On applications of macromolecular QSAR theory

Advanced Methods and Applications in Chemoinformatics: Research Progress and New Applications

Volumn , Issue , 2011, Pages 219-228

  Duchowicz, Pablo R ^a   Castro, Eduardo A ^a  

^a INSTITUTO DE INVESTIGACIONES FISICOQUÍMICAS TEÓRICAS Y APLICADAS INIFTA   (Argentina)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84899242595     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-60960-860-6.ch010     Document Type: Chapter

References (70)

1. Aguero-Chapin, G., Antunes, A., Ubeira, F. M., Chou, K.-C., & González-Díaz, H. (2008). Comparative Study of Topological Indices of Macro/Supramolecular RNA Complex Networks. Journal of Chemical Information and Modeling, 48, 2265-2277. doi:10.1021/ci8001809
2. Caballero, J., Fernandez, L., Abreu, J. I., & Fernandez, M. (2006). Amino acid sequence autocorrelation vectors and ensembles of Bayesian-regularized genetic neural networks for prediction of conformational stability of human lysozyme mutants. Journal of Chemical Information and Modeling, 46, 1255-1268. doi:10.1021/ci050507z
3. Cai, Y. D., & Chou, K. C. (2005). Predicting enzyme subclass by functional domain composition and pseudo amino acid composition. Journal of Proteome Research, 4, 967-971. doi:10.1021/pr0500399
4. Cai, Y. D., & Lin, S. L. (2003). Support vector machines for predicting rRNA-, RNA-, and DNA-binding proteins from amino acid sequence. Biochimica et Biophysica Acta, 1648, 127-133.
5. Chalk, A. J., Worth, C. L., Overington, J. P., & Chan, A. W. (2004). PDBLIG: classification of small molecular protein binding in the Protein Data Bank. Journal of Medicinal Chemistry, 47, 3807-3816. doi:10.1021/jm040804f
6. Chou, K. C., & Cai, Y. D. (2003). Predicting protein quaternary structure by pseudo amino acid composition. Proteins, 53, 282-289. doi:10.1002/prot.10500
7. Chou, K. C., & Cai, Y. D. (2005). Prediction of membrane protein types by incorporating amphipathic effects. Journal of Chemical Information and Modeling, 45, 407-413. doi:10.1021/ci049686v
8. Chou, K. C., & Shen, H. B. (2006). Predicting protein subcellular location by fusing multiple classifiers. Journal of Cellular Biochemistry, 99, 517-527. doi:10.1002/jcb.20879
9. Chou, K. C., & Shen, H. B. (2007). MemType-2L: A web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochemical and Biophysical Research Communications, 360, 339-345. doi:10.1016/j.bbrc.2007.06.027
10. Deng, W., Breneman, C., & Embrechts, M. J. (2004). Predicting protein-ligand binding affinities using novel geometrical descriptors and machine-learning methods. Journal of Chemical Information and Computer Sciences, 44, 699-703. doi:10.1021/ci034246+
11. Diudea, M. V. E. (2001). QSPR/QSAR Studies by Molecular Descriptors. New York: Nova Science Publishers.
12. Dobson, P. D., & Doig, A. J. (2005). Predicting enzyme class from protein structure without alignments. Journal of Molecular Biology, 345, 187-199. doi:10.1016/j.jmb.2004.10.024
13. Duchowicz, P. R., & Castro, E. A. (2008). Partial Order Theory Applied to QSPR-QSAR Studies. In Brueggemann, R. (Ed.), Combinatorial Chemistry and High Throughput Screening (special issue) (pp. 783-793). New York: Bentham Science Publishers.
14. Duchowicz, P. R., & Castro, E. A. (2009a). Application of the Fuzzy Logic Theory to QSAR/QSPR Studies. In Putz, M. (Ed.), Quantum Frontiers of Atoms and Molecules in Physics, Chemistry and Biology. Cairo, Egypt: Hindawi Publishing Corp.
15. Duchowicz, P. R., Fernández, F. M., & Castro, E. A. (2009b). The Employment of Orthogonalization Methods in QSPR-QSAR Studies. In Pandalai, S. G., & Castro, E. A. (Eds.), QSPR-QSAR Studies on Desired Properties for Drug Design. New York: Research Signpost/Transworld Research Network.
