Predicting protein solubility by the general form of chou's pseudo amino acid composition: Approached from chaos game representation and fractal dimension

Protein and Peptide Letters

Volumn 19, Issue 9, 2012, Pages 940-948

  Niu, Xiao Hui ^a   Hu, Xue Hai ^a   Shi, Feng ^a   Xia, Jing Bo ^a  

^a HUAZHONG AGRICULTURAL UNIVERSITY   (China)

Author keywords

Chaos game representation; Fractal dimension; Protein solubility; Support vector machine

Indexed keywords

PROTEIN;

ACCURACY; ALGORITHM; AMINO ACID COMPOSITION; AMINO ACID SEQUENCE; ARTICLE; CHAOS GAME REPRESENTATION; CHAOS THEORY; CHOU PSEUDO AMINO ACID COMPOSITION; CONTROLLED STUDY; EXPERIMENTAL STUDY; FRACTAL DIMENSION; PREDICTION; PROTEIN SOLUBILITY; PROTEIN STRUCTURE; PROTEOMICS; SOLUBILITY; STATISTICAL ANALYSIS; SUPPORT VECTOR MACHINE;

AMINO ACIDS; FRACTALS; MODELS, CHEMICAL; NONLINEAR DYNAMICS; PROTEINS; SOLUBILITY; SUPPORT VECTOR MACHINES;

EID: 84865778459     PISSN: 09298665     EISSN: None     Source Type: Journal    
DOI: 10.2174/092986612802084492     Document Type: Article

References (90)

