Skip to content
2000
Volume 24, Issue 26
  • ISSN: 1568-0266
  • E-ISSN: 1873-4294

Abstract

Background

For cell wall biosynthesis, drug-resistant uses a special protein called PBP2a, even when antibiotics are present and stop its natural processes from working. To combat this, novel therapies are required to specifically target PBP2a with greater efficacy.

Methods

Using computational approaches, we screened nine phenolic compounds from other Bergenia species, including , against the PBP2a allosteric site to explore the potential interaction between phenolic compounds and a specific region of PBP2a known as the allosteric site.

Results

Based on interaction patterns and estimated affinity, vitexin has been found to be the most prominent phenolic compound. We performed MD simulations on vitexin and ceftazidime as control molecules based on the docking results. The binding free energy estimates of vitexin 
(-94.48 +/- 17.92 kJ/mol) using MM/PBSA were lower than those of the control (-67.61 +/- 12.29 kJ/mol), which suggests that vitexin may be able to inhibit PBP2a activity in MRSA.

Conclusion

It has been intriguing to observe a correlation between the affinity of the lead vitexin at the allosteric site and the modification of Tyr446, the active site gatekeeper residue in PBP2a. Our findings have implied that lead vitexin can either directly or indirectly decrease PBP2a activity by inducing allosteric site change in conventional medicine.

Loading

Article metrics loading...

/content/journals/ctmc/10.2174/0115680266312143240805191718
2024-11-01
2025-09-18
Loading full text...

Full text loading...

