Natural/random protein classification models based on star network topological indices

Journal of Theoretical Biology

Volumn 254, Issue 4, 2008, Pages 775-783

  Munteanu, Cristian Robert ^a   González Díaz, Humberto ^b   Borges, Fernanda ^a   de Magalhães, Alexandre Lopes ^a  

^a UNIVERSITY OF PORTO   (Portugal)

^b UNIVERSITY OF SANTIAGO DE COMPOSTELA   (Spain)

Author keywords

GDA; Graph theory; Protein structure; Python applications; Random proteins; S2SNet

Indexed keywords

AMINO ACID; DNA; RNA;

AMINO ACID; CLASSIFICATION; MODELING; PROTEIN; TOPOLOGY;

AMINO ACID SEQUENCE; ARTICLE; MASS SPECTROMETRY; MATHEMATICAL MODEL; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN STRUCTURE; PROTEOMICS; QUALITATIVE ANALYSIS; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SEQUENCE HOMOLOGY;

AMINO ACID SEQUENCE; ANIMALS; DATABASES, PROTEIN; MODELS, BIOLOGICAL; MODELS, STATISTICAL; MOLECULAR SEQUENCE DATA; NEURAL NETWORKS (COMPUTER); PROTEIN CONFORMATION; PROTEINS;

EID: 52149090972     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2008.07.018     Document Type: Article

References (87)