1. Wilkinson, D.L.; Harrison, R.G. Predicting the solubility of recombinant proteins in Escherichia coli. Biotechnology, 1991, 19, 443-448.
2. Davis, G.D.; Elisee, C.; Newham, D.M.; Harrison, R.G. New fusion protein systems designed to give soluble expression in Es-cherichia coli. Biotechnol. Bioeng., 1999, 165, 382-388
3. Zhang, X.P.; Zhang, S.J.; He, X.Z. Prediction of solubility of lysozyme in lysozyme-NaCl-H2O system with artificial neural network. J. Cryst. Growth, 2004, 264, 409-416.
4. Kapust, R.B.; Waugh, D.S. Escherichia coli maltose-binding protein is uncommonly effective at promoting the solubility of poly-peptides to which it is fused. Protein Sci., 1999, 8, 1668-1674.
5. Tresaugues, L.; Collinet, B.; Minard, P.; Henckes, G.; Aufrère, R.; Blondeau, K.; Liger, D.; Zhou, C.Z.; Janin, J.; Van, Tilbeurgh. H.; Quevillon-Cheruel, S. Refolding strategies from inclusion bodies in a structural genomics project. J. Struct. Funct. Genomics, 2004, 5, 195-204.
6. Makrides, S.C. Strategies for achieving high-level expression of genes in Escherichia coli. Microbiol. Rev., 1996, 60, 512-538.
7. Singh, S.M.; Panda, A.K. Solubilization and refolding of bacterial inclusion body proteins. J. Biosci. Bioeng., 2005, 99, 303-310.
8. Smialowski, P.; Martin-Galiano, A.J.; Mikolajka, A.; Girschick, T.; Holak, T.A.; Frishman, D. Protein solubility: sequence based prediction and experimental verification. Bioinformatics, 2007, 23, 2536-2542.
9. Thomas, S.I.; Kulkarni, A.J.; Kulkarni, B.D.; Jayaraman, V.K.; Balaji, P.V. A support vector machine-based method for predicting the propensity of a protein to be soluble or to form inclusion body on overexpression in Escherichia coli. Bioinformatics, 2006, 22, 278-284.
10. Magnan, N.C.; Randall, A.; Baldi, P. SOLpro: accurate sequence-based prediction of protein solubility. Bioinformatics, 2009, 25, 2200-2207.
11. Hao, B.L.; Lee, H.C.; Zhang, S.Y. Fractals related to long DNA sequences and complete genomes. Chaos Solitons Fractals, 2000, 11, 825-836.
12. Jeffrey, H.J.; Chaos game representation of gene structure, Nucleic Acids Res., 1990, 18, 2163-2170.
13. Basu, S.; Pan, A.; Dutta, C.; Das, J. Chaos game representation of proteins, J. Mol. Graph. Model, 1997, 15(5), 279-289.
14. Yu, Z.G.; Anha, V.; Lau, K.S. Chaos game representation of protein sequences based on the detailed HP model and their multifractal and correlation analyses. J. Theor. Biol., 2004, 226, 341-348.
15. Yang, J.Y.; Peng, Z.L.; Yu, Z.G.; Zhang, R.J.; Anh, V.; Wang, D. Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation. J. Theor. Biol., 2009, 257, 618-626.
16. Jingbo, X.; Silan, Z.; Feng, S.; Huijuan, X.; Xuehai, H.; Xiaohui, N.; Zhi, L. Using the concept of pseudo amino acid composition to predict resistance gene against Xanthomonas oryzae pv. oryzae in rice: An approach from chaos games representation. J. Theor. Biol., 2011, 284, 16-23.
17. Liu, X.L.; Lu, J.L.; Hu, X.H. Predicting Thermophilic Proteins with Pseudo Amino Acid Composition: Approached from Chaos Game Representation and Principal Component Analysis. Protein Pept. Lett., 2011, 18, 1244-1250.
18. Mandelbrot, B.B. The Fractal Geometry of Nature. Freeman: San Francisco, 1982.
19. Falconer, K.J. Techniques in Fractal Geometry, Wiley: NewYork, 1997.
20. Baish, J.W.; Jain, R.K. Cancer, angiogenesis and fractals. Nat. Med., 1998, 4, 984.
21. Baish, J.W.; Jain, R.K. Fractals and cancer. Cancer Res., 2000, 60, 3683-3688.
22. Grizzi, F.; Russo, C.; Colombo, P.; Franceschini, B.; Frezza, E.E.; Cobos, E.,; Chiriva-Internati, M. Quantitative evaluation and modeling of two-dimensional neovascular network complexity: the surface fractal dimension. BMC Cancer, 2005, 8, 5-14.
