Mem-PHybrid: Hybrid features-based prediction system for classifying membrane protein types

Analytical Biochemistry

Volumn 424, Issue 1, 2012, Pages 35-44

  Hayat, Maqsood ^a   Khan, Asifullah ^a  

^a PAKISTAN INSTITUTE OF ENGINEERING AND APPLIED SCIENCES   (Pakistan)

Author keywords

ET KNN; Membrane proteins; Multi profiles Bayes; Physicochemical properties; Random forest; Split amino acid composition; Support vector machine

Indexed keywords

AMINO ACIDS; CLASSIFICATION (OF INFORMATION); DECISION TREES; FORECASTING; MEMBRANES; NEAREST NEIGHBOR SEARCH; PHYSICOCHEMICAL PROPERTIES; RANDOM FORESTS; STATISTICAL TESTS; SUPPORT VECTOR MACHINES;

AMINO ACID COMPOSITIONS; ET-KNN; K-NEAREST NEIGHBORS; LEARNING CAPABILITIES; MEMBRANE PROTEINS; MULTI-PROFILES BAYES; PREDICTION PERFORMANCE; PREDICTION SYSTEMS;

PROTEINS;

MEMBRANE PROTEIN; PROTEIN;

ACCURACY; AMINO ACID COMPOSITION; ARTICLE; COMPUTER PREDICTION; CONTROLLED STUDY; DATA BASE; HYBRID; PHYSICAL CHEMISTRY; PRIORITY JOURNAL; RANDOM FOREST; SPLIT AMINO ACID COMPOSITION; SUPPORT VECTOR MACHINE;

EID: 84858731996     PISSN: 00032697     EISSN: 10960309     Source Type: Journal    
DOI: 10.1016/j.ab.2012.02.007     Document Type: Article

References (64)

