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BioMed Research International
Volume 2014, Article ID 935719, 8 pages
http://dx.doi.org/10.1155/2014/935719
Research Article

Predicting the Types of J-Proteins Using Clustered Amino Acids

1School of Public Health, Hebei United University, Tangshan 063000, China
2Key Laboratory for Neuroinformation of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
3Department of Physics, School of Sciences, Center for Genomics and Computational Biology, Hebei United University, Tangshan 063000, China
4The National Research Center for Animal Transgenic Biotechnology, Inner Mongolia University, Hohhot 010021, China

Received 24 January 2014; Revised 4 March 2014; Accepted 13 March 2014; Published 2 April 2014

Academic Editor: Dong Wang

Copyright © 2014 Pengmian Feng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. J. Caplan, D. M. Cyr, and M. G. Douglas, “Eukaryotic homologues of Escherichia coli dnaJ: a diverse protein family that functions with HSP70 stress proteins,” Molecular and Cellular Biology, vol. 4, no. 6, pp. 555–563, 1993. View at Google Scholar · View at Scopus
  2. X. B. Qiu, Y. M. Shao, S. Miao, and L. Wang, “The diversity of the DnaJ/Hsp40 family, the crucial partners for Hsp70 chaperones,” Cellular and Molecular Life Sciences, vol. 63, no. 22, pp. 2560–2570, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. M. E. Cheetham and A. J. Caplan, “Structure, function and evolution of DnaJ: conservation and adaptation of chaperone function,” Cell Stress Chaperones, vol. 3, no. 1, pp. 28–36, 1998. View at Google Scholar
  4. V. B. Rajan and P. D'Silva, “Arabidopsis thaliana J-class heat shock proteins: cellular stress sensors,” Functional & Integrative Genomics, vol. 9, no. 4, pp. 433–446, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. P. Walsh, D. Bursać, Y. C. Law, D. Cyr, and T. Lithgow, “The J-protein family: modulating protein assembly, disassembly and translocation,” EMBO Reports, vol. 5, no. 6, pp. 567–571, 2004. View at Publisher · View at Google Scholar · View at Scopus
  6. E. A. Craig, P. Huang, R. Aron, and A. Andrew, “The diverse roles of J-proteins, the obligate Hsp70 co-chaperone,” Reviews of Physiology, Biochemistry and Pharmacology, vol. 156, pp. 1–21, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. A. S. Sreedhar and P. Csermely, “Heat shock proteins in the regulation of apoptosis: new strategies in tumor therapy—a comprehensive review,” Pharmacology & Therapeutics, vol. 101, no. 3, pp. 227–257, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. T. Gotoh, K. Terada, and M. Mori, “Hsp70-DnaJ chaperone pairs prevent nitric oxide-mediated apoptosis in RAW 264.7 macrophages,” Cell Death & Differentiation, vol. 8, no. 4, pp. 357–366, 2001. View at Publisher · View at Google Scholar · View at Scopus
  9. T. Gotoh, K. Terada, S. Oyadomari, and M. Mori, “hsp70-DnaJ chaperone pair prevents nitric oxide- and CHOP-induced apoptosis by inhibiting translocation of Bax to mitochondria,” Cell Death & Differentiation, vol. 11, no. 4, pp. 390–402, 2004. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Kurisu, A. Honma, H. Miyajima, S. Kondo, M. Okumura, and K. Imaizumi, “MDG1/ERdj4, an ER-resident DnaJ family member, suppresses cell death induced by ER stress,” Genes to Cells, vol. 8, no. 2, pp. 189–192, 2003. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Z. Liu and S. A. Whitham, “Overexpression of a soybean nuclear localized type-III DnaJ domain -containing HSP40 reveals its roles in cell death and disease resistance,” Plant Journal, vol. 74, no. 1, pp. 110–121, 2013. View at Publisher · View at Google Scholar
  12. A. Mitra, L. A. Shevde, and R. S. Samant, “Multi-faceted role of HSP40 in cancer,” Clinical & Experimental Metastasis, vol. 26, no. 6, pp. 559–567, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. J. N. Sterrenberg, G. L. Blatch, and A. L. Edkins, “Human DNAJ in cancer and stem cells,” Cancer Letters, vol. 312, no. 2, pp. 129–142, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. K.-C. Chou, “Some remarks on protein attribute prediction and pseudo amino acid composition,” Journal of Theoretical Biology, vol. 273, no. 1, pp. 236–247, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. R. K. Ratheesh, S. N. Nagarajan, P. A. Arunraj et al., “HSPIR: a manually annotated heat shock protein information resource,” Bioinformatics, vol. 28, no. 21, pp. 2853–2855, 2012. View at Google Scholar
