Predicting drug-target interaction networks based on functional groups and biological features

PLoS ONE

Volumn 5, Issue 3, 2010, Pages

  He, Zhisong ^b,d   Zhang, Jian ^c   Shi, Xiao He ^b   Hu, Le Le ^a   Kong, Xiangyin ^b,e   Cai, Yu Dong ^a,f   Chou, Kuo Chen ^f  

^a SHANGHAI UNIVERSITY   (China)

^b Shanghai Jiao Tong University Affiliated Sixth People s Hospital   (China)

^c Tongji University School of Medicine   (China)

^d FUDAN UNIVERSITY   (China)

^e SHANGHAI JIAO TONG UNIVERSITY   (China)

^f GORDON LIFE SCIENCE INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CELL NUCLEUS RECEPTOR; ENZYME; FUNCTIONAL GROUP; G PROTEIN COUPLED RECEPTOR; ION CHANNEL; PROTEIN; DRUG;

ALGORITHM; AMINO ACID COMPOSITION; ARTICLE; COMPUTER MODEL; COMPUTER NETWORK; DRUG FORMULATION; DRUG TARGETING; MATHEMATICAL MODEL; MAXIMUM RELEVANCE MINIMUM REDUNDANCY; PROTEIN CONFORMATION; PROTEIN CONTENT; PROTEIN INTERACTION; TECHNIQUE; BINDING SITE; BIOLOGY; CHEMISTRY; HUMAN; METABOLISM; METHODOLOGY; PHARMACEUTICS; PROTEIN SECONDARY STRUCTURE; STATISTICAL MODEL;

ALGORITHMS; BINDING SITES; COMPUTATIONAL BIOLOGY; HUMANS; MODELS, STATISTICAL; PHARMACEUTICAL PREPARATIONS; PROTEIN CONFORMATION; PROTEIN STRUCTURE, SECONDARY; PROTEINS; RECEPTORS, G-PROTEIN-COUPLED; TECHNOLOGY, PHARMACEUTICAL;

EID: 77950448057     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0009603     Document Type: Article

References (110)

