QSAR models for proteins of parasitic organisms, plants and human guests: Theory, applications, legal protection, taxes, and regulatory issues

Current Proteomics

Volumn 6, Issue 4, 2009, Pages 214-227

  González Díaz, Humberto ^a   Prado Prado, Francisco ^a   Pérez Montoto, Lázaro G ^a   Duardo Sánchez, Aliuska ^a   López Díaz, Antonio ^a  

^a UNIVERSITY OF SANTIAGO DE COMPOSTELA   (Spain)

Author keywords

Complex networks; Proteins QSAR; Proteomics; Software protection and copyright; Tax issues in bioinformatics

Indexed keywords

EID: 72449158079     PISSN: 15701646     EISSN: None     Source Type: Journal    
DOI: 10.2174/157016409789973789     Document Type: Article

References (148)

1. Menke, R. Linked: The New Science of Networks (review). Perspect. Biol. Méd., 2004, 47, 300-303.
2. Huynen, M.A.; Snel, B.; von Mering, C. and Bork, P. Function prediction and protein networks. Curr. Opin. Cell Biol., 2003, 15, 191-198.
3. Chou, K.C. and Forsen, S. Graphical rules for enzyme-catalyzed rate laws. Biochem. J., 1980, 187, 829-835.
4. 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. 1984, 222, 169-176.
5. Chou, K.C. Graphical rules in steady and non-steady enzyme kinetics J. Biol. Chem., 1989, 264, 12074-12079.
6. Kuzmic, P.; Ng, K.Y. and Heath, T.D. Mixtures of tight-binding enzyme inhibitors. Kinetic analysis by a recursive rate equation. Anal. Biochem., 1992, 200, 68-73.
7. Lin, S.X. and Neet, K.E. Demonstration of a slow conformational change in liver glucokinase by fluorescence spectroscopy J. Biol. Chem., 1990, 265, 9670-9675.
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. Zhang, C.T. and Chou, K.C. Analysis of codon usage in 1562 E. Coli protein coding sequences. J. Mol. Biol., 1994, 238, 1-8.
11. 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., 1993, 268, 14875-14880.
12. 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 nonnucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry, 1993, 32, 6548-6554. (Pubitemid 23217124)
13. 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., 1993, 268, 6119-6124.
14. Chou, K.C.; Kezdy, F.J. and Reusser, F. Review: Steady-state inhibition kinetics of processive nucleic acid polymerases and nucleases. Anal. Biochem., 1994, 221, 217-230.
15. Barabasi, A.L. Sociology. Network theory-the emergence of the creative enterprise. Science, 2005, 308, 639-641.
16. Barabasi, A.L. and Bonabeau, E. Scale-free networks. Sci. Am., 2003, 288, 60-69.
17. Barabasi, A.L. and Oltvai, Z.N. Network biology: understanding the cell's functional organization. Nat. Rev. Genet., 2004, 5, 101-113.
18. 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.
19. Selassie, C.D.; Mekapati, S.B. and Verma, R.P. QSAR: then and now. Curr. Top. Med. Chem., 2002, 2, 1357-1379.
20. Gozalbes, R.; Doucet, J.P. and Derouin, F. Application of topological descriptors in QSAR and drug design: history and new trends. Curr. Drug Targets Infect. Disord., 2002, 2, 93-102.
21. Honey, C.J.; Kotter, R.; Breakspear, M. and Sporns, O. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc. Natl. Acad. Sci. USA, 2007, 104, 10240-10245.
22. McDonald, D.B. Predicting fate from early connectivity in a social network. Proc. Natl. Acad. Sci. USA, 2007, 104, 10910-10914.
23. Hahn, M.W. and Kern, A.D. Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Mol. Biol. Evol., 2005, 22, 803-806.
24. Altmann, M. Reinterpreting network measures for models of disease transmission. Soc. Netw., 1993, 15, 1-17.
25. Bonchev, D. and Buck, G.A. From molecular to biological structure and back. J. Chem. Inf. Model, 2007, 47, 909-917.
26. Boccaletti, S.; Latora, V.; Moreno, Y.; Chavez, M. and Hwang, D.U. Complex networks: structure and dynamics. Phys. Rep., 2006, 424, 175-308.