23. Spasic, S.; Kalauzi, A.; Grbic, G.; Martac, L.; Culic, M. Fractal analysis of rat brain activity after injury. Med. Biol. Eng. Comput., 2005, 43, 35-348.
24. Chou, K.C.; Jiang, S.P.; Liu, W.M.; Fee, C.H. Graph theory of enzyme kinetics: 1. Steady-state reaction system. Scientia Sinica, 1979, 22, 341-358.
25. Chou, K.C. Graphic rules in steady and non-steady enzyme kinetics. J. Biol. Chem., 1989, 264, 12074-12079.
26. Zhou, G.P.; Deng, M.H. An extension of Chou's graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways. Biochem. J., 1984, 222, 169-176.
27. Myers, D.; Palmer, G. Microcomputer tools for steady-state enzyme kinetics. Bioinformatics (original: Computer Applied Bioscience), 1985, 1, 105-110.
28. Andraos, J. Kinetic plasticity and the determination of product ratios for kinetic schemes leading to multiple products without rate laws: new methods based on directed graphs. Can. J. Chem., 2008, 86, 342-357.
29. Chou, K.C. Review: Applications of graph theory to enzyme kinetics and protein folding kinetics. Steady and non-steady state systems. Biophys. Chem., 1990, 35, 1-24.
30. Chou, K.C.; Shen, H.B. FoldRate: A web-server for predicting protein folding rates from primary sequence. Open Bioinforma. J., 2009, 3, 31-50 (openly accessible at http://www.bentham.org/open/tobioij/).
31. Althaus, I.W.; Chou, J.J.; Gonzales, A.J.; Diebel, M.R.; Chou, K.C.; Kezdy, F.J.; Romero, D.L.; Aristoff, P.A.; Tarpley, W.G.; Reusser, F. Steady-state kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-87201E. J. Biol. Chem., 1993, 268, 6119-6124.
32. Althaus, I.W.; Gonzales, A.J.; Chou, J.J.; Diebel, M.R.; Chou, K.C.; Kezdy, F.J.; Romero, D.L.; Aristoff, P.A.; Tarpley, W.G.; Reusser, F. The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. J. Biol. Chem., 1993, 268, 14875-14880.
33. Althaus, I.W.; Chou, J.J.; Gonzales, A.J.; Diebel, M.R.; Chou, K.C.; Kezdy, F.J.; Romero, D.L.; Aristoff, P.A.; Tarpley, W.G.; Reusser, F. Kinetic studies with the nonnucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry, 1993, 32, 6548-6554.
34. Chou, K.C.; Kezdy, F.J.; Reusser, F. Review: Steady-state inhibition kinetics of processive nucleic acid polymerases and nucleases. Anal. Biochem., 1994, 221, 217-230.
35. Chou, K.C. Graphic rule for drug metabolism systems. Curr. Drug Metabol., 2010, 11, 369-378.
36. Chou, K.C.; Lin, W.Z.; Xiao, X. Wenxiang: a web-server for drawing wenxiang diagrams. Nat. Sci., 2011, 3, 862-865 (openly accessible at http://www.scirp.org/journal/NS/).
37. Zhou, G.P. The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism. J. Theor. Biol., 2011, 284, 142-148.
38. Zhou, G.P. The Structural Determinations of the Leucine Zipper Coiled-Coil Domains of the cGMP-Dependent Protein Kinase I alpha and its Interaction with the Myosin Binding Subunit of the Myosin Light Chains Phosphase. Protein Pept. Lett., 2011, 18, 966-978.
39. Chou, K.C. Some remarks on protein attribute prediction and pseudo amino acid composition (50th Anniversary Year Review). J. Theor. Biol., 2011, 273, 236-247.
40. Chou, K.C. Prediction of protein cellular attributes using pseudo amino acid composition. Protein Struc. Funct. Genet. (Erratum: ibid., 2001, Vol.44, 60), 2001, 43, 246-255.
41. Mohabatkar, H. Prediction of cyclin proteins using Chou's pseudo amino acid composition. Protein Pept. Lett., 2010, 17, 1207-1214.
42. Esmaeili, M.; Mohabatkar, H.; Mohsenzadeh, S. Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses. J. Theor. Biol., 2010, 263, 203-209.
43. Lin, H. The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition. J. Theor. Biol., 2008, 252, 350-356.
44. Zeng, Y.H.; Guo, Y.Z.; Xiao, R.Q.; Yang, L.; Yu, L. Z.; Li, M. L. Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. J. Theor. Biol., 2009, 259, 366-372.
