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BioMed Research International
Volume 2016, Article ID 1654623, 8 pages
http://dx.doi.org/10.1155/2016/1654623
Research Article

Identification of Bacterial Cell Wall Lyases via Pseudo Amino Acid Composition

1Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics and Center for Information in Biomedicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
2Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China
3School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
4Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China

Received 24 April 2016; Accepted 30 May 2016

Academic Editor: Qin Ma

Copyright © 2016 Xin-Xin Chen 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. D. Trudil, “Phage lytic enzymes: a history,” Virologica Sinica, vol. 30, no. 1, pp. 26–32, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. Y. Li, C. Wang, Z. Miao et al., “ViRBase: a resource for virus–host ncRNA-associated interactions,” Nucleic Acids Research, vol. 43, no. 1, pp. D578–D582, 2015. View at Publisher · View at Google Scholar · View at Scopus
  3. E. Hankin, “L'action bactericide des eaux de la Jumna et du Gange sur le vibrion du cholera,” Annales de l'Institut Pasteur, vol. 10, pp. 511–523, 1896. View at Google Scholar
  4. V. A. Fischetti, “Bacteriophage lytic enzymes: novel anti-infectives,” Trends in Microbiology, vol. 13, no. 10, pp. 491–496, 2005. View at Publisher · View at Google Scholar · View at Scopus
  5. D. C. Osipovitch, S. Therrien, and K. E. Griswold, “Discovery of novel S. aureus autolysins and molecular engineering to enhance bacteriolytic activity,” Applied Microbiology and Biotechnology, vol. 99, no. 15, pp. 6315–6326, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. C. C. Kietzman, G. Gao, B. Mann, L. Myers, and E. I. Tuomanen, “Dynamic capsule restructuring by the main pneumococcal autolysin LytA in response to the epithelium,” Nature Communications, vol. 7, article 10859, 2016. View at Publisher · View at Google Scholar
  7. H. Oliveir, L. D. R. Melo, S. B. Santos et al., “Molecular aspects and comparative genomics of bacteriophage endolysins,” Journal of Virology, vol. 87, no. 8, pp. 4558–4570, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. R. Brunoghe and J. Maisin, “Essais de therapeutique au moyen du bacteriophage du staphylocoque,” Journal des Comptes Rendus de la Société de Biologie, vol. 85, pp. 1029–1121, 1921. View at Google Scholar
  9. W. R. Maxted, “The active agent in nascent phage lysis of streptococci,” Microbiology, vol. 16, no. 3, pp. 584–595, 1957. View at Publisher · View at Google Scholar · View at Scopus
  10. R. M. Krause, “Studies on the bacteriophages of hemolytic streptococci. II. Antigens released from the streptococcal cell wall by a phage-associated lysin,” The Journal of Experimental Medicine, vol. 108, no. 6, pp. 803–821, 1958. View at Publisher · View at Google Scholar · View at Scopus
  11. V. A. Fischetti, E. C. Gotschlich, and A. W. Bernheimer, “Purification and physical properties of group C streptococcal phage-associated lysin,” The Journal of Experimental Medicine, vol. 133, no. 5, pp. 1105–1117, 1971. View at Publisher · View at Google Scholar · View at Scopus
  12. R. Schuch, D. Nelson, and V. A. Fischetti, “A bacteriolytic agent that detects and kills Bacillus anthracis,” Nature, vol. 418, no. 6900, pp. 884–889, 2002. View at Publisher · View at Google Scholar · View at Scopus
  13. O. Salazar and J. A. Asenjo, “Enzymatic lysis of microbial cells,” Biotechnology Letters, vol. 29, no. 7, pp. 985–994, 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. H. J. Rogers, H. R. Perkins, and J. B. Ward, Microbial Cell Walls and Membranes, Chapman and Hall London, 1980.
  15. M. McCarty, The Transforming Principle: Discovering That Genes Are Made of DNA, W. W. Norton & Company, New York, NY, USA, 1986.
  16. J. M. Sanchez-Puelles, C. Ronda, J. L. Garcia, P. Garcia, R. Lopez, and E. Garcia, “Searching for autolysin functions. Characterization of a pneumococcal mutant deleted in the lytA gene,” European Journal of Biochemistry, vol. 158, no. 2, pp. 289–293, 1986. View at Publisher · View at Google Scholar · View at Scopus
  17. 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
  18. M. K. Leong and T.-H. Chen, “Prediction of cytochrome P450 2B6-substrate interactions using pharmacophore ensemble/support vector machine (PhE/SVM) approach,” Medicinal Chemistry, vol. 4, no. 4, pp. 396–406, 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. B. Liu, D. Zhang, R. Xu et al., “Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection,” Bioinformatics, vol. 30, no. 4, pp. 472–479, 2014. View at Publisher · View at Google Scholar · View at Scopus