1. K.E. Chen, Y. Jiang, L. Du, and L. Kurgan Prediction of integral membrane protein type by collocated hydrophobic amino acid pairs J. Comput. Chem. 30 2009 163 172
2. Z. Aydin, Y. Altunbasak, and H. Erdogan Bayesian models and algorithms for protein â-sheet prediction IEEE/ACM Trans. Comput. Biol. Bioinform. 8 2011 395 409
3. K.C. Chou, and D.W. Elrod Prediction of membrane protein types and subcellular location Proteins Struct. Funct. Genet. 34 1999 137 153
4. P.G. Bagos, T.D. Liakopoulos, I.C. Spyropoulos, and S.J. Hamodrakas PRED-TMBB: a web server for predicting the topology of β-barrel outer membrane proteins Nucleic Acids Res. 32 2004 W400 W404
5. E.L.L. Sonnhammer, G.V. Heijne, A. Krogh, A hidden Markov model for predicting transmembrane helices in protein sequences, in: Proceedings of the Sixth International Conference on Intelligent Systems for, Mol. Biol. (1998), pp. 175-182.
6. G.E. Tusnady, and I. Simon The HMMTOP transmembrane topology prediction server Bioinformatics 17 2001 849 850
7. D.T. Jones Improving the accuracy of transmembrane protein topology prediction using evolutionary information Bioinformatics 23 2007 538 544
8. K.C. Chou Prediction of protein subcellular attributes using pseudo-amino acid composition Proteins 43 2001 246 255
9. M. Wang, J. Yang, G.P. Liu, Z.J. Xu, and K.C. Chou Weighted-support vector machines for predicting membrane protein types based on pseudo amino acid composition Protein Eng. Des. Sel. 17 2004 509 516
10. Y.D. Cai, P.W. Ricardo, C.H. Jen, and K.C. Chou Application of SVM to predict membrane protein types J. Theor. Biol. 226 2004 373 376
11. H. Liu, M. Wang, and K.C. Chou Low-frequency Fourier spectrum for predicting membrane protein types Biochem. Biophys. Res. Commun. 336 2005 737 739
12. M.A. Rezaei, P.A. Maleki, Z. Karami, E.B. Asadabadi, M.A. Sherafat, K.A. Moghaddam, M. Fadaie, and M. Forouzanfar Prediction of membrane protein types by means of wavelet analysis and cascaded neural network J. Theor. Biol. 255 2008 817 820
13. J.D. Qiu, X.U. Sun, J.H. Huang, and R.P. Liang Prediction of the types of membrane proteins based on discrete wavelet transform and support vector machines Protein J. 29 2010 114 119
14. L. Wang, Z. Yuan, X. Chen, and Z. Zhou The prediction of membrane protein types with NPE IEICE Electron. Express 6 2010 397 402
15. M. Hayat, and A. Khan Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition J. Theor. Biol. 271 2011 10 17
16. K.C. Chou, and Y.D. Cai Prediction of membrane protein types by incorporating amphipathic effects J. Chem. Inf. Model. 45 2005 407 413
17. M. Cserzo, E. Wallin, and I. Simon Prediction of transmembrane α-helices in prokaryotic membrane proteins: the dense alignment surface method Protein Eng. Des. Sel. 10 1997 673 676
18. B. Rost, R. Casadio, and P. Fariselli Topology prediction for helical transmembrane proteins at 86% accuracy Protein Sci. 5 1996 1704 1718
19. P.L. Martelli, P. Fariselli, A. Krogh, and R. Casadio A sequence-profile β-based HMM for predicting and discriminating -barrel membrane proteins Bioinformatics 18 2002 46 53
20. G.E. Tusnady, and I. Simon Principles governing amino acid composition of integral membrane proteins: application to topology prediction J. Mol. Biol. 283 1998 489 506
21. M.E. Call, K.W. Wucherpfennig, and J.J. Chou The structural basis for intramembrane assembly of an activating immunoreceptor complex Nat. Immunol. 11 2010 1023 1029
22. R.M. Pielak, and J.J. Chou Influenza M2 proton channels Biochim. Biophys. Acta 2011 1808 522 529
23. R.M. Pielak, and J.J. Chou Solution NMR structure of the V27A drug resistant mutant of influenza A M2 channel Biochem. Biophys. Res. Commun. 401 2010 58 63
24. R.M. Pielak, R. Jason, J.R. Schnell, and J.J. Chou Mechanism of drug inhibition and drug resistance of influenza A M2 channel Proc. Natl. Acad. Sci. USA 106 2009 7379 7384
25. K.C. Chou Structural bioinformatics and its impact to biomedical science Curr. Med. Chem. 11 2004 2105 2134
26. K.C. Chou Insights from modelling the 3D structure of the extracellular domain of α7 nicotinic acetylcholine receptor Biochem. Biophys. Res. Commun. 319 2004 433 438
27. K.C. Chou Molecular therapeutic target for type-2 diabetes J. Proteome Res. 3 2004 1284 1288
28. K.C. Chou Some remarks on protein attribute prediction and pseudo amino acid composition J. Theor. Biol. 273 2011 236 247
29. K.C. Chou, and H.B. Shen MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM Biochem. Biophys. Res. Commun. 360 2007 339 345
30. A. Reinhardt, and T. Hubbard Using neural networks for prediction of the subcellular location of proteins Nucleic Acids Res. 26 1998 2230 2236
31. I. Small, N. Peeters, F. Legeai, and C. Lurin Predotar: a tool for rapidly screening proteomes for N-terminal targeting sequences Proteomics 4 2004 1581 1590
32. H.B. Shen, and K.C. Chou EzyPred: a top-down approach for predicting enzyme functional classes and subclasses Biochem. Biophys. Res. Commun. 364 2007 53 59
33. K.C. Chou, and H.B. Shen A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0 PLoS One 5 2010 e9931