45. Chen, C.; Chen, L.; Zou, X.; Cai, P. Prediction of protein secondary structure content by using the concept of Chou's pseudo amino acid composition and support vector machine. Protein Pept. Lett., 2009, 16, 27-31.
46. Ding, H.; Luo, L.; Lin, H. Prediction of cell wall lytic enzymes using Chou's amphiphilic pseudo amino acid composition. Protein Pept. Lett., 2009, 16, 351-355.
47. Zhou, X.B.; Chen, C.; Li, Z.C.; Zou, X.Y. Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes. J. Theor. Biol., 2007, 248, 546-551.
48. Du, P.; Li, Y. Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physico-chemical features of segmented sequence. BMC Bioinformatics, 2006, 7, 518.
49. Du, P.; Cao, S.; Li, Y. SubChlo: predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic K-nearest neighbor (ET-KNN) algorithm. J. Theor. Biol., 2009, 261, 330-335.
50. Cai, Y.D.; Zhou, G.P.; Chou, K.C. Support vector machines for predicting membrane protein types by using functional domain composition. Biophys. J., 2003, 84, 3257-3263.
51. Cai, Y.D.; Pong-Wong, R.; Feng, K.; Jen, J.C.H.; Chou, K.C. Application of SVM to predict membrane protein types. J. Theor. Biol., 2004, 226, 373-376.
52. Cai, Y.D.; Liu, X.J.; Xu, X.B.; Chou, K.C. Prediction of protein structural classes by support vector machines. Comput. Chem., 2002, 26, 293-296.
53. Chou, K.C.; Cai, Y.D. Using functional domain composition and support vector machines for prediction of protein subcellular location. J. Biol. Chem., 2002, 277, 45765-45769.
54. Cai, Y.D.; Liu, X.J.; Xu, X.B.; Chou, K.C. Support Vector Machines for predicting HIV protease cleavage sites in protein. J. Comput. Chem., 2002, 23, 267-274.
55. Cai, Y.D.; Liu, X.J.; Xu, X.B.; Chou, K.C. Support vector machines for the classification and prediction of beta-turn types. J. Pept. Sci., 2002, 8, 297-301.
56. Cai, Y. D.; Lin, S.; Chou, K.C. Support vector machines for prediction of protein signal sequences and their cleavage sites. Peptides, 2003, 24, 159-161.
57. Cai, Y.D.; Feng, K.Y.; Li, Y.X.; Chou, K.C. Support vector machine for predicting alpha-turn types. Peptides, 2003, 24, 629-630.
58. Cai, Y.D.; Zhou, G.P.; Jen, C.H.; Lin, S.L.; Chou, K.C. Identify catalytic triads of serine hydrolases by support vector machines. J. Theor. Biol., 2004, 228, 551-557.
59. Chen, J.; Liu, H.; Yang, J.; Chou, K.C., Prediction of linear B-cell epitopes using amino acid pair antigenicity scale Amino Acids, 2007, 33, 423-428.
60. Wu, Z.C.; Xiao, X.; Chou, K.C. iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites. Mol. Biosyst., 2011, 7, 3287-3297.
61. Xiao, X.; Wu, Z.C.; Chou, K.C. A multi-label classifier for predicting the subcellular localization of gram-negative bacterial proteins with both single and multiple sites. PLoS ONE, 2011, 6, e20592.
62. Lin, W.Z.; Fang, J.A.; Xiao, X.; Chou, K.C. iDNA-Prot: Identifica-tion of DNA Binding Proteins Using Random Forest with Grey Model. PLoS ONE, 2011, 6, e24756.
63. Wang, P.; Xiao, X.; Chou, K.C. NR-2L: A Two-Level Predictor for Identifying Nuclear Receptor Subfamilies Based on Sequence-Derived Features. PLoS ONE, 2011, 6, e23505.
64. Chou, K.C.; Wu, Z.C.; Xiao, X. iLoc-Hum: Using accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. Mol. Biosyst., 2012, 8, 629-641.
65. Xiao, X.; Wu, Z.C.; Chou, K.C. iLoc-Virus: A multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. J. Theor. Biol., 2011, 284, 42-51.
66. Wu, Z.C.; Xiao, X.; Chou, K.C. iLoc-Gpos: A Multi-Layer Classifier for Predicting the Subcellular Localization of Singleplex and Multiplex Gram-Positive Bacterial Proteins. Protein Pept. Lett., 2012, 19, 4-14.
