Amino Acids

, Volume 38, Issue 4, pp 975–983 | Cite as

Prediction of subcellular location apoptosis proteins with ensemble classifier and feature selection

  • Quan Gu
  • Yong-Sheng Ding
  • Xiao-Ying Jiang
  • Tong-Liang Zhang
Original Article

Abstract

Apoptosis proteins have a central role in the development and the homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. The function of an apoptosis protein is closely related to its subcellular location. It is crucial to develop powerful tools to predict apoptosis protein locations for rapidly increasing gap between the number of known structural proteins and the number of known sequences in protein databank. In this study, amino acids pair compositions with different spaces are used to construct feature sets for representing sample of protein feature selection approach based on binary particle swarm optimization, which is applied to extract effective feature. Ensemble classifier is used as prediction engine, of which the basic classifier is the fuzzy K-nearest neighbor. Each basic classifier is trained with different feature sets. Two datasets often used in prior works are selected to validate the performance of proposed approach. The results obtained by jackknife test are quite encouraging, indicating that the proposed method might become a potentially useful tool for subcellular location of apoptosis protein, or at least can play a complimentary role to the existing methods in the relevant areas. The supplement information and software written in Matlab are available by contacting the corresponding author.

Keywords

Apoptosis protein subcellular location Feature selection Ensemble classifier Fuzzy K-nearest neighbor classifier 

References

  1. Adams JM, Cory S (1998) The Bcl-2 protein family: arbiters of cell survival. Science 281:1322–1326PubMedGoogle Scholar
  2. Argos P, Rao JK, Hargrave PA (1982) Structural prediction of membrane-bound proteins. Eur J Biochem 128:565–575PubMedCrossRefGoogle Scholar
  3. Bulashevska A, Eils R (2006) Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains. BMC Bioinform 7:298–310Google Scholar
  4. 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 305:407–411PubMedGoogle Scholar
  5. Cai YD, Liu XJ, Xu XB, Zhou GP (2001) Support vector machines for predicting structural class. BMC Bioinform 2:3Google Scholar
  6. Cai YD, Zhou GP, Chou KC (2003) Support vector machines for predicting membrane protein types by using functional domain composition. Biophys J 84:3257–3263PubMedGoogle Scholar
  7. Chen YL, Li QZ (2007a) Prediction of the subcellular location of apoptosis proteins. J Theor Biol 245:775–783PubMedGoogle Scholar
  8. Chen YL, Li QZ (2007b) Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition. J Theor Biol 248(2):377–381. doi:10.1016/j.jtbi.2007.05.019 PubMedGoogle Scholar
  9. Chen C, Zhou X, Tian YX, Zou XY, Cai PX (2006a) Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network. Anal Biochem 357:116–121PubMedGoogle Scholar
  10. Chen C, Tian YX, Zou XY, Cai PX, Mo JY (2006b) Using pseudo-amino acid composition and support vector machine to predict protein structural class. J Theor Biol 243:444–448PubMedGoogle Scholar
  11. Chen K, Kurgan LA, Rahbari M (2007a) Prediction of protein crystallization using collocation of amino acid pairs. Biochem Biophys Res Commun 355:764–769PubMedGoogle Scholar
  12. Chen K, Kurgan LA, Ruan JH (2007b) Prediction of flexible/rigid regions from protein sequences using k-spaced amino acid pairs. BMC Struct Biol 7:25PubMedGoogle Scholar
  13. Chou KC (1988) Review: low-frequency collective motion in biomacromolecules and its biological functions. Bio Chem 30:3–48Google Scholar
  14. Chou KC (1992) Energy-optimized structure of antifreeze protein and its binding mechanism. J Mol Biol 223:509–517PubMedGoogle Scholar
