An Ensemble Method for Predicting Subnuclear Localizations from Primary Protein Structures

PLoS ONE

Volumn 8, Issue 2, 2013, Pages

  Han, Guo Sheng ^a   Yu, Zu Guo ^a,b   Anh, Vo ^b   Krishnajith, Anaththa P D ^b   Tian, Yu Chu ^b  

^a XIANGTAN UNIVERSITY   (China)

^b QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACID ANALYSIS; AMINO ACID COMPOSITION; AMINO ACID SEQUENCE; ANALYTIC METHOD; ANALYTICAL PARAMETERS; ARTICLE; AUTOCORRELATION DESCRIPTOR; AUTOCOVARIANCE METHOD; AUTOMATION; CELLULAR DISTRIBUTION; DISCRETE WAVELET TRANSFORM; GLOBAL DESCRIPTOR; HILBERT HUANG TRANSFORM; KERNEL METHOD; LEMPEL ZIV COMPLEXITY; MUTATIONAL ANALYSIS; NUCLEAR LOCALIZATION SIGNAL; PHYSICAL CHEMISTRY; PHYSICOCHEMICAL PROPERTY DISTRIBUTION DESCRIPTOR; PREDICTIVE VALUE; PROCESS DEVELOPMENT; PROCESS MODEL; PROTEIN ANALYSIS; PROTEIN DATABASE; PROTEIN ISOLATION; PROTEIN LOCALIZATION; RECURRENCE QUANTIFICATION ANALYSIS; RELIABILITY; SEQUENCE ORDER DESCRIPTOR; STRUCTURE ANALYSIS; SUPPORT VECTOR MACHINE; VALIDATION PROCESS;

AMINO ACID SEQUENCE; CELL NUCLEUS; DATABASES, PROTEIN; MODELS, MOLECULAR; PROTEIN TRANSPORT; PROTEINS; REPRODUCIBILITY OF RESULTS; ROC CURVE; SEQUENCE ANALYSIS, PROTEIN; SUBCELLULAR FRACTIONS; SUPPORT VECTOR MACHINES;

EID: 84874534864     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0057225     Document Type: Article

References (93)

