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Computational insights into the differentiated binding affinities of Myc, Max, and Omomyc dimers to the E-boxes of DNA

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Abstract

Myc is a bHLHZip protein involved in growth control and cancer, which does not form a homodimer. Myc operates in a network with its heterodimerization partner Max, the latter of which can form homodimer and heterodimer. Omomyc, a polypeptide, can block Myc to treat cancers because it can both homodimerize as efficiently as Max and heterodimerize with both Myc and Max. However, the binding efficiencies to DNA for the mentioned two homodimers (Omomyc-Omomyc and Max-Max) and three heterodimers (Myc-Max, Omomyc-Myc, and Omomyc-Max) are still controversial. By molecular dynamics simulations and MM/GBSA free energy calculation, we ranked the binding affinities of five dimers to DNA and analyzed the contribution of single amino acids to the molecular recognition of dimers to DNA. Our simulation showed that the Omomyc-Omomyc dimer exhibited the highest binding energy to DNA, followed by the Omomyc-Myc, Max-Max, Omomyc-Max, and Myc-Max dimers. Moreover, five Arg residues (i.e., 7, 8, 15, 17, and 18 numbered by Omomyc) and five Lys residues (i.e., 6, 22, 40, 43, and 48 numbered by Omomyc) dominated the binding of various dimers to DNA while the residues Asp23 and Asp37 weakened the affinities via repulsive interaction. Our simulation would provide worthy information for further development of the structure-based design of novel Omomyc-like peptide inhibitors against Myc in the future.

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References

  1. Carroll PA, Freie BW, Mathsyaraja H, Eisenman RN (2018) The MYC transcription factor network: balancing metabolism, proliferation and oncogenesis. Front Med 12(4):412–425. https://doi.org/10.1007/s11684-018-0650-z

    Article  PubMed  PubMed Central  Google Scholar 

  2. Dejure FR, Eilers M (2017) MYC and tumor metabolism: chicken and egg. EMBO J 36(23):3409–3420. https://doi.org/10.15252/embj.201796438

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Perez-Olivares M, Trento A, Rodriguez-Acebes S, Gonzalez-Acosta D, Fernandez-Antoran D, Roman-Garcia S, Martinez D, Lopez-Briones T, Torroja C, Carrasco YR, Mendez J, Moreno de Alboran I (2018) Functional interplay between c-Myc and Max in B lymphocyte differentiation. EMBO Rep 19(10):e45770. https://doi.org/10.15252/embr.201845770

  4. Luo W, Chen J, Li L, Ren X, Cheng T, Lu S, Lawal RA, Nie Q, Zhang X, Hanotte O (2019) c-Myc inhibits myoblast differentiation and promotes myoblast proliferation and muscle fibre hypertrophy by regulating the expression of its target genes, miRNAs and lincRNAs. Cell Death Differ 26(3):426–442. https://doi.org/10.1038/s41418-018-0129-0

    Article  CAS  PubMed  Google Scholar 

  5. Melnik S, Werth N, Boeuf S, Hahn EM, Gotterbarm T, Anton M, Richter W (2019) Impact of c-MYC expression on proliferation, differentiation, and risk of neoplastic transformation of human mesenchymal stromal cells. Stem Cell Res Ther 10(1):73. https://doi.org/10.1186/s13287-019-1187-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Boone DN, Hann SR (2011) The Myc-ARF-Egr1 pathway: unleashing the apoptotic power of c-Myc. Cell Cycle 10(13):2043–2044. https://doi.org/10.4161/cc.10.13.15711

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wang XN, Su XX, Cheng SQ, Sun ZY, Huang ZS, Ou TM (2019) MYC modulators in cancer: a patent review. Expert Opin Ther Pat 29(5):353–367. https://doi.org/10.1080/13543776.2019.1612878

    Article  CAS  PubMed  Google Scholar 

  8. Park S, Chung S, Kim KM, Jung KC, Park C, Hahm ER, Yang CH (2004) Determination of binding constant of transcription factor myc-max/max-max and E-box DNA: the effect of inhibitors on the binding. Biochim Biophys Acta 1670(3):217–228. https://doi.org/10.1016/j.bbagen.2003.12.007

    Article  CAS  PubMed  Google Scholar 

  9. Blackwood EM, Eisenman RN (1991) Max: a helix-loop-helix zipper protein that forms a sequence-specific DNA-binding complex with Myc. Science 251(4998):1211–1217. https://doi.org/10.1126/science.2006410

    Article  CAS  PubMed  Google Scholar 

  10. Soucek L, Helmer-Citterich M, Sacco A, Jucker R, Cesareni G, Nasi S (1998) Design and properties of a Myc derivative that efficiently homodimerizes. Oncogene 17(19):10. https://doi.org/10.1038/sj.onc.1202199

    Article  CAS  Google Scholar 

  11. Masso-Valles D, Soucek L (2020) Blocking Myc to treat cancer: reflecting on two decades of Omomyc. Cells 9(4):883. https://doi.org/10.3390/cells9040883

