Skip to main content
SpringerLink
Log in

Identification of novel inhibitors of S-adenosyl-L-homocysteine hydrolase via structure-based virtual screening and molecular dynamics simulations

  • Original Paper
  • Published:
Journal of Molecular Modeling Aims and scope Submit manuscript

Abstract

S-adenosyl-L-homocysteine hydrolase (SAHase) is an important regulator in the methylation reactions in many organisms and thus is crucial for numerous cellular functions. In recent years, SAHase has become one of the popular targets for drug design, and SAHase inhibitors have exhibited potent antiviral activity. In this study, we established the complex-based pharmacophore models based on the known crystal complex of SAHase (PDB ID: 1A7A) to screen the drug-likeness compounds of ChEMBL database. Then, three molecular docking programs were used to validate the reliability of compounds, involving Libdock, CDOCKER, and AutoDock Vina programs. The four promising hit compounds (CHEMBL420751, CHEMBL346387, CHEMBL1569958, and CHEMBL4206648) were performed molecular dynamics simulations and MM-PBSA calculations to evaluate their stability and binding-free energy in the binding site of SAHase. After screening and analyzing, the hit compounds CHEMBL420751 and CHEMBL346387 were suggested to further research to obtain novel potential SAHase inhibitors.

Graphical abstract

A series of computer-aided drug design methods, including pharmacophore, molecular docking, molecular dynamics simulation and MM-PBSA calculations, were employed in this study to identity novel inhibitors of S-adenosyl-L-homocysteine hydrolase (SAHase). Some compounds from virtual screening could form various interactions with key residues of SAHase. Among them, compounds CHEMBL346387 and CHEMBL420751 exhibited potent binding affinity from molecular docking and MM-PBSA, and maintained good stability at the binding site during molecular dynamics simulations as well. All these results indicated that the selected compounds might have the potential to be novel SAHase inhibitors.

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
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

The software used is commercially available.

References

  1. Gao J, Cahill CM, Huang X (2018) S-Adenosyl methionine and transmethylation pathways in neuropsychiatric diseases throughout life. Neurotherapeutics 15:156–175. https://doi.org/10.1007/s13311-017-0593-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Dai X, Ren T, Zhang Y, Nan N (2021) Methylation multiplicity and its clinical values in cancer. Expert Rev Mol Med 23:1–10. https://doi.org/10.1017/erm.2021.4

    Article  CAS  Google Scholar 

  3. Kachroo P, Morrow JD, Vyhlidal CA, Gaedigk R, Silverman EK, Weiss ST, Tantisira KG, DeMeo DL (2021) DNA methylation perturbations may link altered development and aging in the lung. Aging 13:1742–1764. https://doi.org/10.18632/aging.202544

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Adams JM, Cory S (1975) Modified nucleosides and bizarre 5′-termini in mouse myeloma mRNA. Nature 255:28–33. https://doi.org/10.1038/255028a0

    Article  CAS  PubMed  Google Scholar 

  5. Banerjee AK (1980) 5′-terminal cap structure in eucaryotic messenger ribonucleic acids. Microbiol Rev 44:175–205. https://doi.org/10.1128/mr.44.2.175-205.1980

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Both GW, Banerjee AK, Shatkin AJ (1975) Methylation-dependent translation of viral messenger RNAs in vitro. Proc Natl Acad Sci 72:1189–1193. https://doi.org/10.1073/pnas.72.3.1189

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Roje S (2006) S-Adenosyl-L-methionine: beyond the universal methyl group donor. Phytochemistry 67:1686–1698. https://doi.org/10.1016/j.phytochem.2006.04.019

    Article  CAS  PubMed  Google Scholar 

  8. Youngblood B, Shieh FK, Buller F, Bullock T, Reich NO (2007) S-adenosyl-L-methionine-dependent methyl transfer: observable precatalytic intermediates during DNA cytosine methylation. Biochemistry 46:8766–8775. https://doi.org/10.1021/bi7005948

