Skip to main content

Advertisement

SpringerLink
Log in

Screening of Prospective Antiallergic Compound as FcεRI Inhibitors and Its Antiallergic Efficacy Through Immunoinformatics Approaches

  • Original Paper
  • Published:
Molecular Biotechnology Aims and scope Submit manuscript

Abstract

The occurrence of allergy, a type I hypersensitivity reaction, is rising exponentially all over the world. Sometimes, allergy proves to be fatal for atopic patients, due to the occurrence of anaphylaxis. This study is aimed to find an anti-allergic agent that can inhibit the binding of IgE to Human High Affinity IgE Receptor (FCεRI), thereby preventing the degranulation of mast cells. A considerable number of potential anti-allergic compounds were assessed for their inhibitory strength through ADMET studies. AUTODOCK was used for estimating the binding energy between anti-allergic compounds and FCεRI, along with the interacting amino acids. The docked pose showing favorable binding energy was subjected to molecular dynamics simulation study. Marrubiin, a diterpenoid lactone from Lamiaceae, and epicatechin-3-gallate appears to be effective in blocking the Human High Affinity IgE Receptor (FCεRI). This in-silico study proposes the use of marrubiin and epicatechin-3-gallate, in the downregulation of allergic responses. Due to the better inhibition constant, future direction of this study is to analyze the safety and efficacy of marrubiin in anti-allergic activities through in-vivo clinical human trials.

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

Similar content being viewed by others

Data Availability

The PDB files of FcεRI [PDB ID: 1F2Q] is available in the Protein Data Bank (https://www.rcsb.org/). The PDB file of the ligand marrubiin is available in PubChem (https://pubchem.ncbi.nlm.nih.gov/).

Code Availability

Not Applicable.

Abbreviations

Ag:

Antigen

FcεRI:

High affinity IgE receptor

IP3:

Inositol-1,4,5-trisphosphate

ITAM:

Immunoreceptor tyrosine-based activation motif

MC:

Mast cell

PAINS:

Pan assay interference compounds

PI3K:

Phospho-inositide 3-kinase

PKC:

Protein kinase C

RMSD:

Root means square deviation

References

  1. Nagata, Y., & Suzuki, R. (2022). FcεRI: A master regulator of mast cell functions. Cells, 11(4), 622. https://doi.org/10.3390/cells11040622

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Dispenza, M. C., Bochner, B. S., & MacGlashan, D. W., Jr. (2020). Targeting the FcεRI pathway as a potential strategy to prevent food-induced anaphylaxis. Frontiers in Immunology, 11, 614402. https://doi.org/10.3389/fimmu.2020.614402

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Ando, T., & Kitaura, J. (2021). Tuning IgE: IgE-associating molecules and their effects on IgE-dependent mast cell reactions. Cells, 10(7), 1697. https://doi.org/10.3390/cells10071697

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Lecce, M., Molfetta, R., Milito, N. D., Santoni, A., & Paolini, R. (2020). FcεRI Signaling in the modulation of allergic response: Role of mast cell-derived exosomes. International Journal of Molecular Sciences, 21(15), 5464. https://doi.org/10.3390/ijms21155464

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Sakai, S., Sugawara, T., Matsubara, K., & Hirata, T. (2009). Inhibitory effect of carotenoids on the degranulation of mast cells via suppression of antigen-induced aggregation of high affinity IgE receptors. The Journal of Biological Chemistry, 284(41), 28172–28179. https://doi.org/10.1074/jbc.M109.001099

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Tokura, T., Nakano, N., Ito, T., Matsuda, H., Nagasako-Akazome, Y., Kanda, T., Ikeda, M., Okumura, K., Ogawa, H., & Nishiyama, C. (2005). Inhibitory effect of polyphenol-enriched apple extracts on mast cell degranulation in vitro targeting the binding between IgE and FcepsilonRI. Bioscience, Biotechnology, and Biochemistry, 69(10), 1974–1977. https://doi.org/10.1271/bbb.69.1974

    Article  CAS  PubMed  Google Scholar 

  7. Vo, T. S. (2020). Natural products targeting FcεRI receptor for anti-allergic therapeutics. Journal of Food Biochemistry, 44(8), 13335. https://doi.org/10.1111/jfbc.13335

    Article  CAS  Google Scholar 

  8. Popoola, O. K., Elbagory, A. M., Ameer, F., & Hussein, A. A. (2013). Marrubiin. Molecules (Basel, Switzerland), 18(8), 9049–9060. https://doi.org/10.3390/molecules18089049

