Star graphs of protein sequences and proteome mass spectra in cancer prediction

Current Proteomics

Volumn 6, Issue 4, 2009, Pages 275-288

  Vázquez, José M ^a   Aguiar, Vanessa ^a   Seoane, Jose A ^a   Freire, Ana ^a   Serantes, José A ^a   Dorado, Julián ^a   Pazos, Alejandro ^a   Munteanu, Cristian R ^a  

^a UNIVERSITY OF A CORUÑA   (Spain)

Author keywords

Cancer prediction; Complex network; Graphs; Linear discriminant analysis; Quantitative protein disease relationship

Indexed keywords

EID: 72449164589     PISSN: 15701646     EISSN: None     Source Type: Journal    
DOI: 10.2174/157016409789973752     Document Type: Article

References (98)

1. Welsh, J.B.; Sapinoso, L.M.; Su, A.I.; Kern, S.G.; Wang-Rodriguez, J.; Moskaluk, C.A.; Frierson, H.F.Jr. and Hampton, G.M. Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. Cancer Res., 2001, 61, 5974-5978.
2. Rivera, M.P. and Stover, D.E. Gender and lung cancer. Clin. Chest Med., 2004, 25, 391-400.
3. Kirkegaard, T.; McGlynn, L.M.; Campbell, F.M.; Muller, S.; Tovey, S.M.; Dunne, B.; Nielsen, K.V.; Cooke, T.G. and Bartlett, J.M. Amplified in breast cancer 1 in human epidermal growth factor receptor - positive tumors of tamoxifen-treated breast cancer patients. Clin. Cancer Res., 2007, 13, 1405-1411.
4. Jain, K.K. Recent advances in clinical oncoproteomics. J. BUON., 2007, 12 (Suppl 1), S31-S38.
5. Cho, W.C. and Cheng, C.H. Oncoproteomics: current trends and future perspectives. Expert Rev. Proteomics, 2007, 4, 401-410.
6. González-Díaz, H.; Ferino, G.; Munteanu, C.R.; Vilar, S. and Uriarte, E. In An Omics Perspective of Cancer, Cho, W.C.S. Ed. Springer Science+Business Media B.V.
7. 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.
8. 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.
9. Chou, K.C. and Zhang, C.T. Diagrammatization of codon usage in 339 HIV proteins and its biological implication. AIDS Res. Hum. Retroviruses, 1992, 8, 1967-1976.
10. Qi, X.Q.; Wen, J. and Qi, Z.H. New 3D graphical representation of DNA sequence based on dual nucleotides. J. Theor. Biol., 2007, 249, 681-690.
11. Liao, B.; Xiang, X. and Zhu, W. Coronavirus phylogeny based on 2D graphical representation of DNA sequence. J. Comput. Chem., 2006, 27, 1196-1202.
12. Randic, M. Condensed representation of DNA primary sequences. J. Chem. Inf. Comput. Sci., 2000, 40, 50-56.
13. Randic, M. and Balaban, A.T. On a four-dimensional representation of DNA primary sequences. J. Chem. Inf. Comput. Sci., 2003, 43, 532-539.
14. Perez, M.A.; Sanz, M.B.; Torres, L.R.; Avalos, R.G.; Gonzalez, M.P. and Diaz, H.G. A topological sub-structural approach for predicting human intestinal absorption of drugs. Eur. J. Med. Chem., 2004, 39, 905-916.
15. Agüero-Chapin, G.; Gonzalez-Diaz, H.; Molina, R.; Varona-Santos, J.; Uriarte, E. and Gonzalez-Diaz, Y. Novel 2D maps and coupling numbers for protein sequences. The first QSAR study of polygalacturonases; isolation and prediction of a novel sequence from Psidium guajava L. FEBS Lett., 2006, 580, 723-730.
16. Devillers, J. and Balaban, A.T. Topological Indices and Related Descriptors in QSAR and QSPR, Gordon and Breach: The Netherlands 1999.
17. Barabasi, A.L. and Bonabeau, E. Scale-free networks. Sci. Am., 2003, 288, 60-69.