27. 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.
28. Abraham, M.H.; Kumarsingh, R.; Cometto-Muniz, J.E. and Cain, W.S. Draize eye scores and eye irritation thresholds in man can be combined into one QSAR. Ann. N.Y. Acad. Sci., 1998, 855, 652-656.
29. Agrawal, V.K.; Bano, S. and Khadikar, P.V. Topological approach to quantifying molecular lipophilicity of heterogeneous set of organic compounds. Bioorg. Med. Chem., 2003, 11, 4039-4047.
30. Barratt, M.D. Integrating computer prediction systems with in vitro methods towards a better understanding of toxicology. Toxicol. Lett., 1998, 102-103, 617-621.
31. Benigni, R.; Andreoli, C. and Giuliani, A. QSAR models for both mutagenic potency and activity: application to nitroarenes and aromatic amines. Environ. Mol. Mutagen, 1994, 24, 208-219.
32. Garcia-Garcia, A.; Galvez, J.; de Julian-Ortiz, J.V.; Garcia-Domenech, R.; Munoz, C.; Guna, R. and Borras, R. Search of chemical scaffolds for novel antituberculosis agents. J. Biomol. Screen, 2005, 10, 206-214.
33. Katritzky, A.R. and Gordeeva, E.V. Traditional topological indices vs electronic, geometrical, and combined molecular descriptors in QSAR/QSPR research. J. Chem. Inf. Comput. Sci., 1993, 33, 835-857.
34. Todeschini, R. and Consonni, V. Handbook of Molecular Descriptors, Wiley-VCH 2002.
35. Estrada, E. Generalization of topological indices. Chem. Phys. Lett., 2001, 336, 248-252.
36. Matamala, A.R. and Estrada, E. Simplex Optimization of Generalized Topological Index (GTI-Simplex): A Unified Approach to Optimize QSPR Models. J. Phys. Chem. A., 2005, 109, 9890-9895.
37. Matamala, A.R. and Estrada, E. Generalised topological indices: Optimisation methodology and physico-chemical interpretation. Chem. Phys. Lett., 2005, 410, 343-347.
38. González-Díaz, H.; Cruz-Monteagudo, M.; Vina, D.; Santana, L.; Uriarte, E. and De Clercq, E. QSAR for anti-RNA-virus activity, synthesis, and assay of anti-RSV carbonucleosides given a unified representation of spectral moments, quadratic, and topologic indices. Bioorg. Med. Chem. Lett., 2005, 15, 1651-1657.
39. Estrada, E. and Uriarte, E. Recent advances on the role of topological indices in drug discovery research. Curr. Med. Chem., 2001, 8, 1573-1588.
40. Marrero-Ponce, Y.; Medina-Marrero, R.; Torrens, F.; Martinez, Y.; Romero-Zaldivar, V. and Castro, E.A. Atom, atom-type, and total nonstochastic and stochastic quadratic fingerprints: a promising approach for modeling of antibacterial activity. Bioorg. Med. Chem., 2005, 13, 2881-2899.
41. Marrero-Ponce, Y.; Montero-Torres, A.; Zaldivar, C.R.; Veitia, M.I.; Perez, M.M. and Sanchez, R.N. Non-stochastic and stochastic linear indices of the 'molecular pseudograph's atom adjacency matrix': application to 'in silico' studies for the rational discovery of new antimalarial compounds. Bioorg. Med. Chem., 2005, 13, 1293-1304. (Pubitemid 40164613)
42. Chou, K.C. Prediction of protein cellular attributes using pseudoamino acid composition. Proteins, 2001, (Erratum: ibid., 2001, Vol. 44, 60) 43, 246-255.
43. Chou, K.C. Prediction of protein subcellular locations by incorporating quasi-sequence-order effect. Biochem. Biophys. Res. Commun., 2000, 278, 477-483.