1. Aguero-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. 580 (2006) 723-730
2. Althaus I.W., Chou J.J., Gonzales A.J., Diebel M.R., Chou K.C., Kezdy F.J., Romero D.L., Aristoff P.A., Tarpley W.G., and Reusser F. Steady-state kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-87201E. J. Biol. Chem. 268 (1993) 6119-6124
3. Althaus I.W., Gonzales A.J., Chou J.J., Diebel M.R., Chou K.C., Kezdy F.J., Romero D.L., Aristoff P.A., Tarpley W.G., and Reusser F. The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. J. Biol. Chem. 268 (1993) 14875-14880
4. Althaus I.W., Chou J.J., Gonzales A.J., Diebel M.R., Chou K.C., Kezdy F.J., Romero D.L., Aristoff P.A., Tarpley W.G., and Reusser F. Kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry 32 (1993) 6548-6554
5. Althaus I.W., Chou J.J., Gonzales A.J., Diebel M.R., Chou K.C., Kezdy F.J., Romero D.L., Aristoff P.A., Tarpley W.G., and Reusser F. Steady-state kinetic studies with the polysulfonate U-9843, an HIV reverse transcriptase inhibitor. Experientia 50 (1994) 23-28
6. Althaus I.W., Chou J.J., Gonzales A.J., Diebel M.R., Chou K.C., Kezdy F.J., Romero D.L., Thomas R.C., Aristoff P.A., Tarpley W.G., and Reusser F. Kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-90152E. Biochem. Pharmacol. 47 (1994) 2017-2028
7. Althaus I.W., Chou K.C., Franks K.M., Diebel M.R., Kezdy F.J., Romero D.L., Thomas R.C., Aristoff P.A., Tarpley W.G., and Reusser F. The benzylthio-pyrididine U-31, 355 is a potent inhibitor of HIV-1 reverse transcriptase. Biochem. Pharmacol. 51 (1996) 743-750
8. 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. 86 (2008) 342-357
9. Berman H.M., Westbrook J., Feng Z., Gilliland G., Bhat T.N., Weissig H., Shindyalov I.N., and Bourne P.E. The protein data bank. Nucleic Acids Res. 28 (2000) 235-242
10. Bielinska-Waz D., Nowak W., Waz P., Nandy A., and Clark T. Distribution moments of 2D-graphs as descriptors of DNA sequences. Chem. Phys. Lett. 443 (2007) 408-413
11. Bisquerra Alzina, R., 1989. Introducción conceptual al análisis multivariante: Un enfoque informático con los paquetes SPSS-X, BMDP, LISREL y SPAD. PPU, Barcelona.
12. 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. 248 (2007) 377-381
13. Chen Y.L., and Li Q.Z. Prediction of the subcellular location of apoptosis proteins. J. Theor. Biol. 245 (2007) 775-783
14. Chou K.C. Graphical rules in steady and non-steady enzyme kinetics. J. Biol. Chem. 264 (1989) 12074-12079
15. Chou K.C. Review: applications of graph theory to enzyme kinetics and protein folding kinetics. Steady and non-steady state systems. Biophys. Chem. 35 (1990) 1-24
16. Chou K.C., and Forsen S. Graphical rules for enzyme-catalyzed rate laws. Biochem. J. 187 (1980) 829-835
17. Chou K.C., and Forsen S. Graphical rules of steady-state reaction systems. Can. J. Chem. 59 (1981) 737-755
18. Chou K.C., and Liu W.M. Graphical rules for non-steady state enzyme kinetics. J. Theor. Biol. 91 (1981) 637-654
19. Chou K.C., and Shen H.B. Review: recent progresses in protein subcellular location prediction. Anal. Biochem. 370 (2007) 1-16
20. Chou K.C., and Shen H.B. Cell-PLoc: a package of web-servers for predicting subcellular localization of proteins in various organisms. Nat. Protocols 3 (2008) 153-162
21. Chou K.C., and Zhang C.T. Diagrammatization of codon usage in 339 HIV proteins and its biological implication. AIDS Res. Hum. Retroviruses 8 (1992) 1967-1976
22. Chou K.C., and Zhang C.T. Review: prediction of protein structural classes. Crit. Rev. Biochem. Mol. Biol. 30 (1995) 275-349
23. Chou K.C., Jiang S.P., Liu W.M., and Fee C.H. Graph theory of enzyme kinetics: 1. Steady-state reaction system. Sci. Sin. 22 (1979) 341-358
24. Chou K.C., Kezdy F.J., and Reusser F. Review: steady-state inhibition kinetics of processive nucleic acid polymerases and nucleases. Anal. Biochem. 221 (1994) 217-230
25. Chou K.C., Zhang C.T., and Elrod D.W. Do antisense proteins exist?. J. Protein Chem. 15 (1996) 59-61
26. Devillers J., and Balaban A.T. Topological Indices and Related Descriptors in QSAR and QSPR (1999), Gordon and Breach, The Netherlands
27. Diao Y., Li M., Feng Z., Yin J., and Pan Y. The community structure of human cellular signaling network. J. Theor. Biol. 247 (2007) 608-615
28. Dillon W.R., and Goldstein M. Multivariate Analysis: Methods and Applications (1984), Wiley, New York