18. Balaban, A.T.; Basak, S.C.; Beteringhe, A.; Mills, D. and Supuran, C.T. QSAR study using topological indices for inhibition of carbonic anhydrase II by sulfanilamides and Schiff bases. Mol. Divers., 2004, 8, 401-412.
19. Barabasi, A.L. and Oltvai, Z.N. Network biology: understanding the cell's functional organization. Nat. Rev. Genet., 2004, 5, 101-113.
20. Barabasi, A.L. Sociology. Network theory-the emergence of the creative enterprise. Science, 2005, 308, 639-641.
21. González-Díaz, H.; Vilar, S.; Santana, L. and Uriarte, E. Medicinal chemistry and bioinformatics - current trends in drugs discovery with networks topological indices. Curr. Top. Med. Chem., 2007, 7, 1025-1039.
22. Ferino, G.; Gonzalez-Diaz, H.; Delogu, G.; Podda, G. and Uriarte, E. Using spectral moments of spiral networks based on PSA/mass spectra outcomes to derive quantitative proteome-disease relationships (QPDRs) and predicting prostate cancer. Biochem. Biophys. Res. Commun., 2008, 372, 320-325.
23. Gonzalez-Diaz, H.; Gonzalez-Diaz, Y.; Santana, L.; Ubeira, F.M. and Uriarte, E. Proteomics, networks and connectivity indices. Proteomics, 2008, 8, 750-778.
24. Van Waterbeemd, H. Chemometric methods in molecular design, Wiley-VCH: New York 1995.
25. Harary, F. Graph Theory: MA 1969.
26. Randic, M.; Zupan, J. and Vikic-Topic, D. On representation of proteins by star-like graphs. J. Mol. Graph Model, 2007, 290-305.
27. Munteanu, C.R. and Gonzáles-Diáz H.: S2SNet - Sequence to Star Network, Reg. No. 03/2008/1338, Santiago de Compostela, Spain, 2008.
28. González-Díaz H.; Molina-Ruiz R. and Hernandez I.: MARCHINSIDE v3.0 (MARkov CHains INvariants for SImulation & DEsign), gonzalezdiazh@yahoo.es, 2007.
29. Rappin, N. and Dunn, R. wxPython in Action, Manning Publications Co.: Greenwich, CT 2006.
30. Ferino, G.; Delogu, G.; Podda, G.; Uriarte, E. and González- Díaz, H. Quantitative proteome-disease relationships (QPDRs) in clinical chemistry prediction of prostate cancer with special moments of PSA/MS star networks. In: Clinical Chemistry Research; Mitchem, BHaS, ChL., ed. NY: Nova Science Publisher 2009.
31. Koutsofios, E. and North, S.C. Drawing Graphs with dot, AT&T Bell Laboratories, Murray Hill: NJ, USA 1993.
32. Koschützki, D. pp. CentiBiN Version 1.4.2, Centralities in Biological Networks © 2004-2006 Dirk Koschützki Research Group Network Analysis, IPK Gatersleben, Germany 2006.
33. Batagelj, V. and Mrvar, A.: Pajek 1.15, 2006.
34. Todeschini, R. and Consonni, V. Handbook of Molecular Descriptors, Wiley-VCH 2002.
35. StatSoft.Inc.: STATISTICA (data analysis software system), ver. 6.0, www.statsoft.com, 2002.
36. Witten, I.H. and Frank, E.: WEKA - Waikato Environment for Knowledge Analysis, 2000.
37. Van Waterbeemd, H. In Chemometric methods in molecular design, H. Van Waterbeemd, ed.; Wiley-VCH: New York, 1995; Vol. 2, pp. 265-282.
38. Bisquerra Alzina, R. Introducción conceptual al análisis multivariante: Un enfoque informático con los paquetes SPSS-X, BMDP, LISREL y SPAD, PPU: Barcelona 1989.
39. Stewart, J.G., L. Econometrics London 1998.
40. James A. Hanley, B.J.M. The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) Curve. Radiology, 1982, 143, 29-36.
41. Swets, J.A. Measuring the accuracy of diagnostic systems. Science, 1988, 240, 1285-1293.
42. Bate, P. and Warwicker, J. Enzyme/non-enzyme discrimination and prediction of enzyme active site location using charge-based methods. J. Mol. Biol., 2004, 340, 263-276.