44. Chou, K.C. In Gene Cloning & Expression Technologies P.W. Weinrer; Q. Lu, eds.; Eaton Publishing: Westborough, MA., 2002 pp. 57-70.
45. Chou, K.C. and Cai, Y.D. Prediction of membrane protein types by incorporating amphipathic effects. J. Chem. Inf. Model, 2005, 45, 407-413.
46. Liu, H.; Yang, J.; Wang, M.; Xue, L. and Chou, K.C. Using Fourier spectrum analysis and pseudo amino acid composition for prediction of membrane protein types. Protein J., 2005, 24, 385-389.
47. Shen, H.B. and Chou, K.C. Using optimized evidence-theoretic Knearest neighbor classifier and pseudo amino acid composition to predict membrane protein types. Biochem. Biophys. Res. Commun., 2005, 334, 288-292.
48. Shen, H.B. and Chou, K.C. Using ensemble classifier to identify membrane protein types. Amino Acids 2007, 32, 483-488.
49. Shen, H.B.; Yang, J. and Chou, K.C. Fuzzy KNN for predicting membrane protein types from pseudo amino acid composition. J. Theor. Biol., 2006, 240, 9-13.
50. Chou, K.C. and Shen, H.B. MemType-2L: A Web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochem. Biophys. Res. Commun., 2007, 360(2), 339-345.
51. Cai, Y.D. and Lin, S.L. Support vector machines for predicting rRNA-, RNA-, and DNA-binding proteins from amino acid sequence. Biochim. Biophys. Acta, 2003, 1648, 127-133.
52. Chou, K.C. and Cai, Y.D. Predicting enzyme family class in a hybridization space. Protein Sci., 2004, 13, 2857-2863.
53. Chou, K.C. and Cai, Y.D. Using GO-PseAA predictor to predict enzyme sub-class. Biochem. Biophys. Res. Commun., 2004, 325, 506-509.
54. Chou, K.C. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics, 2005, 21, 10-19.
55. Cai, Y.D. and Chou, K.C. Predicting enzyme subclass by functional domain composition and pseudo amino acid composition. J. Proteome Res., 2005, 4, 967-971.
56. Chou, K.C. and Cai, Y.D. Predicting protein quaternary structure by pseudo amino acid composition. Proteins, 2003, 53, 282-289.
57. Chou, K.C. and Shen, H.B. Predicting protein subcellular location by fusing multiple classifiers. J. Cell Biochem., 2006, 99, 517-527.
58. Mondal, S.; Bhavna, R.; Mohan Babu, R. and Ramakumar, S. Pseudo amino acid composition and multi-class support vector machines approach for conotoxin superfamily classification J. Theor. Biol., 2006, 243, 252-260.
59. Pan, Y.X.; Zhang, Z.Z.; Guo, Z.M.; Feng, G.Y.; Huang, Z.D. and He, L. Application of pseudo amino acid composition for predicting protein subcellular location: stochastic signal processing approach. J. Protein Chem., 2003, 22, 395-402.
60. Chen, C.; Zhou, X.; Tian, Y.; Zou, X. and Cai, P. Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network. Anal. Biochem., 2006, 357, 116-121. (Pubitemid 44331169)
61. Chen, C.; Tian, Y.X.; Zou, X.Y.; Cai, P.X. and Mo, J.Y. Using pseudo-amino acid composition and support vector machine to predict protein structural class. J. Theor. Biol., 2006, 243, 444-448. (Pubitemid 44647324)
62. Zhang, S.W.; Pan, Q.; Zhang, H.C.; Shao, Z.C. and Shi, J.Y. Prediction protein homo-oligomer types by pseudo amino acid composition: Approached with an improved feature extraction and naive Bayes feature fusion. Amino Acids, 2006, 30, 461-468.
63. Du, P. and Li, Y. Prediction of protein submitochondria locations by hybridizing pseudo-amino acid composition with various physicochemical features of segmented sequence. BMC Bioinformatics, 2006, 7, 518.
64. Lin, H. and Li, Q.Z. Predicting conotoxin superfamily and family by using pseudo amino acid composition and modified Mahalanobis discriminant. Biochem. Biophys. Res. Commun., 2007, 354, 548-551.
