Predict potential drug targets from the ion channel proteins based on SVM

Journal of Theoretical Biology

Volumn 262, Issue 4, 2010, Pages 750-756

  Huang, Chen ^a   Zhang, Ruijie ^a   Chen, Zhiqiang ^a   Jiang, Yongshuai ^a   Shang, Zhenwei ^a   Sun, Peng ^a   Zhang, Xuehong ^a   Li, Xia ^a,b  

^a HARBIN MEDICAL UNIVERSITY   (China)

^b Biomedical Engineering Institute of CUMS   (China)

Author keywords

Cross validation; Feature selection; Secondary structure; Subcellular localization; Two stage prediction

Indexed keywords

GLUTAMATE RECEPTOR; GLUTAMATE RECEPTOR 1; ION CHANNEL; KAINIC ACID RECEPTOR;

DRUG; GENETIC ALGORITHM; GENOME; PREDICTION; PROTEIN;

AMINO ACID SEQUENCE; ARTICLE; HUMAN; HYDROPHOBICITY; PRIORITY JOURNAL; PROTEIN LOCALIZATION; PROTEIN SECONDARY STRUCTURE; PROTEIN TARGETING; SUPPORT VECTOR MACHINE;

ALGORITHMS; AMINO ACID SEQUENCE; CHEMISTRY, PHARMACEUTICAL; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; DRUG DESIGN; GENOME; HUMANS; ION CHANNELS; PATTERN RECOGNITION, AUTOMATED; PHARMACEUTICAL PREPARATIONS; PROTEIN FOLDING; PROTEIN STRUCTURE, SECONDARY; PROTEINS; SEQUENCE ANALYSIS, PROTEIN;

EID: 77649337754     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2009.11.002     Document Type: Article

References (65)