References

  1. KalaloM.J. FatimawaliF. KalaloT. RambiC.I.J. Tea bioactive compounds as inhibitor of mrsa penicillin binding protein 2a (pbp2a): a molecular docking study.Pharm. Med. J. (PMJ)2021327010.35799/pmj.3.2.2020.32878
    [Google Scholar]
  2. RahmanM.M. AminK.B. RahmanS.M.M. KhairA. RahmanM. HossainA. RahmanA.K.M.A. ParvezM.S. MiuraN. AlamM.M. Investigation of methicillin-resistant Staphylococcus aureus among clinical isolates from humans and animals by culture methods and multiplex PCR.BMC Vet. Res.201814130010.1186/s12917‑018‑1611‑0 30285752
    [Google Scholar]
  3. Abdel-MoeinK.A. ZaherH.M. Occurrence of multidrug-resistant methicillin-resistant Staphylococcus aureus among healthy farm animals: a public health concern.Int. J. Vet. Sci. Med.201971556010.1080/23144599.2019.1689630 31819891
    [Google Scholar]
  4. StryjewskiM.E. CoreyG.R. Methicillin-resistant Staphylococcus aureus: An evolving pathogen.Clin. Infect. Dis.201358S10S19
    [Google Scholar]
  5. MalikB. BhattacharyyaS. Antibiotic drug-resistance as a complex system driven by socio-economic growth and antibiotic misuse.Sci. Rep.201920199
    [Google Scholar]
  6. VermaA.K. AhmedS.F. HossainM.S. BhojiyaA.A. MathurA. UpadhyayS.K. SrivastavaA.K. VishvakarmaN.K. BarikM. RahamanM.M. BahadurN.M. Molecular docking and simulation studies of flavonoid compounds against PBP-2a of methicillin-resistant Staphylococcus aureus.J. Biomol. Struct. Dyn.20224021105611057710.1080/07391102.2021.1944911 34243699
    [Google Scholar]
  7. LarssonD.G.J. FlachC.F. Antibiotic resistance in the environment.Nat. Rev. Microbiol.202220525726910.1038/s41579‑021‑00649‑x 34737424
    [Google Scholar]
  8. JosephineH.R. CharlierP. DaviesC. NicholasR.A. PrattR.F. Reactivity of penicillin-binding proteins with peptidoglycanmimetic β-lactams: what’s wrong with these enzymes?Biochemistry20064551158731588310.1021/bi061804f 17176110
    [Google Scholar]
  9. ZapunA. Contreras-MartelC. VernetT. Penicillin-binding proteins and β-lactam resistance.FEMS Microbiol. Rev.200832236138510.1111/j.1574‑6976.2007.00095.x 18248419
    [Google Scholar]
  10. SauvageE. KerffF. TerrakM. AyalaJ.A. CharlierP. The penicillin-binding proteins: structure and role in peptidoglycan biosynthesis.FEMS Microbiol. Rev.200832223425810.1111/j.1574‑6976.2008.00105.x 18266856
    [Google Scholar]
  11. ScheffersD.J. PinhoM.G. Bacterial cell wall synthesis: new insights from localization studies.Microbiol. Mol. Biol. Rev.200569458560710.1128/MMBR.69.4.585‑607.2005 16339737
    [Google Scholar]
  12. MeiselJ.E. FisherJ.F. ChangM. MobasheryS. Allosteric Inhibition of Bacterial Targets: An Opportunity for Discovery of Novel Antibacterial Classes.Topics in medicinal chemistry.Springer2017
    [Google Scholar]
  13. OteroL.H. Rojas-AltuveA. LlarrullL.I. Carrasco-LópezC. KumarasiriM. LastochkinE. FishovitzJ. DawleyM. HesekD. LeeM. JohnsonJ.W. FisherJ.F. ChangM. MobasheryS. HermosoJ.A. How allosteric control of Staphylococcus aureus penicillin binding protein 2a enables methicillin resistance and physiological function.Proc. Natl. Acad. Sci. USA201311042168081681310.1073/pnas.1300118110 24085846
    [Google Scholar]
  14. DundasJ. OuyangZ. TsengJ. BinkowskiA. TurpazY. LiangJ. CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues.Nucleic Acids Res.200634Web ServerW116W11810.1093/nar/gkl282 16844972
    [Google Scholar]
  15. FisherJ.F. MobasheryS. β-Lactam Resistance Mechanisms: Gram-Positive Bacteria and Mycobacterium tuberculosis.Cold Spring Harb. Perspect. Med.201665a02522110.1101/cshperspect.a025221 27091943
    [Google Scholar]
  16. AlhadramiH.A. HamedA.A. HassanH.M. BelbahriL. RatebM.E. SayedA.M. Flavonoids as Potential anti-MRSA Agents through Modulation of PBP2a: A Computational and Experimental Study.Antibiotics (Basel)20209956210.3390/antibiotics9090562 32878266
    [Google Scholar]
  17. NewmanD.J. CraggG.M. Natural Products as Sources of New Drugs over the Nearly Four Decades from 01/1981 to 09/2019.J. Nat. Prod.202083377080310.1021/acs.jnatprod.9b01285 32162523