  12. Jung LA, Gebhardt A, Koelmel W, Ade CP, Walz S, Kuper J, von Eyss B, Letschert S, Redel C, d’Artista L, Biankin A, Zender L, Sauer M, Wolf E, Evan G, Kisker C, Eilers M (2017) OmoMYC blunts promoter invasion by oncogenic MYC to inhibit gene expression characteristic of MYC-dependent tumors. Oncogene 36(14):1911–1924. https://doi.org/10.1038/onc.2016.354

    Article  CAS  PubMed  Google Scholar 

  13. Nair SK, Burley SK (2003) X-ray structures of Myc-Max and Mad-Max recognizing DNA. Molecular bases of regulation by proto-oncogenic transcription factors. Cell 112(2):193–205. https://doi.org/10.1016/s0092-8674(02)01284-9

    Article  CAS  PubMed  Google Scholar 

  14. Ferré-D’Amaré AR, Prendergast GC, Ziff EB, Burley SK (1993) Recognition by Max of its cognate DNA through a dimeric b/HLH/Z domain. Nature 363(6424):38–45. https://doi.org/10.1038/363038a0

    Article  PubMed  Google Scholar 

  15. Case DA, Cheatham TE 3rd, Darden T, Gohlke H, Luo R, Merz KM Jr, Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The Amber biomolecular simulation programs. J Comput Chem 26(16):1668–1688. https://doi.org/10.1002/jcc.20290

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Duan Y, Wu C, Chowdhury S, Lee MC, Xiong GM, Zhang W, Yang R, Cieplak P, Luo R, Lee T, Caldwell J, Wang JM, Kollman P (2003) A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. J Comput Chem 24(16):1999–2012. https://doi.org/10.1002/jcc.10349

    Article  CAS  PubMed  Google Scholar 

  17. Gao J, Liang L, Chen Q, Zhang L, Huang T (2018) Insight into the molecular mechanism of yeast acetyl-coenzyme A carboxylase mutants F510I, N485G, I69E, E477R, and K73R resistant to soraphen A. J Comput Aided Mol Des 32(4):547–557. https://doi.org/10.1007/s10822-018-0108-z

    Article  CAS  PubMed  Google Scholar 

  18. Chen H, Wang Y, Gao Z, Yang W, Gao J (2019) Assessing the performance of three resveratrol in binding with SIRT1 by molecular dynamics simulation and MM/GBSA methods: the weakest binding of resveratrol 3 to SIRT1 triggers a possibility of dissociation from its binding site. J Comput-Aided Mol Des 33(4):437–446. https://doi.org/10.1007/s10822-019-00193-0

    Article  CAS  PubMed  Google Scholar 

  19. Shi S, Wang Q, Liu S, Qu Z, Li K, Geng X, Wang T, Gao J (2021) Characterization the performances of twofold resveratrol integrated compounds in binding with SIRT1 by molecular dynamics simulation and molecular mechanics/generalized born surface area (MM/GBSA) calculation. Chem Phys 544:111108. https://doi.org/10.1016/j.chemphys.2021.111108

    Article  CAS  Google Scholar 

  20. Onufriev A, Bashford D, Case DA (2000) Modification of the generalized born model suitable for macromolecules. J Phys Chem B 104(15):3712–3720. https://doi.org/10.1021/jp994072s

    Article  CAS  Google Scholar 

  21. Weiser J, Shenkin PS, Still WC (1999) Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO). J Comput Chem 20(2):217–230. https://doi.org/10.1002/(SICI)1096-987X(19990130)20:2%3c217::AID-JCC4%3e3.0.CO;2-A

    Article  CAS  Google Scholar 

  22. Gao J, Cui W, Du Y, Ji M (2013) Insight into the molecular mechanism about lowered dihydrofolate binding affinity to dihydrofolate reductase-like 1 (DHFRL1). J Mol Model 19(12):5187–5198. https://doi.org/10.1007/s00894-013-2018-2

    Article  CAS  PubMed  Google Scholar 

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Funding

This work was supported by the Six Talent Peaks Project in Jiangsu Province (grant number YY-046), the Qinglan Project of Jiangsu Province of China, and Jiangsu Training Programs of Innovation and Entrepreneurship for Undergraduates (grant number 202110313022Z).

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Authors and Affiliations

  1. Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, People’s Republic of China

    Yuxin Dai, Jinyuan Zhang, Yinchuan Wang, Linlin Liu & Jian Gao

  2. College of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, People’s Republic of China

    Linlin Liu

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  1. Yuxin Dai
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  2. Jinyuan Zhang
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  3. Yinchuan Wang
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  4. Linlin Liu
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Contributions

Jian Gao and Yuxin Dai performed the molecular dynamics simulations; Jinyuan Zhang, Yinchuan Wang, and Yuxin Dai performed the MM/GBSA binding free energy calculations; Jian Gao and Linlin Liu analyzed the data and wrote the paper.

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Correspondence to Linlin Liu or Jian Gao.

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Dai, Y., Zhang, J., Wang, Y. et al. Computational insights into the differentiated binding affinities of Myc, Max, and Omomyc dimers to the E-boxes of DNA. J Mol Model 28, 329 (2022). https://doi.org/10.1007/s00894-022-05261-1

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