In silico analysis of STX2a-PE15-P4A8 chimeric protein as a novel immunotoxin for cancer therapy

Abstract

Today, the targeted therapies like the use of immunotoxins are increased which targeted specific antigens or receptors on the surface of tumor cells. Fibroblast growth factor-inducible 14 (Fn14) is a cytokine receptor which involves several intercellular signaling pathways and can be highly expressed in the surface of cancer cells. Since the cleavage of enzymatic domain of Pseudomonas exotoxin A (PE) occurs in one step by furin protease, we fused enzymatic subunit of Shiga-like toxin type 2a (Stx2a) with domain II and a portion of Ib of PE to increase the toxicity of Stx. Then, we genetically fused the Fv fragment of an anti-Fn14 monoclonal antibody (P4A8) to STX2a-PE15 and evaluated the STX2a-PE15-P4A8 chimeric protein as a new immunotoxin candidate. In silico analysis showed that the STX2a-PE15-P4A8 is a stable chimeric protein with high affinity to the Fn14 receptor. Despite, the STX2a-PE15-P4A8 can be bind to the B cell receptor, but it has been weakly presented by major histocompatibility complex molecules II (MHC-II). So, it may have a little immunogenicity. On the basis of our in-silico studies we predict that STX2a-PE15-P4A8 can be a good candidate for cancer immunotherapy.

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Data availability

All data analyzed during this study are included in this article.

Abbreviations

Stx:

Shiga toxin

Stx2a:

Shiga-like toxin type 2a

PE:

Pseudomonas exotoxin A

MHC-II:

Major histocompatibility complex molecules II

Fn14:

Fibroblast growth factor-inducible 14

ScFv:

Single chain fragment variable

VH:

Heavy chain variable domain

VL:

Light chain variable domain

E.coli :

Escherichia coli

References

  1. Alewine C, Hassan R, Pastan I (2015) Advances in anticancer immunotoxin therapy. Oncologist 20:176

    CAS  Article  Google Scholar 

  2. Amala S (2010) In silico analysis and 3D modeling of ASAH1 protein in farber. Lipogranulomatosis 10:06–08

    Google Scholar 

  3. Bhasin M, Raghava G (2004a) Analysis and prediction of affinity of TAP binding peptides using cascade SVM. Protein Sci 13:596–607

    CAS  Article  Google Scholar 

  4. Bhasin M, Raghava G (2004b) Prediction of CTL epitopes using QM SVM and ANN techniques. Vaccine 22:3195–3204

    CAS  Article  Google Scholar 

  5. Binnington B, Lingwood D, Nutikka A, Lingwood CA (2002) Effect of globotriaosyl ceramide fatty acid α-hydroxylation on the binding by verotoxin 1 and verotoxin 2. Neurochem Res 27:807–813

    CAS  Article  Google Scholar 

  6. Brown S, Richards C, Hanscom H, Feng S, Winkles J (2003) The Fn14 cytoplasmic tail binds tumour-necrosis-factor-receptor-associated factors 1, 2, 3 and 5 and mediates nuclear factor-kappaB activation. Biochem J 371:395–403

    CAS  Article  Google Scholar 

  7. Cancer IAfRo (2014) GLOBOCAN 2012: estimated cancer incidence, mortality and prevalence worldwide in 2012 World Health Organization. https://www.iarc.who.int/news-events/latest-world-cancer-statistics-globocan-2012-estimated-cancer-incidence-mortality-and-prevalence-worldwide-in-2012/

  8. Carbone A, Zinovyev A, Képès F (2003) Codon adaptation index as a measure of dominating codon bias. Bioinformatics 19:2005–2015

    CAS  Article  Google Scholar 

  9. Doytchinova IA, Flower DR (2008) Bioinformatic approach for identifying parasite and fungal candidate subunit vaccines. Open Vaccine J 1:22–26

