Skip to main content
SpringerLink
Log in

Molecular dynamics-guided receptor-dependent 4D-QSAR studies of HDACs inhibitors

  • Original Article
  • Published:
Molecular Diversity Aims and scope Submit manuscript

Abstract

Histone deacetylases (HDACs) were highlighted as a novel category of anticancer targets. Several HDACs inhibitors were approved for therapeutic use in cancer treatment. Comparatively, receptor-dependent 4D-QSAR, LQTA-QSAR, is a new approach which generates conformational ensemble profiles of compounds by molecular dynamics simulations at binding site of enzyme. This work describes a receptor-dependent 4D-QSAR studies on hydroxamate-based HDACs inhibitors. The 4D-QSAR model was generated by multiple linear regression method of QSARINS. Leave-N-out cross-validation (LNO) and Y-randomization were performed to analysis of the independent test set and to verify the robustness of the model. Best 4D-QSAR model showed the following statistics: R2 = 0.8117, Q2LOO = 0.6881, Q2LNO = 0.6830, R2Pred = 0.884. The results may be used for further virtual screening and design for novel HDACs inhibitors.

Graphical abstract

The receptor dependent 4D-QSAR model was developed for the hydroxamate derivatives as HDAC inhibitors by making use of molecular dynamics simulation to obtain conformational ensemble profile for each compound. The multiple linear regression method was used to generate 4D-QSAR model with the suitable predictive ability and the excellent statistical parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Krämer OH, Göttlicher M, Heinzel T (2001) Histone deacetylase as a therapeutic target. Trends Endocrinol Metab 12(7):294–300. https://doi.org/10.1016/s1043-2760(01)00438-6

    Article  PubMed  Google Scholar 

  2. Marks P, Rifkind RA, Richon VM, Breslow R, Miller T, Kelly WK (2001) Histone deacetylases and cancer: causes and therapies. Nat Rev Cancer 1(3):194–202. https://doi.org/10.1038/35106079

    Article  CAS  PubMed  Google Scholar 

  3. Grozinger CM, Schreiber SL (2002) Deacetylase enzymes: biological functions and the use of small-molecule inhibitors. Chem Biol 9(1):3–16. https://doi.org/10.1016/s1074-5521(02)00092-3

    Article  CAS  PubMed  Google Scholar 

  4. Johnstone RW (2002) Histone-deacetylase inhibitors: novel drugs for the treatment of cancer. Nat Rev Drug Discov 1(4):287–299. https://doi.org/10.1038/nrd772

    Article  CAS  PubMed  Google Scholar 

  5. Marks PA, Breslow R (2007) Dimethyl sulfoxide to vorinostat: development of this histone deacetylase inhibitor as an anticancer drug. Nat Biotechnol 25(1):84–90. https://doi.org/10.1038/nbt1272

    Article  CAS  PubMed  Google Scholar 

  6. Marshall JL, Rizvi N, Kauh J, Dahut W, Figuera M, Kang MH, Figg WD, Wainer I, Chaissang C, Li MZ, Hawkins MJ (2002) A phase I trial of depsipeptide (FR901228) in patients with advanced cancer. J Exp Ther Oncol 2(6):325–332. https://doi.org/10.1046/j.1359-4117.2002.01039.x

    Article  CAS  PubMed  Google Scholar 

  7. Shim J, Mackerell AD (2011) Computational ligand-based rational design: role of conformational sampling and force fields in model development. Medchemcomm 2(5):356–370. https://doi.org/10.1039/C1MD00044F

    Article  CAS  PubMed  Google Scholar 

  8. Ghasemi JB, Salahinejad M, Rofouei MK (2011) Review of the quantitative structure–activity relationship modelling methods on estimation of formation constants of macrocyclic compounds with different guest molecules. Supramol Chem 23(9):614–629. https://doi.org/10.1080/10610278.2011.581281

    Article  CAS  Google Scholar 

  9. Hopfinger AJ, Wang S, Tokarski JS, Jin B, Albuquerque MG, Madhav JP, Duraiswami C (1997) Construction of 3D-QSAR models using the 4D-QSAR analysis formalism. J Am Chem Soc 119(43):10509–10524. https://doi.org/10.1021/ja9718937