1. Lei ZD, Dai Y, (2005) An SVM-based system for predicting protein subnuclear localizations. BMC Bioinformatics 6: 291.
2. Mei SY, Fei W (2010) Amino acid classification based spectrum kernel fusion for protein subnuclear localization. BMC Bioinformatics (Suppl 1): S17.
3. Shen HB, Chou KC, (2005) Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition. Biochem Biophys Res Commun 337: 752-756.
4. Phair RD, Misteli T, (2000) High mobility of proteins in the mammalian cell nucleus. Nature 404: 604-609.
5. Murphy RF, Boland MV, Velliste M, (2000) Towards a systematics for protein subcellular location: quantitative description of protein localization patterns and automated analysis of fluorescence microscope images. Proc Int Conf Intell Syst Mol Biol 8: 251-259.
6. Briesemeister S, RahnenfÄuhrer J, Kohlbacher O, (2010) Going from where to why-interpretable prediction of protein subcellular localization. Bioinformatics 26: 1232-1238.
7. Cedano J, Aloy P, Pérez-Pons JA, Querol E, (1997) Relation between amino acid composition and cellular location of proteins. J Mol Biol 266: 594-600.
8. Emanuelsson O, Nielsen H, Brunak S, von Heijne G, (2000) Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J Mol Biol 300: 1005-1016.
9. Emanuelsson O, Brunak S, von Heijne G, Nielsen H, (2007) Locating proteins in the cell using TargetP, SignalP and related tools. Nat Protoc 2: 953-971.
10. Huang WL, Tung CW, Huang HL, Hwang SF, Ho SY, (2007) ProLoc: prediction of protein subnuclear localization using SVM with automatic selection from physicochemical composition features. BioSystems 90: 573-581.
11. Höglund A, Dönnes P, Blum T, Adolph HW, Kohlbacher O, (2006) MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition. Bioinformatics 22: 1158-1165.
12. Nakashima H, Nishikawa K, (1994) Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies. J Mol Biol 238: 54-61.
13. Pierleoni A, Martelli PL, Fariselli P, Casadio R, (2006) BaCelLo: a balanced subcellular localization predictor. Bioinformatics 22: e408-416.
14. Sarda D, Chua GH, Li KB, Krishnan A, (2005) pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties. BMC Bioinformatics 6: 152.
15. Wang J, Sung WK, Krishnan A, Li KB, (2005) Protein subcellular localization prediction for Gram-negative bacteria using amino acid subalphabets and a combination of multiple support vector machines. BMC Bioinformatics 6: 174.
16. Yu NY, Wagner JR, Laird MR, Melli G, Rey S, et al. (2010) PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26: 1608-1615.
17. Zheng XQ, Liu TG, Wang J, (2009) A complexity-based method for predicting protein subcellular location. Amino Acids 37: 427-433.
18. Chou KC, Cai YD, (2002) Using functional domain composition and support vector machines for prediction of protein subcellular location. J Biol Chem 277: 45765-45769.
19. Chou KC, Cai YD, (2004) Prediction of protein subcellular locations by GO-FunD-PseAA predictor. Biochem Biophys Res Commun 320: 1236-1239.
20. Chou KC, Shen HB, (2010) A New Method for Predicting the Subcellular Localization of Eukaryotic Proteins with Both Single and Multiple Sites: Euk-mPLoc 2.0. PLoS One 5: e9931.
21. Lei ZD, Dai Y, (2006) Assessing protein similarity with Gene Ontology and its use in subnuclear localization prediction. BMC Bioinformatics 7: 491.
22. Mei SY, Fei W, Zhou SG, (2011) Gene ontology based transfer learning for protein subcellular localization. BMC Bioinformatics 12: 44.
23. Chang JM, Su EC, Lo A, Chiu HS, Sung TY, et al. (2008) PSLDoc: Protein subcellular localization prediction based on gapped-dipeptides and probabilistic latent semantic analysis. Proteins 72: 693-710.
24. Guo J, Lin YL, (2006) TSSub: eukaryotic protein subcellular localization by extracting features from profiles. Bioinformatics 22: 1784-1785.
25. Mundra P, Kumar M, Kumar KK, Jayaraman VK, Kulkarni BD, (2007) Using pseudo amino acid composition to predict protein subnuclear localization: Approached with PSSM. Pattern Recognit Lett 28: 1610-1615.
26. Shen HB, Chou KC, (2007) Nuc-PLoc: a new web-server for predicting protein subnuclear localization by fusing PseAA composition and PsePSSM. Protein Eng Des Sel 20: 561-567.
27. Xiao RQ, Guo YZ, Zeng YH, Tan HF, Pu XM, et al. (2009) Using position specific scoring matrix and autocovariance to predict protein subnuclear localization. J Bio Sci Eng 2: 51-56.
28. Shin CJ, Wong S, Davis MJ, Ragan MA, (2009) Protein-protein interaction as a predictor of subcellular location. BMC Syst Biol 3: 28.
29. Guda C, Subramaniam S, (2005) pTARGET: a new method for predicting protein subcellular localization in eukaryotes. Bioinformatics 21: 3963-3969.