29. 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. 14 (2007) 811-815
30. Gao L., Ding Y.S., Dai H., Shao S.H., Huang Z.D., and Chou K.C. A novel fingerprint map for detecting SARS-CoV. J. Pharm. Biomed. Anal. 41 (2006) 246-250
31. Garcia-Garcia A., Galvez J., de Julian-Ortiz J.V., Garcia-Domenech R., Munoz C., Guna R., and Borras R. New agents active against Mycobacterium avium complex selected by molecular topology: a virtual screening method. J. Antimicrob. Chemother. 53 (2004) 65-73
32. Gonzalez-Diaz H., Sanchez-Gonzalez A., and Gonzalez-Diaz Y. 3D-QSAR study for DNA cleavage proteins with a potential anti-tumor ATCUN-like motif. J. Inorg. Biochem. 100 (2006) 1290-1297
33. Gonzalez-Diaz 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. 10 (2007) 1015-1029
34. Gonzalez-Diaz H., Bonet I., Teran C., De Clercq E., Bello R., Garcia M.M., Santana L., and Uriarte E. ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds. Eur. J. Med. Chem. 42 (2007) 580-585
35. Gonzalez-Díaz H., Gonzalez-Díaz Y., Santana L., Ubeira F.M., and Uriarte E. Proteomics, networks, and connectivity indices. Proteomics 8 (2008) 750-778
36. Harary, F., 1969. Graph Theory, MA.
37. Hua S., and Sun Z. Support vector machine approach for protein subcellular localization prediction. Bioinformatics 17 (2001) 721-728
38. James A., and Hanley B.J.M. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143 (1982) 29-36
39. Jiang X., Wei R., Zhang T.L., 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. 15 (2008) 392-396
40. Jin Y., Niu B., Feng K.Y., Lu W.C., Cai Y.D., and Li G.Z. Predicting subcellular localization with AdaBoost learner. Protein Pept. Lett. 15 (2008) 286-289
41. Kabsch W., and Sander C.K. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22 (1983) 2577-2637
42. King E.L., and Altman C. A schematic method of deriving the rate laws for enzyme-catalyzed reactions. J. Phys. Chem. 60 (1956) 1375-1378
43. Koutsofios E. Drawing Graphs with Dot. AT&T Bell Laboratories (1993), Murray Hill, NJ, USA
44. Kowalski R.D., and Wold S. Pattern recognition in chemistry. In: Krishnaiah P.R., and Kanal L.N. (Eds). Handbook of Statistic (1982), North Holland Publishing Company, Amsterdam 673-697
45. Kuzmic P., Ng K.Y., and Heath T.D. Mixtures of tight-binding enzyme inhibitors. Kinetic analysis by a recursive rate equation. Anal. Biochem. 200 (1992) 68-73
46. Li F.M., and Li Q.Z. Predicting protein subcellular location using Chou's pseudo amino acid composition and improved hybrid approach. Protein Pept. Lett. 15 (2008) 612-616
47. Liao B., and Ding K. Graphical approach to analyzing DNA sequences. J. Comput. Chem. 26 (2005) 1519-1523
48. Liao B., and Wang T.M. Analysis of similarity/dissimilarity of DNA sequences based on nonoverlapping triplets of nucleotide bases. J. Chem. Inf. Comput. Sci. 44 (2004) 1666-1670
49. Liao B., and Wang T.M. New 2D graphical representation of DNA sequences. J. Comput. Chem. 25 (2004) 1364-1368
50. Liao B., Xiang X., and Zhu W. Coronavirus phylogeny based on 2D graphical representation of DNA sequence. J. Comput. Chem. 27 (2006) 1196-1202
51. Lin H. The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition. J. Theor. Biol. 252 (2008) 350-356
52. Lin H., Ding H., Feng-Biao Guo F.B., Zhang A.Y., and Huang J. Predicting subcellular localization of mycobacterial proteins by using Chou's pseudo amino acid composition. Protein Pept. Lett. 15 (2008) 739-744
53. 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. 12 (2004) 5331-5342
54. Marrero-Ponce Y., Castillo-Garit J.A., Olazabal E., Serrano H.S., Morales A., Castanedo N., Ibarra-Velarde F., Huesca-Guillen A., Sanchez A.M., Torrens F., and Castro E.A. Atom, atom-type and total molecular linear indices as a promising approach for bioorganic and medicinal chemistry: theoretical and experimental assessment of a novel method for virtual screening and rational design of new lead anthelmintic. Bioorg. Med. Chem. 13 (2005) 1005-1020
55. Morales Helguera A., R-B J.E., García-Mera X., Fernández F., Natália M., and Cordeiro D.S. Probing the anticancer activity of nucleoside analogues: a QSAR model approach using an internally consistent training set. J. Med. Chem. 50 (2007) 1537-1545
56. Myers D., and Palmer G. Microcomputer tools for steady-state enzyme kinetics. Bioinformatics (Orig.: Comput. Appl. Biosci.) 1 (1985) 105-110
