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Identification of Distinct Amino Acid Composition of Human Cruciform Binding Proteins

  • STRUCTURAL-FUNCTIONAL ANALYSIS OF BIOPOLYMERS AND THEIR COMPLEXES
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Abstract

Cruciform structures are preferential targets for many architectural and regulatory proteins, as well as a number of DNA binding proteins with weak sequence specificity. Some of these proteins are also capable of inducing the formation of cruciform structures upon DNA binding. In this paper we analyzed the amino acid composition of eighteen cruciform binding proteins of Homo sapiens. Comparison with general amino acid frequencies in all human proteins revealed unique differences, with notable enrichment for lysine and serine and/or depletion for alanine, glycine, glutamine, arginine, tyrosine and tryptophan residues. Based on bootstrap resampling and fuzzy cluster analysis, multiple molecular mechanisms of interaction with cruciform DNA structures could be suggested, including those involved in DNA repair, transcription and chromatin regulation. The proteins DEK, HMGB1 and TOP1 in particular formed a very distinctive group. Nonetheless, a strong interaction network connecting nearly all the cruciform binding proteins studied was demonstrated. Data reported here will be very useful for future prediction of new cruciform binding proteins or even construction of predictive tool/web-based application.

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REFERENCES

  1. Bochman M.L., Paeschke K., Zakian V.A. 2012. DNA secondary structures: Stability and function of G-quadruplex structures. Nat. Rev. Genet. 13, 770–780.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Siddiqui-Jain A., Grand C.L., Bearss D.J., Hurley L.H. 2002. Direct evidence for a G-quadruplex in a promoter region and its targeting with a small molecule to repress c-MYC transcription. Proc. Natl. Acad. Sci. U. S. A. 99, 11593–11598.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Wells R.D. 2007. Non-B DNA conformations, mutagenesis and disease. Trends Biochem. Sci. 32, 271–278.

    Article  CAS  PubMed  Google Scholar 

  4. Zhao J., Bacolla A., Wang G., Vasquez K.M. 2010. Non-B DNA structure-induced genetic instability and evolution. Cell. Mol. Life Sci. 67, 43–62.

    Article  CAS  PubMed  Google Scholar 

  5. Mizuuchi K., Mizuuchi M., Gellert M. 1982. Cruciform structures in palindromic DNA are favored by DNA supercoiling. J. Mol. Biol. 156, 229–243.

    Article  CAS  PubMed  Google Scholar 

  6. Chasovskikh S., Dimtchev A., Smulson M., Dritschilo A. 2005. DNA transitions induced by binding of PARP-1 to cruciform structures in supercoiled plasmids. Cytometry A. 68, 21–27.

    Article  CAS  PubMed  Google Scholar 

  7. Limanskaya O.Y. 2009. Bioinformatic analysis of inverted repeats of coronaviruses genome. Biopolymers Cell. 25, 307–314.

    Article  CAS  Google Scholar 

  8. Werbowy K., Cieśliński H., Kur J. 2009. Characterization of a cryptic plasmid pSFKW33 from Shewanella sp. 33B. Plasmid. 62, 44–49.

    Article  CAS  PubMed  Google Scholar 

  9. Pearson C.E., Zorbas H., Price G.B., Zannis-Hadjopoulos M. 1996. Inverted repeats, stem-loops, and cruciforms: Significance for initiation of DNA replication. J. Cell. Biochem. 63, 1–22.

    Article  CAS  PubMed  Google Scholar 

  10. van Holde K., Zlatanova J. 1994. Unusual DNA structures, chromatin and transcription. Bioessays. 16, 59–68.

    Article  CAS  PubMed  Google Scholar 

  11. Zannis-Hadjopoulos M., Frappier L., Khoury M., Price G.B. 1988. Effect of anti-cruciform DNA monoclonal antibodies on DNA replication. EMBO J. 7, 1837.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Waga S., Mizuno S., Yoshida M. 1990. Chromosomal protein HMG1 removes the transcriptional block caused by the cruciform in supercoiled DNA. J. Biol. Chem. 265, 19424–19428.