16. Estrada, E. (2001). Generalization of topological indices. Chemical Physics Letters, 336, 248-252. doi:10.1016/S0009-2614(01)00127-0
17. Estrada, E. (2003). Application of a novel graph-theoretic folding degree index to the study of steroid-DB3 antibody binding affinity. Computational Biology and Chemistry, 27, 305-313. doi:10.1016/S1476-9271(02)00078-6
18. Fernández, L., Caballero, J., Abreu, J. I., & Fernández, M. (2007). Amino acid sequence autocorrelation vectors and Bayesian regularized genetic neural networks for modeling protein conformational stability: Gene V protein mutants. Proteins, 67, 834-852. doi:10.1002/prot.21349
19. Galindo, J. F., Bermudez, C. I., & Daza, E. E. (2006). tRNA structure from a graph and quantum theoretical perspective. Journal of Theoretical Biology, 240, 574-582. doi:10.1016/j.jtbi.2005.10.017
20. Gan, H. H., Pasquali, S., & Schlick, T. (2003). Exploring the repertoire of RNA secondary motifs using graph theory; implications for RNA design. Nucleic Acids Research, 31, 2926-2943. doi:10.1093/nar/gkg365
21. González-Díaz, H., Agüero-Chapin, G., Varona, J., Molina, R., Delogu, G., & Santana, L. (2007a). 2D-RNA-coupling numbers: A new computational chemistry approach to link secondary structure topology with biological function. Journal of Computational Chemistry, 28, 1049-1056. doi:10.1002/jcc.20576
22. González-Díaz, H., Aguero-Chapin, G., Varona-Santos, J., Molina, R., de la Riva, G., & Uriarte, E. (2005). 2D RNA-QSAR: assigning ACC oxidase family membership with stochastic molecular descriptors; isolation and prediction of a sequence from Psidium guajava L. Bioorganic & Medicinal Chemistry Letters, 15, 2932-2937. doi:10.1016/j.bmcl.2005.03.017
23. González-Díaz, H., Cruz-Monteagudo, M., Vina, D., Santana, L., Uriarte, E., & De Clercq, E. (2005a). QSAR for anti-RNA-virus activity, synthesis, and assay of anti-RSV carbonucleosides given a unified representation of spectral moments, quadratic, and topologic indices. Bioorganic & Medicinal Chemistry Letters, 15, 1651-1657. doi:10.1016/j.bmcl.2005.01.047
24. González-Díaz, H., de Armas, R. R., & Molina, R. (2003a). Markovian negentropies in bioinformatics. 1. A picture of footprints after the interaction of the HIV-1 Psi-RNA packaging region with drugs. Bioinformatics (Oxford, England), 19, 2079-2087. doi:10.1093/bioinformatics/btg285
25. González-Díaz, H., de Armas, R. R., & Molina, R. (2003b). Vibrational Markovian modelling of footprints after the interaction of antibiotics with the packaging region of HIV type 1. Bulletin of Mathematical Biology, 65, 991-1002. doi:10.1016/S0092-8240(03)00064-8
26. González-Díaz, H., Ferino, G., Podda, G., & Uriarte, E. (2008a). Discriminating prostate cancer patients from control group with connectivity indices. ECSOC, 12, 1-10.
27. González-Díaz, H., González-Díaz, Y., Santana, L., Ubeira, F. M., & Uriarte, E. (2008b). Proteomics, networks and connectivity indices. Proteomics, 8, 750-778. doi:10.1002/pmic.200700638
28. González-Díaz, H., Molina, R., & Uriarte, E. (2005b). Recognition of stable protein mutants with 3D stochastic average electrostatic potentials. FEBS Letters, 579, 4297-4301. doi:10.1016/j.febslet.2005.06.065
29. González-Díaz, H., Pérez-Bello, A., Cruz-Monteagudo, M., González-Díaz, Y., Santana, L., & Uriarte, E. (2007b). Chemometrics for QSAR with Low Sequence Homology: Mycobacterial Promoter Sequences Recognition with 2D-RNA Entropies. Chemometrics and Intelligent Laboratory Systems, 85, 20-26. doi:10.1016/j.chemolab.2006.03.005
30. González-Díaz, H., Perez-Bello, A., Uriarte, E., & González-Díaz, Y. (2006a). QSAR study for mycobacterial promoters with low sequence homology. Bioorganic & Medicinal Chemistry Letters, 16, 547-553. doi:10.1016/j.bmcl.2005.10.057
31. González-Díaz, H., Pérez-Castillo, Y., Podda, G., & Uriarte, E. (2007c). Computational chemistry comparison of stable/nonstable protein mutants classification models based on 3D and topological indices. Journal of Computational Chemistry, 28, 1990-1995. doi:10.1002/jcc.20700
32. González-Díaz, H., Prado-Prado, F., Pérez-Montoto, L. G., Duardo-Sánchez, A., & López-Díaz, A. (2009). QSAR Models for Proteins of Parasitic Organisms, Plants and Human Guests: Theory, Applications, Legal Protection, Taxes, and Regulatory Issues. Current Proteomics, 6, 214-227. doi:10.2174/157016409789973789
33. González-Díaz, H., Sanchez-González, A., & González-Díaz, Y. (2006b). 3D-QSAR study for DNA cleavage proteins with a potential antitumor ATCUN-like motif. Journal of Inorganic Biochemistry, 100, 1290-1297. doi:10.1016/j.jinorgbio.2006.02.019