57. Niu B., Cai Y.D., Lu W.C., Zheng G.Y., and Chou K.C. Predicting protein structural class with AdaBoost learner. Protein Pept. Lett. 13 (2006) 489-492
58. Niu B., Jin Y.H., Feng K.Y., Liu L., Lu W.C., Cai Y.D., and Li G.Z. Predicting membrane protein types with bagging learner. Protein Pept. Lett. 15 (2008) 590-594
59. Noel Rappin, R.D., 2006. wxPython in Action.
60. Perez M.A., Sanz M.B., Torres L.R., Avalos R.G., Gonzalez M.P., and Gonzales-Diaz H. A topological sub-structural approach for predicting human intestinal absorption of drugs. Eur. J. Med. Chem. 39 (2004) 905-916
61. Prado-Prado F.J., Gonzalez-Diaz H., de la Vega O.M., Ubeira F.M., and Chou K.C. 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 (2008) 5871-5880
62. Qi X.Q., Wen J., and Qi Z.H. New 3D graphical representation of DNA sequence based on dual nucleotides. J. Ther. Biol. 249 (2007) 681-690
63. Randic M. Condensed representation of DNA primary sequences. J. Chem. Inf. Comput. Sci. 40 (2000) 50-56
64. Randic M., and Balaban A.T. On a four-dimensional representation of DNA primary sequences. J. Chem. Inf. Comput. Sci. 43 (2003) 532-539
65. Randic M., and Basak S.C. Characterization of DNA primary sequences based on the average distances between bases. J. Chem. Inf. Comput. Sci. 41 (2001) 561-568
66. Randic M., Vracko M., Nandy A., and Basak S.C. On 3-D graphical representation of DNA primary sequences and their numerical characterization. J. Chem. Inf. Comput. Sci. 40 (2000) 1235-1244
67. Randic M., Zupan J., and Vikic-Topic D. On representation of proteins by star-like graphs. J. Mol. Graph Model (2007) 290-305
68. Rossum, G.V., 2006. In: Foundation, P.S. (Ed.), Python Reference Manual. Fred L. Drake, Jr.
69. StatSoft.Inc., STATISTICA (data analysis software system), version 6.0, 〈www.statsoft.com〉. Statsoft, Inc., 2002, pp. STATISTICA (data analysis software system), version 6.0, 〈www.statsoft.com.Statsoft〉.
70. Stewart, J.G.L., 1998. Econometrics. London.
71. Todeschini R., and Consonni V. Handbook of Molecular Descriptors (2002), Wiley, New York
72. Van Waterbeemd, H., 1995. Discriminant analysis for activity prediction. In: Manhnhold, R., et al. (Eds.), Method and Principles in Medicinal Chemistry, vol. 2.
73. Wang G., and Dunbrack R.L.J. PISCES: a protein sequence culling server. Bioinformatics 19 (2003) 1589-1591
74. Wang M., Yao J.S., Huang Z.D., Xu Z.J., Liu G.P., Zhao H.Y., Wang X.Y., Yang J., Zhu Y.S., and Chou K.C. A new nucleotide-composition based fingerprint of SARS-CoV with visualization analysis. Med. Chem. 1 (2005) 39-47
75. Wang T., Yang J., Shen H.B., and Chou K.C. Predicting membrane protein types by the LLDA algorithm. Protein Pept. Lett. (2008) (in press)
76. Wolfram S. Cellular automation as models of complexity. Nature 311 (1984) 419-424
77. Wolfram S. A New Kind of Science (2002), Wolfram Media Inc., Champaign, IL
78. Xiao X., and Chou K.C. Digital coding of amino acids based on hydrophobic index. Protein Pept. Lett. 14 (2007) 871-875
79. Xiao X., Shao S., Ding Y., Huang Z., Chen X., and Chou K.C. Using cellular automata to generate image representation for biological sequences. Amino Acids 28 (2005) 29-35
80. Xiao X., Shao S., Ding Y., Huang Z., Chen X., and Chou K.C. An application of gene comparative image for predicting the effect on replication ratio by HBV virus gene missense mutation. J. Theor. Biol. 235 (2005) 555-565
81. Xiao X., Shao S.H., and Chou K.C. A probability cellular automaton model for hepatitis B viral infections. Biochem. Biophys. Res. Commun. 342 (2006) 605-610
82. Xiao X., Shao S.H., Ding Y.S., Huang Z.D., and Chou K.C. Using cellular automata images and pseudo amino acid composition to predict protein subcellular location. Amino Acids 30 (2006) 49-54
83. Zhang C.T., and Chou K.C. Graphic analysis of codon usage strategy in 1490 human proteins. J. Protein Chem. 12 (1993) 329-335
84. Zhang C.T., and Chou K.C. Analysis of codon usage in 1562 E. coli protein coding sequences. J. Mol. Biol. 238 (1994) 1-8
85. 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. 250 (2008) 186-193
86. Zhou G.P., and Deng M.H. An extension of Chou's graphical rules for deriving enzyme kinetic equations to system involving parallel reaction pathways. Biochem. J. 222 (1984) 169-176
87. 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. 248 (2007) 546-551