    Article  CAS  Google Scholar 

  10. Martins JPA, Barbosa EG, Pasqualoto KFM, Ferreira MMC (2009) LQTA-QSAR: a new 4D-QSAR methodology. J Chem Inf Model 49(6):1428–1436. https://doi.org/10.1021/ci900014f

    Article  CAS  PubMed  Google Scholar 

  11. Santosfilho OA, Hopfinger AJ (2002) The 4D-QSAR paradigm: application to a novel set of non- peptidic HIV protease inhibitors. Quant Struct-Act Relat 21(4):369–381. https://doi.org/10.1002/1521-3838(200210)21:4%3c369::AID-QSAR369%3e3.0.CO;2-1

    Article  CAS  Google Scholar 

  12. Dassault SystemesBIOVIA, Discovery Studio, San Diego, California, USA. http://accelrys.com/

  13. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) Autodock4 and Autodocktools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791. https://doi.org/10.1002/jcc.21256

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Csizmadia P, MarvinSketch and MarvinView: molecule applets for the world wide web, https://chemaxon.com/products/marvin

  15. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE (2004) UCSF chimera–a visualization system for exploratory research and analysis. J Comput Chem 25(13):1605–1612. https://doi.org/10.1002/jcc.20084

    Article  CAS  PubMed  Google Scholar 

  16. Thompson MA, Planaria Software LLC, ArgusLab-molecular modeling, graphics & drug design program. http://www.arguslab.com/

  17. DeLano WL (2002) The PyMOL molecular graphics system. http://www.pymol.org.

  18. Pronk S, Pll S, Schulz R, Larsson P, Bjelkmar P, Apostolov R, Shirts MR, Smith JC, Kasson PM, Van Der Spoel D, Hess B, Lindahl E (2013) a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 29(7):845–854. https://doi.org/10.1093/bioinformatics/btt055

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Duke R, Giese T, Gohlke H (2016) AmberTools. University of California, San Francisco. http://ambermd.org/AmberTools.php

  20. Ray BD (2010) GROMACS Contribution Section http://www.gromacs.org/Downloads/User_contributions/Other_software

  21. Gramatica P, Chirico N, Papa E, Cassani S, Kovarich S (2013) QSARINS: a new software for the development, analysis, and validation of QSAR MLR models. J Comput Chem 34(24):2121–2132. https://doi.org/10.1002/jcc.23361

    Article  CAS  Google Scholar 

  22. Lavoie R, Bouchain G, Frechette S, Woo SH, Abou-Khalil E, Leit S, Fournel M, Yan PT, Trachy-Bourget MC, Beaulieu C, Li Z, Besterman J, Delorme D (2001) Design and synthesis of a novel class of histone deacetylase inhibitors. Bioorg Med Chem Lett 11(21):2847–2850. https://doi.org/10.1016/s0960-894x(01)00552-2

    Article  CAS  PubMed  Google Scholar 

  23. Remiszewski SW, Sambucetti LC, Atadja P, Bair KW, Cornell WD, Green MA, Howell KL, Jung M, Kwon P, Trogani N, Walker H (2002) Inhibitors of human histone deacetylase: synthesis and enzyme and cellular activity of straight chain hydroxamates. J Med Chem 45(4):753–757. https://doi.org/10.1021/jm015568c

    Article  CAS  PubMed  Google Scholar 

  24. Woo SH, Frechette S, Abou Khalil E, Bouchain G, Vaisburg A, Bernstein N, Moradei O, Leit S, Allan M, Fournel M, Trachy-Bourget M-C, Li Z, Besterman JM, Delorme D (2002) Structurally simple trichostatin a-like straight chain hydroxamates as potent histone deacetylase inhibitors. J Med Chem 45(13):2877–2885. https://doi.org/10.1021/jm020154k

    Article  CAS  PubMed  Google Scholar 

  25. Finnin MS, Donigian JR, Cohen A, Richon VM, Rifkind RA, Marks PA, Breslow R, Pavletich NP (1999) Structures of a histone deacetylase homologue bound to the tsa and saha inhibitors. Nature 401(6749):188–193. https://doi.org/10.1038/43710