    Article  CAS  PubMed  Google Scholar 

  9. Lee HO, Wang L, Kuo YM, Andrews AJ, Gupta S, Kruger WD (2018) S-adenosylhomocysteine hydrolase over-expression does not alter S-adenosylmethionine or S-adenosylhomocysteine levels in CBS deficient mice. Mol Genet Metab Rep 15:15–21. https://doi.org/10.1016/j.ymgmr.2018.01.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Xiao Y, Su X, Huang W, Zhang J, Peng C, Huang H, Wu X, Huang H, Xia M, Ling W (2015) Role of S-adenosylhomocysteine in cardiovascular disease and its potential epigenetic mechanism. Int J Biochem Cell Biol 67:158–166. https://doi.org/10.1016/j.biocel.2015.06.015

    Article  CAS  PubMed  Google Scholar 

  11. Altintas E, Sezgin O (2004) S-adenosylhomocysteine hydrolase, S-adenosylmethionine, S-adenosylhomocysteine: correlations with ribavirin induced anemia. Med Hypotheses 63:834–837. https://doi.org/10.1016/j.mehy.2004.03.031

    Article  CAS  PubMed  Google Scholar 

  12. Brzezinski K (2020) S-adenosyl-l-homocysteine hydrolase: a structural perspective on the enzyme with two Rossmann-fold domains. Biomolecules 10:1682. https://doi.org/10.3390/biom10121682

    Article  CAS  PubMed Central  Google Scholar 

  13. Converso A, Hartingh T, Fraley ME, Garbaccio RM, Hartman GD, Huang SY, Majercak JM, McCampbell A, Na SJ, Ray WJ, Savage MJ, Wolffe C, Yeh S, Yu Y, White R, Zhang R (2014) Adenosine analogue inhibitors of S-adenosylhomocysteine hydrolase. Bioorg Med Chem Lett 24:2737–2740. https://doi.org/10.1016/j.bmcl.2014.04.034

    Article  CAS  PubMed  Google Scholar 

  14. Turner MA, Yang X, Yin D, Kuczera K, Borchardt RT, Howell PL (2000) Structure and function of S-adenosylhomocysteine hydrolase. Cell Biochem Biophys 33:101–125. https://doi.org/10.1385/CBB:33:2:101

    Article  CAS  PubMed  Google Scholar 

  15. Liu S, Wolfe MS, Borchardt RT (1992) Rational approaches to the design of antiviral agents based on S-adenosyl-L-homocysteine hydrolase as a molecular target. Antiviral Res 19:247–265. https://doi.org/10.1016/0166-3542(92)90083-h

    Article  CAS  PubMed  Google Scholar 

  16. Malanovic N, Streith I, Wolinski H, Rechberger G, Kohlwein SD, Tehlivets O (2008) S-adenosyl-L-homocysteine hydrolase, key enzyme of methylation metabolism, regulates phosphatidylcholine synthesis and triacylglycerol homeostasis in yeast: implications for homocysteine as a risk factor of atherosclerosis. J Biol Chem 283:23989–23999. https://doi.org/10.1074/jbc.M800830200

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Qian G, Chen C, Zhou R (2014) A thermostable S-adenosylhomocysteine hydrolase from Thermotoga maritima: properties and its application on S-adenosylhomocysteine production with enzymatic cofactor regeneration. Enzyme Microb Technol 64:33–37. https://doi.org/10.1016/j.enzmictec.2014.06.007

    Article  CAS  PubMed  Google Scholar 

  18. Chiang PK (1998) Biological effects of inhibitors of S-adenosylhomocysteine hydrolase. Pharmacol Ther 77:115–134. https://doi.org/10.1016/S0163-7258(97)00089-2

    Article  CAS  PubMed  Google Scholar 

  19. Tan X, Wang P, Nian S, Wang G (2014) Design and synthesis of amide derivatives as S-adenosyl-L-homocysteine hydrolase inhibitors. Chem Pharm Bull 2:112–117. https://doi.org/10.1248/cpb.c13-00623

    Article  Google Scholar 

  20. Jia Y, Li P, Song W (2016) Rational design of a profluorescent substrate for S-adenosylhomocysteine hydrolase and its applications in bioimaging and inhibitor screening. ACS Appl Mater Interfaces 8:25818–25824. https://doi.org/10.1021/acsami.6b09190

