Classification of voltage-gated K+ ion channels from 3D pseudo-folding graph representation of protein sequences using genetic algorithm-optimized support vector machines

Journal of Molecular Graphics and Modelling

Volumn 26, Issue 8, 2008, Pages 1306-1314

  Fernández, Michael ^a,b   Fernández, Leyden ^a   Abreu, Jose Ignacio ^a,c   Garriga, Miguel ^a  

^a Faculty of Agronomy   (Cuba)

^b KYUSHU INSTITUTE OF TECHNOLOGY   (Japan)

^c UNIVERSITY OF MATANZAS   (Cuba)

Author keywords

Electrophysiological variables; Graph similarity; Ion channel; Kernel based methods

Indexed keywords

BIOLOGICAL MEMBRANES; GENETIC ALGORITHMS; GRAPHIC METHODS; SUPPORT VECTOR MACHINES;

ELECTROPHYSIOLOGICAL VARIABLES; GRAPH SIMILARITY; ION CHANNELS; KERNEL-BASED METHODS;

PROTEINS;

VOLTAGE GATED POTASSIUM CHANNEL;

AMINO ACID SEQUENCE; ARTICLE; ELECTROPHYSIOLOGY; GENETIC ALGORITHM; PRIORITY JOURNAL; PROTEIN FOLDING; PROTEIN STRUCTURE; X RAY CRYSTALLOGRAPHY;

ALGORITHMS; AMINO ACID SEQUENCE; ARTIFICIAL INTELLIGENCE; MOLECULAR SEQUENCE DATA; POTASSIUM CHANNELS, VOLTAGE-GATED; PROTEIN FOLDING; REPRODUCIBILITY OF RESULTS;

EID: 42749090642     PISSN: 10933263     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmgm.2008.01.001     Document Type: Article

References (61)