    CAS  PubMed  Google Scholar 

  13. Alvarez D., Novac O., Callejo M., Ruiz M.T., Price G.B., Zannis-Hadjopoulos M. 2002. 14-3-3σ is a cruciform DNA binding protein and associates in vivo with origins of DNA replication. J. Cell. Biochem. 87, 194–207.

    Article  CAS  PubMed  Google Scholar 

  14. Brázda V., Laister R.C., Jagelská E.B., Arrowsmith C. 2011. Cruciform structures are a common DNA feature important for regulating biological processes. BMC Mol. Biol. 12, 33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Bianchi M.E., Beltrame M., Paonessa G. 1989. Specific recognition of cruciform DNA by nuclear protein HMG1. Science. 243, 1056.

    Article  CAS  PubMed  Google Scholar 

  16. Waldmann T., Baack M., Richter N., Gruss C. 2003. Structure-specific binding of the proto-oncogene protein DEK to DNA. Nucleic Acids Res. 31, 7003–7010.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Brázda V., Čechová J., Battistin M., Coufal J., Jagelská E.B., Raimondi I., Inga A. 2017. The structure formed by inverted repeats in p53 response elements determines the transactivation activity of p53 protein. Biochem. Biophys. Res. Commun. 483, 516–521.

    Article  CAS  PubMed  Google Scholar 

  18. Jagelská E.B., Pivoňková H., Fojta M., Brázda V. 2010. The potential of the cruciform structure formation as an important factor influencing p53 sequence-specific binding to natural DNA targets. Biochem. Biophys. Res. Commun. 391, 1409–1414.

    Article  CAS  PubMed  Google Scholar 

  19. Cobb A.M., Jackson B.R., Kim E., Bond P.L., Bowater R.P. 2013. Sequence-specific and DNA structure-dependent interactions of Escherichia coli MutS and human p53 with DNA. Anal. Biochem. 442, 51–61.

    Article  CAS  PubMed  Google Scholar 

  20. Pane K., Durante L., Crescenzi O., Cafaro V., Pizzo E., Varcamonti M., Zanfardino A., Izzo V., Di Donato A., Notomista E. 2017. Antimicrobial potency of cationic antimicrobial peptides can be predicted from their amino acid composition: Application to the detection of “cryptic” antimicrobial peptides. J. Theor. Biol. 419, 254–265.

    Article  CAS  PubMed  Google Scholar 

  21. Settanni G., Zhou J., Suo T., Schöttler S., Landfester K., Schmid F., Mailänder V. 2017. Protein corona composition of poly (ethylene glycol)-and poly (phosphoester)-coated nanoparticles correlates strongly with the amino acid composition of the protein surface. Nanoscale. 9, 2138–2144.

    Article  CAS  PubMed  Google Scholar 

  22. Minhas F., Ross E.D., Ben-Hur A. 2017. Amino acid composition predicts prion activity. PLoS Comp. Biol. 13, e1005465.

    Article  CAS  Google Scholar 

  23. The UniProt Consortium. 2017. UniProt: The universal protein knowledgebase. Nucleic Acids Res. 45, D158–D169.

  24. Gasteiger E., Hoogland C., Gattiker A., Duvaud S., Wilkins M.R., Appel R.D., Bairoch A. 2005. Protein identification and analysis tools on the ExPASy server. In: The Proteomics Protocols Handbook. Ed. Walker J.M. Humana Press, pp. 571–607.

    Google Scholar 

  25. Tekaia F., Yeramian E., Dujon B. 2002. Amino acid composition of genomes, lifestyles of organisms, and evolutionary trends: A global picture with correspondence analysis. Gene. 297, 51–60.

    Article  CAS  PubMed  Google Scholar 

  26. Vacic V., Uversky V.N., Dunker A.K., Lonardi S. 2007. Composition Profiler: A tool for discovery and visualization of amino acid composition differences. BMC Bioinform. 8, 211.

    Article  CAS  Google Scholar 

  27. Liu B., Liu F., Wang X., Chen J., Fang L., Chou K.-C. 2015. Pse-in-One: A web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res. 43, W65–W71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Lobanov M.Y., Sokolovskiy I.V., Galzitskaya O.V. 2014. HRaP: Database of occurrence of HomoRepeats and patterns in proteomes. Nucleic Acids Res. 42, D273–D278.

