Abstract
3D-QSAR models were established by collecting 46 multivariate-substituted 4-oxyquinazoline HDAC6 inhibitors. The relationship of molecular structure and inhibitory activity was studied by comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA). The results showed the models established by CoMFA (q2 = 0.590, r2 = 0.965) and CoMSIA (q2 = 0.594, r2 = 0.931) had good prediction ability. At the same time, 3D-QSAR models met the internal verification, external verification and AD test. Ten new compounds were designed based on CoMFA and CoMSIA contour maps and their pharmacokinetic/toxic properties (ADME/T) were evaluated. It was found that most compounds have well safety profile and pharmacokinetic property. Then, we explored the interaction between HDAC6 and compounds by molecular docking. The results showed that the binding mode of the new compounds with HDAC6 was the same as the template compound 46, and the hydrogen bond and hydrophobic bond played a vital role in the binding process. Molecular dynamics simulation results showed that residues Ser531, His574 and Tyr745 played key roles in the binding process. All newly designed compounds had lower energy gap and binding energy than compound 46 according to DFT analysis and free energy analysis. This study provided a theoretical reference for designing compounds of higher activity and a new idea for the development of novel HDAC6 inhibitors.
Graphical abstract
Similar content being viewed by others
References
West AC, Johnstone RW (2014) New and emerging HDAC inhibitors for cancer treatment. J Clin Invest 124:30–39. https://doi.org/10.1172/JCI69738
Oyelere AK, Chen PC, Guerrant W et al (2009) Non-peptide macrocyclic histone deacetylase inhibitors. J Med Chem 52:456–468. https://doi.org/10.1021/jm801128g
Heckman CA, Duan H, Boxer LM (2013) Histone deacetylase inhibitors down-regulate bcl-2 expression and induce apoptosis in t(14;18) lymphomas. Mol Cell Biol 25(5):1608–1619. https://doi.org/10.1128/MCB.25.5.1608-1619.2005
Luo JY, Su F, Chen D et al (2000) Deacetylation of p53 modulates its effect on cell growth and apoptosis. Nature 408:377–381. https://doi.org/10.1038/35042612
Minucci S, Nervi C, Coco FL et al (2001) Histone deacetylases: a common molecular target for differentiation treatment of acute myeloid leukemias? Oncogene 20:3110. https://doi.org/10.1038/sj.onc.1204336
Young PE, Youngwoo W, Jin KS et al (2016) Anticancer effects of a new SIRT inhibitor, MHY2256, against human breast cancer MCF-7 cells via regulation of MDM2-p53 binding. Int J Biol Sci 12:1555–1567. https://doi.org/10.7150/ijbs.13833
Pengyu H, Ingrid AP, Matthew J et al (2017) Selective HDAC inhibition by ACY-241 enhances the activity of paclitaxel in solid tumor models. Oncotarget 8:2694. https://doi.org/10.1158/1535-7163.TARG-15-A187
Salerno S, Settimo D, Taliani S et al (2010) Recent advances in the development of dual topoisomerase I and II inhibitors as anticancer drugs. Curr Med Chem 17:4270–4290. https://doi.org/10.2174/092986710793361252
Nitiss LJ (2009) DNA topoisomerase II and its growing repertoire of biological functions. Nat Rev Cancer 9:327–337. https://doi.org/10.1038/nrc2608
Champoux JJ (2001) DNA topoisomerase I-mediated nicking of circular duplex DNA. Methods Mol Biol 95:81–87. https://doi.org/10.1385/1-59259-057-8:81
Hideshima T, Qi J, Paranal RM et al (2016) Discovery of selective small-molecule HDAC6 inhibitor for overcoming proteasome inhibitor resistance in multiple myeloma. Proc Natl Acad Sci USA 113:13162–13167. https://doi.org/10.1073/pnas.1608067113
LL Zhang, A Ogden, R Aneja et al (2016) Diverse roles of HDAC6 in viral infection: implications for antiviral therapy. Pharmacol Therapeut 164:120–125. https://doi.org/10.1016/j.pharmthera.2016.04.005
Tran DA, Marmo TP, Salam AA et al (2007) HDAC6 deacetylation of tubulin modulates dynamics of cellular adhesions. J Cell Sci 120(8):1469–1479. https://doi.org/10.1242/jcs.03431
Mayr C, Kiesslich T, Erber S et al (2021) HDAC screening identifies the HDAC class I inhibitor romidepsin as a promising epigenetic drug for biliary tract cancer. Cancers 13:3862. https://doi.org/10.1096/FASEBJ.2021.35.S1.05128
Pusoon C (2015) Histone deacetylase inhibitors in hematological malignancies and solid tumors. Arch Pharm Res 38:933–949. https://doi.org/10.1007/s12272-015-0571-1