34. González-Díaz, H., & Uriarte, E. (2005c). Proteins QSAR with Markov average electrostatic potentials. Bioorganic & Medicinal Chemistry Letters, 15, 5088-5094. doi:10.1016/j.bmcl.2005.07.056
35. González-Díaz, H., Uriarte, E., & Ramos de Armas, R. (2005d). Predicting stability of Arc repressor mutants with protein stochastic moments. Bioorganic & Medicinal Chemistry, 13, 323-331. doi:10.1016/j.bmc.2004.10.024
36. González-Díaz, H., Vilar, S., Santana, L., & Uriarte, E. (2007d). Medicinal Chemistry and Bioinformatics-Current Trends in Drugs Discovery with Networks Topological Indices. Current Topics in Medicinal Chemistry, 7, 1015-1029. doi:10.2174/156802607780906771
37. Goodarzi, M., Duchowicz, P. R., Wu, C. H., Fernández, F. M., & Castro, E. A. (2009). New Hybrid Genetic Based Support Vector Regression as QSAR Approach for Analyzing Flavonoids-GABA(A) Complexes. Journal of Chemical Information and Modeling, 49, 1475-1485. doi:10.1021/ci900075f
38. Guyon, I., & Elisseeff, A. (2003). An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 3, 1157-1182. doi:10.1162/153244303322753616
39. Han, L., Cui, J., Lin, H., Ji, Z., Cao, Z., Li, Y., & Chen, Y. (2006). Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity. Proteomics, 6, 4023-4037. doi:10.1002/pmic.200500938
40. Hansch, C., & Fujita, T. (1964). Q-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure. Journal of the American Chemical Society, 86, 1616-1626. doi:10.1021/ja01062a035
41. Hansch, C., & Leo, A. (1995). Exploring QSAR. Fundamentals and Applications in Chemistry and Biology. Washington, D. C.: American Chemical Society.
42. Kalate, R. N., Tambe, S. S., & Kulkarni, B. D. (2003). Artificial neural networks for prediction of mycobacterial promoter sequences. Computational Biology and Chemistry, 27, 555-564. doi:10.1016/j.compbiolchem.2003.09.004
43. Katritzky, A. R., & Goordeva, E. V. (1993). Traditional Topological Indices vs. Electronic, Geometrical, and Combined Molecular Descriptors in QSAR/QSPR Research. Journal of Chemical Information and Computer Sciences, 33, 835-857. doi:10.1021/ci00016a005
44. Katritzky, A. R., Lobanov, V. S., & Karelson, M. (1995). QSPR: the correlation and quantitative prediction of chemical and physical properties from structure. Chemical Society Reviews, 24, 279-287. doi:10.1039/cs9952400279
45. Kubinyi, H. (2008). QSAR: Hansch Analysis and Related Approaches. New York: Wiley-Interscience.
46. Liao, B., & Ding, K. (2005). Graphical approach to analyzing DNA sequences. Journal of Computational Chemistry, 26, 1519-1523. doi:10.1002/jcc.20287
47. Liao, B., & Wang, T. M. (2004). Analysis of similarity/dissimilarity of DNA sequences based on nonoverlapping triplets of nucleotide bases. Journal of Chemical Information and Computer Sciences, 44, 1666-1670. doi:10.1021/ci034271f
48. Liao, B., Xiang, X., & Zhu, W. (2006). Coronavirus phylogeny based on 2D graphical representation of DNA sequence. Journal of Computational Chemistry, 27, 1196-1202. doi:10.1002/jcc.20439
49. Marrero Ponce, Y., Castillo Garit, J. A., & Nodarse, D. (2005). Linear indices of the 'macromolecular graph's nucleotides adjacency matrix' as a promising approach for bioinformatics studies. Part 1: prediction of paromomycin's affinity constant with HIV-1 psi-RNA packaging region. Bioorganic & Medicinal Chemistry, 13, 3397-3404. doi:10.1016/j.bmc.2005.03.010
50. Marrero-Ponce, Y., Medina-Marrero, R., Castillo-Garit, J. A., Romero-Zaldivar, V., Torrens, F., & Castro, E. A. (2005). 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. Bioorganic & Medicinal Chemistry, 13, 3003-3015. doi:10.1016/j.bmc.2005.01.062
51. Marrero-Ponce, Y., Medina-Marrero, R., Castro, E. A., Ramos de Armas, R., González, H., Romero, V., & Torrens, F. (2004a). Protein quadratic indices of the "macromolecular pseudograph's a-carbon atom adjacency Matrix". 1. prediction of Arc repressor alanine-mutant's stability. Molecules (Basel, Switzerland), 9, 1124-1147. doi:10.3390/91201124
52. Marrero-Ponce, Y., Nodarse, D., González-Díaz, H., Ramos de Armas, R., Romero-Zaldivar, V., Torrens, F., & Castro, E. A. (2004b). Nucleic acid quadratic indices of the "macromolecular graph's nucleotides adjacency matrix" modeling of footprints after the interaction of paromomycin with the HIV-1 C-RNA packaging region. International Journal of Molecular Sciences, 5, 276-293. doi:10.3390/i5110276
53. Nandy, A. (1994). Recent investigations into global characteristics of long DNA sequences. Indian Journal of Biochemistry & Biophysics, 31, 149-155.