    [Google Scholar]
  18. ShenB. A New Golden Age of Natural Products Drug Discovery.Cell201516361297130010.1016/j.cell.2015.11.031 26638061
    [Google Scholar]
  19. ZhaoJ. LiuJ. ZhangX. LiuZ. TseringT. ZhongY. NanP. Chemical composition of the volatiles of three wild Bergenia species from western China.Flavour Fragrance J.200621343143410.1002/ffj.1689
    [Google Scholar]
  20. SinghD.P. SrivastavaS.K. GovindarajanR. RawatA.K.S. High-Performance Liquid Chromatographic Determination of Bergenin in Different Bergenia Species.Acta Chromatogr.2007246252
    [Google Scholar]
  21. ChitmeH.R. AlokS. JainS.K. SabharwalM. Herbal Treatment for Urinary Stones.201012431
    [Google Scholar]
  22. ChauhanR. SharmaS. Polypharmacological activities of bergenia species Review Article.Int. J. Pharm. Sci. Rev. Res.201213111
    [Google Scholar]
  23. ChauhanV. RawatP. ChauhanN. REVIEW ON COMPILATION OF ETHNOPHARMACOLOGICAL PROPERTIES OF BERGENIA CILIATA: THE MEDICINAL HERB OF HIMALAYAS. Plant Archives.Plant Arch.20212110.51470/PLANTARCHIVES.2021.v21.no2.063
    [Google Scholar]
  24. ReddyU.D.C. ChawlaA.S. DeepakM. SinghD. HandaS.S. High pressure liquid chromatographic determination of bergenin and (+) -afzelechin from different parts of Paashaanbhed (Bergenia ligulata yeo).Phytochem. Anal.1999101444710.1002/(SICI)1099‑1565(199901/02)10:1<44:AID‑PCA424>3.0.CO;2‑4
    [Google Scholar]
  25. BagulM.S. RavishankaraM.N. PadhH. RajaniM. Phytochemical Evaluation and Free Radical Scavenging Properties of Rhizome of Bergenia Ciliata (Haw.).Sternb. Forma Ligulata Yeo. J. Nat. Rem.200338389
    [Google Scholar]
  26. NirumandM. HajialyaniM. RahimiR. FarzaeiM. ZingueS. NabaviS. BishayeeA. Dietary Plants for the Prevention and Management of Kidney Stones: Preclinical and Clinical Evidence and Molecular Mechanisms.Int. J. Mol. Sci.201819376510.3390/ijms19030765 29518971
    [Google Scholar]
  27. KumarV. TyagiD. Review on phytochemical, ethnomedical and biological studies of medically useful genus Bergenia.Int. J. Curr. Microbiol. Appl. Sci.201325328334
    [Google Scholar]
  28. KraujalienėV. PukalskasA. KraujalisP. VenskutonisP.R. Biorefining of Bergenia crassifolia L. roots and leaves by high pressure extraction methods and evaluation of antioxidant properties and main phytochemicals in extracts and plant material.Ind. Crops Prod.20168939039810.1016/j.indcrop.2016.05.034
    [Google Scholar]
  29. Kader MohiuddinA. Traditional System of Medicine and Nutritional Supplementation: Use vs. Regulation.Botany Research Journal2019121-413010.36478/brj.2019.1.30
    [Google Scholar]
  30. ChauhanR. DwivediJ. Secondary metabolites found in Bergenia species: A compendious review.Int. J. Pharm. Pharm. Sci.201351916
    [Google Scholar]
  31. PatelD.K. PatelK. KumarR. GadewarM. TahilyaniV. Pharmacological and analytical aspects of bergenin: a concise report.Asian Pac. J. Trop. Dis.20122216316710.1016/S2222‑1808(12)60037‑1
    [Google Scholar]
  32. AggarwalD. KaushalR. KaurT. BijarniaR.K. PuriS. SinglaS.K. The most potent antilithiatic agent ameliorating renal dysfunction and oxidative stress from Bergenia ligulata rhizome.J. Ethnopharmacol.2014158Pt A859310.1016/j.jep.2014.10.01325456425
    [Google Scholar]
  33. UddinG. SadatA. SiddiquiB.S. Comparative antioxidant and antiplasmodial activities of 11-O-galloylbergenin and bergenin isolated from Bergenia ligulata.PubMed2014311143148 24862054
    [Google Scholar]
  34. SethiA. JoshiK. SasikalaK. AlvalaM. Molecular Docking in Modern Drug Discovery: Principles and Recent Applications;IntechOpen eBooks2020
    [Google Scholar]
  35. LadeH. KimJ.S. Bacterial Targets of Antibiotics in Methicillin-Resistant Staphylococcus aureus.Antibiotics (Basel)202110439810.3390/antibiotics10040398 33917043
    [Google Scholar]
  36. PandeyR. KumarB. MeenaB. SrivastavaM. MishraT. TiwariV. PalM. NairN.K. UpretiD.K. RanaT.S. Major bioactive phenolics in Bergenia species from the Indian Himalayan region: Method development, validation and quantitative estimation using UHPLC-QqQLIT-MS/MS.PLoS One2017127e018095010.1371/journal.pone.0180950 28749965
