Modeling and interaction analysis of the tumor necrosis factor-alpha with oligopeptides

The aim of the study was the design, characteristics and analysis of the TNFα interaction with oligopeptideanalogs of the interaction site of TNFα with TNFα-R2. Here are the results of the analysis contact zone of TNFα with TNFα-R2, determination of the potentially most effective oligopeptides, study of the binding free energy of oligopeptides and its changes depending on the number of amino acid residues in the peptide chain, as well as the TNFα form (monomer or trimer). Here are described the most typical loci of oligopeptides interaction with cytokine. To confirm the calculations, the effectiveness of the selected oligopeptides was evaluated in experiments in vitro.
For visualization of the molecular complex and work with the pdb file we are used Chimera 1.14 software with AutoDocVina utility. For in vitro studies, were used indirect enzyme immunoassay reagent kits. The initial concentration of oligopeptides is 10 µM, the initial concentration of TNFα (×10–8): 0; 0.0287; 0.0862; 0.2300; 0.5750; 1.4370 µM. When oligopeptides interact with mTNFα, the binding efficiency increase was observed with an increase in the number of amino acid residues in the chain. With tTNFα, such dependence was not observed. A statistically significant difference was  observed in the binding energy of di-, tri-, and tetra peptides with mTNFα, with tTNFα, the differences found were not statistically significant.
Thus, the data were obtained, which allowed us to come to the following conclusions: 1) the energy of interaction of oligopeptides with tTNFα does not depend on the number of amino acid residues in the oligopeptide; 2) the trimerized form of TNFα interacts most effectively with oligopeptides in comparison with mTNFα; 3) oligopeptides containing the -Trp- and being a spatial analogue of the TNFα-R2 fragment (-Trp65-Asn66-Trp67-Val68-Pro69-) interact most effectively; 4) it was selected three oligopeptides are the most promising for the binding of TNFα. The experiments in vitro confirmed the effectiveness only one oligopeptide

1. Aggarwal B., Gupta S., Kim J. Historical perspectivaes on tumor necrosis factor and its superfamily: 25 years later, a golden journey. Blood, 2012, vol. 119, no. 3, pp. 651–665. https://doi.org/10.1182/blood-2011-04-325225

2. Steeland S., Libert C., Vandenbroucke R. E. A new venue of TNF targeting. International Journal of Molecular Sciences, 2018, vol. 19, no. 5, pр. 1–55. https://doi.org/10.3390/ijms19051442

3. Idriss H. T., Naismith J. H. TNFα and the TNF receptor superfamily: structure-function relationship(s). Microscopy Research and Technique, 2000, vol. 50, no. 3, pp. 184–195. https://doi.org/10.1002/1097-0029(20000801)50:3<184::aidjemt2>3.0.co;2-h

4. Mukai Y., Nakamura T., Yoshikawa M., Yoshioka Y., Tsunoda S.-I., Nakagawa S., Yamagata Y., Tsutsumi Y. Solution of the structure of the TNF-TNFR2 complex. Science Signaling, 2010, vol. 3, iss. 148, pp. 1–10. https://doi.org/10.1126/scisignal.2000954

5. Smith R., Baglioni C. The active form of tumor necrosis factor is a trimer. Journal of Biological Chemistry, 1987, vol. 262, no. 15, pp. 6951–6954. https://doi.org/10.1016/s0021-9258(18)48183-5

6. Faustman D., Davis M. TNF receptor 2 pathway: drug target for autoimmune diseases. Nature Reviews Drug Discovery, 2010, vol. 9, no. 6, pp. 482–493. https://doi.org/10.1038/nrd3030

7. Ahmad S., Azid N. A., Boer J. C., Lim J., Chen X., Plebanski M., Mohamud R. The key role of TNF-TNFR2 interactions in the modulation of allergic inflammation: a review. Frontiers in Immunology, 2018, vol. 9, art. 2572. https://doi.org/10.3389/fimmu.2018.02572