    Article  CAS  PubMed  Google Scholar 

  9. Sim, L. Y., Abd Rani, N. Z., & Husain, K. (2019). Lamiaceae: An insight on their anti-allergic potential and its mechanisms of action. Frontiers in Pharmacology, 10, 677. https://doi.org/10.3389/fphar.2019.00677

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Laha, A., Bandopadhyay, R., Sarkar, A., Chakraborty, P., & Panja, A. S. (2023). Efficacy screening of prospective anti-allergic drug candidates: An in silico study. Current Bioinformatics, 18(2), 143–153. https://doi.org/10.2174/1574893618666221019092212

    Article  CAS  Google Scholar 

  11. Mnonopi, N., Levendal, R. A., Davies-Coleman, M. T., & Frost, C. L. (2011). The cardioprotective effects of marrubiin, a diterpenoid found in Leonotis leonurus extracts. Journal of Ethnopharmacology, 138(1), 67–75. https://doi.org/10.1016/j.jep.2011.08.041

    Article  CAS  PubMed  Google Scholar 

  12. Aćimović, M., Jeremić, K., Salaj, N., Gavarić, N., Kiprovski, B., Sikora, V., & Zeremski, T. (2020). Marrubium vulgare L.: A phytochemical and pharmacological overview. Molecules (Basel, Switzerland), 25(12), 2898. https://doi.org/10.3390/molecules25122898

    Article  CAS  PubMed  Google Scholar 

  13. Kapoor, Y., & Kumar, K. (2020). Structural and clinical impact of anti-allergy agents: An overview. Bioorganic Chemistry, 94, 103351. https://doi.org/10.1016/j.bioorg.2019.103351

    Article  CAS  PubMed  Google Scholar 

  14. Burley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chen, L., Crichlow, G. V., Duarte, J. M., Dutta, S., Fayazi, M., Feng, Z., Flatt, J. W., Ganesan, S. J., Goodsell, D. S., Ghosh, S., Kramer Green, R., Guranovic, V., Henry, J., Hudson, B. P., Lawson, C. L., … Zardecki, C. (2022). RCSB Protein Data Bank: Celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D. Protein Science, 31(1), 187–208. https://doi.org/10.1002/pro.4213

    Article  CAS  PubMed  Google Scholar 

  15. Kim, S., Cheng, T., He, S., Thiessen, P. A., Li, Q., Gindulyte, A., & Bolton, E. E. (2022). PubChem protein, gene, pathway, and taxonomy data collections: bridging biology and chemistry through target-centric views of PubChem data. Journal of molecular biology,434(11), 167514.https://doi.org/10.1016/j.jmb.2022.167514

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Xiong, G., Wu, Z., Yi, J., Fu, L., Yang, Z., Hsieh, C., Yin, M., Zeng, X., Wu, C., Lu, A., Chen, X., Hou, T., & Cao, D. (2021). ADMETlab 20: An integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Research, 49(W1), W5–W14. https://doi.org/10.1093/nar/gkab255

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785–2791. https://doi.org/10.1002/jcc.21256

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ahmad, M. I., Potshangbam, A. M., Javed, M., & Ahmad, M. (2020). Studies on conformational changes induced by binding of pendimethalin with human serum albumin. Chemosphere, 243, 125270. https://doi.org/10.1016/j.chemosphere.2019.125270

    Article  CAS  PubMed  Google Scholar 

  19. Bjelkmar, P., Larsson, P., Cuendet, M. A., Hess, B., & Lindahl, E. (2010). Implementation of the CHARMM force field in GROMACS: Analysis of protein stability effects from correction maps, virtual interaction sites, and water models. Journal of Chemical Theory and Computation, 6(2), 459–466. https://doi.org/10.1021/ct900549r

    Article  CAS  PubMed  Google Scholar 

  20. GROMACS development team. (2021). Gromacs Documentation Release 2021.2. https://doi.org/10.5281/zenodo.4723561

  21. Grace Development Team. (2008). Grace. https://plasma-gate.weizmann.ac.il/Grace/

  22. Attique, S. A., Hassan, M., Usman, M., Atif, R. M., Mahboob, S., Al-Ghanim, K. A., Bilal, M., & Nawaz, M. Z. (2019). A molecular docking approach to evaluate the pharmacological properties of natural and synthetic treatment candidates for use against hypertension. International Journal of Environmental Research and Public Health, 16(6), 923. https://doi.org/10.3390/ijerph16060923

