Skip to main content
Log in

In silico studies of the interaction between BRN2 protein and MORE DNA

  • Original Paper
  • Published:
Journal of Molecular Modeling Aims and scope Submit manuscript

Abstract

The incidence of skin cancer has increased in recent decades, and melanoma is the most aggressive form with the lowest chance of successful treatment. Currently, drug design projects are in progress, but available treatments against metastatic melanoma have not significantly increased survival, and few patients are cured. Thus, new therapeutic agents should be developed as more effective therapeutic options for melanoma. High levels of the BRN2 transcription factor have been related to melanoma development. However, neither the three-dimensional (3D) structure of BRN2 protein nor its POU domain has been determined experimentally. Construction of the BRN2 3D structure, and the study of its interaction with its DNA target, are important strategies for increasing the structural and functional knowledge of this protein. Thus, the aim of this work was to study the interaction between BRN2 and MORE DNA through in silico methods. The full-length BRN2 3D structure was built using the PHYRE2 and Swiss-Model programs, and molecular dynamics of this protein in complex with MORE DNA was simulated for 20 ns by the NAMD program. The BRN2 model obtained includes helix and loop regions, and the BRN2 POU domain shares structural similarity with other members of the transcription factor family. No significant conformational change of this protein occurred during dynamics simulation. These analyses revealed BRN2 residues important for the specific interaction with nucleotide bases and with more than one DNA nucleotide. This study may contribute to the design of inhibitors against BRN2 or MORE DNA as molecular targets of melanoma skin cancer.

Model of complete Brn2 protein in complex with MORE DNA after building through comparative modeling and refinement by molecular dynamics simulation

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5a–d
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. WHO (2016) Skin cancers. World Health Organization. http://www.who.int/uv/faq/skincancer/en/. Accessed 30 March 2016

  2. Karimkhani C, Gonzalez R, Dellavalle RP (2014) A review of novel therapies for melanoma. Am J Clin Dermatol 15:323–337. doi:10.1007/s40257-014-0083-7

    Article  Google Scholar 

  3. Garbe C, Leiter U (2008) Epidemiology of melanoma and nonmelanoma skin cancer-the role of sunlight. Adv Exp Med Biol 624:89–103. doi:10.1007/978-0-387-77574-6_8

    Article  Google Scholar 

  4. Eisen T, Easty DJ, Bennett DC, Goding CR (1995) The POU domain transcription factor Brn-2: elevated expression in malignant melanoma and regulation of melanocyte-specific gene expression. Oncogene 11:2157–2164

    CAS  Google Scholar 

  5. Flammiger A, Besch R, Cook AL et al (2009) SOX9 and SOX10 but not BRN2 are required for nestin expression in human melanoma cells. J Investig Dermatol 129:945–953. doi:10.1038/jid.2008.316

    Article  CAS  Google Scholar 

  6. Goodall J, Wellbrock C, Dexter TJ et al (2004) The Brn-2 transcription factor links activated BRAF to melanoma proliferation. Mol Cell Biol 24:2923–2931. doi:10.1128/MCB.24.7.2923-2931.2004

    Article  CAS  Google Scholar 

  7. Sturm RA, O’Sullivan BJ, Thomson JA et al (1994) Expression studies of pigmentation and POU-domain genes in human melanoma cells. Pigment Cell Res 7:235–240

    Article  CAS  Google Scholar 

  8. Thomson JA, Murphy K, Baker E et al (1995) The brn-2 gene regulates the melanocytic phenotype and tumorigenic potential of human melanoma cells. Oncogene 11:691–700

    CAS  Google Scholar 

  9. Ryan AK, Rosenfeld MG (1997) POU domain family values: flexibility, partnerships, and developmental codes. Genes Dev 11:1207–1225. doi:10.1101/gad.11.10.1207

    Article  CAS  Google Scholar 

  10. Besch R, Berking C (2014) POU transcription factors in melanocytes and melanoma. Eur J Cell Biol 93:55–60. doi:10.1016/j.ejcb.2013.10.001

    Article  CAS  Google Scholar 

  11. Arozarena I, Sanchez-Laorden B, Packer L et al (2011) Oncogenic BRAF induces melanoma cell invasion by downregulating the cGMP-specific phosphodiesterase PDE5A. Cancer Cell 19:45–57. doi:10.1016/j.ccr.2010.10.029

    Article  CAS  Google Scholar 

  12. Berlin I, Denat L, Steunou A-L et al (2012) Phosphorylation of BRN2 modulates its interaction with the Pax3 promoter to control melanocyte migration and proliferation. Mol Cell Biol 32:1237–1247. doi:10.1128/MCB.06257-11