    CAS  Article  Google Scholar 

  10. Hassan R et al (2014) Phase 1 study of the antimesothelin immunotoxin SS1P in combination with pemetrexed and cisplatin for front‐line therapy of pleural mesothelioma and correlation of tumor response with serum mesothelin, megakaryocyte potentiating factor, and cancer antigen 125. Cancer 120:3311–3319

    CAS  Article  Google Scholar 

  11. Ivanciuc O, Schein CH, Braun W (2003) SDAP: database and computational tools for allergenic proteins. Nucl Acids Res 31:359–362

    CAS  Article  Google Scholar 

  12. Kawa S, Onda M, Ho M, Kreitman RJ, Bera TK, Pastan I (2011) The improvement of an anti-CD22 immunotoxin. MAbs 3(5):479–486

    Article  Google Scholar 

  13. Keshtvarz M et al (2017) Bioinformatic prediction and experimental validation of a PE38-based recombinant immunotoxin targeting the Fn14 receptor in cancer cells. Immunotherapy 9:387–400

    CAS  Article  Google Scholar 

  14. Kreitman RJ (2006) Immunotoxins for targeted cancer therapy. AAPS J 8:E532–E551

    CAS  Article  Google Scholar 

  15. Kringelum JV, Lundegaard C, Lund O, Nielsen M (2012) Reliable B cell epitope predictions: impacts of method development and improved benchmarking. PLoS Comput Biol 8:e1002829

    CAS  Article  Google Scholar 

  16. Larsen MV, Lundegaard C, Lamberth K, Buus S, Lund O, Nielsen M (2007) Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinform 8:424

    Article  Google Scholar 

  17. Lovell SC et al (2003) Structure validation by Cα geometry: ϕ, ψ and Cβ deviation proteins: structure. Funct Bioinform 50:437–450

    CAS  Article  Google Scholar 

  18. Matar AJ et al (2012) Effect of pre-existing anti-diphtheria toxin antibodies on T cell depletion levels following diphtheria toxin-based recombinant anti-monkey CD3 immunotoxin treatment. Transpl Immunol 27:52–54

    CAS  Article  Google Scholar 

  19. Michalska M, Wolf P (2015) Pseudomonas exotoxin A: optimized by evolution for effective killing. Front Microbiol 6:963

    Article  Google Scholar 

  20. Nielsen M, Lundegaard C, Lund O, Keşmir C (2005) The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage. Immunogenetics 57:33–41

    CAS  Article  Google Scholar 

  21. Pastan I, Hassan R, FitzGerald DJ, Kreitman RJ (2006) Immunotoxin therapy of cancer. Nat Rev Cancer 6:559–565

    CAS  Article  Google Scholar 

  22. Rezaie E et al (2019) Different frequencies of memory B-cells induced by tetanus, botulinum, and heat-labile toxin binding domains. Microb Pathog 127:225–232

    CAS  Article  Google Scholar 

  23. Rezaie E, Bidmeshki AP, Amani J, Mahmoodzadeh Hosseini H (2020a) Bioinformatics predictions, expression, purification and structural analysis of the PE38KDEL-scfv immunotoxin against EPHA2 receptor. Int J Pept Res Ther 26:979–996. https://doi.org/10.1007/s10989-019-09901-8

    CAS  Article  Google Scholar 

  24. Rezaie E, Amani J, Pour AB, Hosseini HM (2020b) A new scfv-based recombinant immunotoxin against EPHA2-overexpressing breast cancer cells. High in vitro anti-cancer potency. Eur J Pharmacol 870:172912

    CAS  Article  Google Scholar 

  25. Saha S, Raghava G (2006) Prediction of continuous B-cell epitopes in an antigen using recurrent neural network proteins: structure. Funct Bioinform 65:40–48

    CAS  Article  Google Scholar 

  26. Sarkar A, Banik A, Pathak BK, Mukhopadhyay SK, Chatterjee S (2013) Envelope protein gene based molecular characterization of Japanese encephalitis virus clinical isolates from West Bengal, India: a comparative approach with respect to SA14–14–2 live attenuated vaccine strain. BMC Infect Dis 13:368