    Article  CAS  PubMed  Google Scholar 

  29. Wei T., Wei M.T. 2016. Package ‘corrplot.’ Statistician. 56, 316–324.

    Google Scholar 

  30. Ishida T., Kinoshita K. 2007. PrDOS: Prediction of disordered protein regions from amino acid sequence. Nucleic Acids Res. 35, W460–W464.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Lobanov M.Y., Sokolovskiy I.V., Galzitskaya O.V. 2013. IsUnstruct: Prediction of the residue status to be ordered or disordered in the protein chain by a method based on the Ising model. J. Biomol. Struct. Dyn. 31, 1034–1043.

    Article  CAS  PubMed  Google Scholar 

  32. Suzuki R., Shimodaira H. 2013. Hierarchical clustering with P-values via multiscale bootstrap resampling. R package. https://cran.r-project.org/web/packages/pvclust/ index.html.

  33. Maechler M., Rousseeuw P., Struyf A. 2014. Package ‘cluster’. R package. https://cran.r-project.org/web/ packages/cluster/index.html.

  34. Szklarczyk D., Franceschini A., Wyder S., Forslund K., Heller D., Huerta-Cepas J., Simonovic M., Roth A., Santos A., Tsafou K.P., Kuhn M., Bork P., Jensen L.J., von Mering C. 2015. STRING v10: Protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452.

    Article  CAS  Google Scholar 

  35. Tompa P. 2002. Intrinsically unstructured proteins. Trends Biochem. Sci. 27, 527–533.

    Article  CAS  PubMed  Google Scholar 

  36. Blander G., Kipnis J., Leal J.F.M., Yu C.-E., Schellenberg G.D., Oren M. 1999. Physical and functional interaction between p53 and the Werner’s syndrome protein. J. Biol. Chemistry. 274, 29463–29469.

    Article  CAS  Google Scholar 

  37. Kawai H., Li H., Chun P., Avraham S., Avraham H.K. 2002. Direct interaction between BRCA1 and the estrogen receptor regulates vascular endothelial growth factor (VEGF) transcription and secretion in breast cancer cells. Oncogene. 21, 7730.

    Article  CAS  PubMed  Google Scholar 

  38. Ross E.D., Ben-Hur A. 2017. Amino acid composition predicts prion activity. PLoS Comput. Biol. 13, e1005465.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Sukackaite R., Jensen M.R., Mas P.J., Blackledge M., Buonomo S.B., Hart D.J. 2014. Structural and biophysical characterization of murine rif1 C terminus reveals high specificity for DNA cruciform structures. J. Biol. Chem. 289, 13903–13911.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Adhikari U.K., Rahman M.M. 2016. In silico identification and comparative analyses of active sites of copper containing nitrite reductase (CuNiR) in fungal and bacterial spp. J. Biol. Eng. Res. Rev. 3, 08–18.

  41. Wang L., Huang C., Yang M.Q., Yang J.Y. 2010. BindN+ for accurate prediction of DNA and RNA-binding residues from protein sequence features. BMC Syst. Biol. 4, S3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Keller H., Kiosze K., Sachsenweger J., Haumann S., Ohlenschläger O., Nuutinen T., Syväoja J.E., Görlach M., Grosse F., Pospiech H. 2014. The intrinsically disordered amino-terminal region of human RecQL4: Multiple DNA-binding domains confer annealing, strand exchange and G4 DNA binding. Nucleic Acids Res. 42, 12614–12627.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Laptenko O., Tong D.R., Manfredi J., Prives C. 2016. The tail that wags the dog: How the disordered C-terminal domain controls the transcriptional activities of the p53 tumor-suppressor protein. Trends Biochem. Sci. 41, 1022–1034.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Benham C.J., Savitt A.G., Bauer W.R. 2002. Extrusion of an imperfect palindrome to a cruciform in superhelical DNA: Complete determination of energetics using a statistical mechanical model. J. Mol. Biol. 316, 563–581.

    Article  CAS  PubMed  Google Scholar 

  45. Reddy K., Tam M., Bowater R.P., Barber M., Tomlinson M., Nichol Edamura K., Wang Y.-H., Pearson C.E. 2011. Determinants of R-loop formation at convergent bidirectionally transcribed trinucleotide repeats. Nucleic Acids Res. 39, 1749–1762.