  15. Chou KC (1993) A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins. J Biol Chem 268:16938–16948PubMedGoogle Scholar
  16. Chou KC (1995) A novel approach to predicting protein structural classes in a (20–1)-D amino acid composition space. Proteins 21:319–344PubMedGoogle Scholar
  17. Chou KC (1996) Review: prediction of HIV protease cleavage sites in proteins. Anal Biochem 233:1–14PubMedGoogle Scholar
  18. Chou KC (2000) Review: prediction of protein structural classes and subcellular locations. Curr Protein Pept Sci 1:171–208PubMedGoogle Scholar
  19. Chou KC (2001) Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins Struct Funct Genet 43(3):246–255PubMedGoogle Scholar
  20. Chou KC (2002) A new branch of proteomics: prediction of protein cellular attributes. In: Weinrer PW, Lu Q (eds) Gene cloning and expression technologies. Eaton Publishing, Westborough, pp 57–70Google Scholar
  21. Chou KC (2004a) Review: structural bioinformatics and its impact to biomedical science. Curr Med Chem 11:2105–2134PubMedGoogle Scholar
  22. Chou KC (2004b) Insights from modelling the 3D structure of the extracellular domain of alpha7 nicotinic acetylcholine receptor. Biochem Biophys Res Commun 319:433–438PubMedGoogle Scholar
  23. Chou KC (2004c) Modelling extracellular domains of GABA-A receptors: subtypes 1, 2, 3, and 5. Biochem Biophys Res Commun 316:636–642PubMedGoogle Scholar
  24. Chou KC (2004d) Molecular therapeutic target for type-2 diabetes. J Proteome Res 3:1284–1288PubMedGoogle Scholar
  25. Chou KC (2005a) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21:10–19PubMedGoogle Scholar
  26. Chou KC (2005b) Coupling interaction between thromboxane A2 receptor and alpha-13 subunit of guanine nucleotide-binding protein. J Proteome Res 4:1681–1686PubMedGoogle Scholar
  27. Chou KC (2005c) Prediction of G-protein-coupled receptor classes. J Proteome Res 4:1413–1418PubMedGoogle Scholar
  28. Chou KC, Cai YD (2002) Using functional domain composition and support vector machines for prediction of protein subcellular location. J Biol Chem 277:45765–45769PubMedGoogle Scholar
  29. Chou KC, Cai YD (2004) Prediction of protein subcellular locations by GO-FunD-PseAA predictor. Biochem Biophys Res Commun 320:1236–1239PubMedGoogle Scholar
  30. Chou KC, Cai YD (2005) Predicting protein localization in budding yeast. Bioinformatics 21:944–950PubMedGoogle Scholar
  31. Chou KC, Elrod DW (1999) Prediction of membrane protein types and subcellular locations. Proteins: Struct Funct Genet 34:137–153Google Scholar
  32. Chou KC, Elrod DW (2002) Bioinformatical analysis of G-protein-coupled receptors. J Proteome Res 1:429–433PubMedGoogle Scholar
  33. Chou KC, Jiang SP (1974) Studies on the rate of diffusion-controlled reactions of enzymes. Sci Sinica 17:664–680Google Scholar
  34. Chou KC, Shen HB (2006a) Hum-PLoc: a novel ensemble classifier for predicting human protein subcellular localization. Biochem Biophys Res Commun 347:150–157PubMedGoogle Scholar
  35. Chou KC, Shen HB (2006b) Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-nearest neighbor classifiers. J Proteome Res 5:1888–1897PubMedGoogle Scholar
  36. Chou KC, Shen HB (2006c) Predicting protein subcellular location by fusing multiple classifiers. J Cell Biochem 99:517–527PubMedGoogle Scholar
  37. Chou KC, Shen HB (2007a) Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. J Proteome Res 6:1728–1734PubMedGoogle Scholar
  38. Chou KC, Shen HB (2007b) MemType-2L: a Web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochem Biophys Res Comm 360:339–345PubMedGoogle Scholar
  39. Chou KC, Shen HB (2007c) Review: recent progresses in protein subcellular location prediction. Anal Biochem 370:1–16PubMedGoogle Scholar
  40. Chou KC, Shen HB (2007d) Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. Biochem Biophys Res Comm 357:633–640PubMedGoogle Scholar