    CAS  Article  Google Scholar 

  27. Sen TZ, Jernigan RL, Garnier J, Kloczkowski A (2005) GOR V server for protein secondary structure prediction. Bioinformatics 21:2787–2788

    CAS  Article  Google Scholar 

  28. Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. Cancer J Clin 69:7–34

    Article  Google Scholar 

  29. Trapani G, Denora N, Trapani A, Laquintana V (2012) Recent advances in ligand targeted therapy. J Drug Target 20:1–22

    Article  Google Scholar 

  30. Tredget EE, Shankowsky HA, Rennie R, Burrell RE, Logsetty S (2004) Pseudomonas infections in the thermally injured patient. Burns 30:3–26

    Article  Google Scholar 

  31. Weldon JE, Pastan I (2011) A guide to taming a toxin–recombinant immunotoxins constructed from Pseudomonas exotoxin A for the treatment of cancer. FEBS J 278:4683–4700

    CAS  Article  Google Scholar 

  32. Whitsett TG et al (2012) Elevated expression of Fn14 in non-small cell lung cancer correlates with activated EGFR and promotes tumor cell migration and invasion. Am J Pathol 181:111–120

    CAS  Article  Google Scholar 

  33. Winkles JA (2008) The TWEAK–Fn14 cytokine–receptor axis: discovery, biology and therapeutic targeting. Nat Rev Drug Discov 7:411–425

    CAS  Article  Google Scholar 

  34. Wu H-C, Chang D-K, Huang C-T (2006) Targeted therapy for cancer. J Cancer Mol 2:57–66

    CAS  Google Scholar 

  35. Yaraguppi DA, Udapudi BB, Patil LR, Hombalimath V, Shet AR (2021) IN-silico analysis for predicting protein ligand interaction for snake venom protein. J Adv Bioinfo Res 3:345–356

    Google Scholar 

  36. Zauber AG et al (2012) Colonoscopic polypectomy and long-term prevention of colorectal-cancer deaths. N Engl J Med 366:687–696

    CAS  Article  Google Scholar 

  37. Zhang Y (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinform 9:40

    Article  Google Scholar 

  38. Zhang Q et al (2008) Immune epitope database analysis resource (IEDB-AR). Nucl Acids Res 36:W513–W518

    CAS  Article  Google Scholar 

  39. Zhou H, Marks JW, Hittelman WN, Yagita H, Cheung LH, Rosenblum MG, Winkles JA (2011) Development and characterization of a potent immunoconjugate targeting the Fn14 receptor on solid tumor cells. Mol Cancer Ther 10:1276–1288

    CAS  Article  Google Scholar 

  40. Zuker M (2003) Mfold web server for nucleic acid folding and hybridization prediction. Nucl Acids Res 31:3406–3415

    CAS  Article  Google Scholar 

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Funding

This study is part of a research project with Grant No. 29903 that supported by Tehran University of Medical Sciences & Health Services.

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Authors

Contributions

MK, JA, and JF designed the chimeric protein for the article. MK, ER and JA assessed the chimeric protein through various bioinformatic softwares and drafted the early version of the manuscript. MD and JF evaluated and discussed the software and the results. MD, ER and JF provided comments on the manuscript and contributed to editing of the manuscript. MD received the grant for the current study. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jafar Amani or Masoumeh Douraghi.

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The authors declare that they have no competing interests.

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Keshtvarz, M., Salimian, J., Amani, J. et al. In silico analysis of STX2a-PE15-P4A8 chimeric protein as a novel immunotoxin for cancer therapy. In Silico Pharmacol. 9, 19 (2021). https://doi.org/10.1007/s40203-021-00079-w

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Keywords

  • Cancer
  • Exotoxin A
  • Fn14 receptor
  • P4A8
  • STX2a