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A knowledge-based method to predict the cooperative relationship between transcription factors

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Abstract

Identifying the cooperation between transcription factors is crucial and challenging to uncover the mystery behind the complex gene expression patterns. Computational methods aimed to infer transcription factor cooperation are expected to get good results if we can integrate the knowledge (existed functional/structural annotations) of proteins. In this contribution, we proposed an information integrative computational framework to infer the cooperation between transcription factors, which relies on the hybridization-space method that can integrate the annotation information of proteins. In our computational experiments, by using function domain annotations only, on our testing dataset, the overall prediction accuracy and the specificity reaches 84.3% and 76.9%, respectively, which is a fairly good result and outperforms the prediction by both amino acid composition-based method and BLAST-based approach. The corresponding online service TFIPS (Transcription Factor Interaction Prediction System) is available on http://pcal.biosino.org/cgi-bin/TFIPS/TFIPS.pl.

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References

  1. van Dam H, Castellazzi M (2001) Distinct roles of Jun: Fos and Jun: ATF dimers in oncogenesis. Oncogene 20: 2453–2464. doi:10.1038/sj.onc.1204239

    Article  PubMed  Google Scholar 

  2. Benbrook DM, Jones NC (1990) Heterodimer formation between CREB and JUN proteins. Oncogene 5: 295–302

    CAS  PubMed  Google Scholar 

  3. Ivashkiv LB et al (1990) mXBP/CRE-BP2 and c-Jun form a complex which binds to the cyclic AMP, but not to the 12-O-tetradecanoylphorbol-13-acetate, response element. Mol Cell Biol 10: 1609–1621

    CAS  PubMed  Google Scholar 

  4. Karin M, Hunter T (1995) Transcriptional control by protein phosphorylation: signal transmission from the cell surface to the nucleus. Curr Biol 5: 747–757. doi:10.1016/S0960-9822

    Article  CAS  PubMed  Google Scholar 

  5. Karin M, Liu Z, Zandi E (1997) AP-1 function and regulation. Curr Opin Cell Biol 9: 240–246. doi:10.1016/S0955-0674

    Article  CAS  PubMed  Google Scholar 

  6. Huguier S et al (1998) Transcription factor ATF2 cooperates with v-Jun to promote growth factor-independent proliferation in vitro and tumor formation in vivo. Mol Cell Biol 18: 7020–7029

    CAS  PubMed  Google Scholar 

  7. Yuan Z et al (2009) Opposing roles for ATF2 and c-Fos in c-Jun-mediated neuronal apoptosis. Mol Cell Biol 29: 2431–2442. doi:10.1128/MCB.01344-08

    Article  CAS  PubMed  Google Scholar 

  8. Lin WC et al (2002) Transcriptional activation of C/EBPbeta gene by c-Jun and ATF2. DNA Cell Biol 21: 551–560

    Article  CAS  PubMed  Google Scholar 

  9. Kato M et al (2004) Identifying combinatorial regulation of transcription factors and binding motifs. Genome Biol 5: R56. doi:10.1186/gb-2004-5-8-r56

    Article  PubMed  Google Scholar 

  10. Nagamine N, Kawada Y, Sakakibara Y (2005) Identifying cooperative transcriptional regulations using protein-protein interactions. Nucleic Acids Res 33: 4828–4837. doi:10.1093/nar/gki793

    Article  CAS  PubMed  Google Scholar 

  11. Yu X et al (2006) Genome-wide prediction and characterization of interactions between transcription factors in Saccharomyces cerevisiae. Nucleic Acids Res 34: 917–927. doi:10.1093/nar/gkj487

    Article  CAS  PubMed  Google Scholar 

  12. Cai YD, Chou KC (2005) Using functional domain composition to predict enzyme family classes. J Proteome Res 4: 109–111. doi:10.1021/pr049835p

    Article  CAS  PubMed  Google Scholar 

  13. Qian Z, Cai YD, Li YX (2006) Automatic transcription factor classifier based on functional domain composition. Biochem Biophys Res Commun 347: 141–144. doi:10.1016/j.bbrc.2006.06.060

    Article  CAS  PubMed  Google Scholar 

  14. Mulder NJ et al (2002) InterPro: an integrated documentation resource for protein families, domains and functional sites. Brief Bioinform 3: 225–235. doi:10.1093/bib/3.3.225

    Article  CAS  PubMed  Google Scholar 

  15. Cai YD, Chou KC (2004) Predicting 22 protein localizations in budding yeast. Biochem Biophys Res Commun 323: 425–428. doi:10.1016/j.bbrc.2004.08.113

    Article  CAS  PubMed  Google Scholar 

  16. Chou KC, Cai YD (2006) Predicting protein-protein interactions from sequences in a hybridization space. J Proteome Res 5: 316–322. doi:10.1021/pr050331g

