Skip to main content
SpringerLink
Log in

Identification of novel C-15 fluoro isosteviol derivatives for GABA-AT inhibition by in silico investigations

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

Abstract

Context

The treatment of epilepsy is associated with the inhibition of γ-aminobutyric acid-aminotransferase (GABA-AT), which suppresses the concentration of a key neurotransmitter GABA. Isosteviol, a natural bioactive molecule, has been reported to possess an anticonvulsant property. In this work, we first reported a series of C-15 fluoro isosteviol analogs which are bearing different functional groups at C-16 to investigate the interactions with GABA-AT by applying molecular docking and molecular dynamic simulation approach. The results revealed that all fluoro isosteviol analogs displayed a greater binding affinity than references vigabatrin, an FDA-approved GABA-AT inactivator, and CPP-115, which has Orphan Drug Designation status, and positioned at the same binding site as references. Furthermore, molecular dynamic (MD) simulation studies on minimum (A1), maximum (E1) binding energy score of fluoro isosteviol analogs, and isosteviol (G1) revealed their stable complex formation in terms of RMSD, RMSF, RG, and hydrogen bond formation. All analogs were found to have drug-like nature, non-toxic, >80% absorption, and the majority tend to penetrate brain-blood-barrier (BBB). The investigations found in this study can help in the development of isosteviol derivatives as drugs for the treatment of epilepsy.

Methods

The two-dimensional (2D) ligand structures were drawn using ChembioDraw Ultra 14.0. Molecular docking with Autodock4 and molecular dynamic simulation with GROMACS version 2020.1 were performed. The CHARMM27 all-atom force field was applied for writing the topology. Biovia Discovery Studio DS2021 was used for viewing and analyzing the protein-ligand complexes. The data generated from molecular dynamic simulation trajectories were plotted using the Origin® 8 software. The Open Babel software was utilized for extracting SMILEs files of all the fluoro isosteviol analogs. The drug-likeness and ADMET of the molecules were evaluated by SwissADME and ADMETlab 2.0 web tools.

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

Similar content being viewed by others

Availability of data and material

All data produced or analyzed during this study are incorporated in the article and supporting information. All data are available from the corresponding authors upon reasonable request.

References

  1. Mukhopadhyay HK, Kandar CC, Das SK, et al Epilepsy and its management: a review. 7

  2. Brigo F, Bonavita S, Leocani L et al (2020) Telemedicine and the challenge of epilepsy management at the time of COVID-19 pandemic. Epilepsy Behav 110:107164

  3. Beghi E (2020) The epidemiology of epilepsy. Neuroepidemiology 54:185–191. https://doi.org/10.1159/000503831

    Article  PubMed  Google Scholar 

  4. 高明青柳, 孝雄和田, 真知子永井 et al (1990) Increased γ-aminobutyrate aminotransferase activity in brain of patients with Alzheimer’s disease. Chem Pharm Bull (Tokyo) 38:1748–1749. https://doi.org/10.1248/cpb.38.1748

    Article  Google Scholar 

  5. Nishino N, Fujiwara H, Noguchi-Kuno SA, Tanaka C (1988) GABAA receptor but not muscarinic receptor density was decreased in the brain of patients with Parkinson’s disease. Jpn J Pharmacol 48:331–339. https://doi.org/10.1254/jjp.48.331

    Article  CAS  PubMed  Google Scholar 

  6. Garret M, Du Z, Chazalon M et al (2018) Alteration of GABAergic neurotransmission in Huntington’s disease. CNS Neurosci Ther 24:292–300. https://doi.org/10.1111/cns.12826

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Pan Y, Gerasimov MR, Kvist T et al (2012) (1 S, 3 S )-3-Amino-4-difluoromethylenyl-1-cyclopentanoic Acid ( CPP-115), a potent γ-aminobutyric acid aminotransferase inactivator for the treatment of cocaine addiction. J Med Chem 55:357–366. https://doi.org/10.1021/jm201231w