Zang LL, Wang XJ, Li XB et al (2014) SAHA-based novel HDAC inhibitor design by core hopping method. J Mol Graph Model 54:10–18. https://doi.org/10.1016/j.jmgm.2014.08.005
Kirschbaum MH, Foon KA, Frankel P et al (2014) A phase 2 study of belinostat (PXD101) in patients with relapsed or refractory acute myeloid leukemia or patients over the age of 60 with newly diagnosed acute myeloid leukemia: a California cancer consortium study. Leuk Lymphoma 168(6):811–819. https://doi.org/10.1111/bjh.13222
Felice C, Lewis A, Armuzzi A et al (2015) Review article: selective histone deacetylase isoforms as potential therapeutic targets in inflammatory bowel diseases. Aliment Pharmacol Ther 41:26–38. https://doi.org/10.1111/apt.13008
Savona M, Odenike O, Amrein PC et al (2018) Pharmacokinetic- and pharmacodynamic-guided phase 1 study of an oral fixed-dose combination of decitabine and the cytidine deaminase inhibitor cedazuridine in myelodysplastic syndromes. Soc Sci Electron Publ. https://doi.org/10.2139/ssrn.3287512
Thakur A, Tawa G, Henderson M et al (2020) Design, synthesis, and biological evaluation of quinazolin-4-one-based hydroxamic acids as dual PI3K/HDAC inhibitors. J Med Chem 63:4256–4292. https://doi.org/10.1021/acs.jmedchem.0c00193
Anh DT, Hai PT, Huy LD et al (2021) Novel 4-oxoquinazoline-based N-Hydroxypropenamides as histone deacetylase inhibitors: design, synthesis, and biological evaluation. ACS Omega 6:4907–4920. https://doi.org/10.1021/acsomega.0c05870
Cramer RD, Soltanshahi F, Jilek R et al (2007) AllChem: generating and searching 10(20) synthetically accessible structures. J Comput Aided Mol Des 21:341–350. https://doi.org/10.1007/s10822-006-9093-8
Pirhadi S, Ghasemi JB (2010) 3D-QSAR analysis of human immunodeficiency virus entry-1 inhibitors by CoMFA and CoMSIA. Eur J Med Chem 45(11):4897–4903. https://doi.org/10.1016/j.ejmech.2010.07.062
Clark M, Iii R, Opdenbosch NV (2010) Validation of the general purpose tripos 5.2 force field. J Comput Chem 10:982–1012. https://doi.org/10.1002/jcc.540100804
Gasteiger J, Marsili M (1980) Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges-science direct. Tetrahedron 36:3219–3228. https://doi.org/10.1016/0040-4020(80)80168-2
Bush BL, Nachbar RB (1993) Sample-distance partial least squares: PLS optimized for many variables, with application to CoMFA. J Comput Aided Mol Des 7:587–619. https://doi.org/10.1007/BF00124364
Ghaleb A, Aouidate A, Ghamali M et al (2017) 3D-QSAR modeling and molecular docking studies on a series of 2,5 disubstituted 1,3,4-oxadiazoles. J Mol Struct 1145:278–284. https://doi.org/10.9734/IRJPAC/2017/37695
Golbraikh A, Tropsha A (2002) Beware of q2! J Mol Graph Model 20:269–276. https://doi.org/10.1016/S1093-3263(01)00123-1
Golbraikh A, Tropsha A (2002) Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. J Comput Aid Mol Des 16:357–369. https://doi.org/10.1023/a:1021372108686
Mitra I, Roy PP, Kar S et al (2010) On further application of r2m as a metric for validation of QSAR models. J Chemometr 24:22–33. https://doi.org/10.1002/cem.1268
Roy PP, Paul S, Mitra I et al (2009) On two novel parameters for validation of predictive QSAR models. Molecules 15(1):604–605. https://doi.org/10.3390/molecules15010604
Fu L, Chen Y, Guo HM et al (2020) A selectivity study of polysubstituted pyridinylimidazoles as dual inhibitors of JNK3 and p38α MAPK based on 3D-QSAR, molecular docking, and molecular dynamics simulation. Struct Chem. https://doi.org/10.1007/s11224-020-01668-9
Tropsha A, Gramatica P, Gombar V (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. Mol Inform 22:69–77. https://doi.org/10.1002/qsar.200390007
Kaneko H, Funatsu K (2014) Applicability domain based on ensemble learning in classification and regression analyses. J Chem Inf Model 54:2469–2482. https://doi.org/10.1021/ci500364e
Hai Y, Christianson DW (2016) Histone deacetylase 6 structure and molecular basis of catalysis and inhibition. Nat Chem Biol 12:741–747. https://doi.org/10.1038/nchembio.2134
Hamdani H, Amane M (2019) Preparation, spectral, antimicrobial properties and anticancer molecular docking studies of new metal complexes [M(caffeine)4] (PF6)2; M = Fe(II), Co(II), Mn(II), Cd(II), Zn(II), Cu(II), Ni(II). J Mol Struct 1184:262–270. https://doi.org/10.1016/j.molstruc.2019.02.049