  20. D. Li, Y. Ju, and Q. Zou, “Protein folds prediction with hierarchical structured SVM,” Current Proteomics, vol. 13, no. 2, pp. 79–85, 2016. View at Publisher · View at Google Scholar
  21. A. Reinhardt and T. Hubbard, “Using neural networks for prediction of the subcellular location of proteins,” Nucleic Acids Research, vol. 26, no. 9, pp. 2230–2236, 1998. View at Publisher · View at Google Scholar · View at Scopus
  22. X. Zhao, Q. Zou, B. Liu, and X. Liu, “Exploratory predicting protein folding model with random forest and hybrid features,” Current Proteomics, vol. 11, no. 4, pp. 289–299, 2014. View at Google Scholar · View at Scopus
  23. H. Shen and K.-C. Chou, “Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo-amino acid composition to predict membrane protein types,” Biochemical and Biophysical Research Communications, vol. 334, no. 1, pp. 288–292, 2005. View at Publisher · View at Google Scholar · View at Scopus
  24. C. Yan, J. Hu, and Y. Wang, “Discrimination of outer membrane proteins using a K-nearest neighbor method,” Amino Acids, vol. 35, no. 1, pp. 65–73, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. T.-L. Zhang, Y.-S. Ding, and K.-C. Chou, “Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern,” Journal of Theoretical Biology, vol. 250, no. 1, pp. 186–193, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  26. A. Bulashevska, M. Stein, D. Jackson, and R. Eils, “Prediction of small molecule binding property of protein domains with Bayesian classifiers based on Markov chains,” Computational Biology and Chemistry, vol. 33, no. 6, pp. 457–460, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. A. Bulashevska and R. Eils, “Using Bayesian multinomial classifier to predict whether a given protein sequence is intrinsically disordered,” Journal of Theoretical Biology, vol. 254, no. 4, pp. 799–803, 2008. View at Publisher · View at Google Scholar · View at Scopus
  28. H. Lin and Q.-Z. Li, “Using pseudo amino acid composition to predict protein structural class: approached by incorporating 400 dipeptide components,” Journal of Computational Chemistry, vol. 28, no. 9, pp. 1463–1466, 2007. View at Publisher · View at Google Scholar · View at Scopus
  29. H. Lin, “The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition,” Journal of Theoretical Biology, vol. 252, no. 2, pp. 350–356, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  30. C. Lin, W. Chen, C. Qiu, Y. Wu, S. Krishnan, and Q. Zou, “LibD3C: ensemble classifiers with a clustering and dynamic selection strategy,” Neurocomputing, vol. 123, pp. 424–435, 2014. View at Publisher · View at Google Scholar · View at Scopus
  31. X. Zeng, S. Yuan, X. Huang, and Q. Zou, “Identification of cytokine via an improved genetic algorithm,” Frontiers of Computer Science, vol. 9, no. 4, pp. 643–651, 2015. View at Publisher · View at Google Scholar · View at Scopus
  32. L. Song, D. Li, X. Zeng, Y. Wu, L. Guo, and Q. Zou, “nDNA-prot: identification of DNA-binding proteins based on unbalanced classification,” BMC Bioinformatics, vol. 15, article 298, 2014. View at Publisher · View at Google Scholar · View at Scopus
  33. B. Liu, J. Chen, and X. Wang, “Application of learning to rank to protein remote homology detection,” Bioinformatics, vol. 31, no. 21, pp. 3492–3498, 2015. View at Publisher · View at Google Scholar · View at Scopus
  34. T. G. Dietterich, “Ensemble methods in machine learning,” in Multiple Classifier Systems, pp. 1–15, Springer, 2000. View at Google Scholar
  35. T. G. Dietterich, “Ensemble learning,” in The Handbook of Brain Theory and Neural Networks, vol. 2, pp. 110–125, MIT Press, 2002. View at Google Scholar
  36. M. H. Smith, “The amino acid composition of proteins,” Journal of Theoretical Biology, vol. 13, pp. 261–282, 1966. View at Publisher · View at Google Scholar · View at Scopus
  37. J. Cedano, P. Aloy, J. A. Perez-Pons, and E. Querol, “Relation between amino acid composition and cellular location of proteins,” Journal of Molecular Biology, vol. 266, no. 3, pp. 594–600, 1997. View at Publisher · View at Google Scholar · View at Scopus
  38. K.-C. Chou, “Prediction of protein cellular attributes using pseudo-amino acid composition,” Proteins: Structure, Function, and Bioinformatics, vol. 43, no. 3, pp. 246–255, 2001. View at Publisher · View at Google Scholar · View at Scopus
  39. S. Saha and G. P. S. Raghava, “BcePred: prediction of continuous B-cell epitopes in antigenic sequences using physico-chemical properties,” in Artificial Immune Systems, G. Nicosia, V. Cutello, P. J. Bentley, and J. Timmis, Eds., vol. 3239 of Lecture Notes in Computer Science, pp. 197–204, Springer, New York, NY, USA, 2004. View at Publisher · View at Google Scholar