    Article  CAS  Google Scholar 

  13. Bonvin E, Falletta P, Shaw H et al (2012) A phosphatidylinositol 3-Kinase-Pax3 axis regulates Brn-2 expression in melanoma. Mol Cell Biol 32:4674–4683. doi:10.1128/MCB.01067-12

    Article  CAS  Google Scholar 

  14. Goodall J, Martinozzi S, Dexter TJ et al (2004) Brn-2 expression controls melanoma proliferation and is directly regulated by beta-catenin. Mol Cell Biol 24:2915–2922. doi:10.1128/MCB.24.7.2915-2922.2004

    Article  CAS  Google Scholar 

  15. Nieto L, Joseph G, Stella A et al (2007) Differential effects of phosphorylation on DNA binding properties of N Oct-3 are dictated by protein/DNA complex structures. J Mol Biol 370:687–700. doi:10.1016/j.jmb.2007.04.072

    Article  CAS  Google Scholar 

  16. Cabos-Siguier B, Steunou AL, Joseph G et al (2009) Expression and purification of human full-length N Oct-3, a transcription factor involved in melanoma growth. Protein Expr Purif 64:39–46. doi:10.1016/j.pep.2008.10.009

    Article  CAS  Google Scholar 

  17. Cook AL, Sturm RA (2008) POU domain transcription factors: BRN2 as a regulator of melanocytic growth and tumourigenesis. Pigment Cell Melanoma Res 21:611–626. doi:10.1111/j.1755-148X.2008.00510.x

    Article  CAS  Google Scholar 

  18. Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res. doi:10.1093/nar/28.1.235

    Google Scholar 

  19. Bordoli L, Kiefer F, Arnold K et al (2009) Protein structure homology modeling using SWISS-MODEL workspace. Nat Protoc 4:1–13. doi:10.1038/nprot.2008.197

    Article  CAS  Google Scholar 

  20. Kelley LA, Mezulis S, Yates CM et al (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10:845–858. doi:10.1038/nprot.2015.053

    Article  CAS  Google Scholar 

  21. Gelpi J, Hospital A, Goñi R, Orozco M (2015) Molecular dynamics simulations: advances and applications. Adv Appl Bioinforma Chem 8:37. doi:10.2147/AABC.S70333

    Google Scholar 

  22. Millevoi S, Thion L, Joseph G et al (2001) Atypical binding of the neuronal POU protein N-Oct3 to noncanonical DNA targets. Implications for heterodimerization with HNF-3 beta. Eur J Biochem 268:781–791

    Article  CAS  Google Scholar 

  23. Jauch R, Choo SH, Ng CKL, Kolatkar PR (2011) Crystal structure of the dimeric Oct6 (POU3f1) POU domain bound to palindromic MORE DNA. Proteins Struct Funct Bioinf 79:674–677. doi:10.1002/prot.22916

    Article  CAS  Google Scholar 

  24. Pruitt KD, Brown GR, Hiatt SM et al (2014) RefSeq: an update on mammalian reference sequences. Nucleic Acids Res 42:D756–D763. doi:10.1093/nar/gkt1114

    Article  CAS  Google Scholar 

  25. Avery CL, Sitlani CM, Arking DE et al (2014) Drug-gene interactions and the search for missing heritability: a cross-sectional pharmacogenomics study of the QT interval. Pharmacogenomics J 14:6–13. doi:10.1038/tpj.2013.4

    Article  CAS  Google Scholar 

  26. Biasini M, Bienert S, Waterhouse A et al (2014) SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res 42:W252–W258. doi:10.1093/nar/gku340

    Article  CAS  Google Scholar 

  27. Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26:283–291. doi:10.1107/S0021889892009944

    Article  CAS  Google Scholar 

  28. Bowie JU, Lüthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253:164–170. doi:10.1126/science.1853201

    Article  CAS  Google Scholar 

  29. Lüthy R, Bowie JU, Eisenberg D (1992) Assessment of protein models with three-dimensional profiles. Nature 356:83–85. doi:10.1038/356083a0

    Article  Google Scholar 

  30. Melo F, Feytmans E (1998) Assessing protein structures with a non-local atomic interaction energy. J Mol Biol 277:1141–1152. doi:10.1006/jmbi.1998.1665

    Article  CAS  Google Scholar 

  31. Inc AS (2013) Discovery studio modeling environment, release 4.0, in Accelrys Discovery Studio. Accelrys Software Inc, San Diego

    Google Scholar 

  32. Best RB, Zhu X, Shim J et al (2012) Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ(1) and χ(2) dihedral angles. J Chem Theory Comput 8:3257–3273. doi:10.1021/ct300400x

    Article  CAS  Google Scholar 

  33. Hart K, Foloppe N, Baker CM et al (2012) Optimization of the CHARMM additive force field for DNA: improved treatment of the BI/BII conformational equilibrium. J Chem Theory Comput 8:348–362. doi:10.1021/ct200723y