Li A (2001) Screening for human ADME/Tox drug properties in drug discovery. Drug Discov Today 6:357–366. https://doi.org/10.1016/S1359-6446(01)01712-3
Bharadwaj S, Rao AK, Dwivedi VD et al (2020) Structure-based screening and validation of bioactive compounds as Zika virus methyltransferase (MTase) inhibitors through first-principle density functional theory, classical molecular simulation and QM/MM affinity estimation. J Biomol Struct Dyn 39:2338–2351. https://doi.org/10.1080/07391102.2020.1747545
Cross JB, Thompson DC, Rai BK et al (2009) Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. J Chem Inf Model 49:1455–1474. https://doi.org/10.1021/ci900056c
Panwar U, Singh SK (2021) Atom-based 3D-QSAR, molecular docking, DFT, and simulation studies of acylhydrazone, hydrazine, and diazene derivatives as IN-LEDGF/p75 inhibitors. Struct Chem 32:337–352. https://doi.org/10.1007/s11224-020-01628-3
Panwar U, Singh SK (2018) Structure-based virtual screening toward the discovery of novel inhibitors for impeding the protein-protein interaction between HIV-1 integrase and human lens epithelium-derived growth factor (LEDGF/p75). J Biomol Struct Dyn 36(12):3199–3217. https://doi.org/10.1080/07391102.2017.1384400
NM O’boyle, AL Tenderholt, KM Langner, (2010) cclib: a library for package-independent computational chemistry algorithms. J Comput Chem 29:839–845. https://doi.org/10.1002/jcc.20823
Pearlman DA, Case DA, Caldwell JW et al (2016) Amber 16. University of California, San Francisco
Götz AW, Williamson MJ, Xu D et al (2012) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. Generalized born J Chem Theory Comput 8:1542–1555. https://doi.org/10.1021/ct200909j
Bergonzo C, Henriksen NM, Roe DR et al (2014) Multidimensional replica exchange molecular dynamics yields a converged ensemble of an RNA tetranucleotide. J Chem Theory Comput 10:492–499. https://doi.org/10.1021/ct400862k
Lindorff-Larsen K, Piana S, Palmo K et al (2010) Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 78:1950–1958. https://doi.org/10.1002/prot.22711
Sprenger GK, Vance JW, Jim P (2015) The general AMBER force field (GAFF) can accurately predict thermodynamic and transport properties of many ionic liquids. J Phys Chem B 119:5882–5895. https://doi.org/10.1021/acs.jpcb.5b00689
Santo L, Hideshima T, Kung AL et al (2012) Preclinical activity, pharmacodynamic, and pharmacokinetic properties of a selective HDAC6 inhibitor, ACY-1215, in combination with bortezomib in multiple myeloma. Blood. https://doi.org/10.1182/blood.V118.21.2912.2912
SFerrer Romelia, AW Götz, D Poole, et al (2013) Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. J Chem Theory Comput 9:3878–3888. https://doi.org/10.1021/ct400314y
Wang YQ, Guo HQ, Feng ZA et al (2019) PD-1-targeted discovery of peptide inhibitors by virtual screening, molecular dynamics simulation, and surface plasmon resonance. Molecules 24:3784. https://doi.org/10.3390/molecules24203784
Loncharich RJ, Brooks BR, Pastor RW (1992) Langevin dynamics of peptides: The frictional dependence of isomerization rates of N-acetylalanyl-N′-methylamide. Biopolymers 32:523–535. https://doi.org/10.1002/bip.360320508
Izaguirre JA, Catarello DP, Wozniak JM et al (2001) Langevin stabilization of molecular dynamics. J Chem Phys 114:2090–2098. https://doi.org/10.1063/1.1332996
Darden T, York D, Pedersen L (1993) Particle mesh Ewald: an N⋅log (N) method for Ewald sums in large systems. J Chem Phys 98:10089–10092. https://doi.org/10.1063/1.464397
Essmann U, Perera L, Berkowitz ML et al (1995) A smooth particle mesh Ewald method. J Chem Phys 103:8577–8593. https://doi.org/10.1063/1.470117
Bharadwaj S, Dubey A, Kamboj NK et al (2021) Drug repurposing for ligand-induced rearrangement of Sirt2 active site-based inhibitors via molecular modeling and quantum mechanics calculations. Sci Rep 11(1):10169. https://doi.org/10.1038/s41598-021-89627-0
Hou T, Wang J, Li Y et al (2011) Assessing the performance of the MM/PBSA and MM/GBSA methods: I. The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Model 51:69–82. https://doi.org/10.1021/ci100275a
Huang K, Luo S, Cong Y et al (2020) An accurate free energy estimator: based on MM/PBSA combined with interaction entropy for protein–ligand binding affinity. Nanoscale. https://doi.org/10.1039/C9NR10638C