  16. W. Li and A. Godzik, “Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences,” Bioinformatics, vol. 22, no. 13, pp. 1658–1659, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. P. D. Thomas and K. A. Dill, “An iterative method for extracting energy-like quantities from protein structures,” Proceedings of the National Academy of Sciences of the United States of America, vol. 93, no. 21, pp. 11628–11633, 1996. View at Publisher · View at Google Scholar · View at Scopus
  18. L. A. Mirny and E. I. Shakhnovich, “Universally conserved positions in protein folds: reading evolutionary signals about stability, folding kinetics and function,” Journal of Molecular Biology, vol. 291, no. 1, pp. 177–196, 1999. View at Publisher · View at Google Scholar · View at Scopus
  19. A. D. Solis and S. Rackovsky, “Optimized representations and maximal information in proteins,” Proteins, vol. 38, no. 2, pp. 49–164, 2000. View at Google Scholar
  20. A. G. de Brevern, “New assessment of a structural alphabet,” In Silico Biology, vol. 5, no. 3, pp. 283–289, 2005. View at Google Scholar · View at Scopus
  21. A. G. de Brevern, C. Etchebest, and S. Hazout, “Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks,” Proteins, vol. 41, no. 3, pp. 271–287, 2000. View at Google Scholar
  22. A. P. Joseph, G. Agarwal, S. Mahajan et al., “A short survey on protein blocks,” Biophysical Reviews, vol. 2, no. 3, pp. 137–147, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. C. Etchebest, C. Benros, A. Bornot, A.-C. Camproux, and A. G. de Brevern, “A reduced amino acid alphabet for understanding and designing protein adaptation to mutation,” European Biophysics Journal, vol. 36, no. 8, pp. 1059–1069, 2007. View at Publisher · View at Google Scholar · View at Scopus
  24. Y. C. Zuo and Q. Z. Li, “Using reduced amino acid composition to predict defensin family and subfamily: Integrating similarity measure and structural alphabet,” Peptides, vol. 30, no. 10, pp. 1788–1793, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. W. Chen, P. M. Feng, and H. Lin, “Prediction of ketoacyl synthase family using reduced amino acid alphabets,” Journal of Industrial Microbiology and Biotechnology, vol. 39, no. 4, pp. 579–584, 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. Y. L. Chen, Q. Z. Li, and L. Q. Zhang, “Using increment of diversity to predict mitochondrial proteins of malaria parasite: integrating pseudo-amino acid composition and structural alphabet,” Amino Acids, vol. 42, no. 4, pp. 1309–1316, 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. P. M. Feng, W. Chen, H. Lin, and K. C. Chou, “iHSP-PseRAAAC: identifying the heat shock protein families using pseudo reduced amino acid alphabet composition,” Analytical Biochemistry, vol. 442, no. 1, pp. 118–125, 2013. View at Publisher · View at Google Scholar
  28. K. C. Chou and Y. D. Cai, “Using functional domain composition and support vector machines for prediction of protein subcellular location,” The Journal of Biological Chemistry, vol. 277, no. 48, pp. 45765–45769, 2002. View at Publisher · View at Google Scholar · View at Scopus
  29. Y. D. Cai, G. P. Zhou, and K. C. Chou, “Support vector machines for predicting membrane protein types by using functional domain composition,” Biophysical Journal, vol. 84, no. 5, pp. 3257–3263, 2003. View at Google Scholar · View at Scopus
  30. W. Chen and H. Lin, “Prediction of midbody, centrosome and kinetochore proteins based on gene ontology information,” Biochemical and Biophysical Research Communications, vol. 401, no. 3, pp. 382–384, 2010. View at Publisher · View at Google Scholar · View at Scopus
  31. M. Hayat and A. Khan, “MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM,” Journal of Theoretical Biology, vol. 292, pp. 93–102, 2012. View at Publisher · View at Google Scholar · View at Scopus
  32. H. Lin and H. Ding, “Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition,” Journal of Theoretical Biology, vol. 269, no. 1, pp. 64–69, 2011. View at Publisher · View at Google Scholar · View at Scopus
  33. X. Xiao, P. Wang, and K.-C. Chou, “iNR-physchem: a sequence-based predictor for identifying nuclear receptors and their subfamilies via physical-chemical property matrix,” PLoS ONE, vol. 7, no. 2, Article ID e30869, 2012. View at Publisher · View at Google Scholar · View at Scopus