65. Lin, H. and Li, Q.Z. Using pseudo amino acid composition to predict protein structural class: approached by incorporating 400 dipeptide components. J. Comput. Chem., 2007, 28, 1463-1466.
66. Shi, J.Y.; Zhang, S.W.; Pan, Q.; Cheng, Y.-M. and Xie, J. Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition. Amino Acids, 2007, 33(1), 69-74.
67. Pu, X.; Guo, J.; Leung, H. and Lin, Y. Prediction of membrane protein types from sequences and position-specific scoring matrices. J. Theor. Biol., 2007, 247, 259-265.
68. 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(2), 377-381.
69. Kurgan, L.A.; Stach, W. and Ruan, J. Novel scales based on hydrophobicity indices for secondary protein structure. J. Theor. Biol., 2007, 248(2), 354-366.
70. Caballero, J.; Fernandez, L.; Abreu, J.I. and Fernandez, M. Amino acid sequence autocorrelation vectors and ensembles of bayesianregularized genetic neural networks for prediction of conformational stability of human lysozyme mutants. J. Chem. Inf. Model., 2006, 46, 1255-1268.
71. Fernández, L.; Caballero, J.; Abreu, J.I. and Fernández, M. Amino acid sequence autocorrelation vectors and bayesian-regularized genetic neural networks for modeling protein conformational Stability: gene V protein mutants. Proteins, 2007, 67, 834-852. (Pubitemid 46753933)
72. Chou, K.C. and Shen, H.B. Hum-PLoc: A novel ensemble classifier for predicting human protein subcellular localization. Biochem. Biophys. Res. Commun., 2006, 348, 1479.
73. Tashiro, M.; Tejero, R.; Zimmerman, D.E.; Celda, B.; Nilsson, B. and Montelione, G.T. High-resolution solution NMR structure of the Z domain of staphylococcal protein A. J. Mol. Biol., 1997, 272, 573-590.
74. Navarro, E.; Tejero, R.; Fenude, E. and Celda, B. Solution NMR structure of a D,L-alternating oligonorleucine as a model of betahelix. Biopolymers, 2001, 59, 110-119.
75. Navarro, E.; Fenude, E. and Celda, B. Conformational and structural analysis of the equilibrium between single- and double-strand beta-helix of a D,L-alternating oligonorleucine. Biopolymers, 2004, 73, 229-241.
76. Navarro, E.; Fenude, E. and Celda, B. Solution structure of a D,Lalternating oligonorleucine as a model of double-stranded antiparallel beta-helix. Biopolymers, 2002, 64, 198-209.
77. Marrero-Ponce, Y.; Medina-Marrero, R.; Castillo-Garit, J.A.; Romero-Zaldivar, V.; Torrens, F. and Castro, E.A. Protein linear indices of the 'macromolecular pseudograph alpha-carbon atom adjacency matrix' in bioinformatics. Part 1: prediction of protein stability effects of a complete set of alanine substitutions in Arc repressor. Bioorg. Med. Chem., 2005, 13, 3003-3015.
78. Marrero-Ponce, Y.; Medina-Marrero, R.; Castro, A.E.; Ramos de Armas, R.; González-Díaz, H.; Romero-Zaldivar, V. and Torrens, F. Protein quadratic indices of the "Macromolecular Pseudograph's α-Carbon Atom Adjacency Matrix". 1. Prediction of Arc repressor alanine-mutant's stability. Molecules, 2004, 9, 1124-1147.
79. Liu, J.; Yang, L.; Li, Y.; Pan, D. and Hopfinger, A.J. Prediction of plasma protein binding of drugs using Kier-Hall valence connectivity indices and 4D-fingerprint molecular similarity analyses. J. Comput. Aided. Mol. Des., 2005, 19, 567-583.
80. Chalk, A.J.; Worth, C.L.; Overington, J.P. and Chan, A.W. PDBLIG: classification of small molecular protein binding in the Protein Data Bank. J. Med. Chem., 2004, 47, 3807-3816.