  41. Chou KC, Shen HB (2008a) Cell-PLoc: a package of web-servers for predicting subcellular localization of proteins in various organisms. Nat Protoc 3:153–162PubMedGoogle Scholar
  42. Chou KC, Shen HB (2008b) ProtIdent: a web server for identifying proteases and their types by fusing functional domain and sequential evolution information. Biochem Biophys Res Comm 376(2):321–325. doi:10.1016/j.bbrc.2008.08.125 PubMedGoogle Scholar
  43. Chou KC, Zhang CT (1995) Review: prediction of protein structural classes. Crit Rev Biochem Mol Bio 30:275–349Google Scholar
  44. Chou KC, Zhou GP (1982) Role of the protein outside active site on the diffusion-controlled reaction of enzyme. J Am Chem Soc 104:1409–1413Google Scholar
  45. Chou KC, Nemethy G, Scheraga HA (1984) Energetic approach to packing of a-helices: 2. General treatment of nonequivalent and nonregular helices. J American Chem Soc 106:3161–3170Google Scholar
  46. Chou KC, Maggiora GM, Nemethy G, Scheraga HA (1988) Energetics of the structure of the four-alpha-helix bundle in proteins. Proc Natl Acad Sci U S A 85:4295–4299PubMedGoogle Scholar
  47. Chou KC, Zhang TC, Maggiora MG (1997) Disposition of amphiphilic helices in heteropolar environments. Proteins 28:99–108PubMedGoogle Scholar
  48. Chou JJ, Li H, Salvessen GS, Yuan J, Wagner G (1999) Solution structure of BID, an intracellular amplifier of apoptotic signalling. Cell 96:615–624PubMedGoogle Scholar
  49. Chou KC, Tomasselli AG, Heinrikson RL (2000) Prediction of the tertiary structure of a caspase-9/inhibitor complex. FEBS Lett 470:249–256PubMedGoogle Scholar
  50. Chou KC, Wei DQ, Zhong WZ (2003) Binding mechanism of coronavirus main proteinase with ligands and its implication to drug design against SARS (Erratum: ibid., 2003, Vol. 310, 675). Biochem Biophys Res Comm 308:148–151PubMedGoogle Scholar
  51. Chou KC, Wei DQ, Du QS, Sirois S, Zhong WZ (2006) Review: progress in computational approach to drug development against SARS. Curr Med Chem 13:3263–3270PubMedGoogle Scholar
  52. Cosic I (1994) Macromolecular bioactivity: is it resonant interaction between macromolecules?—theory and applications. IEEE Trans Biomed Eng 41:1101–1114PubMedGoogle Scholar
  53. Dea-Ayuela MA, Perez-Castillo Y, Meneses-Marcel A, Ubeira FM, Bolas-Fernandez F, Chou KC, Gonzalez-Diaz H (2008) HP-Lattice QSAR for dynein proteins: Experimental proteomics (2D-electrophoresis, mass spectrometry) and theoretic study of a Leishmania infantum sequence. Bioorg Med Chem 16:7770–7776PubMedGoogle Scholar
  54. Du PF, Li YD (2006) Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence. BMC Bioinform 7:518–526Google Scholar
  55. Du QS, Mezey PG, Chou KC (2005) Heuristic molecular lipophilicity potential (HMLP): a 2D-QSAR study to LADH of molecular family pyrazole and derivatives. J Comput Chem 26:461–470PubMedGoogle Scholar
  56. Du QS, Huang RB, Chou KC (2008a) Review: recent advances in QSAR and their applications in predicting the activities of chemical molecules, peptides and proteins for drug design. Curr Protein Pept Sci 9:248–259PubMedGoogle Scholar
  57. Du QS, Huang RB, Wei YT, Du LQ, Chou KC (2008b) Multiple field three dimensional quantitative structure-activity relationship (MF-3D-QSAR). J Comput Chem 29:211–219PubMedGoogle Scholar
  58. Emanuelsson O, Brunak S, von Heijne G, Nielsen H (2007) Locating proteins in the cell using TargetP, SignalP and related tools. Nat Protoc 2:953–971PubMedGoogle Scholar
  59. Evan G, Littlewood T (1998) A matter of life and cell death. Science 281:1317–1322PubMedGoogle Scholar
  60. Falquet L, Pagni M, Bucher P, Hulo N, Sigrist CJ, Hofmann K, Bairoch A (2002) The PROSITE database, its status in 2002. Nucleic Acids Res 30:235–238PubMedGoogle Scholar
  61. Fauchere JL, Charton M, Kier LB, Verloop A, Pliska V (1988) Amino acid side chain parameters for correlation studies in biology and pharmacology. Int J Pept Protein Res 32:269–278PubMedGoogle Scholar
