A novel approach to predict protein-protein interactions related to Azheimer's disease based on complex network

Protein and Peptide Letters

Volumn 17, Issue 3, 2010, Pages 356-366

  Yang, Jie ^a   Jiang, Xiong Fei ^a  

^a NANJING UNIVERSITY   (China)

Author keywords

AD associated protein interaction network; Conservative interaction pattern; Signal pathway

Indexed keywords

AATF PROTEIN, HUMAN; APOPTOSIS REGULATORY PROTEIN; PROTEIN; REPRESSOR PROTEIN;

ALGORITHM; ALZHEIMER DISEASE; AMINO ACID SEQUENCE; ARTICLE; BIOLOGY; HUMAN; METABOLISM; METHODOLOGY; MOLECULAR GENETICS; PROTEIN ANALYSIS; SIGNAL TRANSDUCTION; STATISTICAL MODEL;

ALGORITHMS; ALZHEIMER DISEASE; AMINO ACID SEQUENCE; APOPTOSIS REGULATORY PROTEINS; COMPUTATIONAL BIOLOGY; HUMANS; MODELS, STATISTICAL; MOLECULAR SEQUENCE DATA; PROTEIN INTERACTION MAPPING; PROTEINS; REPRESSOR PROTEINS; SIGNAL TRANSDUCTION;

EID: 77949945267     PISSN: 09298665     EISSN: None     Source Type: Journal    
DOI: 10.2174/092986610790780323     Document Type: Article

References (76)