    Article  CAS  PubMed  Google Scholar 

  8. Le HV, Hawker DD, Wu R et al (2015) Design and mechanism of tetrahydrothiophene-based γ-aminobutyric acid aminotransferase inactivators. J Am Chem Soc 137:4525–4533. https://doi.org/10.1021/jacs.5b01155

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Silverman RB (2018) Design and mechanism of GABA aminotransferase inactivators. Treatments for epilepsies and addictions. Chem Rev 118:4037–4070. https://doi.org/10.1021/acs.chemrev.8b00009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Cano A, Fonseca E, Ettcheto M et al (2021) Epilepsy in neurodegenerative diseases: related drugs and molecular pathways. Pharmaceuticals 14:1057. https://doi.org/10.3390/ph14101057

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Storici P, Biase DD, Bossa F et al (2004) Structures of γ-aminobutyric acid (GABA) aminotransferase, a pyridoxal 5′-phosphate, and [2Fe-2S] cluster-containing enzyme, complexed with γ-ethynyl-GABA and with the antiepilepsy drug vigabatrin *. J Biol Chem 279:363–373. https://doi.org/10.1074/jbc.M305884200

    Article  CAS  PubMed  Google Scholar 

  12. Boonstra E, de Kleijn R, Colzato LS et al (2015) Neurotransmitters as food supplements: the effects of GABA on brain and behavior. Front Psychol 6:1520. https://doi.org/10.3389/fpsyg.2015.01520

    Article  PubMed  PubMed Central  Google Scholar 

  13. Sirven JI, Noe K, Hoerth M, Drazkowski J (2012) Antiepileptic drugs 2012: recent advances and trends. Mayo Clin Proc 87:879–889. https://doi.org/10.1016/j.mayocp.2012.05.019

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Cruz MP (2010) Vigabatrin (Sabril), a gamma-aminobutyric acid transaminase inhibitor for the treatment of two catastrophic seizure disorders. Pharm Ther 35:20–23

    Google Scholar 

  15. Uysal E, Konkan R, Solmaz M Psychotic episodes after vigabatrine treatment: a case report. 3

  16. Qiu J, Silverman RB (2000) A new class of conformationally rigid analogues of 4-amino-5-halopentanoic acids, potent inactivators of γ-aminobutyric acid aminotransferase. J Med Chem 43:706–720. https://doi.org/10.1021/jm9904755

    Article  CAS  PubMed  Google Scholar 

  17. Juncosa JI, Takaya K, Le HV et al (2018) Design and mechanism of (s)-3-amino-4-(difluoromethylenyl)cyclopent-1-ene-1-carboxylic acid, a highly potent γ-aminobutyric acid aminotransferase inactivator for the treatment of addiction. J Am Chem Soc 140:2151–2164. https://doi.org/10.1021/jacs.7b10965

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Clift MD, Silverman RB (2008) Synthesis and evaluation of novel aromatic substrates and competitive inhibitors of GABA aminotransferase. Bioorg Med Chem Lett 18:3122–3125. https://doi.org/10.1016/j.bmcl.2007.10.060

    Article  CAS  PubMed  Google Scholar 

  19. Quantitative structure-activity relationship and molecular docking studies of a series of quinazolinonyl analogues as inhibitors of gamma amino butyric acid aminotransferase | Elsevier Enhanced Reader. https://reader.elsevier.com/reader/sd/pii/S2090123216300790?token=2C6442EE9A6CAA78006E1C35E671719A63394A81DBD73478E0ACA3753B8C66DB9C213C6C0896BAA2A74EB3462C8D976D&originRegion=eu-west-1&originCreation=20220822144924. Accessed 22 Aug 2022

  20. Qiu J, Stevenson SH, O’Beirn MJ, Silverman RB (1999) 2,6-Difluorophenol as a bioisostere of a carboxylic acid: bioisosteric analogues of γ-aminobutyric acid. J Med Chem 42:329–332. https://doi.org/10.1021/jm980435l