43. Cai, Y.D.; Zhou, G.P. and Chou, K.C. Predicting enzyme family classes by hybridizing gene product composition and pseudo-amino acid composition. J. Theor. Biol., 2005, 234, 145-149.
44. Cai, Y.D. and Chou, K.C. Using functional domain composition to predict enzyme family classes. J. Proteome Res., 2005, 4, 109-111.
45. Chou, K.C. and Elrod, D.W. Prediction of enzyme family classes. J. Proteome Res., 2003, 2, 183-190.
46. Chou, K.C. and Cai, Y.D. Predicting protein structural class by functional domain composition. Biochem. Biophys. Res. Commun., 2004, 321, 1007-1009.
47. Dobson, P.D. and Doig, A.J. Distinguishing enzyme structures from non-enzymes without alignments. J. Mol. Biol., 2003, 330, 771-783.
48. Dobson, P.D. and Doig, A.J. Predicting enzyme class from protein structure without alignments. J. Mol. Biol., 2005, 345, 187-199.
49. Shen, H.B. and Chou, K.C. PseAAC: A flexible web server for generating various kinds of protein pseudo amino acid composition. Anal. Biochem., 2008, 373, 386-388.
50. Shen, H.B. and Chou, K.C. EzyPred: a top-down approach for predicting enzyme functional classes and subclasses. Biochem. Biophys. Res. Commun., 2007, 364, 53-59.
51. Munteanu, C.R.; Gonzalez-Diaz, H. and Magalhaes, A.L. Enzymes/ non-enzymes classification model complexity based on composition, sequence, 3D and topological indices. J. Theor. Biol., 2008, 254, 476-482.
52. Munteanu, C.R. and Magalhaes, A.L. Prot-2S: a new python web tool for protein secondary structure studies. Int. J. Bioinform. Res. Appl., 2009, 5, 402-416.
53. Fujibuchi, W.; Goto, S.; Migimatsu, H.; Uchiyama, I.; Ogiwara, A.; Akiyama, Y. and Kanehisa, M. In Pacific Symposium on Biocomputing '98; eds Altman R.B., Dunker K., Hunter L., Klein T.E. (World Scientific, Singapore), Singapore, 1997, pp. 683-694.
54. Bairoch, A. and Apweiler, R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res., 2000, 28, 45-48.
55. Chandonia, J.M.; Walker, N.S.; Lo Conte, L.; Koehl, P.; Levitt, M. and Brenner, S.E. ASTRAL compendium enhancements. Nucleic Acids Res., 2002, 30, 260-263.
56. Wang, G. and Dunbrack, J. PISCES: a protein sequence culling server. Bioinformatics, 2003, 19, 1589-1591.
57. Rossum, G.V. In: Python Reference Manual. Foundation, P.S. Ed. Fred, Z.D. Jr. 2006.
58. Levine, M. and Tjian, R. Transcription regulation and animal diversity. Nature, 2003, 424, 147-151.
59. Wyrick, J.J. and Young, R.A. Deciphering gene expression regulatory networks. Curr. Opin. Genet. Dev., 2002, 12, 130-136.
60. Perez-Bello, A.; Munteanu, C.R.; Ubeira, F.M.; De Magalhaes, A.L.; Uriarte, E. and Gonzalez-Diaz, H. Alignment-free prediction of mycobacterial DNA promoters based on pseudo-folding lattice network or star-graph topological indices. J. Theor. Biol., 2009, 256, 458-466.
61. Griffiths, A.J.F.; Miller, J.H.; Suzuki, D.T.; Lewontin, R.C. and Gelbart, W.M. Introduction to Genetic Analysis, W. H. Freeman & Co.: New York 1999.
62. WHO; World Health Organization, Fact sheet N°297, http://www.who.int/mediacentre/factsheets/fs297/en/, 2008.
63. Jemal, A.; Siegel, R.; Ward, E.; Hao, Y.; Xu, J.; Murray, T. and Thun, M.J. Cancer statistics, 2008. CA Cancer J. Clin., 2008, 58, 71-96.