  62. Feng ZP (2002) An overview on predicting the subcellular location of a protein. In Silico Biol 2:291–303PubMedGoogle Scholar
  63. Gao QB, Wang ZZ, Yan C, Du YH (2005a) Prediction of protein subcellular location using a combined feature of sequence. FEBS Lett 579:3444–3448PubMedGoogle Scholar
  64. Gao Y, Shao SH, Xiao X, Ding YS, Huang YS, Huang ZD, Chou KC (2005b) Using pseudo amino acid composition to predict protein subcellular location: approached with Lyapunov index, Bessel function, and Chebyshev filter. Amino Acids 28:373–376PubMedGoogle Scholar
  65. Gao WN, Wei DQ, Li Y, Gao H, Xu WR, Li AX, Chou KC (2007) Agaritine and its derivatives are potential inhibitors against HIV proteases. Med Chem 3:221–226PubMedGoogle Scholar
  66. Gonzalez-Diaz H, Sanchez-Gonzalez A, Gonzalez-Diaz Y (2006) 3D-QSAR study for DNA cleavage proteins with a potential anti-tumor ATCUN-like motif. J Inorg Biochem 100:1290–1297PubMedGoogle Scholar
  67. Gonzalez-Díaz H, Gonzalez-Díaz Y, Santana L, Ubeira FM, Uriarte E (2008) Proteomics, networks, and connectivity indices. Proteomics 8:750–778PubMedGoogle Scholar
  68. Hong B, Tang QY, Yang FS (1999) Apen and Cross-ApEn: property, fast algorithm and preliminary application to the study of EEG and cognition. Signal Process 15:100–108 (in Chinese)Google Scholar
  69. Hopp TP, Woods KR (1981) Prediction of protein antigenic determinants from amino acid sequences. Proc Natl Acad Sci USA 78:3824–3828PubMedGoogle Scholar
  70. Huang Y, Li YD (2004) Prediction of protein subcellular locations using fuzzy k-NN method. Bioinformatics 20(1):21–28PubMedGoogle Scholar
  71. Huang J, Shi F (2005) Support vector machines for predicting apoptosis proteins types. Acta Biotheor 53:39–47PubMedGoogle Scholar
  72. Huang WL, Chen HM, Hwang SF, Ho SY (2006) Accurate prediction of enzyme subfamily class using an adaptive fuzzy K-nearest neighbor method. BioSystems 90(2):405–413. doi:10.1016/j.biosystems.2006.10.004 PubMedGoogle Scholar
  73. Janin J (1979) Surface and inside volumes in globular proteins. Nature 277:491–492PubMedGoogle Scholar
  74. Janin J, Wodak S (1978) Conformation of amino acid side-chains in proteins. J Mol Biol 125:357–386PubMedGoogle Scholar
  75. Kawashima S, Ogata H, Kanehisa M (1999) AAindex: amino acid index database. Nucleic Acids Res 27:368–369PubMedGoogle Scholar
  76. Kedarisetti KD, Kurgan LA, Dick S (2006) Classifier ensembles for protein structural class prediction with varying homology. Biochem Biophys Res Commun 348:981–988PubMedGoogle Scholar
  77. Keller JM, Gray MR, Givens JA (1985) A fuzzy k-nearest neighbors algorithm. IEEE Trans Syst Man Cybern 15:580–585Google Scholar
  78. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, vol 4, Perth, Australia, pp 1942–1948Google Scholar
  79. Kennedy J, Eberhart RC (1997) A discrete binary version of the particles warm algorithm. Systems, man and cybernetics, 1997. In: Proceedings of the IEEE International Conference on Computational Cybernetics and Simulation, vol 5, October 12–15, pp 4104–4108Google Scholar
  80. Kennedy J, Eberhart RC, Shi Y (2001) Swarm intelligence. Morgan Kaufman, San MateoGoogle Scholar
  81. Kerr JF, Wyllie AH, Currie AR (1972) Apoptosis: a basic biological phenomenon with wide-ranging implications in tissue kinetics. Br J Cancer 26:239–257PubMedGoogle Scholar
  82. Li TT, Chou KC (1976) The quantitative relations between diffusion-controlled reaction rate and characteristic parameters in enzyme-substrate reaction system: 1. Neutral substrate. Sci Sinica 19:117–136PubMedGoogle Scholar
  83. Li Y, Wei DQ, Gao WN, Gao H, Liu BN, Huang CJ, Xu WR, Liu DK, Chen HF, Chou KC (2007) Computational approach to drug design for oxazolidinones as antibacterial agents. Med Chem 3:576–582PubMedGoogle Scholar
