, H.C.S., and Hu, Z., 2017, Using PyMOL as a platform for computational drug design, WIREs Comput. Mol. Sci., 7 (2), e1298.

[28] Raj, S., Sasidharan, S., Dubey, V.K., and Saudagar, P., 2019, Identification of lead molecules against potential drug target protein MAPK4 from L. donovani: An in-silico approach using docking, molecular dynamics and binding free energy calculation, PLoS One, 14 (8), e0221331.

[29] Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M.R., Smith, J.C., Kasson, P.M., van der Spoel, D., Hess, B., and Lindahl, E., 2013, GROMACS 4.5: A high-throughput and highly parallel open source molecular simulation toolkit, Bioinformatics, 29 (7), 845–854.

[30] Chen, P., Nishiyama, Y., and Mazeau, K., 2014, Atomic partial charges and one Lennard-Jones parameter crucial to model cellulose allomorphs, Cellulose, 21 (4), 2207–2217.

[31] Yousefpour, A., Amjad Iranagh, S., Nademi, Y., and Modarress, H., 2013, Molecular dynamics simulation of nonsteroidal antiinflammatory drugs, naproxen and relafen, in a lipid bilayer membrane, Int. J. Quantum Chem., 113 (15), 1919–1930.

[32] Tummanapelli, A.K., and Vasudevan, S., 2015, Ab initio molecular dynamics simulations of amino acids in aqueous solutions: Estimating pK_a values from metadynamics sampling, J. Phys. Chem. B, 119 (37), 12249–12255.

[33] Prakash, M., Lemaire, T., Di Tommaso, D., de Leeuw, N., Lewerenz, M., Caruel, M., and Naili, S., 2017, Transport properties of water molecules confined between hydroxyapaptite surfaces: A Molecular dynamics simulation approach, Appl. Surf. Sci., 418, 296–301.

[34] Huang, Y., Chen, W., Wallace, J.A., and Shen, J., 2016, All-atom continuous constant pH molecular dynamics with particle mesh ewald and titratable water, J. Chem. Theory Comput., 12 (11), 5411–5421.

[35] Abraham, M.J., Murtola, T., Schulz, R., Páll, S., Smith, J.C., Hess, B., and Lindah, E., 2015, GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers, SoftwareX, 1-2, 19–25.

[36] Anbarasu, K., and Jayanthi, S., 2018, Identification of curcumin derivatives as human LMTK₃ inhibitors for breast cancer: A docking, dynamics, and MM/PBSA approach, 3 Biotech, 8 (5), 228.

[37] Serhan, M., Jackemeyer, D., Long, M., Sprowls, M., Diez Perez, I., Maret, W., Chen, F., Tao, N., and Forzani, E., 2020, Total iron measurement in human serum with a smartphone-based assay, IEEE J. Transl. Eng. Health Med., 8, 1–9.

[38] Bernstein, H.J., 2000, Recent changes to RasMol, recombining the variants, Trends Biochem. Sci., 25 (9), 453–455.

[39] Wang, E., Sun, H., Wang, J., Wang, Z., Liu, H., Zhang, J.Z.H., and Hou, T., 2019, End-point binding free energy calculation with MM/PBSA and MM/GBSA: Strategies and applications in drug design, Chem. Rev., 119 (16), 9478–9508.

[40] Lagorce, D., Bouslama, L., Becot, J., Miteva, M.A., and Villoutreix, B.O., 2017, FAF-Drugs4: Free ADME-tox filtering computations for chemical biology and early stages drug discovery, Bioinformatics, 33 (22), 3658–3660.

[41] Paligaspe, P., Weerasinghe, S., Dissanayake, D.P., and Senthilnithy, R., 2021, Impact of Cd(II) on the stability of human uracil DNA glycosylase enzyme; An implication of molecular dynamics trajectories on stability analysis, J. Biomol. Struct. Dyn., 0 (0), 1–8.

[42] Heinig, M., and Frishman, D., 2004, STRIDE: A web server for secondary structure assignment from known atomic coordinates of proteins, Nucleic Acids Res., 32 (Suppl. 2), W500–W502.

[43] Fährrolfes, R., Bietz, S., Flachsenberg, F., Meyder, A., Nittinger, E., Otto, T., Volkamer, A., and Rarey, M., 2017, ProteinsPlus: A web portal for structure analysis of macromolecules, Nucleic Acids Res., 45 (W1), W337–W343.

[44] Sowmya, H., 2019, A comparative study of homology modeling algorithms for NPTX2 structure prediction, Res. J. Pharm. Technol., 12 (4), 1895–1900.

[45] Agoni, C., Ramharack, P., and Soliman, M.E.S., 2018, Co-inhibition as a strategic therapeutic approach to overcome rifampin resistance in tuberculosis therapy: Atomistic insights, Future Med. Chem., 10 (14), 1665–1675.

[46] Paligaspe, P., Weerasinghe, S., Dissanayake, D.P., and Senthilnithy, R., 2021, Identify the effect of As(III) on the structural stability of monomeric PKM2 and its carcinogenicity: A molecular dynamics and QM/MM based approach, J. Mol. Struct., 1235, 130257.

[47] Dushanan, R., Samantha, W., Dhammike, D., and Senthilnithy, R., 2022, Implication of Ab Initio, QM/MM, and molecular dynamics calculations on the prediction of the therapeutic potential of some selected HDAC inhibitors, Mol. Simul., 48 (16), 1464–1475.

DOI: https://doi.org/10.22146/ijc.74167