64. Ward, D.G.; Suggett, N.; Cheng, Y.; Wei, W.; Johnson, H.; Billingham, L.J.; Ismail, T.; Wakelam, M.J.; Johnson, P.J. and Martin, A. Identification of serum biomarkers for colon cancer by proteomic analysis. Br. J. Cancer, 2006, 94, 1898-1905.
65. Wang, J.; Zhang, X.; Ge, X.; Guo, H.; Xiong, G. and Zhu, Y. Proteomic studies of early-stage and advanced ovarian cancer patients. Gynecol. Oncol., 2008, 111, 111-119.
66. Hilario, M.; Kalousis, A.; Muller, M. and Pellegrini, C. Machine learning approaches to lung cancer prediction from mass spectra. Proteomics, 2003, 3, 1716-1719.
67. Munteanu, C.R.; Magalhaes, A.L.; Uriarte, E. and Gonzalez-Diaz, H. Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices. J. Theor. Biol., 2009, 257, 303-311.
68. Sjoblom, T.; Jones, S.; Wood, L.D.; Parsons, D.W.; Lin, J.; Barber, T.D.; Mandelker, D.; Leary, R.J.; Ptak, J.; Silliman, N.; Szabo, S.; Buckhaults, P.; Farrell, C.; Meeh, P.; Markowitz, S.D.; Willis, J.; Dawson, D.; Willson, J.K.; Gazdar, A.F.; Hartigan, J.; Wu, L.; Liu, C.; Parmigiani, G.; Park, B.H.; Bachman, K.E.; Papadopoulos, N.; Vogelstein, B.; Kinzler, K.W. and Velculescu, V.E. The consensus coding sequences of human breast and colorectal cancers. Science, 2006, 314, 268-274.
69. Dobson, P.D.; Cai, Y.D.; Stapley, B.J. and Doig, A.J. Prediction of protein function in the absence of significant sequence similarity. Curr. Med. Chem., 2004, 11, 2135-2142.
70. Kowalski, R.D. and Wold, S. In Handbook of Statistic, Krishnaiah, P. R. and Kanal, L. N. Eds. North Holland Publishing Company: Amsterdam 1982, pp. 673-697.
71. Chou, K.C. and Zhang, C.T. Prediction of protein structural classes. Crit. Rev. Biochem. Mol. Biol., 1995, 30, 275-349.
72. Chou, K.C. and Shen, H.B. Recent progress in protein subcellular location prediction. Anal. Biochem., 2007, 370, 1-16.
73. Chou, K.C. and Shen, H.B. Cell-PLoc: A package of web-servers for predicting subcellular localization of proteins in various organisms. Nat. Protoc., 2008, 3, 153-162.
74. Chen, Y.L. and Li, Q.Z. Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo amino acid composition. J. Theor. Biol., 2007, 248, 377-381.
75. Chen, Y.L. and Li, Q.Z. Prediction of the subcellular location of apoptosis proteins. J. Theor. Biol., 2007, 245, 775-783.
76. Diao, Y.; Li, M.; Feng, Z.; Yin, J. and Pan, Y. The community structure of human cellular signaling network. J. Theor. Biol., 2007, 247, 608-615.
77. Ding, Y.S.; Zhang, T.L. and 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.
78. Jiang, X.; Wei, R.; Zhang, T. and Gu, Q. Using the concept of Chou's pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy. Protein Pept. Lett., 2008, 15, 392-396.
79. Li, F.M. and Li, Q.Z. Using pseudo amino acid composition to predict protein subnuclear location with improved hybrid approach. Amino Acids, 2008, 34, 119-125.
80. 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.
81. Niu, B.; Cai, Y.D.; Lu, W.C.; Li, G.Z. and Chou, K.C. Predicting protein structural class with AdaBoost Learner. Protein Pept. Lett., 2006, 13, 489-492.
82. Xiao, X. and Chou, K.C. Digital coding of amino acids based on hydrophobic index. Protein Pept. Lett., 2007, 14, 871-875.
83. Zhou, X.B.; Chen, C.; Li, Z.C. and 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.
84. Zhang, T.L.; Ding, Y.S. and Chou, K.C. Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern. J. Theor. Biol., 2008, 250, 186-193.
