In silico screening of protein-protein interaction modulators using the P53 and 14-3-3γ proteins as an example

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Abstract

The study of the p53 protein and its interactions with other proteins is key to understanding the mechanisms by which p53 affects tumorigenesis. Mutations in the TP53 gene, which occur in approximately 50% of human cancers, often disrupt its function, highlighting its key role in tumorigenesis. Although structurally challenging due to the presence of unstructured regions, p53 has a well-documented role in DNA damage signaling and cancer progression. In this study, the interaction between p53 and 14-3-3γ monomers was studied using in silico methods. Using tertiary structure modeling, molecular dynamics, molecular docking, and virtual ligand screening, we identified small molecule compounds that can modulate the interaction of p53 with 14-3-3γ. Key findings of the study include identification of a ligand binding pocket in the p53–14-3-3γ interaction interface, generation of full-length models of 14-3-3γ and p53 using in silico methods, and selection of potential protein-protein modulators with high affinity for the proteins under study.

About the authors

A. A. Sargsyan

Laboratory of Computational Modeling of Biological Processes, Institute of Molecular Biology, National Academy of Sciences of the Republic of Armenia (NAS RA); Russian-Armenian University

Email: g_arakelov@mb.sci.am
Armenia, Yerevan, 0014; Yerevan, 0051

N. G. Muradyan

Laboratory of Computational Modeling of Biological Processes, Institute of Molecular Biology, National Academy of Sciences of the Republic of Armenia (NAS RA)

Email: g_arakelov@mb.sci.am
Armenia, Yerevan, 0014

V. G. Arakelov

Laboratory of Computational Modeling of Biological Processes, Institute of Molecular Biology, National Academy of Sciences of the Republic of Armenia (NAS RA)

Email: g_arakelov@mb.sci.am
Armenia, Yerevan, 0014

A. K. Paronyan

Laboratory of Computational Modeling of Biological Processes, Institute of Molecular Biology, National Academy of Sciences of the Republic of Armenia (NAS RA); Russian-Armenian University

Email: g_arakelov@mb.sci.am
Armenia, Yerevan, 0014; Yerevan, 0051

G. G. Arakelov

Laboratory of Computational Modeling of Biological Processes, Institute of Molecular Biology, National Academy of Sciences of the Republic of Armenia (NAS RA); Russian-Armenian University

Email: g_arakelov@mb.sci.am
Armenia, Yerevan, 0014; Yerevan, 0051

K. B. Nazaryan

Laboratory of Computational Modeling of Biological Processes, Institute of Molecular Biology, National Academy of Sciences of the Republic of Armenia (NAS RA); Russian-Armenian University

Author for correspondence.
Email: g_arakelov@mb.sci.am
Armenia, Yerevan, 0014; Yerevan, 0051