    Article  CAS  PubMed  Google Scholar 

  17. Qian Z, Cai YD, Li YX (2006) A novel computational method to predict transcription factor DNA binding preference. Biochem Biophys Res Commun 348: 1034–1037. doi:10.1016/j.bbrc.2006.07.149

    Article  CAS  PubMed  Google Scholar 

  18. Yu X, Wang C, Li YX (2006) Classification of protein quaternary structure by functional domain composition. BMC Bioinformatics 7: 187–192. doi:10.1186/1471-2105-7-187

    Article  PubMed  Google Scholar 

  19. Matys V et al (2006) TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res 34: D108–D110. doi:10.1093/nar/gkj143

    Article  CAS  PubMed  Google Scholar 

  20. Wingender E et al (1996) TRANSFAC: a database on transcription factors and their DNA binding sites. Nucleic Acids Res 24: 238–241

    Article  CAS  PubMed  Google Scholar 

  21. Cai YD, Chou KC (2006) Predicting membrane protein type by functional domain composition and pseudo-amino acid composition. J Theor Biol 238: 395–400. doi:10.1016/j.jtbi.2005.05.035

    Article  CAS  PubMed  Google Scholar 

  22. Cai YD, Chou KC (2004) Predicting subcellular localization of proteins in a hybridization space. Bioinformatics 20: 1151–1156

    Article  CAS  PubMed  Google Scholar 

  23. Lu L et al (2007) ECS: an automatic enzyme classifier based on functional domain composition. Comput Biol Chem 31: 226–332. doi:10.1016/j.compbiolchem.2007.03.008

    Article  CAS  PubMed  Google Scholar 

  24. Altschul SF et al (1990) Basic local alignment search tool. J Mol Biol 215: 403–410

    CAS  PubMed  Google Scholar 

  25. Cai YD, Doig AJ (2004) Prediction of Saccharomyces cerevisiae protein functional class from functional domain composition. Bioinformatics 20: 1292–1300

    Article  CAS  PubMed  Google Scholar 

  26. Cai YD, Bork P (1998) Homology-based gene prediction using neural nets. Anal Biochem 265: 269–274. doi:10.1006/abio.1998.2876

    Article  CAS  PubMed  Google Scholar 

  27. Cai CZ et al (2003) SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res 31: 3692–3697

    Article  CAS  PubMed  Google Scholar 

  28. Cai YD, Chou KC (2003) Nearest neighbour algorithm for predicting protein subcellular location by combining functional domain composition and pseudo-amino acid composition. Biochem Biophys Res Commun 305: 407–411. doi:10.1016/S0006-291X(03)00775-7

    Article  CAS  PubMed  Google Scholar 

  29. Cai YD, Chou KC (2005) Using functional domain composition to predict enzyme family classes. J Proteome Res 4: 109–111. doi:10.1021/pr049835p

    Article  CAS  PubMed  Google Scholar 

  30. Quevillon E et al (2005) InterProScan: protein domains identifier. Nucleic Acids Res 33: W116–W120. doi:10.1093/nar/gki442

    Article  CAS  PubMed  Google Scholar 

  31. Zdobnov EM, Apweiler R (2001) InterProScan–an integration platform for the signature-recognition methods in InterPro. Bioinformatics 17: 847–848

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Key Lab of Molecular Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China

    Lingyi Lu, Ziliang Qian & Yixue Li

  2. Graduate School of the Chinese Academy of Sciences, 19 Yuquan Road, Beijing, 100039, China

    Lingyi Lu & Ziliang Qian

  3. Institute of Health Science Shanghai Institute for Biological Science, Chinese Academy of Science, 225 South ChongQing Road, Shanghai, 200025, China

    XiaoHe Shi

  4. CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China

    Haipeng Li

  5. Institute of System Biology, Shanghai University, 99 ShangDa Road, Shanghai, 200244, China

    Yu-Dong Cai

  6. Centre for Computational Systems Biology, Fudan University, 220 HanDan Road, Shanghai, 200433, China

    Yu-Dong Cai

  7. Shanghai Center for Bioinformation Technology, 100 Qinzhou Road, Shanghai, 200235, China

    Yixue Li

Authors
  1. Lingyi Lu
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  2. Ziliang Qian
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  3. XiaoHe Shi
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  4. Haipeng Li
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  5. Yu-Dong Cai
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  6. Yixue Li
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Corresponding authors

Correspondence to Haipeng Li, Yu-Dong Cai or Yixue Li.

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Lu, L., Qian, Z., Shi, X. et al. A knowledge-based method to predict the cooperative relationship between transcription factors. Mol Divers 14, 815–819 (2010). https://doi.org/10.1007/s11030-009-9177-1

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  • DOI: https://doi.org/10.1007/s11030-009-9177-1

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