  84. Lin H, Li QZ (2007a) Predicting conotoxin superfamily and family by using pseudo amino acid composition and modified Mahalanobis discriminant. Biochem Biophys Res Commun 354:548–551PubMedGoogle Scholar
  85. Lin H, Li QZ (2007b) Using pseudo amino acid composition to predict protein structural class: approached by incorporating 400 dipeptide components. J Comput Chem 28:1463–1466PubMedGoogle Scholar
  86. Liu H, Wang M, Chou KC (2005a) Low-frequency Fourier spectrum for predicting membrane protein types. Biochem Biophys Res Commun 336:737–739PubMedGoogle Scholar
  87. Liu H, Yang J, Wang M, Xue L, Chou KC (2005b) Using Fourier spectrum analysis and pseudo amino acid composition for prediction of membrane protein types. Protein J 24:385–389PubMedGoogle Scholar
  88. Nakashima H, Nishikawa K (1994) Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies. J Mol Biol 238(1):54–61PubMedGoogle Scholar
  89. Park KJ, Kanehisa M (2003) Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs. Bioinformatics 19(13):1656–1663PubMedGoogle Scholar
  90. Peter ME, Heufelder AE, Hengartner MO (1997) Advances in apoptosis research. Proc Natl Acad Sci USA 94:12736–12737PubMedGoogle Scholar
  91. Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 88:2297–2301PubMedGoogle Scholar
  92. Prado-Prado FJ, Gonzalez-Diaz H, de la Vega OM, Ubeira FM, Chou KC (2008) Unified QSAR approach to antimicrobials. Part 3: First multi-tasking QSAR model for Input-Coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds. Bioorg Med Chem 16:5871–5880PubMedGoogle Scholar
  93. Reed JC, Paternostro G (1999) Postmitochondrial regulation of apoptosis during heart failure. Proc Natl Acad Sci USA 96:7614–7616PubMedGoogle Scholar
  94. Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278(6):H2039–H2049PubMedGoogle Scholar
  95. Schulz JB, Weller M, Moskowitz MA (1999) Caspases as treatment targets in stroke and neurodegenerative diseases. Ann Neurol 45:421–429PubMedGoogle Scholar
  96. Shen HB, Chou KC (2006a) Ensemble classifier for protein fold pattern recognition. Bioinformatics 22:1717–1722PubMedGoogle Scholar
  97. Shen HB, Chou KC (2006b) Using ensemble classifier to identify membrane protein types. Amino Acids 32(4):483–488. doi:10.1007/s00726-006-0439-2 PubMedGoogle Scholar
  98. Shen HB, Chou KC (2007a) Hum-mPLoc: an ensemble classifier for large-scale human protein subcellular location prediction by incorporating samples with multiple sites. Biochem Biophys Res Commun 355(4):1006–1011PubMedGoogle Scholar
  99. Shen HB, Chou KC (2007b) EzyPred: a top-down approach for predicting enzyme functional classes and subclasses. Biochem Biophys Res Comm 364:53–59PubMedGoogle Scholar
  100. Shen HB, Chou KC (2007c) Signal-3L: a 3-layer approach for predicting signal peptide. Biochem Biophys Res Comm 363:297–303PubMedGoogle Scholar
  101. Shen HB, Chou KC (2008) HIVcleave: a web-server for predicting HIV protease cleavage sites in proteins. Anal Biochem 375:388–390PubMedGoogle Scholar
  102. Shen HB, Yang J, Liu XJ, Chou KC (2005) Using supervised fuzzy clustering to predict protein structural classes. Biochem Biophys Res Commun 334:577–581PubMedGoogle Scholar
  103. Shen HB, Yang J, Chou KC (2006) Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition. J Theor Biol 240:9–13PubMedGoogle Scholar
  104. Shen HB, Yang J, Chou KC (2007a) Euk-PLoc: an ensemble classifier for large-scale eukaryotic protein subcellular location prediction. Amino Acids 33(1):57–59PubMedGoogle Scholar
  105. Shen HB, Yang J, Chou KC (2007b) Methodology development for predicting subcellular location and other attributes of proteins. Expert Rev Proteomics 4(4):453–463PubMedGoogle Scholar
  106. Shi JY, Zhang SW, Pan Q, Cheng YM, Xie J (2007) Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition. Amino Acids 33:69–74PubMedGoogle Scholar