81. Estrada, E. Application of a novel graph-theoretic folding degree index to the study of steroid-DB3 antibody binding affinity. Comput. Biol. Chem., 2003 27, 305-313.
82. Estrada, E. Characterization of the folding degree of proteins. Bioinformatics, 2002, 18, 697-704.
83. Estrada, E.; Uriarte, E. and Vilar, S. Effect of protein backbone folding on the stability of protein-ligand complexes. J. Proteome Res., 2006, 5, 105-111.
84. Dobson, P.D. and Doig, A.J. Predicting enzyme class from protein structure without alignments. J. Mol. Biol., 2005, 345, 187-199.
85. Estrada, E. Tight-binding "dihedral orbitals" approach to the degree of folding of macromolecular chains. J. Phys. Chem. B., 2007, 111, 13611-13618. (Pubitemid 350275465)
86. Plaxco, K.W.; Simons, K.T. and Baker, D. Contact order, transition state placement and the refolding rates of single domain proteins. J. Mol. Biol., 1998, 277, 985-994.
87. Plaxco, K.W.; Simons, K.T.; Ruczinski, I. and Baker, D. Topology, stability, sequence, and length: defining the determinants of twostate protein folding kinetics. Biochemistry, 2000, 39, 11177-11183.
88. Dixit, P.D. and Weikl, T.R. A simple measure of native-state topology and chain connectivity predicts the folding rates of two-state proteins with and without crosslinks. Proteins 2006, 64, 193-197.
89. González-Díaz, H. and Uriarte, E. Proteins QSAR with Markov average electrostatic potentials. Bioorg. Med. Chem. Lett., 2005, 15, 5088-5094.
90. González-Díaz, H.; Pérez-Castillo, Y.; Podda, G. and Uriarte, E. Computational chemistry comparison of stable/nonstable protein mutants classification models based on 3D and topological indices. J. Comput. Chem., 2007, 28, 1990-1995.
91. González-Díaz, H.; Sanchez-Gonzalez, A. and Gonzalez-Diaz, Y. 3D-QSAR study for DNA cleavage proteins with a potential antitumor ATCUN-like motif. J. Inorg. Biochem., 2006, 100, 1290-1297.
92. Saiz-Urra, L.; González-Díaz, H. and Uriarte, E. Proteins Markovian 3D-QSAR with spherically-truncated average electrostatic potentials. Bioorg. Med. Chem., 2005, 13, 3641-3647.
93. Gonzalez-Diaz, H.; Molina, R. and Uriarte, E. Recognition of stable protein mutants with 3D stochastic average electrostatic potentials. FEBS Lett., 2005, 579, 4297-4301.
94. 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.
95. Agarwala, R.; Batzoglou, S.; Dancik, V.; Decatur, S.E.; Hannenhalli, S.; Farach, M.; Muthukrishnan, S. and Skiena, S. Local rules for protein folding on a triangular lattice and generalized hydrophobicity in the HP model. J. Comput. Biol., 1997, 4, 275-296.
96. Berger, B. and Leighton, T. Protein folding in the hydrophobichydrophilic (HP) model is NP-complete. J. Comput. Biol., 1998, 5, 27-40.
97. Chen, M. and Huang, W.Q. A branch and bound algorithm for the protein folding problem in the HP lattice model. Genomics Proteomics Bioinformatics, 2005, 3, 225-230.
98. Gupta, A.; Manuch, J. and Stacho, L. Inverse protein folding in 2D HP model. Proc. IEEE Comput. Syst. Bioinform. Conf., 2004, 311-318.
99. Gupta, A.; Manuch, J. and Stacho, L. Structure-approximating inverse protein folding problem in the 2D HP model. J. Comput. Biol., 2005, 12, 1328-1345.
100. Jiang, M. and Zhu, B. Protein folding on the hexagonal lattice in the HP model. J. Bioinform. Comput. Biol., 2005, 3, 19-34.
101. Thachuk, C.; Shmygelska, A. and Hoos, H.H. A replica exchange Monte Carlo algorithm for protein folding in the HP model. BMC Bioinformatics, 2007, 8, 342.