    Article  CAS  PubMed  Google Scholar 

  21. Bansal SK, Sinha BN, Khosa RL (2013) γ-Amino butyric acid analogs as novel potent GABA-AT inhibitors: molecular docking, synthesis, and biological evaluation. Med Chem Res 22:134–146. https://doi.org/10.1007/s00044-012-0023-0

    Article  CAS  Google Scholar 

  22. Bansal SK, Sinha BN, Khosa RL (2012) Docking-based virtual screening of Schiff’s bases of GABA: a prospective to novel GABA-AT inhibitors. Med Chem Res 21:3063–3072. https://doi.org/10.1007/s00044-011-9843-6

    Article  CAS  Google Scholar 

  23. Pinto A, Tamborini L, Pennacchietti E et al (2016) Bicyclic γ-amino acids as inhibitors of γ-aminobutyrate aminotransferase. J Enzyme Inhib Med Chem 31:295–301. https://doi.org/10.3109/14756366.2015.1021251

    Article  CAS  PubMed  Google Scholar 

  24. Tovar-Gudiño E, Guevara-Salazar JA, Bahena-Herrera JR et al (2018) Novel-substituted heterocyclic GABA analogues. Enzymatic activity against the GABA-AT enzyme from Pseudomonas fluorescens and in silico molecular modeling. Molecules 23:1128. https://doi.org/10.3390/molecules23051128

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. (2019) Docking studies of biologically active substances from plant extracts with anticonvulsant activity. J Appl Pharm Sci 9:66–72. https://doi.org/10.7324/JAPS.2019.90110

  26. Asrani U, Thakur DA (2020) A comprehensive review on uses of Stevia rebaudiana plant. Clin Med 07:6

    Google Scholar 

  27. Das K (2013) Wound healing potential of aqueous crude extract of Stevia rebaudiana in mice. Rev Bras Farmacogn 23:351–357. https://doi.org/10.1590/S0102-695X2013005000011

    Article  Google Scholar 

  28. Gupta E, Purwar S, Sundaram S, Rai GK Nutritional and therapeutic values of Stevia rebaudiana: a review. 11

  29. Marcinek K, Krejpcio Z (2016) Stevia rebaudiana Bertoni: health promoting properties and therapeutic applications. J Für Verbraucherschutz Leb 11:3–8. https://doi.org/10.1007/s00003-015-0968-2

    Article  CAS  Google Scholar 

  30. Sci-Hub | Computer-Aided Identification of Anticonvulsant Effect of Natural Nonnutritive Sweeteners Stevioside and Rebaudioside A. ASSAY and Drug Development Technologies, 13 (6), 313–318 | 10.1089 / adt.2015.29010.meddrrr. https://sci-hub.se/https://doi.org/10.1089/adt.2015.29010.meddrrr. Accessed 22 Aug 2022

  31. Shah P, Westwell AD (2007) The role of fluorine in medicinal chemistry. J Enzyme Inhib Med Chem 22:527–540

    Article  CAS  PubMed  Google Scholar 

  32. Chen P, Liu G (2015) Advancements in aminofluorination of alkenes and alkynes: convenient access to β-fluoroamines. Eur J Org Chem 2015:4295–4309

    Article  CAS  Google Scholar 

  33. Bartholomees U, Struyf T, Lauwers O et al (2016) Validation of an HPLC method for direct measurement of steviol equivalents in foods. Food Chem 190:270–275. https://doi.org/10.1016/j.foodchem.2015.05.102

    Article  CAS  PubMed  Google Scholar 

  34. Arcon JP, Modenutti CP, Avendaño D et al (2019) AutoDock bias: improving binding mode prediction and virtual screening using known protein–ligand interactions. Bioinformatics 35:3836–3838. https://doi.org/10.1093/bioinformatics/btz152

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Morris GM, Huey R, Lindstrom W et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791. https://doi.org/10.1002/jcc.21256

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Hsu K-C, Chen Y-F, Lin S-R, Yang J-M (2011) iGEMDOCK: a graphical environment of enhancing GEMDOCK using pharmacological interactions and post-screening analysis. BMC Bioinformatics 12:S33. https://doi.org/10.1186/1471-2105-12-S1-S33