85. Vilar, S.; Gonzalez-Diaz, H.; Santana, L. and Uriarte, E. QSAR model for alignment-free prediction of human breast cancer biomarkers based on electrostatic potentials of protein pseudofolding HP-lattice networks. J. Comput. Chem., 2008, 29, 2613-2622.
86. Marrero-Ponce, Y.; Diaz, H.G.; Zaldivar, V.R.; Torrens, F. and Castro, E.A. 3D-chiral quadratic indices of the 'molecular pseudograph's atom adjacency matrix' and their application to central chirality codification: classification of ACE inhibitors and prediction of sigma-receptor antagonist activities. Bioorg. Med. Chem., 2004, 12, 5331-5342.
87. Morales, A.H.; Cabrera Perez, M.A. and Gonzalez, M.P. A radialdistribution-function approach for predicting rodent carcinogenicity. J. Mol. Model., 2006, 12, 769-780.
88. Estrada, E. and Molina, E. 3D connectivity indices in QSPR/QSAR studies. J. Chem. Inf. Comput. Sci., 2001, 41, 791-797.
89. Castillo-Garit, J.A.; Marrero-Ponce, Y.; Torrens, F.; Garcia-Domenech, R. and Romero-Zaldivar, V. Bond-based 3D-chiral linear indices: Theory and QSAR applications to central chirality codification. J. Comput. Chem., 2008, 29, 2500-2512.
90. Ferino, G.; Gonzalez-Diaz, H.; Delogu, G.; Podda, G. and Uriarte, E. Using spectral moments of spiral networks based on PSA/mass spectra outcomes to derive quantitative proteome-disease relationships (QPDRs) and predicting prostate cancer. Biochem. Biophys. Res. Commun., 2008, 372(2), 320-323.
91. Fox, B.A.; Yee, V.C.; Pedersen, L.C.; Le Trong, I.; Bishop, P.D.; Stenkamp, R.E. and Teller, D.C. Identification of the calcium binding site and a novel ytterbium site in blood coagulation factor XIII by x-ray crystallography. J. Biol. Chem., 1999, 274, 4917-4923.
92. Anwar, R.; Miloszewski, K.J. and Markham, A.F. New splicing mutations in the human factor XIIIA gene, each producing multiple mutant transcripts of varying abundance. Thromb. Haemost., 1998, 79, 1151-1156.
93. Costantini, V.; Zacharski, L.R.; Memoli, V.A.; Kisiel, W.; Kudryk, B.J. and Rousseau, S.M. Fibrinogen deposition without thrombin generation in primary human breast cancer tissue. Cancer Res., 1991, 51, 349-353.
94. Yee, V.C.; Pedersen, L.C.; Le Trong, I.; Bishop, P.D.; Stenkamp, R.E. and Teller, D.C. Three-dimensional structure of a transglutaminase: human blood coagulation factor XIII. Proc. Natl. Acad. Sci. USA, 1994, 91, 7296-7300.
95. Takahashi, H.; Isobe, T.; Horibe, S.; Takagi, J.; Yokosaki, Y.; Sheppard, D. and Saito, Y. Tissue transglutaminase, coagulation factor XIII, and the pro-polypeptide of von Willebrand factor are all ligands for the integrins alpha 9beta 1 and alpha 4beta 1. J. Biol. Chem., 2000, 275, 23589-23595.
96. González-Díaz, H.; Ferino, G.; Podda, G. and Uriarte, E. Discriminating prostate cancer patients from control group with connectivity indices. ECSOC, 2007, 11, G1:1-10.
97. Petricoin, E.F.; Ornstein, D.K.; Paweletz, C.P.; Ardekani, A.; Hackett, P.S.; Hitt, B.A.; Velassco, A.; Trucco, C.; Wiegand, L.; Wood, K.; Simone, C.B.; Levine, P.J.; Linehan, W.M.; Emmert-Buck, M.R.; Steinberg, S.M.; Kohn, E.C. and Liotta, L.A. Serum proteomic patterns for detection of prostate cancer. J. Natl. Cancer Inst., 2002, 94.
98. Brzezinska, E. The QSAR analysis of tricyclic non-nucleoside inhibitors of HIV-1 reverse transcriptase. Acta. Polym. Pharm., 2003, 60, 3-13.