    [Google Scholar]
  37. BermanH.M. WestbrookJ. FengZ. GillilandG. BhatT.N. WeissigH. ShindyalovI.N. BourneP.E. The Protein Data Bank.Nucleic Acids Res.200028123524210.1093/nar/28.1.235 10592235
    [Google Scholar]
  38. BurleyS.K. BhikadiyaC. BiC. BittrichS. ChenL. CrichlowG.V. ChristieC.H. DalenbergK. Di CostanzoL. DuarteJ.M. DuttaS. FengZ. GanesanS. GoodsellD.S. GhoshS. GreenR.K. GuranovićV. GuzenkoD. HudsonB.P. LawsonC.L. LiangY. LoweR. NamkoongH. PeisachE. PersikovaI. RandleC. RoseA. RoseY. SaliA. SeguraJ. SekharanM. ShaoC. TaoY.P. VoigtM. WestbrookJ.D. YoungJ.Y. ZardeckiC. ZhuravlevaM. RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences.Nucleic Acids Res.202149D1D437D45110.1093/nar/gkaa1038 33211854
    [Google Scholar]
  39. MahasenanK.V. MolinaR. BouleyR. BatuecasM.T. FisherJ.F. HermosoJ.A. ChangM. MobasheryS. Conformational Dynamics in Penicillin-Binding Protein 2a of Methicillin-Resistant Staphylococcus aureus, Allosteric Communication Network and Enablement of Catalysis.J. Am. Chem. Soc.201713952102211010.1021/jacs.6b12565 28099001
    [Google Scholar]
  40. FrischM.J. TrucksG.W. SchlegelH.B. ScuseriaG.E. RobbM.A. CheesemanJ.R. Gaussian 09, Revision B.01. Gaussian Inc., Wallingford.2009Available from: https://www.scirp.org/reference/referencespapers?referenceid=1989943 (accessed on 15- 7-2024)
    [Google Scholar]
  41. GaussView. Version 6.Shawnee Mission, KSSemichem Inc.2016
    [Google Scholar]
  42. YusofI. SegallM.D. Considering the impact drug-like properties have on the chance of success.Drug Discov. Today20131813-1465966610.1016/j.drudis.2013.02.008 23458995
    [Google Scholar]
  43. PiresD.E.V. BlundellT.L. AscherD.B. pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures.J. Med. Chem.20155894066407210.1021/acs.jmedchem.5b00104 25860834
    [Google Scholar]
  44. TrottO. OlsonA.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.J. Comput. Chem.201031245546110.1002/jcc.21334 19499576
    [Google Scholar]
  45. DelanoW.L. The PyMOL Molecular Graphics System.2002Available from: https://www.scirp.org/reference/ReferencesPapers?ReferenceID=1958992 (accessed on 15-7-2024)
    [Google Scholar]
  46. JejurikarB.L. RohaneS.H. Drug Designing in Discovery Studio.Asian J. Res. Chem202114135138
    [Google Scholar]
  47. AbrahamM.J. MurtolaT. SchulzR. PállS. SmithJ.C. HessB. LindahlE. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers.SoftwareX20151-2192510.1016/j.softx.2015.06.001
    [Google Scholar]
  48. XueW. YangF. WangP. ZhengG. ChenY. YaoX. ZhuF. What Contributes to Serotonin-Norepinephrine Reuptake Inhibitors’ Dual-Targeting Mechanism? The Key Role of Transmembrane Domain 6 in Human Serotonin and Norepinephrine Transporters Revealed by Molecular Dynamics Simulation.ACS Chem. Neurosci.2018951128114010.1021/acschemneuro.7b00490 29300091
    [Google Scholar]
  49. SchüttelkopfA.W. van AaltenD.M.F. PRODRG: a tool for high-throughput crystallography of protein-ligand complexes.Acta Crystallogr. D Biol. Crystallogr.20046081355136310.1107/S0907444904011679 15272157
    [Google Scholar]
  50. LemkulJ. From Proteins to Perturbed Hamiltonians: A Suite of Tutorials for the GROMACS-2018 Molecular Simulation Package [Article v1.0].Living J. Comput. Mol. Sci.2019111 [Article v1.0]10.33011/livecoms.1.1.5068
    [Google Scholar]
  51. BerendsenH.J.C. PostmaJ.P.M. van GunsterenW.F. DiNolaA. HaakJ.R. Molecular dynamics with coupling to an external bath.J. Chem. Phys.19848183684369010.1063/1.448118
    [Google Scholar]
  52. ZhangY. ZhengG. FuT. HongJ. LiF. YaoX. XueW. ZhuF. The binding mode of vilazodone in the human serotonin transporter elucidated by ligand docking and molecular dynamics simulations.Phys. Chem. Chem. Phys.20202295132514410.1039/C9CP05764A 32073004
    [Google Scholar]
  53. ParrinelloM. RahmanA. Polymorphic transitions in single crystals: A new molecular dynamics method.J. Appl. Phys.198152127182719010.1063/1.328693