1. Yellen G. The voltage-gated potassium channels and their relatives. Nature 419 (2002) 35-42
2. + channel. Nature 423 (2003) 33-41
3. + channel S4. Neuron 16 (1996) 387-397
4. Sands Z., Grottesi A., and Sansom M.S. Voltage-gated ion channels. Curr. Biol. 15 (2005) 44-47
5. Jentsch T.J. Neuronal KCNQ potassium channels: physiology and role in disease. Nat. Rev. Neurosci. 1 (2000) 21-30
6. Comu S., Giuliani M., and Narayanan V. Episodic ataxia and myokymia syndrome: a new mutation of potassium channel gene Kv1.1. Ann. Neurol. 40 (1996) 684-687
7. Abdul M., and Hoosein N. Voltage-gated potassium ion channels in colon cancer. Oncol. Rep. 9 (2002) 961-964
8. Koni P.A., Khanna R., Chang M.C., Tang M.D., Kaczmarek L.K., Schlichter L.C., and Flavella R.A. Compensatory anion currents in Kv1.3 channel-deficient thymocytes. J. Biol. Chem. 278 (2003) 39443-39451
9. Cooper E.C. Potassium channels: how genetic studies of epileptic syndromes open paths to new therapeutic targets and drugs. Epilepsia 42 (2001) 49-54
10. Ford J.W., Stevens E.B., Treherne J.M., Packer J., and Bushfield M. Potassium channels: gene family, therapeutic relevance, high-throughput screening technologies and drug discovery. Prog. Drug Res. 58 (2002) 133-168
11. + channels. Science 258 (1992) 1152-1155
12. Miller C. 1990: annus mirabilis of potassium channels. Science 252 (1991) 1092-1096
13. Li B., and Gallin W.J. Computational identification of residues that modulate voltage sensitivity of voltage-gated potassium channels. BMC Struct. Biol. 5 5 (2005) 16
14. Ramos de Armas R., González-Díaz H., Molina R., and Uriarte E. Markovian backbone negentropies: molecular descriptors for protein research. I. Predicting protein stability in arc repressor mutants. Proteins 56 (2004) 715-723
15. 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 α-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. 13 (2005) 3003-3015
16. Caballero J., Fernández L., Abreu J.I., and Fernández M. Amino acid sequence autocorrelation vectors and ensembles of Bayesian-regularized genetic neural networks for prediction of conformational stability of human lysozyme mutants. J. Chem. Inform. Model 46 (2006) 1255-1268
17. 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 67 (2007) 834-852
18. Caballero J., Fernández L., Gariga 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. 26 (2007) 166-178
19. Fernández M., Caballero J., Fernández L., Abreu J.I., and Acosta G. Classification of conformational stability of protein mutants from 3D pseudo-folding graph representation of protein sequences using support vector machines. Proteins 70 (2008) 167-175
20. Fernández M., Caballero J., Fernández L., Abreu J.I., and Acosta G. Classification of conformational stability of protein mutants from 2D graph representation of protein sequences using support vector machines. Mol. Simulat. 33 (2007) 889-896
21. Li B., and Gallin W.J. VKCDB: voltage-gated potassium channel database. BMC Struct. Biol. 5 (2004) 3
22. Randić M., Butina D., and Zupan J. Novel 2-D graphical representation of proteins. Chem. Phys. Lett. 419 (2006) 528-532
23. Raychaudhury C., and Nandy A. Indexing scheme and similarity measures for macromolecular sequences. J. Chem. Inform. Comput. Sci. 39 (1999) 243-247
24. Agüero-Chapin G., González-Díaz H., Molina R., Varona-Santos J., Uriarte E., and González-Díaz 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
25. Jeffrey H.I. Chaos game representation of gene structure. Nucleic Acid Res. 18 (1990) 2163-2170
26. Bai F., and Wang T. On graphical and numerical representation of protein sequences. J. Biomol. Struct. Dyn. 23 (2006) 537-545
27. 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. 7 (2007) 1025-1039
28. Chou K.C. Prediction of protein subcellular locations by incorporating quasi-sequence-order effect. Biochem. Biophys. Res. Commun. 278 (2000) 477-483
29. 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. 28 (2007) 1463-1466
30. 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. 4 (1997) 275-296
31. Berger B., and Leighton T. Protein folding in the hydrophobic-hydrophilic (HP) model is NP complete. J. Comput. Biol. 5 (1998) 27-40
32. Chen M., and Huang W.Q. A branch and bound algorithm for the protein folding problem in the HP lattice model. Geno. Prot. Bioinfo. 3 (2005) 225-230
33. Gupta A., Manuch J., and Stacho L. Structure-approximating inverse protein folding problem in the 2D HP model. J. Comput. Biol. 12 (2005) 1328-1345
34. González-Díaz H., Pérez-Castillo Y., Podda G., and Uriarte E. Comparison of stable/nonstable protein mutants classification models based on 3D and topological indices. J. Comput. Chem. 28 (2007) 1990-1995
35. 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. 6 (2007) 904-908
36. 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. 28 (2007) 1042-1048
37. Chou K.-C., and Shen H.B. Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. J. Proteome Res. 6 (2007) 1728-1734
38. Chou K.C., and Cai Y.D. Predicting protein-protein interactions from sequences in a hybridization space. J. Proteome Res. 5 (2006) 316-322
39. Bajzer Z., Randic M., Plavšic D., and Basak S.C. Novel map descriptors for characterization of toxic effects in proteomics maps. J. Mol. Graph. Model. 22 (2003) 1-9
40. Randić M., Lers N., Vukicević D., Plavsić D., Basak S.C., and Gute B.D. Canonical labeling of proteome maps. J. Proteome Res. 4 (2005) 1347-1352
41. Ping P., Vondriska T.M., Creighton C.J., Gandhi T.K., Yang Z., Menon R., Kwon M.-S., Drwal G., Kellman M., Peri S., Suresh S., Gronborg M., Molina H., Chaerkady R., Rekha B., Muthusamy B., Shet A.S., Gerszten R.E., Wu H., Raftery M., Wasinger V., Schulze-Knappe P., Hanash S.M., Paik Y.-K., Hancock W.S., States D.J., Omenn G.S., and Pandey A. A functional annotation of subproteomes in human plasma. Proteomics 5 (2005) 3506-3519
42. Randić M., Zupan J., and Vikić-Topić D. On representation of proteins by star-like graphs. J. Mol. Graph. Model. 26 (2007) 290-305
43. Randic M., and Krilov G. Characterization of 3D sequences of proteins. Chem. Phys. Lett. 272 (1997) 115-119
44. MATLAB 7.0. program, available from The Mathworks Inc., Natick, MA, http://www.mathworks.com.
45. Cortes C., and Vapnik V. Support-vector networks. Mach. Learn. 20 (1995) 273-297
46. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2 (1998) 1-47
47. Vapnik V. Statistical Learning Theory (1998), Wiley, New York
48. C.-C. Chang, C.-J. Lin, LIBSVM: a library for support vector machines, 2001, Software available at http://www.csie.ntu.edu.tw/∼cjlin/libsvm.
49. Lua W., Donga N., and Náray-Szabó G. Predicting anti-HIV-1 activities of hept-analog compounds by using support vector classification. QSAR Comb. Sci. 24 (2005) 1021-1025
50. Yao X., Liu H., Zhang R., Liu M., Hu Z., Panaye A., Doucet J.P., and Fan B. QSAR and classification study of 1,4-dihydropyridine calcium channel antagonists based on least squares support vector machines. Mol. Pharm. 2 (2005) 348-356
51. Fröhlich H., Wegner J.K., and Zell A. Towards optimal descriptor subset selection with support vector machines in classification and regression. QSAR Comb. Sci. 23 (2004) 311-318
52. Hemmateenejad B., Safarpour M.A., Miri R., and Nesari N. Toward an optimal procedure for PC-ANN model building: prediction of the carcinogenic activity of a large set of drugs. J. Chem. Inform. Model. 45 (2005) 190-199
53. + channel by some triarylmethanes using topological charge indexes descriptors. J. Comput. Aided Mol. Des. 19 (2005) 771-789
54. Caballero J., and Fernández M. Linear and nonlinear modeling of antifungal activity of some heterocyclic ring derivatives using multiple linear regression and Bayesian-regularized neural networks. J. Mol. Model. 12 (2006) 168-181
55. Holland H. Adaption in Natural and Artificial Systems (1975), The University of Michigan Press, Ann Arbor, MI
56. Cartwright H.M. Applications of Artificial Intelligence in Chemistry (1993), Oxford University Press, Oxford
57. Fröhlich H., Chapelle O., and Schölkopf B. Feature selection for support vector machines by means of genetic algorithms. Proceedings of the 15th IEEE International Conference on Tools with AI (2003) 142-148
58. GlibSVM Toolbox for Matlab Version 1.0 (2007), Molecular Modeling Group, University of Matanzas
59. The MathWorks Inc. Genetic Algorithm and Direct Search Toolbox User's Guide for use with MATLAB (2004), The Mathworks Inc., MA
60. Sands Z., Grottesi A., and Sansom M.S. Voltage-gated ion channels. Curr. Biol. 15 44 (2005) 7
61. Witten I.H., and Frank E. Data Mining: Practical Machine Learning Tools and Techniques. 2nd edition (2005), Morgan Kaufmann, San Francisco