    Article  CAS  PubMed  Google Scholar 

  26. Gordon JC, Myers JB, Folta T, Shoja V, Heath LS, Onufriev A (2005) H++: a server for estimating pkas and adding missing hydrogens to macromolecules. Nucleic Acids Res 33:W368–W371. https://doi.org/10.1093/nar/gki464

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Mark P, Nilsson L (2001) Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. J Phys Chem A 105(43):9954–9960. https://doi.org/10.1021/jp003020w

    Article  CAS  Google Scholar 

  28. 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

    Article  CAS  Google Scholar 

  29. MacKerell Jr AD, Banavali NK (2000) All‐atom empirical force field for nucleic acids: II. application to molecular dynamics simulations of DNA and RNA in solution. J Comput Chem 21(2): 105–120. https://doi.org/https://doi.org/10.1002/(SICI)1096-987X(20000130)21:2<105::AID-JCC3>3.0.CO;2-P

  30. Barbosa EG, Pasqualoto KFM, Ferreira MMC (2012) The receptor-dependent LQTA-QSAR: application to a set of trypanothione reductase inhibitors. J Comput Aided Mol Des 26(9):1055–1065. https://doi.org/10.1007/s10822-012-9598-2

    Article  CAS  PubMed  Google Scholar 

  31. Patil RB, Barbosa EG, Sangshetti JN, Sawant SD, Zambre VP (2018) LQTA-R: a new 3D-QSAR methodology applied to a set of DGAT1 inhibitors. Comput Biol Chem 74:123–131. https://doi.org/10.1016/j.compbiolchem.2018.02.021

    Article  CAS  PubMed  Google Scholar 

  32. Golbraikh A, Tropsha A (2002) Beware of q2! J Mol Graph Model 20(4):269–276. https://doi.org/10.1016/s1093-3263(01)00123-1

    Article  CAS  PubMed  Google Scholar 

  33. Golbraikh A, Tropsha A (2002) Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. J Comput Aided Mol Des 16(5–6):357–369. https://doi.org/10.1023/a:1020869118689

    Article  CAS  PubMed  Google Scholar 

  34. Chirico N, Gramatica P (2011) Real external predictivity of QSAR models: how to evaluate it? comparison of different validation criteria and proposal of using the concordance correlation coefficient. J Chem Inf Model 51(9):2320–2335. https://doi.org/10.1021/ci200211n

    Article  CAS  PubMed  Google Scholar 

  35. Ghasemi JB, Safavi-Sohi R, Barbosa EG (2012) 4D-LQTA-QSAR and docking study on potent gram-negative specific LPXC inhibitors: a comparison to COMFA modeling. Mol Divers 16(1):203–213. https://doi.org/10.1007/s11030-011-9340-3

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The project was supported by the Nanchang University Teaching Reform Foundation (NCUJGLX-19-130, NCUJGLX-19-124), the Graduate Innovation Foundation of Jiangxi Province (CX2018190) and the Undergraduate Innovation and Entrepreneurship Foundation (2020CX289)

Author information

Authors and Affiliations

  1. Department of Medicinal Chemistry, School of Pharmaceutical Science, NanChang University, Nanchang, 330006, China

    Zhihao Hu, Tiansheng Zhao, Bowen Yang & Guogang Tu

  2. Jiangxi University of Traditional Chinese Medicine, Nanchang, 330006, Jiangxi, People’s Republic of China

    Qianxia Lin & Haiyun Liu

Authors
  1. Zhihao Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qianxia Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haiyun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tiansheng Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bowen Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Guogang Tu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guogang Tu.

Ethics declarations

Conflict of interest

The author declares no conflict of interest, financial or otherwise.

Human and animal rights

No Animals/Humans were used for studies that are basis of this research.

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 Supplementary Information.

Supplementary material 1 (DOCX 553 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, Z., Lin, Q., Liu, H. et al. Molecular dynamics-guided receptor-dependent 4D-QSAR studies of HDACs inhibitors. Mol Divers 26, 757–768 (2022). https://doi.org/10.1007/s11030-021-10181-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11030-021-10181-y

Keywords

Search

Navigation