    Article  CAS  PubMed  Google Scholar 

  46. Lu S., Wang G., Bacolla A., Zhao J., Spitser S., Vasquez K.M. 2015. Short inverted repeats are hotspots for genetic instability: Relevance to cancer genomes. Cell Rep. 10, 1674–1680.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Brázda V., Kolomazník J., Lỳsek J., Hároníková L., Coufal J., Št’astnỳ J. 2016. Palindrome analyser: A new web-based server for predicting and evaluating inverted repeats in nucleotide sequences. Biochem. Biophys. Res. Commun. 478, 1739–1745.

    Article  CAS  PubMed  Google Scholar 

  48. Faller M. 1999. Emboss-palindrome. Online tool. http:// www.bioinformatics.nl/cgi-bin/emboss/help/palindrome.

  49. Ye C., Ji G., Li L., Liang C. 2014. DetectIR: A novel program for detecting perfect and imperfect inverted repeats using complex numbers and vector calculation. PLoS One. 9, e113349.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Fernandes-Alnemri T., Yu J.-W., Wu J., Datta P., Alnemri E.S. 2009. AIM2 activates the inflammasome and cell death in response to cytoplasmic DNA. Nature. 458, 509–513.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Rogacheva M.V., Manhart C.M., Chen C., Guarne A., Surtees J., Alani E. 2014. Mlh1–Mlh3, a meiotic crossover and DNA mismatch repair factor, is a Msh2-Msh3-stimulated endonuclease. J. Biol. Chemistry. 289, 5664–5673.

    Article  CAS  Google Scholar 

  52. Monroe D.G., Secreto F.J., Hawse J.R., Subramaniam M., Khosla S., Spelsberg T.C. 2006. Estrogen receptor isoform-specific regulation of the retinoblastoma-binding protein 1 (RBBP1) gene: Roles of AF1 and enhancer elements. J. Biol. Chem. 281, 28596–28604.

    Article  CAS  PubMed  Google Scholar 

  53. Pietrosemoli N., García-Martín J.A., Solano R., Pazos F. 2013. Genome-wide analysis of protein disorder in Arabidopsis thaliana: Implications for plant environmental adaptation. PLoS One. 8, e55524.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Lobanov M.Y., Galzitskaya O.V. 2015. How common is disorder? Occurrence of disordered residues in four domains of life. Int. J. Mol. Sci. 16, 19490–19507.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Stefanovsky V.Y., Moss T. 2015. The cruciform DNA mobility shift assay: A tool to study proteins that recognize bent DNA. In: DNA–Protein Interactions. Eds Leblanc B.P., Rodrigue S. New York: Springer, pp. 195–203.

    Google Scholar 

  56. Čechová J., Lýsek J., Bartas M., Brázda V. 2018. Complex analyses of inverted repeats in mitochondrial genomes revealed their importance and variability. Bioinformatics. https://doi.org/10.1093/bioinformatics/btx729.

  57. Yoshida Y., Izumi H., Torigoe T., Ishiguchi H., Itoh H., Kang D., Kohno K. 2003. P53 physically interacts with mitochondrial transcription factor A and differentially regulates binding to damaged DNA. Cancer Res. 63, 3729–3734.

    CAS  PubMed  Google Scholar 

  58. Zhang H., Meng L.-H., Pommier Y. 2007. Mitochondrial topoisomerases and alternative splicing of the human TOP1mt gene. Biochimie. 89, 474–481.

    Article  CAS  PubMed  Google Scholar 

  59. Ito H., Fujita K., Tagawa K., Chen X., Homma H., Sasabe T., Shimizu J., Shimizu S., Tamura T., Muramatsu S. 2015. HMGB1 facilitates repair of mitochondrial DNA damage and extends the lifespan of mutant ataxin-1 knock-in mice. EMBO Mol. Med. 7, 78–101.

    Article  CAS  PubMed  Google Scholar 

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Correspondence to P. Pečinka.

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Bartas, M., Bažantová, P., Brázda, V. et al. Identification of Distinct Amino Acid Composition of Human Cruciform Binding Proteins. Mol Biol 53, 97–106 (2019). https://doi.org/10.1134/S0026893319010023

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