    Article  PubMed  PubMed Central  Google Scholar 

  37. BIOVIA DS (2017) BIOVIA Discovery Studio-BIOVIA-Dassault Systèmes®

  38. Lee H, Le HV, Wu R et al (2015) Mechanism of inactivation of GABA aminotransferase by (E)- and (Z)-(1 S,3 S )-3-amino-4-fluoromethylenyl-1-cyclopentanoic acid. ACS Chem Biol 10:2087–2098. https://doi.org/10.1021/acschembio.5b00212

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Bitencourt-Ferreira G, Pintro VO, Azevedo WF de (2019) Docking with autodock4. In: docking screens for drug discovery. Springer, pp 125–148

  40. Parves M, Riza YM, Alam S, Jaman S (2023) Molecular dynamics-based insight of VEGFR-2 kinase domain: a combined study of pharmacophore modeling and molecular docking and dynamics. J Mol Model 29:1–15

    Article  Google Scholar 

  41. Oshima H, Re S, Sugita Y (2020) Prediction of protein–ligand binding pose and affinity using the gREST+ FEP method. J Chem Inf Model 60:5382–5394

    Article  CAS  PubMed  Google Scholar 

  42. Saha A, Shih AY, Mirzadegan T, Seierstad M (2018) Predicting the binding of fatty acid amide hydrolase inhibitors by free energy perturbation. J Chem Theory Comput 14:5815–5822

    Article  CAS  PubMed  Google Scholar 

  43. Rizvi SMD, Shakil S, Haneef M (2013) A simple click by click protocol to perform docking: AutoDock 4.2 made easy for non-bioinformaticians. EXCLI J 12:831

    PubMed  PubMed Central  Google Scholar 

  44. Schneider G, Böhm H-J (2002) Virtual screening and fast automated docking methods. Drug Discov Today 7:64–70

    Article  CAS  PubMed  Google Scholar 

  45. Lindahl A, Hess van der S, van der Spoel D (2020) GROMACS 2020 source code. Zenodo April 302020:

  46. Bjelkmar P, Larsson P, Cuendet MA et al (2010) Implementation of the CHARMM force field in GROMACS: analysis of protein stability effects from correction maps, virtual interaction sites, and water models. J Chem Theory Comput 6:459–466. https://doi.org/10.1021/ct900549r

    Article  CAS  PubMed  Google Scholar 

  47. Zoete V, Cuendet MA, Grosdidier A, Michielin O (2011) SwissParam: a fast force field generation tool for small organic molecules. J Comput Chem 32:2359–2368. https://doi.org/10.1002/jcc.21816

    Article  CAS  PubMed  Google Scholar 

  48. O’Boyle NM, Banck M, James CA et al (2011) Open Babel: an open chemical toolbox. J Cheminformatics 3:33. https://doi.org/10.1186/1758-2946-3-33

    Article  CAS  Google Scholar 

  49. 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

    Article  PubMed  PubMed Central  Google Scholar 

  50. Xiong G, Wu Z, Yi J et al (2021) ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res 49:W5–W14. https://doi.org/10.1093/nar/gkab255

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Shrivastava SK, Sinha O, Kumar M et al (2022) Synthesis, characterization, and biological evaluation of some novel ϒ-aminobutyric acid aminotransferase (GABA-AT) inhibitors. Med Chem Res 31:1594–1610. https://doi.org/10.1007/s00044-022-02935-6

    Article  CAS  Google Scholar 

  52. Williams MA, Ladbury JE (2003) Hydrogen bonds in protein-ligand complexes. In: Böhm H-J, Schneider G (eds) Methods and principles in medicinal chemistry, 1st edn. Wiley, pp 137–161

    Google Scholar 

  53. Bojarska J, Remko M, Breza M et al (2020) A supramolecular approach to structure-based design with a focus on synthons hierarchy in ornithine-derived ligands: review, synthesis, experimental and in silico studies. Molecules 25:1135. https://doi.org/10.3390/molecules25051135