102. González-Díaz, H.; Saiz-Urra, L.; Molina, R.; Santana, L. and Uriarte, E. A Model for the Recognition of Protein Kinases Based on the Entropy of 3D van der Waals Interactions. J. Proteome Res., 2007, 6, 904-908.
103. González-Díaz, H.; Saiz-Urra, L.; Molina, R.; Gonzalez-Diaz, Y. and Sanchez-Gonzalez, A. Computational chemistry approach to protein kinase recognition using 3D stochastic van der Waals spectral moments. J. Comput. Chem., 2007, 28, 1042-1048.
104. Gonzalez-Diaz, H.; Dea-Ayuela, M.A.; Perez-Montoto, L.G.; Prado-Prado, F.J.; Aguero-Chapin, G.; Bolas-Fernandez, F.; Vazquez-Padron, R.I. and Ubeira, F.M. QSAR for RNases and theoreticexperimental study of molecular diversity on peptide mass fingerprints of a new Leishmania infantum protein. Mol. Divers., 2009, [Epub ahead of print]
105. Dea-Ayuela, M.A.; Perez-Castillo, Y.; Meneses-Marcel, A.; Ubeira, F.M.; Bolas-Fernandez, F.; Chou, K.C. and Gonzalez-Diaz, H. HP-Lattice QSAR for dynein proteins: experimental proteomics (2D-electrophoresis, mass spectrometry) and theoretic study of a Leishmania infantum sequence. Bioorg. Med. Chem., 2008, 16, 7770-7776.
106. Concu, R.; Dea-Ayuela, M.A.; Perez-Montoto, L.G.; Bolas-Fernandez, F.; Prado-Prado, F.J.; Podda, G.; Uriarte, E.; Ubeira, F.M. and Gonzalez-Diaz, H. Prediction of enzyme classes from 3D structure: a general model and examples of experimental-theoretic scoring of peptide mass fingerprints of leishmania proteins. J. Proteome Res., 2009, 8, 4372-4382.
107. Concu, R.; Dea-Ayuela, M.A.; Perez-Montoto, L.G.; Prado-Prado, F.J.; Uriarte, E.; Bolas-Fernandez, F.; Podda, G.; Pazos, A.; Munteanu, C.R.; Ubeira, F.M. and Gonzalez-Diaz, H. 3D entropy and moments prediction of enzyme classes and experimental-theoretic study of peptide fingerprints in leishmania parasites. Biochim. Biophys. Acta, 2009, 1794(12), 1784-1794.
108. Munteanu, C.R.; Vázquez, J.M.; Dorado, J.; Pazos-Sierra, A.; Sánchez-González, A.; Prado-Prado, F.J. and González- Díaz, H. Complex network spectral moments for ATCUN motif DNA cleavage: first predictive study on proteins of human pathogen parasites. J. Proteome. Res., 2009, [Epub ahead of Print].
109. Fenwick & West LLP: International Legal Protection for Software 2007; San Francisco Vol. 2007. Available from: http://www.softwareprotection.com/ 〈http://www.softwareprotection.com/〉 [cited 2007 25/9/2007].
110. Stewart, S.M. International Copyright and Neighbouring Rights, Butterworths: London 1989.
111. Horton, K.L.; Jean Monnet Chair, New York School of Law: New York, 1997.
112. Steering Committee for Intellectual Property Issues in Software Computer Science and Telecommunications Board Commission on Physical Sciences, M., and Applications National Research Council. Intellectual Property Issues In software, National Academy Press: Washington, D.C 1991.
113. WIPO. Berne Convention for the Protection of Literary and Artistic Works: Available from: http://www.wipo.int/treaties/en/ip/berne/-trtdocs-wo001.html
114. WTO.; World Trade Organization: Geneva. WTO. Trade-Related Aspects of Intellectual Property Rights. Annex 1C of the Marrakech Agreement Establishing the World Trade Organization, Available from: http://www.wto.org/english/docs-e/ legal-e/27-trips-01-e.htm
115. EC. Commission of the European Council White Paper: Completing the Internal Market., 1985.
116. IPR-Helpdesk.; Type Briefing Papers, ed., IPR-Helpdesk. Software Copyright. 2004, Vol. 2007. Available from: http://www.ipr-helpdesk. org〈http://www.ipr-helpdesk.org/〉; [Accessed on 20 Oct. 2007].