    [Google Scholar]
  54. HessB. BekkerH. BerendsenH.J.C. FraaijeJ.G.E.M. LINCS: A linear constraint solver for molecular simulations.J. Comput. Chem.199718121463147210.1002/(SICI)1096‑987X(199709)18:12<1463:AID‑JCC4>3.0.CO;2‑H
    [Google Scholar]
  55. RoweisS.T. EM Algorithms for PCA and SPCA.Neural Information Processing Systems199710626632
    [Google Scholar]
  56. KitaoA. Principal component analysis and related methods for investigating the dynamics of biological macromolecules. J20225298317
    [Google Scholar]
  57. VermaA.K. DubeyS. SrivastavaS.K. Identification of alkaloid compounds as potent inhibitors of Mycobacterium tuberculosis NadD using computational strategies.Comput. Biol. Med.202315810686310.1016/j.compbiomed.2023.106863 37030267
    [Google Scholar]
  58. JencksW.P. On the attribution and additivity of binding energies.Proc. Natl. Acad. Sci. USA19817874046405010.1073/pnas.78.7.4046 16593049
    [Google Scholar]
  59. KumariR. KumarR. LynnA. g_mmpbsa-a GROMACS tool for high-throughput MM-PBSA calculations.J. Chem. Inf. Model.20145471951196210.1021/ci500020m 24850022
    [Google Scholar]
  60. XueW. FuT. DengS. YangF. YangJ. ZhuF. Molecular Mechanism for the Allosteric Inhibition of the Human Serotonin Transporter by Antidepressant Escitalopram.ACS Chem. Neurosci.202213334035110.1021/acschemneuro.1c00694 35041375
    [Google Scholar]
  61. CousinsK.R. Computer Review of ChemDraw Ultra 12.0.J. Am. Chem. Soc.201113321838810.1021/ja204075s 21561109
    [Google Scholar]
  62. GuanL. YangH. CaiY. SunL. DiP. LiW. LiuG. TangY. ADMET-score - a comprehensive scoring function for evaluation of chemical drug-likeness.MedChemComm201910114815710.1039/C8MD00472B 30774861
    [Google Scholar]
  63. LuL. QiangM. LiF. ZhangH. ZhangS. Theoretical investigation on the antioxidative activity of anthocyanidins: A DFT/B3LYP study.Dyes Pigments201410317518210.1016/j.dyepig.2013.12.015
    [Google Scholar]
  64. GültekinZ. DemircioğluZ. FreyW. BüyükgüngörO. A combined experimental (XRD, FT-IR, UV-VIS and NMR) and theoretical (NBO, NLO, local & global chemical activity) studies of methyl 2-((3R,4R)-3-(naphthalen-1-yl)-4-(phenylsulfonyl) isoxazolidin-2-yl) acetate.J. Mol. Struct.2020119912697010.1016/j.molstruc.2019.126970
    [Google Scholar]
  65. RodríguezmoralesS. CompadreR. CastilloR. BreenP. CompadreC. 3D-QSAR, synthesis, and antimicrobial activity of 1-alkylpyridinium compounds as potential agents to improve food safety.Eur. J. Med. Chem.200540984084910.1016/j.ejmech.2005.02.012 16194718
    [Google Scholar]
  66. KabschW. SanderC. Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features.Biopolymers198322122577263710.1002/bip.360221211 6667333
    [Google Scholar]
  67. YangJ. ChenY. YaoG. WangZ. FuX. TianY. LiY. Key factors selection on adolescents with non-suicidal self-injury: A support vector machine based approach.Front. Public Health202210104906910.3389/fpubh.2022.1049069 36438278
    [Google Scholar]
  68. HotellingH. Analysis of a complex of statistical variables into principal components.J. Educ. Psychol.193324641744110.1037/h0071325
    [Google Scholar]
  69. KollmanP.A. MassovaI. ReyesC. KuhnB. HuoS. ChongL. LeeM. LeeT. DuanY. WangW. DoniniO. CieplakP. SrinivasanJ. CaseD.A. CheathamT.E.III Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models.Acc. Chem. Res.2000331288989710.1021/ar000033j 11123888
    [Google Scholar]
  70. SinghE. JhaR.K. KhanR.J. KumarA. JainM. MuthukumaranJ. SinghA.K. A computational essential dynamics approach to investigate structural influences of ligand binding on Papain like protease from SARS-CoV-2.Comput. Biol. Chem.20229910772110.1016/j.compbiolchem.2022.107721 35835027
    [Google Scholar]
/content/journals/ctmc/10.2174/0115680266312143240805191718
Loading
/content/journals/ctmc/10.2174/0115680266312143240805191718
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher's website along with the published article.

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test