1. Chou, K.C.; Howe, W.J. Prediction of the tertiary structure of the beta-secretase zymogen. Biochem. Biophy. Res. Commun., 2002, 292, 702-708.
2. Haugabook, S.J.; Yager, D.M.; Eckman, E.A.; Golde, T.E.; Younkin, S.G.; Eckman, C.B. High throughput screens for the identification of compounds that alter the accumulation of the Alzheimer's amyloid peptide (A). J. Neurosci. Methods, 2001, 108, 171-179.
3. Lorenzo, A.; Yankner, B. A. Beta-amyloid neurotoxicity requires fibril formation and is inhibited by Congo red. Proc. Natl. Acad. Sci. USA, 1994, 91,12243-12247.
4. Carter, D.B.; Chou, K.C. A model for structure dependent binding of Congo Red to Alzeheimer beta-amyloid fibrils. Neurobiol. Aging, 1998, 19, 37-40.
5. Chou, K.C. Review: Structural bioinformatics and its impact to biomedical science. Curr. Med. Chem., 2004, 11, 2105-2134.
6. Wei, D.Q.; Sirois, S.; Du, Q.S.; Arias, H.R.; Chou, K.C. Theoretical studies of Alzheimer's disease drug candidate [(2,4-dimethoxy) benzylidene]-anabaseine dihydrochloride (GTS-21) and its derivatives. Biochem. Biophy. Res. Commun., 2005, 338, 1059-1064.
7. Wang, H.-Y.; Lee, D.H.S.; Davis, C.B.; Shank, R.P. Amyloid peptide A 1-42 binds selectively and with picomolar affinity to alpha7 nicotinic receptors. J. Neurochem., 2000, 75, 1155-1161.
8. Zheng, H.; Wei, D.Q.; Zhang, R.; Wang, C.; Wei, H.; Chou, K.C. Screening for New Agonists against Alzheimer's Disease. Med. Chem., 2007, 3, 488-493.
9. Whitehouse, P.J.; Price, D.L.; Struble, R.G.; Clark, A.W.; Coyle, J.T.; DeLong, M.R. Alzheimer's disease and senile dementia: loss of neurons in the basal forebrain. Science, 1982, 215, 1237-1239.
10. Raves, M.L.; Harel, M.; Pang, Y.P.; Silman, I.; Kozikowski, A.P.; Sussman, J.L. Structure of acetylcholinesterase complexed with the nootropic alkaloid, (-)-huperzine A. Nat. Struct. Biol., 1997, 4(1), 57-63.
11. Ge, H.M.; Zhu, C.H.; Shi, D.H.; Zhang, L.D.; Xie, D.Q.; Yang, J.; Ng, S.W.; Tan, R.X. Hopeahainol A, An Acetylcholinesterase Inhibitor from Hopea hainanensis. Chem. Eur. J., 2008, 14(1), 376-381.
12. Jellinger, KA. General aspects of neurodegeneration. J. Neural Transm. Suppl., 2003, 65, 101-44.
13. Jeong, H.; Mason, S.P.; Barabási, A.-L.; Oltvai, Z.N. Lethality and centrality in protein networks. Nature, 2002, 411, 41-42.
14. Eisenberg, D.; Marcotte, E.M.; Xenarios, I.; Yeates, T.O. Protein function in the post-genomicera. Nature, 2000, 405, 823-826.
15. Albert, R.; Barabási, A.-L. Statistical mechanics of complex networks. Rev. Mod. Phys., 2002, 74, 47-97.
16. Erdös, P.; Rényi, A. On random graphs. I. Publ. Math., 1959, 6, 290-297.
17. Milgram, S. The small world problem. Psychol. Today, 1967, 1, 60-67.
18. Granovetter, M. S. The Strength of Weak Ties. Am. J. Sociol., 1973, 78, 1360-1380.
19. Watts, D. J.; Strogatz, S.H. Collective dynamics of "small-world" networks. Nature, 1998, 393, 440-442.
20. Barabasi, A.-L.; Albert, R. Emergence of scaling in random networks. Science, 1999, 286, 509-512.
21. Uetz, P.; Giot, L.; Cagney, G.; Mansfield, T. A.; Judson, R. S.; Narayan, V.; Lockshon, D.; Srinivasan, M.; Pochart, P.; QureshiEmili, A.; Li, Y.; Godwin, B.; Conover, D.; Kalbfleisch, T.; Vijayadamodar, G.; Yang, M.; Johnston, M.; Fields, S.; Rothberg, J.M. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature, 2000, 403, 623-627.
22. Fields, S.; Song, O. A novel genetic system to detect proteinprotein interactions. Nature, 1989, 340, 245-246.
23. Gavin, A.C.; Bösche, M.; Krause, R.; Grandi, P.; Marcioch, M.; Bauer, A.; Schultz, J.; Rick, J.M.; Michon, A.M.; Cruciat, C.M. Functional organisation of the yeast proteome by systematic analysis of protein complexes. Nature, 2002, 415, 141-147.
24. Ho, Y.; Gruhler, A.; Heilbut, A.; Bader, G.D.; Moore, L.; Adams, S.L.; Millar, A.; Taylor, P.; Bennett, K.; Boutilier, K. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature, 2002, 415, 180-183.
25. Zhu, H.; Bilgin, M.; Bangham, R.; Hall, D.; Casamayor, A.; Bertone, P.; Lan, N.; Jansen, R.; Bidlingmaier, S.; Houfek, T. Global analysis of protein activities using proteome chips. Science, 2002, 293, 2101-2105.
26. Tong, A.H.; Drees, B.; Nardelli, G.; Bader, G.D.; Brannetti, B.; Castagnoli, L.; Evangelista, M.; Ferracuti, S.; Nelson, B.; Paoluzi, S. A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules. Science, 2002, 295, 321-324.