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Abdullahi, M., & Adeniji, S. E. (2020). In-silico molecular docking and ADME/pharmacokinetic prediction studies of some novel carboxamide derivatives as anti-tubercular agents. Chemistry Africa, 3, 989–1000. https://doi.org/10.1007/s42250-020-00162-3

    Article  CAS  Google Scholar 

  24. Umar, A. B., Uzairu, A., Shallangwa, G. A., & Uba, S. (2020). Design of potential anti-melanoma agents against SK-MEL-5 cell line using QSAR modeling and molecular docking methods. SN Applied Sciences., 2, 815. https://doi.org/10.1007/s42452-020-2620-8

    Article  CAS  Google Scholar 

  25. Lipinski, C. A. (2004). Lead- and drug-like compounds: The rule-of-five revolution. Drug Discovery Today, 1(4), 337–341. https://doi.org/10.1016/j.ddtec.2004.11.007

    Article  CAS  PubMed  Google Scholar 

  26. Chagas, C. M., Moss, S., & Alisaraie, L. (2018). Drug metabolites and their effects on the development of adverse reactions: Revisiting Lipinski’s Rule of Five. International journal of pharmaceutics, 549(1–2), 133–149. https://doi.org/10.1016/j.ijpharm.2018.07.046

    Article  CAS  PubMed  Google Scholar 

  27. Volpe, D. A. (2011). Drug-permeability and transporter assays in Caco-2 and MDCK cell lines. Future Medicinal Chemistry, 3(16), 2063–2077. https://doi.org/10.4155/fmc.11.149

    Article  CAS  PubMed  Google Scholar 

  28. McGill, M. R., & Jaeschke, H. (2019). Biomarkers of drug-induced liver injury. Advances in Pharmacology (San Diego, Calif), 85, 221–239. https://doi.org/10.1016/bs.apha.2019.02.001

    Article  CAS  PubMed  Google Scholar 

  29. Wang, J., & Hou, T. (2015). Advances in computationally modeling human oral bioavailability. Advanced Drug Delivery Reviews, 86, 11–16. https://doi.org/10.1016/j.addr.2015.01.001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Holt, K., Nagar, S., & Korzekwa, K. (2019). Methods to predict volume of distribution. Current Pharmacology Reports, 5(5), 391–399. https://doi.org/10.1007/s40495-019-00186-5

    Article  PubMed  PubMed Central  Google Scholar 

  31. Svennebring, A. (2016). The connection between plasma protein binding and acute toxicity as determined by the LD50 value. Drug Development Research, 77(1), 3–11. https://doi.org/10.1002/ddr.21286

    Article  CAS  PubMed  Google Scholar 

  32. McCormick, A., Swaisland, H., Reddy, V. P., Learoyd, M., & Scarfe, G. (2018). In vitro evaluation of the inhibition and induction potential of olaparib, a potent poly(ADP-ribose) polymerase inhibitor, on cytochrome P450. Xenobiotica, 48(6), 555–564. https://doi.org/10.1080/00498254.2017.1346332

    Article  CAS  PubMed  Google Scholar 

  33. Smith, D. A., Beaumont, K., Maurer, T. S., & Di, L. (2018). Relevance of half-life in drug design. Journal of Medicinal Chemistry, 61(10), 4273–4282. https://doi.org/10.1021/acs.jmedchem.7b00969

    Article  CAS  PubMed  Google Scholar 

  34. Wempe, M. F. (2022). New insights into ion channels: Predicting hERG-drug interactions. International Journal of Molecular Sciences, 23(18), 10732. https://doi.org/10.3390/ijms231810732

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zeiger, E. (2019). The test that changed the world: The Ames test and the regulation of chemicals. Mutation Research Genetic Toxicology and Environmental Mutagenesis, 841, 43–48. https://doi.org/10.1016/j.mrgentox.2019.05.007

    Article  CAS  PubMed  Google Scholar 

  36. Sepay, N., Sekar, A., Halder, U. C., Alarifi, A., & Afzal, M. (2021). Anti-COVID-19 terpenoid from marine sources: A docking, admet and molecular dynamics study. Journal of Molecular Structure, 1228, 129433. https://doi.org/10.1016/j.molstruc.2020.129433

    Article  CAS  PubMed  Google Scholar 

  37. Papadatos, G., Davies, M., Dedman, N., Chambers, J., Gaulton, A., Siddle, J., Koks, R., Irvine, S. A., Pettersson, J., Goncharoff, N., Hersey, A., & Overington, J. P. (2016). SureChEMBL: A large-scale, chemically annotated patent document database. Nucleic acids research, 44(D1), D1220–D1228. https://doi.org/10.1093/nar/gkv1253