30. Shen HB, Chou KC, (2009) A top-down approach to enhance the power of predicting human protein subcellular localization: Hum-mPLoc 2.0. Anal Biochem 394: 269-274.
31. Carmo-Fonseca M, (2002) The contribution of nuclear compartmentalization to gene regulation. Cell 108: 513-521.
32. Hancock R, (2004) Internal organisation of the nucleus: assembly of compartments by macromolecular crowding and the nuclear matrix model. Biol Cell 96: 595-601.
33. Sutherland HG, Mumford GK, Newton K, Ford LV, Farrall R, et al. (2001) Large-scale identification of mammalian proteins localized to nuclear sub-compartments. Hum Mol Genet 10: 1995-2011.
34. Dubchak I, Muchnik I, Holbrook SR, Kim SH, (1995) Prediction of protein folding class using global description of amino acid sequence. Proc Natl Acad Sci U S A 92: 8700-8704.
35. Lempel A, Ziv J, (1976) On the complexity of finite sequence. IEEE Trans Inf Theory 22: 75-81.
36. Li ZR, Lin HH, Han LY, Jiang L, Chen X, et al. (2008) PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucleic Acids Res 34: W32-W37.
37. Chou KC, (2000) Prediction of protein subcellular locations by incorporating quasi-sequence-order effect. Biochem Biophys Res Commun 278: 477-483.
38. Wold S, Jonsson J, Sjöström M, Sandberg M, Rännar S, (1993) DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures. Anal Chim Acta 277: 239-253.
39. Yang L, Li YZ, Xiao RQ, Zeng YH, Xiao JM, et al. (2010) Using auto covariance method for functional discrimination of membrane proteins based on evolution information. Amino Acids 38: 1497-1503.
40. Zeng YH, Guo YZ, Xiao RQ, Yang L, Yu LZ, et al. (2009) Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. J Theor Biol 259: 366-372.
41. Webb-Robertson BJ, Ratuiste KG, Oehmen CS, (2010) Physicochemical property distributions for accurate and rapid pairwise protein homology detection. BMC Bioinformatics 11: 145.
42. Webber CL, Zbilut JP, (1994) Dynamical assessment of physiological systems and states using recurrence plot strategies. J Appl Physiol 76: 965-973.
43. Mori K, Kasashima N, Yoshioka T, Ueno Y, (1996) Prediction of spalling on a ball bearing by applying the discrete wavelet transform to vibration signals. Wear 195: 162-168.
44. Huang NE, Shen Z, Long SR, Wu MC, Shih SH, et al. (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc R Soc A 454: 903-995.
45. Shi F, Chen QJ, Li NN, (2008) Hilbert Huang transform for predicting proteins subcellular location. J Biomed Sci Eng 1: 59-63.
46. Peng H, Long F, Ding C, (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27: 1226-1238.
47. Dellaire G, Farrall R, Bickmore WA, (2003) The Nuclear Protein Database (NPD): subnuclear localisation and functional annotation of the nuclear proteome. Nucleic Acids Res 31: 328-330.
48. Dill KA, (1985) Theory for the folding and stability of globular proteins. Biochemistry 24: 1501-1509.
49. Yu ZG, Anh V, Lau KS, (2004) Fractal analysis of measure representation of large proteins based on the detailed HP model. Physica A 337: 171-184.
50. Shen J, Zhang J, Luo X, Zhu W, Yu K, et al. (2007) Predicting protein-protein interactions based only on sequences information. Proc Natl Acad Sci U S A 104: 4337-4341.
51. Sánchez-Flores A, Pérez-Rueda E, Segovia L, (2008) Protein homology detection and fold inference through multiple alignment entropy profiles. Proteins 70: 248-256.
52. Murphy LR, Wallqvist A, Levy RM, (2000) Simplified amino acid alphabets for protein fold recognition and implications for folding. Protein Eng 13: 149-152.
53. Basu S, Pan A, Dutta C, Das J, (1997) Chaos game representation of proteins. J Mol Graph Model 15: 279-289.
54. Kawashima S, Kanehisa M, (2000) AAindex: amino acid index database. Nucleic Acids Res 28: 374.
55. Bhasin M, Raghava GP, (2004) ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST. Nucleic Acids Res 32: W414-419.
56. Vapnik VN (1995) The Nature of Statistical Learning Theory. Springer.
57. Platt JC, Cristianini N, Shawe-Taylor J (2000) Large margin DAGs for multiclass classification. Advances in Neural Information Processing Systems. Cambridge: 547-553.
58. Wang J, Lu HP, Plataniotis KN, Lu JW, (2009) Gaussian kernel optimization for pattern classification. Pattern Recognit 42: 1237-1247.
59. Yin JB, Li T, Shen HB, (2011) Gaussian kernel optimization: Complex problem and a simple solution. Neurocomputing 74: 3816-3822.
60. Blum T, Briesemeister S, Kohlbacher O, (2009) MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction. BMC Bioinformatics 10: 274.
61. Huang T, Shi XH, Wang P, He ZS, Feng KY, et al. (2010) Analysis and Prediction of the Metabolic Stability of Proteins Based on Their Sequential Features, Subcellular Locations and Interaction Networks. PLoS One 5: e10972.
62. Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines. Available: http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf.
63. Chou KC, (1995) A novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space. Proteins 21: 319-344.
64. Swets JA, (1988) Measuring the accuracy of diagnostic systems. Science 240: 1285-1293.
65. Bradley AP, (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 30: 1145-1159.
66. Gardy JL, Laird MR, Chen F, Rey S, Walsh CJ, et al. (2005) PSORTb v.2.0: expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis. Bioinformatics 21: 617-623.
67. Breman L, (2001) Random forest. Machine Learning 45: 5-32.
68. randomforest-matlab. Available: http://code.google.com/p/randomforest-matlab/.
69. Nguyen MN, Rajapakse JC, (2005) Prediction of protein relative solvent accessibility with a two-stage SVM approach. Proteins 59: 30-37.
70. Nguyen MN, Rajapakse JC, (2007) Prediction of Protein Secondary Structure with two-stage multi-class SVMs. Int J Data Min Bioinform 1: 248-269.
71. Gubbi J, Shilton A, Parker M, Palaniswami M, (2006) Protein topology classification using two-stage support vector machines. Genome Inform 17: 259-269.
72. Nguyen DV, Rocke DM, (2002) Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 18: 39-50.
73. Tan YX, Shi LM, Tong WD, Wang C, (2005) Multi-class cancer classification by total principal component regression (TPCR) using microarray gene expression data. Nucleic Acids Res 33: 56-65.
74. Silhavy TJ, Benson SA, Emr SD, (1983) Mechanisms of Protein Localization. Microbiol Rev 47: 313-344.
75. Yang JY, Zhou Y, Yu ZG, Anh V, Zhou LQ, (2008) Human Pol II promoter recognition based on primary sequences and free energy of dinucleotides. BMC Bioinformatics 9: 11.
76. Han GS, Yu ZG, Anh V, Chan RH (2009) Distinguishing coding from non-coding sequences in a prokaryote complete genome based on the global descriptor. Proceedings of The 6th International Conference on Fuzzy Systems and Knownledge Discovery: 42-46.
77. Otu HH, Sayood K, (2003) A new sequence distance measure for phylogenetic tree construction. Bioinformatics 19: 2122-2130.
78. Liu TG, Zheng XQ, Wang J, (2010) Prediction of protein structural class using a complexity-based distance measure. Amino Acids 38: 721-728.
79. Peng ZL, Yang JY, Chen X, (2010) An improved classification of G-protein-coupled receptors using sequence-derived features. BMC Bioinformatics 11: 420.
80. Eckmann JP, Kamphorst SO, Ruelle D, (1987) Recurrence plots of dynamical systems. Europhys Lett 4: 973-977.
81. Riley MA, Van OGC (2005) Tutorials in contemporary nonlinear methods for the behavioral sciences. Available: http://www.nsf.gov/sbe/bcs/pac/nmbs/nmbs.jsp.
82. Giuliani A, Benigni R, Zbilut JP, Webber CL, Sirabella P, et al. (2002) Nonlinear signal analysis methods in the elucidation of protein sequence-structure relationships. Chem Rev 102: 1471-1492.
83. Marwan N, Romano MC, Thiel M, Kurths J, (2007) Recurrence plots for the analysis of complex systems. Phys Rep 438: 237-329.
84. Yang JY, Peng ZL, Yu ZG, Zhang RJ, Anh V, et al. (2009) Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation. J Theor Biol 257: 618-626.
85. Yang YC, Tantoso E, Li KB, (2008) Remote protein homology detection using recurrence quantification analysis and amino acid physicochemical properties. J Theor Biol 252: 145-154.
86. Han GS, Yu ZG, Anh V, (2011) Predicting the subcellular location of apoptosis proteins based on recurrence quantification analysis and the Hilbert-Huang transform. Chin Phys B 20: 100504.
87. Yang JY, Chen X, (2011) Improving taxonomy-based protein fold recognition by using global and local features. Proteins 79: 2053-2064.
88. Zhou Y, Yu ZG, Anh V, (2007) Cluster protein structures using recurrence quantification analysis on coordinates of alpha-carbon atoms of proteins. Phys Lett A 368: 314-319.
89. Chou KC, (1988) Low-frequency collective motion in biomacromolecules and its biological functions. Biophys Chem 30: 3-48.
90. Mallat SG, (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11: 674-693.
91. Kandaswamy A, Kumar CS, Ramanathan RP, Jayaraman S, Malmurugan N, (2004) Neural classification of lung sounds using wavelet coefficients. Comput Biol Med 34: 523-537.
92. Shi SP, Qiu JD, Sun XY, Huang JH, Huang SY, et al. (2011) Identify submitochondria and subchloroplast locations with pseudo amino acid composition: approach from the strategy of discrete wavelet transform feature extraction. Biochim Biophys Acta 1813: 424-430.
93. Yu ZG, Anh V, Wang Y, Mao D, Wanliss J, (2010) Modelling and simulation of the horizontal component of the geomagnetic field by fractional stochastic differential equations in conjunction with empirical mode decomposition. J Geophys Res 115: A10219.