  34. W. Chen, P. M. Feng, H. Lin, and K. C. Chou, “iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition,” Nucleic Acids Research, vol. 41, no. 6, p. e68, 2013. View at Publisher · View at Google Scholar
  35. P. M. Feng, H. Lin, and W. Chen, “Identification of antioxidants from sequence information using Naive Bayes,” Computational and Mathematical Methods in Medicine, vol. 2013, Article ID 567529, 5 pages, 2013. View at Publisher · View at Google Scholar
  36. B. Liu, X. Wang, Q. Zou, Q. Dong, and Q. Chen, “Protein remote homology detection by combining chou's pseudo amino acid composition and profile-based protein representation,” Molecular Informatics, vol. 32, no. 9-10, pp. 775–782, 2013. View at Publisher · View at Google Scholar
  37. B. Liu, X. Wang, Q. Chen, Q. Dong, and X. Lan, “Using amino acid physicochemical distance transformation for fast protein remote homology detection,” PLoS ONE, vol. 7, no. 9, Article ID e46633, 2012. View at Google Scholar
  38. P. M. Feng, H. Ding, W. Chen, and H. Lin, “Naive Bayes classifier with feature selection to identify phage virion proteins,” Computational and Mathematical Methods in Medicine, vol. 2013, Article ID 530696, 6 pages, 2013. View at Publisher · View at Google Scholar
  39. B. Liu, X. Wang, L. Lin, B. Tang, Q. Dong, and X. Wang, “Prediction of protein binding sites in protein structures using hidden Markov support vector machine,” BMC Bioinformatics, vol. 10, article 381, 2009. View at Publisher · View at Google Scholar · View at Scopus
  40. B. Liu, X. Wang, L. Lin, Q. Dong, and X. Wang, “Exploiting three kinds of interface propensities to identify protein binding sites,” Computational Biology and Chemistry, vol. 33, no. 4, pp. 303–311, 2009. View at Publisher · View at Google Scholar · View at Scopus
  41. B. Liu, X. Wang, L. Lin, Q. Dong, and X. Wang, “A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis,” BMC Bioinformatics, vol. 9, article 510, 2008. View at Publisher · View at Google Scholar · View at Scopus
  42. C. C. Chang and C. J. Lin, “LIBSVM: a library for support vector machines,” 2001, http://www.csie.ntu.edu.tw/~cjlin/libsvm. View at Google Scholar
  43. K. C. Chou and C. T. Zhang, “Prediction of protein structural classes,” Critical Reviews in Biochemistry and Molecular Biology, vol. 30, no. 4, pp. 275–349, 1995. View at Google Scholar · View at Scopus
  44. K. C. Chou and H. B. Shen, “Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms,” Nature Protocols, vol. 3, no. 2, pp. 153–162, 2008. View at Publisher · View at Google Scholar · View at Scopus
  45. M. Esmaeili, H. Mohabatkar, and S. Mohsenzadeh, “Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses,” Journal of Theoretical Biology, vol. 263, no. 2, pp. 203–209, 2010. View at Publisher · View at Google Scholar · View at Scopus
  46. S. Mei, “Predicting plant protein subcellular multi-localization by Chou's PseAAC formulation based multi-label homolog knowledge transfer learning,” Journal of Theoretical Biology, vol. 310, pp. 80–87, 2012. View at Publisher · View at Google Scholar
  47. K. C. Chou, Z. C. Wu, and X. Xiao, “iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins,” PLoS ONE, vol. 6, no. 3, Article ID e18258, 2011. View at Publisher · View at Google Scholar · View at Scopus
  48. H. Mohabatkar, “Prediction of cyclin proteins using Chou's pseudo amino acid composition,” Protein and Peptide Letters, vol. 17, no. 10, pp. 1207–1214, 2010. View at Publisher · View at Google Scholar · View at Scopus
  49. K. C. Chou, Z. C. Wu, and X. Xiao, “ILoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites,” Molecular BioSystems, vol. 8, no. 2, pp. 629–641, 2012. View at Publisher · View at Google Scholar · View at Scopus
  50. H. Mohabatkar, M. Mohammad Beigi, and A. Esmaeili, “Prediction of GABAA receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine,” Journal of Theoretical Biology, vol. 281, no. 1, pp. 18–23, 2011. View at Publisher · View at Google Scholar · View at Scopus
  51. C. Ding, L. F. Yuan, S. H. Guo, H. Lin, and W. Chen, “Identification of mycobacterial membrane proteins and their types using over-represented tripeptide compositions,” Journal of Protemics, vol. 77, pp. 321–328, 2012. View at Google Scholar
  52. K. Ohtsuka and M. Hata, “Mammalian HSP40/DNAJ homologs: cloning of novel cDNAs and a proposal for their classification and nomenclature,” Cell Stress and Chaperones, vol. 5, no. 2, pp. 98–112, 2000. View at Google Scholar · View at Scopus