27. Chou, K.C.; Cai, Y.D. Predicting protein-protein interactions from sequences in a hybridization space. J. Proteome Res., 2006, 5, 316-322.
28. Yang, Z.R.; Chou, K.C. Correlation of metabolic pathways with the primary structure in acetylated proteins. Open Bioinform. J., 2008, 2, 90-96.
29. Chou, K.C.; Cai, Y.D.; Zhong, W.Z. Predicting networking couples for metabolic pathways of Arabidopsis. EXCLI J. (Exp. Clin. Sci. Int. Online J. Adv. Sci.), 1995, 5, 55-65.
30. 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.
31. Chou, K.C. In Automation in Proteomics and Genomics: An Engineering Case-Based Approach (Harvard-MIT interdisciplinary special studies courses), Chap.5; Alterovitz, G., Benson, R., and Ramoni, M. F., Ed.; Wiley & Sons, Ltd.: West Sussex, UK, 2009; pp. 97-143.
32. Chou, K.C.; Shen, H.B. Review: Recent progresses in protein subcellular location prediction. Anal. Biochem., 2007, 370, 1-16.
33. Chou, K.C.; Zhang, C.T. Review: Prediction of protein structural classes. Crit. Rev. Biochem. Mol. Biol., 1995, 30, 275-349.
34. Bairoch, A.; Apweiler, R. The SWISS-PROT protein sequence data bank and its supplement TREMBL. Nucl. Acids Res., 1997, 25, 3136.
35. Hermjakob, H.; Montecchi-Palazzi, L.; Lewington, C.; Mudali, S.; Kerrien, S.; Orchard, S.; Vingron, M.; Roechert, B.; Roepstorff, P.; Valencia, A.; Margalit, H.; Armstrong, J.; Bairoch, A.; Cesareni, G.; Sherman, D.; Apweiler, R. IntAct - an open source molecular interaction database. Nucl. Acids. Res., 2004, 32, D452-D455.
36. Wagner, A.; Fell, D.A. The small world inside large metabolic networks. Proc. Biol. Sci., 2001, 268 (1478), 1803-1810. (Proc. R. Soc. Land. B)
37. Amaral, L.A.; Scala, A.; Barthélemy, M.; Stanley, H.E. Classes of small-world networks. Proc. Natl. Acad. Sci. USA, 2000, 97, 11149-11152.
38. Albert, R.; Jeong, H.; Barabási, A.-L. Attack and error tolerance of complex networks. Nature, 2000, 406, 378-382.
39. Cohen, R.; Erez, K.; Avraham, D.; Havlin, S. Resilience of the internet to random breakdowns. Phys. Rev. Lett., 2000, 85(21), 4626-4628.
40. Chou, K.C.; Shen, H.B. Hum-PLoc: A novel ensemble classifier for predicting human protein subcellular localization. Biochem. Biophys. Res. Comm., 2006, 347, 150-157.
41. Chou, K.C.; Shen, H.B. Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-nearest neighbor classifiers. J. Proteome Res., 2006, 5, 1888-1897.
42. Chou, K.C.; Shen, H.B. Large-scale plant protein subcellular location prediction. J. Cell. Biochem., 2007, 100, 665-678.
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. Zhang, G.Y.; Fang, B.S. Predicting the cofactors of oxidoreductases based on amino acid composition distribution and Chou's amphiphilic pseudo amino acid composition. J. Theor. Biol., 2008, 253, 310-315.
45. Du, P.; Li, Y. Prediction of C-to-U RNA editing sites in plant mitochondria using both biochemical and evolutionary information. J. Theor. Biol., 2008, 253, 579-589.
46. Xiao, X.; Chou, K.C. Digital coding of amino acids based on hydrophobic index. Protein Pept. Lett., 2007, 14, 871-875.
47. Ding, Y.S.; Zhang, T.L.; Chou, K.C. Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network. Protein Pept. Lett., 2007, 14, 811-815.
48. Niu, B.; Jin, Y.H.; Feng, K.Y.; Liu, L.; Lu, W.C.; Cai, Y.D.; Li, G.Z. Predicting membrane protein types with bagging learner. Protein Pept. Lett., 2008, 15, 590-594.
49. Lin, H.; Ding, H.; Guo, F.B.; Zhang, A.Y.; Huang, J. Predicting subcellular localization of mycobacterial proteins by using Chou's pseudo amino acid composition. Protein Pept. Lett., 2008, 15, 739-744.
50. Shi, M.G.; Huang, D.S.; Li, X.L. A Protein Interaction Network Analysis for Yeast Integral Membrane Protein. Protein Pept. Lett., 2008, 15, 692-699.
51. Wang, T.; Yang, J.; Shen, H.B.; Chou, K.C. Predicting membrane protein types by the LLDA algorithm. Protein Pept. Lett., 2008, 15, 915-921.
52. Kannan, S.; Hauth, A.M.; Burger, G. Function prediction of hypothetical proteins without sequence similarity to proteins of known function. Protein Pept. Lett., 2008, 15, 1107-1116.