117. Westkamp, G.N. Protección del material biológico mediante derechos de autor. ¿Vuelta de la Bioinformática a los Derechos de autor en la biotecnología? IPR-Helpdesk Bulletin, 2005.
118. Story, A. In Intellectual Property Rights and Sustainable Development, ICTSD-UNCTAD, ed.; Imprimerie Typhon: Chavanod, 2004, p. 12.
119. Morcon, C.; Roughton, A. and Gaham, J. The Modern Law of Trade Marks, Butterworth: London 2005.
120. WIPO. WIPO Database of Intellectual Property and Legislative Texts. WIPO. Madrid Agreement Concerning the International Registration of Marks of April 14, 1891, Available from: http://-www.wipo.int/madrid/en/legal-texts/trtdocs- wo015.html
121. WIPO. World Intellectual Property Organization. WIPO. Protocol Relating to the Madrid Agreement Concerning the International Registration of Marks adopted at Madrid on June 27, 1989 and amended on October 3, 2006. Available from: http://www.wipo.-int/madrid/en/legal-texts/trtdocs-wo016.html
122. Rowland, D. and Campbell, A. Supply of Software: Copyright and Contract Issues. Int. J. Law Inform. Technol., 2002, 10, 23-40.
123. Davidson SJ, Bergs SJ, Kapsner M. Open, click, download, send ⋯ What have you agreed to? The possibilities seem endless. Computer Associations Law Conference (CLA 2001). February 4-6,2001.
124. IPR-Helpdesk.; Briefing Papers, ed., 2003; Vol. 10/20/2007, pp. 2-3.
125. OECD. OECD. Organization for Economic Co-operation and Development. Articles of The Model Convention Whit Respect to Taxes on Income and on Capital. 2005; Vol. 2007; Available from: http://www.oecd.org/dataoecd/50/49/35363840. pdf [Accessed on September 15, 2007].
126. Delgado, A. In Cinco Días, 2003, pp. 29.
127. Commission of the European Communities. Council Directive of 3 June 2003 on a common system of taxation applicable to interest and royalty payments made between associated companies of different Member States.: Official Journal 2003, Vol. 2003/49/EC, pp.0049 - 0054.
128. Caballero, J.; Fernández, L.; Garriga, M.; Abreu, J.I.; Collina, S. and Fernández, M. Proteometric study of ghrelin receptor function variations upon mutations using amino acid sequence autocorrelation vectors and genetic algorithm-based least square support vector machines. J. Mol. Graph. Model., 2007, 26, 166-178.
129. Fernández, M.; Caballero, J.; Fernández, L.; Abreu, J.I. and Garriga, M. Protein radial distribution function (P-RDF) and Bayesian-Regularized Genetic Neural Networks for modeling protein conformational stability: Chymotrypsin inhibitor 2 mutants. J. Mol. Graph. Model., 2007, 26, 748-759. (Pubitemid 47628843)
130. Estrada, E. Characterization of the folding degree of proteins. Bioinformatics, 2002, 18, 697-704.
131. Estrada, E. A protein folding degree measure and its dependence on crystal packing, protein size, secondary structure, and domain structural class. J. Chem. Inf. Comput. Sci., 2004, 44, 1238-1250.
132. Estrada, E.; Uriarte, E. and Vilar, S. Effect of protein backbone folding on the stability of protein-ligand complexes. J. Proteome Res., 2006, 5, 105-111.
133. Estrada, E. and Uriarte, E. Folding degrees of azurins and pseudoazurins. Implications for structure and function. Comput. Biol. Chem., 2005, 29, 345-353.
134. Estrada, E. Characterization of the amino acid contribution to the folding degree of proteins. Proteins, 2004, 54, 727-737.
135. Estrada, E. Application of a novel graph-theoretic folding degree index to the study of steroid-DB3 antibody binding affinity. Comput. Biol. Chem., 2003, 27, 305-313.