  40. L. Wei, M. Liao, X. Gao, and Q. Zou, “An improved protein structural classes prediction method by incorporating both sequence and structure information,” IEEE Transactions on NanoBioscience, vol. 14, no. 4, pp. 339–349, 2015. View at Publisher · View at Google Scholar · View at Scopus
  41. C.-S. Yu, C.-J. Lin, and J.-K. Hwang, “Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions,” Protein Science, vol. 13, no. 5, pp. 1402–1406, 2004. View at Publisher · View at Google Scholar · View at Scopus
  42. B. Liu, F. Liu, X. Wang, J. Chen, L. Fang, and K. Chou, “Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences,” Nucleic Acids Research, vol. 43, no. W1, pp. W65–W71, 2015. View at Publisher · View at Google Scholar
  43. H. Ding, L. Luo, and H. Lin, “Prediction of cell wall lytic enzymes using Chou's amphiphilic pseudo amino acid composition,” Protein and Peptide Letters, vol. 16, no. 4, pp. 351–355, 2009. View at Publisher · View at Google Scholar · View at Scopus
  44. A. M. Bairoch, R. Apweiler, C. H. Wu et al., “The universal protein resource (UniProt),” Nucleic Acids Research, vol. 33, pp. D154–D159, 2005. View at Publisher · View at Google Scholar · View at Scopus
  45. 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
  46. K.-C. Chou, “Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes,” Bioinformatics, vol. 21, no. 1, pp. 10–19, 2005. View at Publisher · View at Google Scholar · View at Scopus
  47. H. Tang, W. Chen, and H. Lin, “Identification of immunoglobulins using Chou's pseudo amino acid composition with feature selection technique,” Molecular BioSystems, vol. 12, no. 4, pp. 1269–1275, 2016. View at Publisher · View at Google Scholar
  48. P.-P. Zhu, W.-C. Li, Z.-J. Zhong et al., “Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition,” Molecular BioSystems, vol. 11, no. 2, pp. 558–563, 2015. View at Publisher · View at Google Scholar · View at Scopus
  49. W.-C. Li, E.-Z. Deng, H. Ding, W. Chen, and H. Lin, “IORI-PseKNC: a predictor for identifying origin of replication with pseudo k-tuple nucleotide composition,” Chemometrics and Intelligent Laboratory Systems, vol. 141, pp. 100–106, 2015. View at Publisher · View at Google Scholar · View at Scopus
  50. 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
  51. C. Chen, X. Zhou, Y. Tian, X. Zou, and P. Cai, “Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network,” Analytical Biochemistry, vol. 357, no. 1, pp. 116–121, 2006. View at Publisher · View at Google Scholar · View at Scopus
  52. M. J. Anderson, “A new method for non-parametric multivariate analysis of variance,” Austral Ecology, vol. 26, no. 1, pp. 32–46, 2001. View at Google Scholar · View at Scopus
  53. R. Wang, Y. Xu, and B. Liu, “Recombination spot identification Based on gapped k-mers,” Scientific Reports, vol. 6, article 23934, 2016. View at Publisher · View at Google Scholar
  54. J. Chen, X. Wang, and B. Liu, “IMiRNA-SSF: improving the identification of microrna precursors by combining negative sets with different distributions,” Scientific Reports, vol. 6, article 19062, 2016. View at Publisher · View at Google Scholar · View at Scopus
  55. P. Feng, H. Lin, W. Chen, and Y. Zuo, “Predicting the types of J-proteins using clustered amino acids,” BioMed Research International, vol. 2014, Article ID 935719, 8 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  56. W. Chen and H. Lin, “Identification of voltage-gated potassium channel subfamilies from sequence information using support vector machine,” Computers in Biology and Medicine, vol. 42, no. 4, pp. 504–507, 2012. View at Publisher · View at Google Scholar · View at Scopus
  57. 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
  58. C.-C. Chang and C.-J. Lin, “LIBSVM: a Library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article 27, 2011. View at Publisher · View at Google Scholar · View at Scopus
  59. 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 Publisher · View at Google Scholar · View at Scopus
  60. Y.-C. Wang, X.-B. Wang, Z.-X. Yang, and N.-Y. Deng, “Prediction of enzyme subfamily class via pseudo amino acid composition by incorporating the conjoint triad feature,” Protein and Peptide Letters, vol. 17, no. 11, pp. 1441–1449, 2010. View at Publisher · View at Google Scholar · View at Scopus
  61. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, “The WEKA data mining software,” ACM SIGKDD Explorations Newsletter, vol. 11, no. 1, pp. 10–18, 2009. View at Publisher · View at Google Scholar