8. Hamilton K. E., Simmons J. G., Ding S., van Landeghem L., Lund P. K. Cytokine induction of tumor necrosis factor receptor 2 is mediated by STAT3 in colon cancer cells. Molecular Cancer Research, 2011, vol. 9, no. 12, pp. 1718–1731. https://doi.org/10.1158/1541-7786.mcr-10-0210

9. Sheng Y., Li F., Qin Z. Makes tumor necrosis factor a friend of tumors. Frontiers in Immunology, 2018, vol. 9, art. 1170. https://doi.org/10.3389/fimmu.2018.01170

10. Palladino M. A., Bahjat F. R., Teodorakis E. A., Moldawer L. L. Anti-TNF-α therapies: the next generation. Nature Reviews, 2003, vol. 2, no. 9, pp. 736‒744. https://doi.org/10.1038/nrd1175

11. Desai S., Furst D. E. Problems encountered during anti-tumor necrosis factor therapy. Best Practice and Research Clinical Rheumatology, 2006, vol. 20, no. 4, pp. 757‒790. https://doi.org/10.1016/j.berh.2006.06.002

12. Sharma R., Sharma C. L. TNF-alpha inhibitors: current indications. Journal of Critical Care Medicine, 2007, vol. 11, no. 3, pp. 139‒148. https://doi.org/10.4103/0972-5229.35087

13. Boshtam M., Asgary S., Kouhpayeh S., Shariati L., Khanahmad H. Aptamers against pro- and anti-inflammatory cytokines: a review. Inflammation, 2016, vol. 40, no. 1, pp. 340‒349. https://doi.org/10.1007/s10753-016-0477-1

14. Fosgerau K., Hoffmann T. Peptide therapeutics: current status and future directions. Drug Discovery Today, 2015, vol. 20, no. 1, pp. 122‒128. https://doi.org/10.1016/j.drudis.2014.10.003

15. Jiang S.-J., Tsai P.-I., Peng S.-Y., Chang C.-C., Chung Y., Tsao H.-H., Huang H.-T., Chen S.-Y., Hsu H.-J. A potential peptide derived from cytokine receptors can bind proinfammatory cytokines as a therapeutic strategy for antiinflammation. Scientific Reports, 2019, vol. 9, art. 2317. https://doi.org/10.1038/s41598-018-36492-z

16. Zoete V., Grosdidier A., Michielin O. J. Docking, virtual high through put screening and in silico fragment-based drug design. Journal of Cellular and Molecular Medicine, 2009, vol. 13, no. 2, pp. 238–248. https://doi.org/10.1111/j.1582-4934.2008.00665.x

17. Chen S., Feng Z., Wang Y., Ma S., Hu Z., Yang P., Chai Y., Xie X. Discovery of novel ligands for TNF-α and TNF Receptor-1 through structure-based virtual screening and biological assay. Journal of Chemical Information and Modeling, 2017, vol. 57, no. 5, pp. 1101–1111. https://doi.org/10.1021/acs.jcim.6b00672

18. Zia K., Ashraf S., Jabeen A., Saeed M., Nur-e-Alam M., Ahmed S., Al-Rehaily A. J., Ul-Haq Z. Identification of potential TNF‑α inhibitors: from in silico to in vitro studies. Scientific Reports, 2020, vol. 10, art. 20974. https://doi.org/10.1038/s41598-020-77750-3

19. Morris G. M., Huey R., Lindstrom W., Sanner M. F., Belew R. K., Goodsell D. S., Olson A. J. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. Journal of Computational Chemistry, 2009, vol. 30, no. 16, pp. 2785‒2791. https://doi.org/10.1002/jcc.21256

20. Gureev М. А., Kadochnikov V. V., Porosov Yu. B. Molecular docking and its verification in the context of virtual screening. St. Petersburg, Saint Petersburg National Research University of Information Technologies, Mechanics and Optics, 2018. 50 p. (in Russian).