136. Agüero-Chapín, G.; González-Díaz, H.; de la Riva, G.; Rodríguez, E.; Sánchez-Rodríguez, A.; Podda, G. and Vazquez-Padrón, R.I. MMM-QSAR recognition of ribonucleases without alignment: comparison with HMM model and isolation from schizosaccharomyces pombe, prediction, and experimental assay of a new sequence. J. Chem. Inf. Model., 2008, 48, 434-448.
137. González-Díaz, H.; Dea-Ayuela, M.A.; Pérez-Montoto, L.G.; Prado-Prado, F.J.; Agüero-Chapín, G.; Bolas-Fernández, F.; Vazquez-Padrón, R.I. and Ubeira, F.M. QSAR for RNases and theoreticexperimental study of molecular diversity on peptide mass fingerprints of a new leishmania infantum protein. Mol. Divers., 2009, [Epub ahead of print]
138. Gonzalez-Diaz, H.; Saiz-Urra, L.; Molina, R.; Gonzalez-Diaz, Y. and Sanchez-Gonzalez, A. Computational chemistry approach to protein kinase recognition using 3D stochastic van der Waals spectral moments. J. Comput. Chem., 2007, 28, 1042-1048.
139. Concu, R.; Podda, G.; Uriarte, E. and González-Díaz, H. In Handbook of Computational Chemistry Research, Robson C.T.; Collett, C.D. Eds. Nova Science Publishers 2009.
140. Concu, R.; Podda, G.; Uriarte, E. and Gonzalez-Diaz, H. Computational chemistry study of 3D-structure-function relationships for enzymes based on Markov models for protein electrostatic, HINT, and van der Waals potentials. J. Comput. Chem., 2009, 30, 1510-1520.
141. Concu, R.; Podda, G. and González-Díaz, H. In Quantitative Structure-Property Relationships from Bio-Molecular to Social Networks, González-Díaz, H. Ed.; Nova Science Publisher: NY, 2009.
142. 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, 476-482.
143. Vilar, S.; González-Díaz, 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.
144. González-Díaz, H.; Ferino, G.; Munteanu, C.R.; Vilar, S. and Uriarte, E. In Oncoproteomics, Cho, W. Ed.; Springer Verlag, 2009.
145. Aguero-Chapin, G.; Varona-Santos, J.; de la Riva, G.A.; Antunes, A.; Gonzalez-Villa, T.; Uriarte, E. and Gonzalez-Diaz, H. Alignment-free prediction of polygalacturonases with pseudofolding topological indices: experimental isolation from coffea arabica and prediction of a new sequence. J. Proteome Res., 2009, 8, 2122-2128.
146. González-Díaz, H.; Agüero-Chapin, G.; Varona, J.; Molina, R.; Delogu, G.; Santana, L.; Uriarte, E. and Gianni, P. 2D-RNAcoupling numbers: a new computational chemistry approach to link secondary structure topology with biological function. J. Comput. Chem., 2007, 28, 1049-1056.
147. González-Díaz, H.; Aguero-Chapin, G.; Varona-Santos, J.; Molina, R.; de la Riva, G. and Uriarte, E. 2D RNA-QSAR: assigning ACC oxidase family membership with stochastic molecular descriptors; isolation and prediction of a sequence from Psidium guajava L. Bioorg. Med. Chem. Lett., 2005, 15, 2932-2937.
148. Munteanu, C.R.; Dorado, J.; Pazos-Sierra, A.; Prado-Prado, F.; Perez-Montoto, L.G.; Vilar, S.; Ubeira, F.M.; Sanchez-Gonzalez, A.; Cruz-Monteagudo, M.; Arrasate, S.; Sotomayor, N.; Lete, E.; Duardo-Sanchez, A.; Diaz-Lopez, A.; Patlewicz, G. and Gonzalez-Diaz, H. In Towards an information theory of Complex Networks: Statistical Methods and Applications., Emmert-Streib, F.; Dehmer, M. and Mehler, A. Eds. Birkhäuser/Springer Publisher: Germany, 2009.