1. An J., Totrov M., Abagyan R. Comprehensive identification of "druggable" protein ligand binding sites. Genome Inf. 2004, 15:31-41.
2. Bakheet T.M., Doig A.J. Properties and identification of human protein drug targets. Bioinformatics 2009, 25:451-457.
3. Baldi P., Brunak S., Chauvin Y., Andersen C.A., Nielsen H. Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 2000, 16:412-424.
4. Bao L., Sun Z. Identifying genes related to drug anticancer mechanisms using support vector machine. FEBS Lett. 2002, 521:109-114.
5. Bareil C., Hamel C.P., Delague V., Arnaud B., Demaille J., Claustres M. Segregation of a mutation in CNGB1 encoding the beta-subunit of the rod cGMP-gated channel in a family with autosomal recessive retinitis pigmentosa. Hum. Genet. 2001, 108:328-334.
6. Berman H.M., Bhat T.N., Bourne P.E., Feng Z., Gilliland G., Weissig H., Westbrook J. The Protein Data Bank and the challenge of structural genomics. Nat. Struct. Biol. 2000, 7(Suppl.):957-959.
7. Bhardwaj N., Langlois R.E., Zhao G., Lu H. Kernel-based machine learning protocol for predicting DNA-binding proteins. Nucleic Acids Res. 2005, 33:6486-6493.
8. Cai C.Z., Han L.Y., Ji Z.L., Chen Y.Z. Enzyme family classification by support vector machines. Proteins 2004, 55:66-76.
9. Cai C.Z., Han L.Y., Ji Z.L., Chen X., Chen Y.Z. SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res. 2003, 31:3692-3697.
10. Cai Y.D., Zhou G.P., Chou K.C. Support vector machines for predicting membrane protein types by using functional domain composition. Biophys. J. 2003, 84:3257-3263.
11. Cai Y.D., Liu X.J., Xu X.B., Chou K.C. Support Vector Machines for predicting HIV protease cleavage sites in protein. J. Comput. Chem. 2002, 23:267-274.
12. Cai Y.D., Liu X.J., Xu X.B., Chou K.C. Support vector machines for predicting the specificity of GalNAc-transferase. Peptides 2002, 23:205-208.
13. Cai Y.D., Ricardo P.W., Jen C.H., Chou K.C. Application of SVM to predict membrane protein types. J. Theor. Biol. 2004, 226:373-376.
14. Cai Y.D., Zhou G.P., Jen C.H., Lin S.L., Chou K.C. Identify catalytic triads of serine hydrolases by support vector machines. J. Theor. Biol. 2004, 228:551-557.
15. Chou K.C. A novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space. Proteins 1995, 21:319-344.
16. Chou K.C. Insights from modeling three-dimensional structures of the human potassium and sodium channels. J. Proteome Res. 2004, 3:856-861.
17. Chou K.C. Prediction of G-protein-coupled receptor classes. J. Proteome Res. 2005, 4:1413-1418.
18. Chou K.C., Zhang C.T. Prediction of protein structural classes. Crit. Rev. Biochem. Mol. Biol. 1995, 30:275-349.
19. Chou K.C., Cai Y.D. Using functional domain composition and support vector machines for prediction of protein subcellular location. J. Biol. Chem. 2002, 277:45765-45769.
20. Chou K.C., Shen H.B. Recent progress in protein subcellular location prediction. Anal. Biochem. 2007, 370:1-16.
21. Chou K.C., Shen H.B. Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. J. Proteome Res. 2007, 6:1728-1734.
22. Chou K.C., 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.
23. Culiat C.T., Stubbs L.J., Montgomery C.S., Russell L.B., Rinchik E.M. Phenotypic consequences of deletion of the gamma 3, alpha 5, or beta 3 subunit of the type A gamma-aminobutyric acid receptor in mice. Proc. Natl. Acad. Sci. USA 1994, 91:2815-2818.
24. Ding C.H., Dubchak I. Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 2001, 17:349-358.
25. Ding Y.S., Zhang T.L., 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.
26. Ding Y.S., Zhang T.L., Gu Q., Zhao P.Y., Chou K.C. Using maximum entropy model to predict protein secondary structure with single sequence. Issue Series Title: Protein Pept. Lett. 2009, 16:552-560.
27. Dobson P.D., Doig A.J. Distinguishing enzyme structures from non-enzymes without alignments. J. Mol. Biol. 2003, 330:771-783.
28. Drews J. Drug discovery: a historical perspective. Science 2000, 287:1960-1964.
29. Du Q.S., Huang R.B., Wang C.H., Li X.M., Chou K.C. Energetic analysis of the two controversial drug binding sites of the M2 proton channel in influenza A virus. J. Theor. Biol. 2009, 259:159-164.
30. Dubchak I., Muchnik I., Holbrook S.R., Kim S.H. Prediction of protein folding class using global description of amino acid sequence. Proc. Natl. Acad. Sci. USA 1995, 92:8700-8704.
31. Dunlop J., Bowlby M., Peri R., Vasilyev D., Arias R. High-throughput electrophysiology: an emerging paradigm for ion-channel screening and physiology. Nat. Rev. Drug Discovery 2008, 7:358-368.
32. Guimera R., Sales-Pardo M., Amaral L.A. A network-based method for target selection in metabolic networks. Bioinformatics 2007, 23:1616-1622.
33. Hajduk P.J., Huth J.R., Fesik S.W. Druggability indices for protein targets derived from NMR-based screening data. J. Med. Chem. 2005, 48:2518-2525.
34. Hajduk P.J., Huth J.R., Tse C. Predicting protein druggability. Drug Discovery Today 2005, 10:1675-1682.
35. Han L., Cui J., Lin H., Ji Z., Cao Z., Li Y., Chen Y. Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity. Proteomics 2006, 6:4023-4037.