  107. Shi JY, Zhang SW, Pan Q, Zhou GP (2008) Using pseudo amino acid composition to predict protein subcellular location: approached with amino acid composition distribution. Amino Acids 35:321–327PubMedGoogle Scholar
  108. Sirois S, Wei DQ, Du QS, Chou KC (2004) Virtual screening for SARS-CoV protease based on KZ7088 pharmacophore points. J Chem Inf Comput Sci 44:1111–1122PubMedGoogle Scholar
  109. Steller H (1995) Mechanisms and genes of cellular suicide. Science 267:1445–1449PubMedGoogle Scholar
  110. Tanford C (1962) Contribution of hydrophobic interactions to the stability of the globular conformation of proteins. J Am Chem Soc 84:4240–4274Google Scholar
  111. Wang JF, Wei DQ, Chen C, Li Y, Chou KC (2008) Molecular modeling of two CYP2C19 SNPs and its implications for personalized drug design. Protein Pept Lett 15:27–32PubMedGoogle Scholar
  112. Xiao X, Shao SH, Ding YS, Huang ZD, Huang YS, Chou KC (2005) Using complexity measure factor to predict protein subcellular location. Amino Acids 28:57–61PubMedGoogle Scholar
  113. Xiao X, Shao SH, Ding YS, Huang ZD, Chou KC (2006a) Using cellular automata images and pseudo amino acid composition to predict protein subcellular location. Amino Acids 30(1):49–54PubMedGoogle Scholar
  114. Xiao X, Shao SH, Huang ZD, Chou KC (2006b) Using pseudo amino acid composition to predict protein structural classes: approached with complexity measure factor. J Comput Chem 27:478–482PubMedGoogle Scholar
  115. Zhang R, Wei DQ, Du QS, Chou KC (2006a) Molecular modeling studies of peptide drug candidates against SARS. Med Chem 2:309–314PubMedGoogle Scholar
  116. Zhang TL, Ding YS, Chou KC (2006b) Prediction of protein subcellular location using hydrophobic patterns of amino acid sequence. Comput Biol Chem 30:367–371PubMedGoogle Scholar
  117. Zhang ZH, Wang ZH, Zhang ZR, Wang YX (2006c) A novel method for apoptosis protein subcellular localization prediction combining encoding based on grouped weight and support vector machine. FEBS Lett 580:6169–6174PubMedGoogle Scholar
  118. Zhang TL, Ding YS, Chou KC (2008) Prediction protein structural classes with pseudo amino acid composition: approximate entropy and hydrophobicity pattern. J Theor Biol 250(1):186–193PubMedGoogle Scholar
  119. Zheng H, Wei DQ, Zhang R, Wang C, Wei H, Chou KC (2007) Screening for new agonists against Alzheimer’s Disease. Med Chem 3:488–493PubMedGoogle Scholar
  120. Zhou GP (1998) An Intriguing controversy over protein structural class prediction. J Protein Chem 17:729–738PubMedGoogle Scholar
  121. Zhou GP, Assa-Munt N (2001) Some insights into protein structural class prediction. Proteins 44:57–59PubMedGoogle Scholar
  122. Zhou GP, Cai YD (2006) Predicting protease types by hybridizing gene ontology and pseudo amino acid composition. Proteins 63(3):681–684PubMedGoogle Scholar
  123. Zhou GP, Doctor K (2003) Subcellular location prediction of apoptosis proteins. Proteins: Struct Funct Genet 50:44–48Google Scholar
  124. Zhou XB, Chen C, Li ZC, Zou XY (2008) Improved prediction of subcellular location for apoptosis proteins by the dual-layer support vector machine. Amino Acids 35(2):383–388PubMedGoogle Scholar
  125. Zimmerman JM, Eliezer N, Simha R (1968) The characterization of amino acid sequences in proteins by statistical methods. J Theor Biol 21:170–201PubMedGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Quan Gu
    • 1
  • Yong-Sheng Ding
    • 1
    • 2
  • Xiao-Ying Jiang
    • 3
  • Tong-Liang Zhang
    • 4
  1. 1.College of Information Sciences and TechnologyDonghua UniversityShanghaiChina
  2. 2.Engineering Research Center of Digitized Textile & Fashion TechnologyMinistry of EducationShanghaiChina
  3. 3.School of Chemistry and Chemical EngineeringHenan Institute of Science and TechnologyHenanChina
  4. 4.Research Institute of HighwayResearch Institute of Highway Ministry of CommunicationsBeijingChina

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