53. Chou, K.C.; Shen, H.B. ProtIdent: A web server for identifying proteases and their types by fusing functional domain and sequential evolution information. Biochem. Biophys. Res. Comm., 2008, 376, 321-325.
54. Munteanu, C.B.; Gonzalez-Diaz, H.; Magalhaes, A.L. Enzymes/non-enzymes classification model complexity based on composition, sequence, 3D and topological indices. J. Theor. Biol., 2008, 254, 476-482.
55. Munteanu, C.R.; Gonzalez-Diaz, H.; Borges, F.; de Magalhaes, A.L. Natural/random protein classification models based on star network topological indices. J. Theor. Biol., 2008, 254, 775-783.
56. Rezaei, M.A.; Abdolmaleki, P.; Karami, Z.; Asadabadi, E.B.; Sherafat, M.A.; Abrishami-Moghaddam, H.; Fadaie, M.; Forouzanfar, M. Prediction of membrane protein types by means of wavelet analysis and cascaded neural networks. J. Theor. Biol., 2008, 254, 817-820.
57. Barbato, C.; Corbi, N.; Canu, N.; Fanciulli, M.; Serafino, A.; Ciotti, M.; Libri, V.; Bruno, T.; Amadoro, G.; De Angelis, R.; Calissano, P.; Passananti, C. Rb binding protein Che-1 interacts with Tau in cerebellar granule neurons. Mol. Cell. Neurosci., 2003, 24, 10381050.
58. Zhan, C.Y.; Yang, J.; Dong, X.C.; Wang, Y.L. Molecular Modeling of Purinergic Receptor P2Y12 and Interaction with its antagonists. J. Mol. Graph. Model., 2007, 26, 20-31.
59. Yang, J.; Dong, X.C.; Leng, Y. Conformation Biases of Amino Acids Based on Tripeptide Microenvironment from PDB database. J. Theor. Biol., 2006, 240 (3), 374-384.
60. Yang, J.; Dong, X.C.; Leng, Y. Application of FTTP to alpha-helix or beta-strand motifs. J. Theor. Biol., 2006, 242, 199-219.
61. Huynen, M.A.; Snel, B. Exploiting the variations in the genomic associations of genes to predict pathways and reconstruct their evolution. In Frontiers in Computational Genomics. Edited by Galperin, M.Y.; Koonin, E.V. Norfolk: Caisters Academic Press, 2003.
62. Zhang, W.; Han, S.W.; McKeel, D.W.; Goate, A.; Wu, J.Y. Interaction of presenilins with the filamin family of actin-binding proteins. J. Neurosci., 1998, 18, 914-922.
63. Chow, N.; Korenberg, J.R.; Chen, X.-N.; Neve, R.L. APP-BP1, a novel protein that binds to the carboxyl-terminal region of the amyloid precursor protein. J. Biol. Chem., 1996, 271, 11339-11346.
64. Xie, J.; Guo, Q. PAR-4 Is Involved in Regulation of -Secretase Cleavage of the Alzheimer Amyloid Precursor Protein. J. Biol. Chem., 2005, 280(14), 13824-13832.
65. Tanahashi, H.; Tabira, T. X11L2, a new member of X11 protein family interacts with Alzheimer's beta-amyloid precursor protein. Biochem. Biophys. Res. Commun., 1999, 255, 663-667.
66. Esler, W. P.; Wolfe, M. S. A Portrait of Alzheimer Secretases-New Features and Familiar Faces. Science, 2001, 293, 1449-1454.
67. Hardy, J.; Selkoe, D. J. The Amyloid Hypothesis of Alzheimer's Disease: Progress and Problems on the Road to Therapeutics. Science, 2002, 297, 353-356.
68. Selkoe, D. J. Alzheimer's Disease Is a Synaptic Failure. Science, 2002, 298, 789-791.
69. Hulo, N.; Sigrist, C.J.A.; Le Saux, V.; Langendijk-Genevaux, P.S.; Bordoli, L.; Gattiker, A.; De Castro, E.; Bucher, P.; Bairoch, A. Recent improvements to the PROSITE database. Nucl. Acids. Res., 2004, 32, D134-D137
70. Sigrist, C.J.A.; Cerutti, L.; Hulo, N.; Gattiker, A.; Falquet, L.; Pagni, M.; Bairoch, A.; Bucher, P. PROSITE: a documented database using patterns and profiles as motif descriptors. Brief Bioinform., 2002, 3, 265-274.
71. Jonassen, I.; Collins, J.F.; Higgins, D.G. Finding flexible patterns in unaligned protein sequences. Protein Sci., 1995, 4, 1587-1595.
72. Cascante, M.; Boros, L.G.; Comin-Anduix, B.; de Atauri, P.; Centelles, J.J.; Lee, P.W. Metabolic control analysis in drug discovery and disease. Nat. Biotechnol., 2002, 20, 243-249.
73. Huang, S. Rational drug discovery: what can we learn from regulatory networks? Drug Discov. Today, 2002, 7, S163-S169.
74. Milo, R.; Shen-Orr, S.S.; Itzkovitz, S.; Kashtan, N.; Chklovskii, D.; Alon, U. Network motifs: Simple building locks of complex networks. Science, 2002, 298, 824-827.
75. Shen-Orr, S.S.; Milo, R.; Mangan, S.; Alon, U. Network motifs in the transcriptional regulation network of E.coli. Nature Genet., 2002, 31, 64-68.
76. Jonassen, I. Efficient discovery of conserved patterns using a pattern graph. CABIOS, 1997, 13, 509-522.