36. Han L.Y., Zheng C.J., Xie B., Jia J., Ma X.H., Zhu F., Lin H.H., Chen X., Chen Y.Z. Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness. Drug Discovery Today 2007, 12:304-313.
37. Hopkins A.L., Groom C.R. The druggable genome. Nat. Rev. Drug Discovery 2002, 1:727-730.
38. Huang R.B., Du Q.S., Wang C.H., Chou K.C. An in-depth analysis of the biological functional studies based on the NMR M2 channel structure of influenza A virus. Biochem. Biophys. Res. Commun. 2008, 377:1243-1247.
39. Kinnings S.L., Liu N., Buchmeier N., Tonge P.J., Xie L., Bourne P.E. Drug discovery using chemical systems biology: repositioning the safe medicine Comtan to treat multi-drug and extensively drug resistant tuberculosis. PLoS Comput. Biol. 2009, 5:e1000423.
40. Kurgan L.H. Prediction of structural classes for protein sequences and domains-impact of prediction algorithms, sequence representation and homology, and test procedures on accuracy. Pattern Recognition 2006, 39:2323-2343.
41. Li Q., Lai L. Prediction of potential drug targets based on simple sequence properties. BMC Bioinf. 2007, 8:353.
42. Lin, W.Z., Xiao, X., Chou, K.C., 2009. GPCR-GIA: a web-server for identifying G-protein coupled receptors and their families with grey incidence analysis. Protein Eng. Des. Sel. 22, 699-705.
43. Mardia K.V., Kent J.T., Bibby J.M. Multivariate Analysis: Chapter 11, Discriminant analysis; Chapter 12, Multivariate analysis of variance; Chapter 13, Cluster analysis 1979, Academic Press, London, pp. 322-381.
44. Mullner S., Neumann T., Lottspeich F. Proteomics-a new way for drug target discovery. Arzneimittelforschung 1998, 48:93-95.
45. Nakanishi S. Molecular diversity of glutamate receptors and implications for brain function. Science 1992, 258:597-603.
46. Overington J.P., Al Lazikani B., Hopkins A.L. How many drug targets are there?. Nat. Rev. Drug Discovery 2006, 5:936-993.
47. Pielak R.M., Schnell J.R., Chou J.J. Mechanism of drug inhibition and drug resistance of influenza A M2 channel. Proc. Natl. Acad. Sci. USA 2009, 106:7379-7384.
48. ProtComp, SoftBerry website. http://linux1.softberry.com/berry.phtml.
49. PSSfinder, SoftBerry website. http://linux1.softberry.com/berry.phtml.
50. Rong En Fan P.H.C., Chih J.L. Working set selection using second order information for training support vector machines. J. Mach. Learn. Res. 2005, 6:1889-1918.
51. Russ A.P., Lampel S. The druggable genome: an update. Drug Discovery Today 2005, 10:1607-1610.
52. Schnell J.R., Chou J.J. Structure and mechanism of the M2 proton channel of influenza A virus. Nature 2008, 451:591-595.
53. Shen H.B., Chou K.C. Hum-mPLoc: an ensemble classifier for large-scale human protein subcellular location prediction by incorporating samples with multiple sites. Biochem. Biophys. Res. Commun. 2007, 355:1006-1011.
54. Sun M., Goldin E., Stahl S., Falardeau J.L., Kennedy J.C., Acierno J.S., Bove C., Kaneski C.R., Nagle J., Bromley M.C., Colman M., Schiffmann R., Slaugenhaupt S.A. Mucolipidosis type IV is caused by mutations in a gene encoding a novel transient receptor potential channel. Hum. Mol. Genet. 2000, 9:2471-2478.
55. Wang J.F., Wei D.Q., Chou K.C. Insights from investigating the interactions of adamantane-based drugs with the M2 proton channel from the H1N1 swine virus. Biochem. Biophys. Res. Commun. 2009, 388:413-417.
56. Wang M., Yang J., Liu G.P., Xu Z.J., Chou K.C. Weighted-support vector machines for predicting membrane protein types based on pseudo-amino acid composition. Protein Eng. Des. Sel. 2004, 17:509-516.
57. Wang T., Yang J., Shen H.B., Chou K.C. Predicting membrane protein types by the LLDA algorithm. Protein Pept. Lett. 2008, 15:915-921.
58. Xiao X., Chou K.C. Digital coding of amino acids based on hydrophobic index. Protein Pept. Lett. 2007, 14:871-875.
59. Xiao X., Wang P., Chou K.C. Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image. J. Theor. Biol. 2008, 254:691-696.
60. Xiao X., Wang P., Chou K.C. GPCR-CA: a cellular automaton image approach for predicting G-protein-coupled receptor functional classes. J. Comput. Chem. 2009, 30:1414-1423.
61. Xie L., Li J., Xie L., Bourne P.E. Drug discovery using chemical systems biology: identification of the protein-ligand binding network to explain the side effects of CETP inhibitors. PLoS Comput. Biol. 2009, 5:e1000387.
62. Zhang T.L., Ding Y.S., Chou K.C. Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern. J. Theor. Biol. 2008, 250:186-193.
63. Zhou G.P. An intriguing controversy over protein structural class prediction. J. Protein Chem. 1998, 17:729-738.
64. Zhou Y., Dong Q., Louahed J., Dragwa C., Savio D., Huang M., Weiss C., Tomer Y., McLane M.P., Nicolaides N.C., Levitt R.C. Characterization of a calcium-activated chloride channel as a shared target of Th2 cytokine pathways and its potential involvement in asthma. Am. J. Respir. Cell Mol. Biol. 2001, 25:486-491.
65. Zuo J., De Jager P.L., Takahashi K.A., Jiang W., Linden D.J., Heintz N. Neurodegeneration in Lurcher mice caused by mutation in delta2 glutamate receptor gene. Nature 1997, 388:769-773.