67. Niu, X.H.; Li, N.N.; Shi, F.; Hu, X.H.; Xia, J.B.; Xiong, H.J. Predicting Protein Solubility with a Hybrid Approach by Pseudo Amino Acid Composition. Protein Pept. Lett., 2010, 17, 1466-1472.
68. Huang Y.; Niu B.F.; Gao Y.; Fu L.M.; Li W.Z. CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics, 2010, 26(5), 680-682.
69. Chou, K.C.; Shen, H.B. Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization. PLoS ONE, 2010, 5, e11335.
70. Chou, K.C.; Wu, Z.C.; Xiao, X. iLoc-Euk: A Multi-Label Classi-fier for Predicting the Subcellular Localization of Singleplex and Multiplex Eukaryotic Proteins. PLoS ONE, 2011, 6, e18258.
71. Soddell, J.; Seviour, R.A comparison of methods for determining the fractal dimensions of colonies of filamentous bacteria. Binary, 1994, 6, 21-31.
72. Foroutan, P.K.; Dutilleul, P.; Smith, D.L. Advances in the implementation of the box-counting method of fractal dimension estimation. Appl. Math. Comput., 1999, 105, 195-210.
73. Vapnik, V. Statistical Learning Theory, Wiley Interscience: New York, 1998.
74. Chou, K.C.; Zhang, C.T. Review: Prediction of protein structural classes, Crit. Rev. Biochem. Mol. Biol., 1995, 30, 275-349.
75. Chou, K.C.; Shen, H.B. Cell-PLoc:a package of Web servers for predicting subcellular localization of proteins in various organisms. Nat. Protocols, 2008, 3, 153-162.
76. Chou, K.C.; Shen, H.B. Review: recent advances in developing web-servers for predicting protein attributes. Natural Science, 2009, 2, 63-92 (openly accessible at
77. http://www.scirp.org/journal/NS/).
78. Cai, Y. D.; He, J.; Li, X.; Feng, K.; Lu, L.; Kong, X.; Lu, W. Pre-dicting protein subcellular locations with feature selection and analysis. Protein Pept. Lett., 2010, 17, 464-472.
79. Chen, L.; Huang, T.; Shi, X.H.; Cai, Y.D.; Chou, K.C. Analysis of protein pathway networks using hybrid properties. Molecules, 2010, 15, 8177-8192 (Openly accessible at
80. www.mdpi.com/journal/molecules).
81. Ding, H.; Liu, L.; Guo, F.B.; Huang, J.; Lin, H. Identify Golgi protein types with modified mahalanobis discriminant algorithm and pseudo amino acid composition. Protein Pept. Lett., 2011, 18, 58-63.
82. Gu, Q.; Ding, Y.S.; Zhang, T.L. Prediction of G-Protein-Coupled Receptor Classes in Low Homology Using Chou's Pseudo Amino Acid Composition with Approximate Entropy and Hydrophobicity Patterns. Protein Pept. Lett., 2010, 17, 559-567.
83. Hayat, M.; Khan, A. Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid com-position. J. Theor. Biol., 2011, 271, 10-17.
84. Hu, L.; Huang, T.; Shi, X.; Lu, W.C.; Cai, Y.D.; Chou, K.C. Pre-dicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties PLoS ONE, 2011, 6, e14556.
85. Ji, G.; Wu, X.; Shen, Y.; Huang, J.; Quinn Li, Q. A classification-based prediction model of messenger RNA polyadenylation sites. J. Theor. Biol., 2010, 265, 287-296.
86. Joshi, R. R.; Sekharan, S. Characteristic peptides of protein secon-dary structural motifs. Protein Pept. Lett., 2010, 17, 1198-1206.
87. Kandaswamy, K.K.; Pugalenthi, G.; Moller, S.; Hartmann, E.; Kalies, K.U.; Suganthan, P.N.; Martinetz, T. Prediction of Apopto-sis Protein Locations with Genetic Algorithms and Support Vector Machines Through a New Mode of Pseudo Amino Acid Composi-tion. Protein Pept. Lett., 2010, 17, 1473-1479.
88. Masso, M.; Vaisman, I.I. Knowledge-based computational mutage-nesis for predicting the disease potential of human non-synonymous single nucleotide polymorphisms. J. Theor. Biol., 2010, 266, 560-568.
89. Xiao, X.; Wang, P.; Chou, K.C. GPCR-2L: Predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions. Mol. Biosyst., 2011, 7, 911-919.
90. Guo, J.; Rao, N.; Liu, G.; Yang, Y.; Wang, G. Predicting protein folding rates using the concept of Chou's pseudo amino acid com-position. J. Comput. Chem., 2011, 32, 1612-1617.