    Article  CAS  PubMed  Google Scholar 

  38. Yang, Z. Y., Yang, Z. J., Lu, A. P., Hou, T. J., & Cao, D. S. (2021). Scopy: an integrated negative design python library for desirable HTS/VS database design. Briefings in Bioinformatics, 22(3), 194. https://doi.org/10.1093/bib/bbaa194

    Article  CAS  Google Scholar 

  39. Baell, J. B., & Holloway, G. A. (2010). New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. Journal of Medicinal Chemistry, 53(7), 2719–2740. https://doi.org/10.1021/jm901137j

    Article  CAS  PubMed  Google Scholar 

  40. Bickerton, G. R., Paolini, G. V., Besnard, J., Muresan, S., & Hopkins, A. L. (2012). Quantifying the chemical beauty of drugs. Nature chemistry, 4(2), 90–98. https://doi.org/10.1038/nchem.1243

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Dhorajiwala, T., Halder, S., & Samant, L. (2019). Comparative in silico molecular docking analysis of L-threonine-3-dehydrogenase, a protein target against african trypanosomiasis using selected phytochemicals. Journal of Applied Biotechnology Reports, 6(3), 101–108. https://doi.org/10.29252/JABR.06.03.04

    Article  CAS  Google Scholar 

  42. Garman, S. C., Kinet, J. P., & Jardetzky, T. S. (1998). Crystal structure of the human high-affinity IgE receptor. Cell, 95(7), 951–961. https://doi.org/10.1016/s0092-8674(00)81719-5

    Article  CAS  PubMed  Google Scholar 

  43. Laha, A., Bandopadhyay, R., & Panja, A. S. (2021). Structural phylogeny of different allergens may reveal common epitopic footprint. Protein and Peptide Letters, 28(10), 1099–1107. https://doi.org/10.2174/0929866528666210622145710

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We are thankful to UGC-Center of Advanced Study, Department of Botany, The University of Burdwan, Burdwan and DST-FIST [DST No. SR/FST/LS-1/2018/188(C)] for providing us with all the necessary research facilities.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. UGC Centre for Advanced Study, Department of Botany, The University of Burdwan, Golapbag, Burdwan, West Bengal, 713104, India

    Anubhab Laha & Rajib Bandopadhyay

  2. Department of Botany, Chandernagore College, Chandernagore, Hooghly, West Bengal, 712136, India

    Anubhab Laha

  3. Post-Graduate Department of Biotechnology, Oriental Institute of Science and Technology, Vidyasagar University, Midnapore, West Bengal, India

    Aniket Sarkar

  4. Department of Biotechnology, Molecular Informatics Laboratory, Oriental Institute of Science and Technology, Vidyasagar University, Midnapore, West Bengal, 721102, India

    Anindya Sundar Panja

Authors
  1. Anubhab Laha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aniket Sarkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anindya Sundar Panja
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rajib Bandopadhyay
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RB conceptualized the project. AL performed the in-silico study under the supervision of RB and ASP. AL, AS and ASP have analyzed the data. The analysis tools were provided by ASP and RB. AL wrote the manuscript. AS helped in writing the draft paper. All authors have edited and read the manuscript.

Corresponding author

Correspondence to Rajib Bandopadhyay.

Ethics declarations

Competing interest

Anubhab Laha, Aniket Sarkar, Anindya Sundar Panja and Rajib Bandopadhyay declare that this article content has no conflict of interest.

Consent to Participate

This article contains information and analysis of in-silico data only, and it does not involve studies with animal or human subjects performed by any of the authors. Thus the need for consent to participate is not applicable for this research article.

Consent for Publication

This article does not contain studies with animal or human subjects performed by any of the authors. So, requirement of consent for publication is inapplicable for this article.

Ethical Approval

This article contains information and analysis of in-silico data only. This article does not involve studies with animal or human subjects performed by any of the authors, which should be approved by Ethics Committee.

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 (XLSX 375 kb)

Supplementary file2 (XLSX 16 kb)

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

Laha, A., Sarkar, A., Panja, A.S. et al. Screening of Prospective Antiallergic Compound as FcεRI Inhibitors and Its Antiallergic Efficacy Through Immunoinformatics Approaches. Mol Biotechnol 66, 26–33 (2024). https://doi.org/10.1007/s12033-023-00728-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12033-023-00728-9

Keywords

Search

Navigation