    Article  CAS  Google Scholar 

  34. MacKerell AD, Banavali NK (2000) All-atom empirical force field for nucleic acids: II. Application to molecular dynamics simulations of DNA and RNA in solution. J Comput Chem 21:105–120. doi:10.1002/(SICI)1096-987X(20000130)21:2<105::AID-JCC3>3.0.CO;2-P

    Article  CAS  Google Scholar 

  35. MacKerell AD, Feig M, Brooks CL (2004) Improved treatment of the protein backbone in empirical force fields. J Am Chem Soc 126:698–699. doi:10.1021/ja036959e

    Article  CAS  Google Scholar 

  36. Phillips JC, Braun R, Wang W et al (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26:1781–1802. doi:10.1002/jcc.20289

    Article  CAS  Google Scholar 

  37. Jorgensen WL, Chandrasekhar J, Madura JD et al (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926. doi:10.1063/1.445869

    Article  CAS  Google Scholar 

  38. Mahoney MW, Jorgensen WL (2000) A five-site model for liquid water and the reproduction of the density anomaly by rigid, nonpolarizable potential functions. J Chem Phys 112:8910. doi:10.1063/1.481505

    Article  CAS  Google Scholar 

  39. Ibragimova GT, Wade RC (1998) Importance of explicit salt ions for protein stability in molecular dynamics simulation. Biophys J 74:2906–2911. doi:10.1016/S0006-3495(98)77997-4

    Article  CAS  Google Scholar 

  40. Darden T, York D, Pedersen L (1993) Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems. J Chem Phys 98:10089. doi:10.1063/1.464397

    Article  CAS  Google Scholar 

  41. Drabik P, Liwo A, Czaplewski C, Ciarkowski J (2001) The investigation of the effects of counterions in protein dynamics simulations. Protein Eng 14:747–752. doi:10.1093/protein/14.10.747

    Article  CAS  Google Scholar 

  42. Miyamoto S, Kollman PA (1992) SETTLE: an analytical version of the SHAKE and RATTLE algorithm for rigid water models. J Comput Chem 13:952–962. doi:10.1002/jcc.540130805

    Article  CAS  Google Scholar 

  43. Feller SE, Zhang Y, Pastor RW, Brooks BR (1995) Constant pressure molecular dynamics simulation: the Langevin piston method. J Chem Phys 103:4613. doi:10.1063/1.470648

    Article  CAS  Google Scholar 

  44. Martyna GJ, Tobias DJ, Klein ML (1994) Constant pressure molecular dynamics algorithms. J Chem Phys 101:4177. doi:10.1063/1.467468

    Article  CAS  Google Scholar 

  45. Hutchinson EG, Thornton JM (2008) PROMOTIF-A program to identify and analyze structural motifs in proteins. Protein Sci 5:212–220. doi:10.1002/pro.5560050204

    Article  Google Scholar 

  46. Roe DR, Cheatham TE (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 9:3084–3095. doi:10.1021/ct400341p

    Article  CAS  Google Scholar 

  47. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14:33–38. doi:10.1016/0263-7855(96)00018-5

    Article  CAS  Google Scholar 

  48. Luscombe NM, Laskowski RA, Thornton JM (1997) NUCPLOT: a program to generate schematic diagrams of protein-nucleic acid interactions. Nucleic Acids Res 25:4940–4945

    Article  CAS  Google Scholar 

  49. Chothia C, Lesk AM (1986) The relation between the divergence of sequence and structure in proteins. EMBO J 5:823–826

    CAS  Google Scholar 

  50. Benner SA et al (1997) Bona fide predictions of protein secondary structure using transparent analyses of multiple sequence alignments. Chem Rev 97:2725–2844. doi:10.1021/cr940469a

    Article  CAS  Google Scholar 

  51. Lobanov MY, Bogatyreva NS, Galzitskaya OV (2008) Radius of gyration as an indicator of protein structure compactness. Mol Biol 42:623–628. doi:10.1134/S0026893308040195

    Article  CAS  Google Scholar 

  52. Bissantz C, Kuhn B, Stahl M (2010) A medicinal chemist’s guide to molecular interactions. J Med Chem 53:5061–5084. doi:10.1021/jm100112j

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors are grateful for the support given from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) (APQ-00557-14 and APQ-02860-16), and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (449984/2014-1).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alex Gutterres Taranto.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

This paper belongs to Topical Collection Brazilian Symposium of Theoretical Chemistry (SBQT 2015)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

do Vale Coelho, I.E., Arruda, D.C. & Taranto, A.G. In silico studies of the interaction between BRN2 protein and MORE DNA. J Mol Model 22, 228 (2016). https://doi.org/10.1007/s00894-016-3078-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00894-016-3078-x

Keywords

Navigation