    Article  CAS  PubMed  Google Scholar 

  21. Lu W, Zhang R, Jiang H, Zhang H, Luo C (2018) Computer-aided drug design in epigenetics. Front Chem 6:57. https://doi.org/10.3389/fchem.2018.00057

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Glaab E (2016) Building a virtual ligand screening pipeline using free software: a survey. Brief Bioinform 17:352–366. https://doi.org/10.1093/bib/bbv037

    Article  CAS  PubMed  Google Scholar 

  23. Forli S (2015) Charting a path to success in virtual screening. Molecules 20:18732–18758. https://doi.org/10.3390/molecules201018732

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Li Q, Shah S (2017) Structure-based virtual screening. Methods Mol Biol 1558:111–124. https://doi.org/10.1007/978-1-4939-6783-4_5

    Article  CAS  PubMed  Google Scholar 

  25. Kontoyianni M (2017) Docking and virtual screening in drug discovery. Methods Mol Biol 1647:255–266. https://doi.org/10.1007/978-1-4939-7201-2_18

    Article  CAS  PubMed  Google Scholar 

  26. Liu X, Shi D, Zhou S, Liu H, Liu H, Yao X (2018) Molecular dynamics simulations and novel drug discovery. Expert Opin Drug Discov 13:23–37. https://doi.org/10.1080/17460441.2018.1403419

    Article  CAS  PubMed  Google Scholar 

  27. Aci-Sèche S, Ziada S, Braka A, Arora R, Bonnet P (2016) Advanced molecular dynamics simulation methods for kinase drug discovery. Future Med Chem 8:545–566. https://doi.org/10.4155/fmc.16.9

    Article  CAS  PubMed  Google Scholar 

  28. Wang E, Sun H, Wang J, Wang Z, Liu H, Zhang JZH, Hou T (2019) End-point binding free energy calculation with MM/PBSA and MM/GBSA: strategies and applications in drug design. Chem Rev 119:9478–9508. https://doi.org/10.1021/acs.chemrev.9b00055

    Article  CAS  PubMed  Google Scholar 

  29. Clercq DE (1987) S-adenosylhomocysteine hydrolase inhibitors as broad-spectrum antiviral agents. Biochem Pharmacol 36:2567–2575. https://doi.org/10.1016/0006-2952(87)90533-8

    Article  PubMed  Google Scholar 

  30. Yuan CS, Saso Y, Lazarides E, Borchardt RT, Robins MJ (1999) Recent advances in S-adenosyl-L-homocysteine hydrolase inhibitors and their potential clinical applications. Expert Opin Ther Pat 9:1197–1206. https://doi.org/10.1517/13543776.9.9.1197

    Article  CAS  Google Scholar 

  31. Seidel T, Schuetz DA, Garon A, Langer T (2019) The pharmacophore concept and its applications in computer-aided drug design. Prog Chem Org Nat Prod 110:99–141. https://doi.org/10.1007/978-3-030-14632-0_4

    Article  CAS  PubMed  Google Scholar 

  32. Rao SN, Head MS, Kulkarni A, LaLonde JM (2007) Validation studies of the site-directed docking program LibDock. J Chem Inf Model 47:2159–2171. https://doi.org/10.1021/ci6004299

    Article  CAS  PubMed  Google Scholar 

  33. Wu H, Liu Y, Guo M, Xie J, Jiang X (2014) A virtual screening method for inhibitory peptides of angiotensin I-converting enzyme. J Food Sci 79:1635–1642. https://doi.org/10.1111/1750-3841.12559

    Article  CAS  Google Scholar 

  34. Wu G, Robertson DH, Brooks CL, Vieth M (2003) Detailed analysis of grid-based molecular docking: a case study of CDOCKER-A CHARMm-based MD docking algorithm. J Comput Chem 24:1549–1562. https://doi.org/10.1002/jcc.10306

    Article  CAS  PubMed  Google Scholar 

  35. Jaghoori MM, Bleijlevens B, Olabarriaga SD (2016) 1001 Ways to run AutoDock Vina for virtual screening. J Comput Aided Mol Des 30:237–249. https://doi.org/10.1007/s10822-016-9900-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Collier TA, Piggot TJ, Allison JR (2020) Molecular dynamics simulation of proteins. Methods Mol Biol 2073:311–327. https://doi.org/10.1007/978-1-4939-9869-2_17