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Ertl P, Rohde B, Selzer P (2000) Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J Med Chem 43:3714–3717. https://doi.org/10.1021/jm000942e

    Article  CAS  PubMed  Google Scholar 

  55. ImranH Khan, Patel NB, Patel VM (2018) Synthesis, in silico molecular docking and pharmacokinetic studies, in vitro antimycobacterial and antimicrobial studies of new imidozolones clubbed with thiazolidinedione. Curr Comput Aided Drug Des 14:269–283. https://doi.org/10.2174/1573409914666180516113552

    Article  CAS  Google Scholar 

  56. Alavijeh MS, Chishty M, Qaiser MZ, Palmer AM (2005) Drug metabolism and pharmacokinetics, the blood-brain barrier, and central nervous system drug discovery. NeuroRX 2:554–571. https://doi.org/10.1602/neurorx.2.4.554

    Article  PubMed  PubMed Central  Google Scholar 

  57. Ertl P, Schuffenhauer A (2009) Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminformatics 1:8. https://doi.org/10.1186/1758-2946-1-8

    Article  CAS  Google Scholar 

  58. Wang S, Li Y, Xu et al (2013) Recent developments in computational prediction of hERG blockage. Curr Top Med Chem 13:1317–1326

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to acknowledge the National Institute of Technology Andhra Pradesh, Tadepalligudem, for providing the necessary facility to carry out the computational studies.

Author information

Authors and Affiliations

  1. School of Sciences (Chemistry), National Institute of Technology Andhra Pradesh, Tadepalligudem, Andhra Pradesh, India

    Punam Salaria & Amarendar Reddy M

  2. Department of Science and Humanities, MLR Institute of Technology, Dundigal, Hyderabad, Telangana, India

    Parameswari Akshinthala

  3. Department of Pharmaceutical Chemistry and Photochemistry, Nirmala College of Pharmacy, Mangalagiri, Andhra Pradesh, India

    Ravikumar Kapavarapu

Authors
  1. Punam Salaria
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Parameswari Akshinthala
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ravikumar Kapavarapu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amarendar Reddy M
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

PunamSalaria (design, conception, computational studies, data analysis, manuscript drafting, and revision), Parameswari Akshinthala (revision), Ravikumar Kapavarapu (data interpretation), and Amarendar Reddy M (design, conception, data analysis, manuscript drafting, and critical revision).

Corresponding author

Correspondence to Amarendar Reddy M.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

Yes.

Consent for publication

Yes.

Competing interests

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

Additional file 1:

Table S1. GABA-AT (4ZSW) active siteamino acid residues involved ininteraction with ligands and bindingenergyscores. Table S2. Screening of C-15 fluoro isosteviol derivativesand standards using SwissADMEonline server to evaluate their drug-likeness, syntheticaccessibility (S.A), and oral bioavailability assays. Table S3. ADMET Screening of C-15 fluoro isosteviolderivatives using ADMETlab online designed web. Fig. S1. 2D interactionspose of isosteviol analogues: A2 (a), B1 (b), B2 (c),C1(d),C2(e),D1 (f), D2 (g),E2 (h), G2 (i), G3 (j), G4 (k) and vigabatrin (l)with 1OHW in complex form at active domain of GABA-AT enzyme. Interactions representedby different colour (m). Fig.S2. Hydrogenbond formation in,(a) A1-bound, (b) E1-bound, (c) G1-bound and(d) vigabatrin-bound complexes.  Free enzyme shown in black colour. A1-GABA-AT in red, E1-GABA-AT in cyan, G1-GABA-AT in blue and vigabatrin GABA-AT in pink colour.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) 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

Salaria, P., Akshinthala, P., Kapavarapu, R. et al. Identification of novel C-15 fluoro isosteviol derivatives for GABA-AT inhibition by in silico investigations. J Mol Model 29, 76 (2023). https://doi.org/10.1007/s00894-023-05479-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00894-023-05479-7

Keywords

Search

Navigation