Sitkoff D, Sharp KA, Honig B (1994) Accurate calculation of hydration free energies using macroscopic solvent models. J Chem Phys 98:1978–1988. https://doi.org/10.1021/j100058a043
Still WC, Tempczyk A, Hawley RC et al (1990) Semianalytical treatment of solvation for molecular mechanics and dynamics. J Am Chem Soc 112:6127–6129. https://doi.org/10.1021/ja00172a038
Weiser J, Shenkin PS, Still W (1999) Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO). J Comput Chem 20:217–230. https://doi.org/10.1002/(SICI)1096-987X(19990130)20:2%3c217::AID-JCC4%3e3.0.CO;2-A
Han V, Gifford E (2003) ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov 2:192–204. https://doi.org/10.1038/NRD1032
TA Davidson, KB Wagener (2013) Acyclic diene metathesis (ADMET) polymerization. Weinheim, Germany
J D, NN Wang, ZJ Yao, et al (2018) ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. J Cheminform 10:29. https://doi.org/10.1186/s13321-018-0283-x
Ghaleb A, Aouidate A, Bouachrine M et al (2019) In silico exploration of aryl halides analogues as checkpoint kinase 1 inhibitors by using 3D-QSAR, molecular docking study, and ADMET Screening. Adv Pharm Bull 9(1):84–92. https://doi.org/10.15171/apb.2019.011
Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7:42717. https://doi.org/10.1038/srep42717
Clark M, Crameriii R, Jones DM et al (1990) Comparative molecular field analysis (CoMFA). 2. Toward its use with 3D-structural databases. Tetrahedron Comput Methodol 3:47–59. https://doi.org/10.1016/0898-5529(90)90120-w
Shiv B, Amit D, Umesh Y et al (2021) Exploration of natural compounds with anti-SARS-CoV-2 activity via inhibition of SARS-CoV-2 Mpro. Brief Bioinform 22(2):1361–1377. https://doi.org/10.1093/bib/bbaa382
Noureddine NI, Al-Dossary O (2021) DFT and molecular docking study of chloroquine derivatives as antiviral to coronavirus COVID-19. J King Saud Univer Sci 33:101248. https://doi.org/10.1016/j.jksus.2020.101248
Srivastava AK, Pandey AK, Jain S et al (2015) FT-IR spectroscopy, intra-molecular C-HO interactions, HOMO, LUMO, MESP analysis and biological activity of two natural products, triclisine and rufescine: DFT and QTAIM approaches. Spectrochim Acta A 136:682–689. https://doi.org/10.1016/j.saa.2014.09.082
Zarezade V, Abolghasemi M, Rahim F et al (2018) In silico assessment of new progesterone receptor inhibitors using molecular dynamics: a new insight into breast cancer treatment. J Mol Model 24(12):377. https://doi.org/10.1007/s00894-018-3858-6
Du J, Wang X, Nie Q et al (2017) Computational study of the binding mechanism of medium chain acyl-CoA synthetase with substrate in Methanosarcina acetivorans. J Biotechnol 259:160–167. https://doi.org/10.1016/j.jbiotec.2017.07.025
Shirvani P, Fassihi A (2021) In silico design of novel FAK inhibitors using integrated molecular docking, 3D-QSAR and molecular dynamics simulation studies. J Biomol Struct Dyn 6:1–19. https://doi.org/10.1080/07391102.2021.1875880
Acknowledgements
The author would like to thank Chongqing for its financial support Entrepreneurship and Innovation Support Program for Returned Overseas Students (cx2020012), State Key Laboratory of Silkworm Genome Biology Funded by State Key Laboratory of Silkworm Genome Biology, Science and Technology Bureau of Banan District, Chongqing (sklsgb1819-2), the Scientific Research Foundation of Chongqing University of Technology, Chongqing Information Center technology and computing support university.
Author information
Authors and Affiliations
Contributions
Linan Zhao involved in conceptualization and writing–original draft. Le Fu involved in software and visualization. Guangping Li involved in date curation. Yongxin Yu involved in date curation. Juan Wang involved in project administration. Haoran Liang involved in revision and research funds. Mao Shu involved in revision and editing. Zhihua Lin involved in guiding the research process. Yuanqiang Wang involved in writing–review and editing and research funding.
Corresponding authors
Ethics declarations
Conflict of interest
No conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Zhao, L., Fu, L., Li, G. et al. Three-dimensional quantitative structural-activity relationship and molecular dynamics study of multivariate substituted 4-oxyquinazoline HDAC6 inhibitors. Mol Divers 27, 1123–1140 (2023). https://doi.org/10.1007/s11030-022-10474-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11030-022-10474-w