    Article  CAS  PubMed  Google Scholar 

  37. Hollingsworth SA, Dror RO (2018) Molecular dynamics simulation for all. Neuron 99:1129–1143. https://doi.org/10.1016/j.neuron.2018.08.011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Warren GL, Andrews CW, Capelli AM, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S, Tedesco G, Wall ID, Woolven JM, Peishoff CE, Head MS (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912–5931. https://doi.org/10.1021/jm050362n

    Article  CAS  PubMed  Google Scholar 

  39. Kontoyianni M, McClellan LM, Sokol GS (2004) Evaluation of docking performance: comparative data on docking algorithms. J Med Chem 47:558–565. https://doi.org/10.1021/jm0302997

    Article  CAS  PubMed  Google Scholar 

  40. Jain AN (2006) Scoring functions for protein-ligand docking. Curr Protein Pept Sci 7:407–420. https://doi.org/10.2174/138920306778559395

    Article  CAS  PubMed  Google Scholar 

  41. Turner MA, Yuan CS, Borchardt RT, Hershfield MS, Smith GD, Howell PL (1998) Structure determination of selenomethionyl S-adenosylhomocysteine hydrolase using data at a single wavelength. Nat Struct Biol 5:369–376. https://doi.org/10.1038/nsb0598-369

    Article  CAS  PubMed  Google Scholar 

  42. Joshi T, Joshi T, Sharma P, Chandra S, Pande V (2021) Molecular docking and molecular dynamics simulation approach to screen natural compounds for inhibition of Xanthomonas oryzae pv. Oryzae by targeting peptide deformylase. J Biomol Struct Dyn 39:823–840. https://doi.org/10.1080/07391102.2020.1719200

    Article  CAS  PubMed  Google Scholar 

  43. Şahİn K, DurdaĞi S (2020) Combined ligand and structure-based virtual screening approaches for identification of novel AChE inhibitors. Turk J Chem 44:574–588. https://doi.org/10.3906/kim-1911-57

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Tirado-Rives J, Jorgensen WL (2006) Contribution of conformer focusing to the uncertainty in predicting free energies for protein-ligand binding. J Med Chem 49:5880–5884. https://doi.org/10.1021/jm060763i

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We are grateful to the reviewers for their invaluable suggestions.

Funding

This work was supported by the National Natural Science Foundation of China (No. 82060627), Guangxi Natural Science Foundation of China (No. 2020GXNSFAA159149, No. 2018GXNSFAA281114), Innovation Project of Guangxi Graduate Education (YCSW2022367).

Author information

Authors and Affiliations

  1. College of Pharmacy, Guilin Medical University, Guilin, 541199, China

    Cong Chen, Xiang-Hui Zhou, Wa Cheng, Yan-Fen Peng, Qi-Ming Yu & Xiang-Duan Tan

Authors
  1. Cong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiang-Hui Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wa Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yan-Fen Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qi-Ming Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiang-Duan Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Cong Chen: methodology, software, writing—original draft. Xiang-Hui Zhou: data analysis, manuscript editing. Wa Cheng: data analysis, software. Yan-Fen Peng: investigation, validation. Qi-Ming Yu: investigation, visualization, validation. Xiang-Duan Tan: conceptualization, writing—review and editing, project management.

Corresponding authors

Correspondence to Qi-Ming Yu or Xiang-Duan Tan.

Ethics declarations

Consent to participate

All authors have seen the manuscript and approved to submit the manuscript.

Consent to publish

All authors consent to the publication of the manuscript.

Conflict of interest

The authors declare no competing interests.

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.

Supplementary file1 (DOCX 945 KB)

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, C., Zhou, XH., Cheng, W. et al. Identification of novel inhibitors of S-adenosyl-L-homocysteine hydrolase via structure-based virtual screening and molecular dynamics simulations. J Mol Model 28, 336 (2022). https://doi.org/10.1007/s00894-022-05298-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00894-022-05298-2

Keywords

Search

Navigation