34. K.C. Chou, and H.B. Shen Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localization PLoS One 5 2010 e11335
35. T. Huang, X.H. Shi, P. Wang, Z. He, K.Y. Feng, L. Hu, X. Kong, Y.X. Li, Y.D. Cai, and K.C. Chou Analysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations, and interaction networks PLoS One 5 2010 e10972
36. T. Huang, S. Wan, Z. Xu, Y. Zheng, K.Y. Feng, H.P. Li, X. Kong, and Y.D. Cai Analysis and prediction of translation rate based on sequence and functional features of the mRNA PLoS One 6 2011 e16036
37. I. Dubchak, I. Muchnik, S. Holbrook, and S. Kim Prediction of protein folding class using global description of amino acid sequence Proc. Natl. Acad. Sci. USA 92 1995 8700 8704
38. I. Dubchak, I. Muchnik, C. Mayor, I. Dralyuk, and S. Kim Recognition of a protein fold in the context of the SCOP classification Protein Struct. Funct. Genet. 35 1999 401 407
39. C.Z. Cai, L.Y. Han, Z.L. Ji, X. Chen, and Y.Z. Chen SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence Nucleic Acids Res. 31 2003 3692 3697
40. L. Chen, T. Huang, X.H. Shi, Y.D. Cai, and K.C. Chou Analysis of protein pathway networks using hybrid properties Molecules 15 2010 8177 8192
41. Z.R. Li, H.H. Lin, L.Y. Han, L. Jiang, X. Chen, and Y.Z. Chen PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence Nucleic Acids Res. 34 2006 W32 W37
42. Q.B. Gao, X.F. Ye, Z.C. Jin, and J. He Improving discrimination of outer membrane proteins by fusing different forms of pseudo amino acid composition Anal. Biochem. 398 2010 52 59
43. K.C. Chou, and H.B. Shen Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-nearest neighbor classifiers J. Proteome Res. 5 2006 1888 1897
44. K.C. Chou, and H.B. Shen Hum-PLoc: a novel ensemble classifier for predicting human protein subcellular localization Biochem. Biophys. Res. Commun. 347 2006 150 157
45. J. Shao, D. Xu, S. Tsai, Y. Wang, and S. Ngai Computational identification of protein methylation sites through bi-profile Bayes feature extraction PLoS One 4 2009 e4920
46. A.A. Freitas, C.D. Wieser, and R. Apweiler On the importance of comprehensible classification models for protein function prediction IEEE/ACM Trans. Comput. Biol. Bioinform. 7 2010 172 182
47. L. Breiman Random forest Mach. Learn. 45 2001 5 32
48. M. Kubat, R. Holte, S. Matwin, Learning when negative examples abound, in: European Conference on, Machine Learning (ECML-97), (1997), pp. 146-153.
49. T. Denoeux A K-nearest neighbor classification rule based on Dempster-Shafer theory IEEE Trans. Syst. Man. Cybern. 25 1995 804 813
50. H. Shen, and K.C. Chou Using optimized evidence-theoretic K-nearest neighbor classifier and pseudo-amino acid composition to predict membrane protein types Biochem. Biophys. Res. Commun. 334 2005 288 292
51. A. Yazdani, T. Ebrahimi, U. Hoffmanm, Classification of EEG signals using Dempster-Shafer theory and a K-nearest neighbor classifier, in: Proceedings of the 4th International IEEE EMBS Conference on Neural Engineering, Antalya, Turkey, 2009, pp. 327-330.
52. G. Shafer A Mathematical Theory of Evidence 1976 Princeton University Press Princeton, NJ
53. V.N. Vapnik The Nature of Statistical Learning Theory 1995 Springer-Verlag London
54. J.D. Qiu, J.H. Huang, R.P. Liang, and X.Q. Lu Prediction of G-protein-coupled receptor classes based on the concept of Chou's pseudo amino acid composition: an approach from discrete wavelet transform Anal. Biochem. 390 2009 68 73
55. Z.U. Rehman, and A. Khan G-protein-coupled receptor prediction using pseudo amino acid composition and multi-scale energy representation of different physiochemical properties Anal. Biochem. 412 2011 173 182
56. A. Khan, S.F. Tahir, A. Majid, and T.-S. Choi Machine learning based adaptive watermark decoding in view of an anticipated attack Pattern Recognit. 41 2008 2594 2610
57. A. Khan, S. F. Tahir, T-S. Choi, Intelligent extraction of a digital watermark from a distorted image, IEICE Trans. Inform. Syst. E91-D (2008) pp. 2072-2075.
58. A. Khan, M.F. Khan, and T.S. Choi Proximity based GPCRs prediction in transform domain Biochem. Biophys. Res. Commun. 371 2008 411 415
59. M. Tahir, A. Khan, and A. Majid Protein subcellular localization of fluorescence imagery using spatial and transform domain features Bioinformatics 28 2012 91 97
60. V.N. Vapnik Statistical Learning Theory 1998 John Wiley New York
61. A. Khan, A. Majid, and T.S. Choi Predicting protein subcellular location: exploiting amino acid based sequence of feature spaces and fusion of diverse classifiers Amino Acids 38 2010 347 350
62. L. Nanni, and A. Lumini An ensemble of support vector machines for predicting the membrane protein type directly from the amino acid sequence Amino Acids 35 2008 573 580
63. K.C. Chou, and H.B. Shen Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms Nat. Sci. 2 2010 1090 1103
64. Z.S. He, J. Zhang, X.H. Shi, L.L. Hu, X.G. Kong, Y.D. Cai, and K.C. Chou Predicting drug-target interaction networks based on functional groups and biological features PLoS One 5 2010 e9603