References

  1. Gotz C., Montenarh M. (1995) P53 and its implication in apoptosis (review). Int. J. Oncol. 6(5), 1129–1135. https://doi.org/10.3892/ijo.6.5.1129
  2. Bode A.M., Dong Z. (2004) Post-translational modification of p53 in tumorigenesis. Nat. Rev. Cancer. 4(10), 793–805. https://doi.org/10.1038/nrc1455
  3. Baugh E.H., Ke H., Levine A.J., Bonneau R.A., Chan C.S. (2018) Why are there hotspot mutations in the TP53 gene in human cancers? Cell Death Differ. 25(1), 154–160. https://doi.org/10.1038/cdd.2017.180
  4. McKinney K., Mattia M., Gottifredi V., Prives C. (2004) p53 linear diffusion along DNA requires its C terminus. Mol. Cell. 16(3), 413–424. https://doi.org/10.1016/j.molcel.2004.09.032
  5. Joerger A.C., Fersht A.R. (2008) Structural biology of the tumor suppressor p53. Annu. Rev. Biochem. 77, 557–582. https://doi.org/10.1146/annurev.biochem.77.060806.
  7. Friedler A., Veprintsev D.B., Freund S.M., von Glos K.I., Fersht A.R. (2005) Modulation of binding of DNA to the C-terminal domain of p53 by acetylation. Structure. 13(4), 629–636. https://doi.org/10.1016/j.str.2005.01.020
  8. Kuusk A., Boyd H., Chen H., Ottmann C. (2020) Small-molecule modulation of p53 protein-protein interactions. Biol. Chem. 401(8), 921–931. https://doi.org/10.1515/hsz-2019-0405
  9. Moore B.W., Perez V.J., Gehring M. (1968) Assay and regional distribution of a soluble protein characteristic of the nervous system. J. Neurochem. 15, 265–272. https://doi.org/10.1111/j.1471-4159.1968.tb11610.x
  10. Fu H., Subramanian R.R., Masters S.C. (2000) 14-3-3 proteins: structure, function, and regulation. Annu. Rev. Pharmacol. Toxicol. 40, 617–647. https://doi.org/10.1146/annurev.pharmtox.40.1.617
  11. Yang X., Lee W.H., Sobott F., Papagrigoriou E., Robinson C.V., Grossmann J.G., Sundström M., Doyle D.A., Elkins J.M. (2006) Structural basis for protein-protein interactions in the 14-3-3 protein family. Proc. Natl. Acad. Sci. USA. 103(46), 17237–17242. https://doi.org/10.1073/pnas.0605779103
  12. Lee M.H., Lozano G. (2006) Regulation of the p53-MDM2 pathway by 14-3-3σ and other proteins. Semin. Cancer Biol. 16(3), 225–234. https://doi.org/10.1016/j.semcancer.2006.03.009
  13. Ginalski K. (2006) Comparative modeling for protein structure prediction. Curr. Opin. Struct. Biol. 16(2), 172–177. https://doi.org/10.1016/j.sbi.2006.02.003
  14. Jumper J., Evans R., Pritzel A., Green T., Figurnov M., Ronneberger O., Tunyasuvunakool K., Bates R., Žídek A., Potapenko A., Bridgland A., Meyer C., Kohl S.A.A., Ballard A.J., Cowie A., Romera-Paredes B., Nikolov S., Jain R., Adler J., Back T., Petersen S., Reiman D., Clancy E., Zielinski M., Steinegger M., Pacholska M., Berghammer T., Bodenstein S., Silver D., Vinyals O., Senior AW., Kavukcuoglu K., Kohli P., Hassabis D. (2021) Highly accurate protein structure prediction with AlphaFold. Nature. 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2
  15. Evans R., O’Neill M., Pritzel A., Antropova N., Senior A., Green T., Žídek A., Bates R., Blackwell S., Yim J., Ronneberger O., Bodenstein S., Zielinski M., Bridgland A., Potapenko A., Cowie A., Tunyasuvunakool K., Jain R., Clancy E., Kohli P., Jumper J., Hassabis D. (2022) Protein complex prediction with AlphaFold-Multimer. bioRxiv. https://doi.org/10.1101/2021.10.04.463034
  16. Izadi S., Anandakrishnan R., Onufriev A.V. (2014) Building water models: a different approach. J. Phys. Chem. Lett. 5(21), 3863–3871. https://doi.org/10.1021/jz501780a
  17. Raguette L.E., Cuomo A.E., Belfon K.A.A., Tian C., Hazoglou V., Witek G., Telehany S.M., Wu Q., Simmerling C. (2024) phosaa14SB and phosaa19SB: updated Amber force field parameters for phosphorylated amino acids. J. Chem. Theory Comput. 20(16), 7199–7209. https://doi.org/10.1021/acs.jctc.4c00732
  18. Tian C., Kasavajhala K., Belfon K.A.A., Raguette L., Huang H., Migues A.N., Bickel J., Wang Y., Pincay J., Wu Q., Simmerling C. (2020) ff19SB: amino-acid-specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. J. Chem. Theory Comput. 16(1), 528–552. https://doi.org/10.1021/acs.jctc.9b00591
  19. Kumari R., Kumar R., Open Source Drug Discovery Consortium, Lynn A. (2014) g_mmpbsa–a GROMACS tool for high-throughput MM-PBSA calculations. J. Chem. Inf. Model. 54(7), 1951–1962. https://doi.org/10.1021/ci500020m
  20. Massova I., Kollman P.A. (2000) Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding. Perspect. Drug Discov. Des. 18(1), 113–135. https://doi.org/10.1023/A:1008763014207
  21. Tubiana T., Carvaillo J.C., Boulard Y., Bressanelli S. (2018) TTClust: a versatile molecular simulation trajectory clustering program with graphical summaries. J. Chem. Inf. Model. 58(11), 2178–2182. https://doi.org/10.1021/acs.jcim.8b00512
  22. Abagyan R., Totrov M. (1994) Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins. J. Mol. Biol. 235(3), 983–1002. https://doi.org/10.1006/jmbi.1994.1052
  23. Hainaut P., Mann K. (2001) Zinc binding and redox control of p53 structure and function. Antioxid. Redox Signal. 3(4), 611–623. https://doi.org/10.1089/15230860152542961
  24. Falcicchio M., Ward J.A., Macip S., Doveston R.G. (2020) Regulation of p53 by the 14-3-3 protein interaction network: new opportunities for drug discovery in cancer. Cell Death Discov. 6(1), 126. https://doi.org/10.1038/s41420-020-00362-3

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