54. Nandy, A. (1996). Two-dimensional graphical representation of DNA sequences and intron-exon discrimination in intronrich sequences. Computer Applications in the Biosciences, 12, 55-62.
55. Nandy, A., Harle, M., & Basak, S. C. (2006). Mathematical descriptors of DNA sequences: Development and applications. ARKIVOC, 9, 211-238.
56. Navarro, E., Fenude, E., & Celda, B. (2002). Solution structure of a D,L-alternating oligonorleucine as a model of doublestranded antiparallel betahelix. Biopolymers, 64, 198-209. doi:10.1002/bip.10172
57. Navarro, E., Fenude, E., & Celda, B. (2004). Conformational and structural analysis of the equilibrium between single- and double-strand beta-helix of a D,L-alternating oligonorleucine. Biopolymers, 73, 229-241. doi:10.1002/bip.10549
58. Puzyn, T., Leszczynski, J., & Cronin, M. T. (2009). Recent Advances in QSAR Studies: Methods and Applications. New York: Springer.
59. Ramos de Armas, R., González-Díaz, H., Molina, R., Pérez-Gonzalez, M., & Uriarte, E. (2004a). Stochastic-based descriptors studying peptides biological properties: Modeling the bitter tasting threshold of dipeptides. Bioorganic & Medicinal Chemistry, 12, 4815-4822. doi:10.1016/j.bmc.2004.07.017
60. Ramos de Armas, R., González-Díaz, H., Molina, R., & Uriarte, E. (2004b). Markovian Backbone Negentropies: Molecular descriptors for protein research. I. Predicting protein stability in Arc repressor mutants. Proteins, 56, 715-723. doi:10.1002/prot.20159
61. Randic, M., & Balaban, A. T. (2003). On a four-dimensional representation of DNA primary sequences. Journal of Chemical Information and Computer Sciences, 43, 532-539. doi:10.1021/ci020051a
62. Randic, M., Guo, X., & Basak, S. C. (2001). On the characterization of DNA primary sequences by triplet of nucleic acid bases. Journal of Chemical Information and Computer Sciences, 41, 619-626. doi:10.1021/ci000120q
63. Randic, M., Lers, N., Plavsic, D., Basak, S., & Balaban, A. T. (2005). Four-color map representation of DNA or RNA sequences and their numerical characterization. Chemical Physics Letters, 407, 205-208. doi:10.1016/j.cplett.2005.03.086
64. Randic, M., Vracko, M., Nandy, A., & Basak, S. C. (2000). On 3-D graphical representation of DNA primary sequences and their numerical characterization. Journal of Chemical Information and Computer Sciences, 40, 1235-1244. doi:10.1021/ci000034q
65. Randic, M., Zupan, J., & Vikic-Topic, D. (2007). On representation of proteins by star-like graphs. Journal of Molecular Graphics & Modelling, 26, 290-305. doi:10.1016/j.jmgm.2006.12.006
66. Saiz-Urra, L., González-Díaz, H., & Uriarte, E. (2005). Proteins Markovian 3D-QSAR with spherically-truncated average electrostatic potentials. Bioorganic & Medicinal Chemistry, 13, 3641-3647. doi:10.1016/j.bmc.2005.03.041
67. Selassie, C. D., Mekapati, S. B., & Verma, D. P. (2002). QSAR: Then and Now. Current Topics in Medicinal Chemistry, 2, 1357-1379. doi:10.2174/1568026023392823
68. Todeschini, R., & Consonni, V. (2009). Molecular Descriptors for Chemoinformatics. Weinheim: Wiley-VCH.
69. Trinajstic, N. (1992). Chemical Graph Theory. Boca Raton, FL: CRC Press.
70. Zhang, S., Golbraikh, A., & Tropsha, A. (2006). Development of quantitative structure-binding affinity relationship models based on novel geometrical chemical descriptors of the protein-ligand interfaces. Journal of Medicinal Chemistry, 49, 2713-2724. doi:10.1021/jm050260x