1. Knowles J, Gromo G (2003) A guide to drug discovery: Target selection in drug discovery. Nat Rev Drug Discov 2: 63-69.
2. Johnson DE, Wolfgang GH (2000) Predicting human safety: screening and computational approaches. Drug Discov Today 5: 445-454.
3. Sirois S, Hatzakis GE, Wei DQ, Du QS, Chou KC (2005) Assessment of chemical libraries for their druggability. Computational Biology & Chemistry 29: 55-67.
4. Chou KC, Wei DQ, Du QS, Sirois S, Zhong WZ (2006) Review: Progress in computational approach to drug development against SARS. Current Medicinal Chemistry 13: 3263-3270.
5. Wang JF, Wei DQ, Li L, Zheng SY, Li YX, et al. (2007) 3D structure modeling of cytochrome P450 2C19 and its implication for personalized drug design. Biochem Biophys Res Commun (Corrigendum: ibid, 2007, Vol357, 330) 355: 513-519.
6. Wang JF, Wei DQ, Chen C, Li Y, Chou KC (2008) Molecular modeling of two CYP2C19 SNPs and its implications for personalized drug design. Protein & Peptide Letters 15: 27-32.
7. Wang JF, Wei DQ, Li L, Chou KC (2008) Review: Pharmacogenomics and personalized use of drugs. Current Topics of Medicinal Chemistry 8: 1573-1579.
8. Wang JF, Zhang CC, Chou KC, Wei DQ (2009) Review: Structure of cytochrome P450s and personalized drug. Current Medicinal Chemistry 16: 232-244.
9. Cheng AC, Coleman RG, Smyth KT, Cao Q, Soulard P, et al. (2007) Structure-based maximal affinity model predicts small-molecule druggability. Nat Biotechnol 25: 71-75.
10. Rarey M, Kramer B, Lengauer T, Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261: 470-489.
11. Chou KC (2004) Review: Structural bioinformatics and its impact to biomedical science. Current Medicinal Chemistry 11: 2105-2134.
12. Chou KC, Wei DQ, Zhong WZ (2003) Binding mechanism of coronavirus main proteinase with ligands and its implication to drug design against SARS. (Erratum: ibid., 2003, Vol.310, 675). Biochem Biophys Res Comm 308: 148-151.
13. Zhu S, Okuno Y, Tsujimoto G, Mamitsuka H (2005) A probabilistic model for mining implicit 'chemical compound-gene' relations from literature. Bioinformatics 21 Suppl 2: ii245-251.
14. Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M (2008) Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24: i232-240.
15. Nagamine N, Sakakibara Y (2007) Statistical prediction of protein chemical interactions based on chemical structure and mass spectrometry data. Bioinformatics 23: 2004-2012.
16. Nagamine N, Shirakawa T, Minato Y, Torii K, Kobayashi H, et al. (2009) Integrating statistical predictions and experimental verifications for enhancing protein-chemical interaction predictions in virtual screening. PLoS Comput Biol 5: e1000397.
17. Vina D, Uriarte E, Orallo F, Gonzalez-Diaz H (2009) Alignment-free prediction of a drug-target complex network based on parameters of drug connectivity and protein sequence of receptors. Mol Pharm 6: 825-835.
18. Chou KC (1993) A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins. Journal of Biological Chemistry 268: 16938-16948.
19. Chou KC (1996) Review: Prediction of HIV protease cleavage sites in proteins. Analytical Biochemistry 233: 1-14.
20. Xiao X, Wang P, Chou KC (2009) GPCR-CA: A cellular automaton image approach for predicting G-protein-coupled receptor functional classes. Journal of Computational Chemistry 30: 1414-1423.
21. Lin WZ, Xiao X, Chou KC (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.
22. Chou KC, Shen HB (2007) Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides. Biochem Biophys Res Comm 357: 633-640.
23. Chou KC, Cai YD (2002) Using functional domain composition and support vector machines for prediction of protein subcellular location. Journal of Biological Chemistry 277: 45765-45769.
24. Chou KC, Shen HB (2008) Cell-PLoc: A package of web-servers for predicting subcellular localization of proteins in various organisms. Nature Protocols 3: 153-162.
25. Chou KC, Shen HB (2007) Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. Journal of Proteome Research 6: 1728-1734.
26. Chou KC (1995) A sequence-coupled vector-projection model for predicting the specificity of GalNAc-transferase. Protein Science 4: 1365-1383.
27. Chou KC, Shen HB (2008) ProtIdent: A web server for identifying proteases and their types by fusing functional domain and sequential evolution information. Biochem Biophys Res Comm 376: 321-325.
28. Chou KC, Cai YD (2006) Prediction of protease types in a hybridization space. Biochem Biophys Res Comm 339: 1015-1020.
29. Chou KC, Elrod DW (1999) Prediction of membrane protein types and subcellular locations. PROTEINS: Structure, Function, and Genetics 34: 137-153.
30. Liu H, Wang M, Chou KC (2005) Low-frequency Fourier spectrum for predicting membrane protein types. Biochem Biophys Res Commun 336: 737-739.
31. Chou KC, Shen HB (2007) MemType-2L: A Web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochem Biophys Res Comm 360: 339-345.
32. Cai YD, Zhou GP, Chou KC (2003) Support vector machines for predicting membrane protein types by using functional domain composition. Biophysical Journal 84: 3257-3263.
33. Chou KC, Shen HB (2009) Review: recent advances in developing web-servers for predicting protein attributes. Natural Science 2: 63-92. (openly accessible at http://www.scirp.org/journal/NS/).
34. Denoeux T (1995) A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Transactions on Systems, Man and Cybernetics 25: 804-813.
35. Mucchielli-Giorgi MH, Hazout S, Tuffery P (1999) PredAcc: prediction of solvent accessibility. Bioinformatics 15: 176-177.
36. Chou KC (2001) Prediction of protein cellular attributes using pseudo amino acid composition. PROTEINS: Structure, Function, and Genetics (Erratum: ibid, 2001, Vol44, 60) 43: 246-255.
37. Chou KC (2005) Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21: 10-19.
38. Xiao X, Chou KC (2007) Digital coding of amino acids based on hydrophobic index. Protein & Peptide Letters 14: 871-875.
39. Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27: 1226-1238.
40. Chou KC (2004) Insights from modelling three-dimensional structures of the human potassium and sodium channels. Journal of Proteome Research 3: 856-861.
41. Oxenoid K, Chou JJ (2005) The structure of phospholamban pentamer reveals a channel-like architecture in membranes. Proc Natl Acad Sci U S A 102: 10870-10875.
42. Pielak RM, Jason R, Schnell JR, Chou JJ (2009) Mechanism of drug inhibition and drug resistance of influenza A M2 channel. Proceedings of National Academy of Science, USA 106: 7379-7384.
43. Schnell JR, Chou JJ (2008) Structure and mechanism of the M2 proton channel of influenza A virus. Nature 451: 591-595.
44. Chou KC (2005) Prediction of G-protein-coupled receptor classes. Journal of Proteome Research 4: 1413-1418.
45. Chou KC (2005) Coupling interaction between thromboxane A2 receptor and alpha-13 subunit of guanine nucleotide-binding protein. Journal of Proteome Research 4: 1681-1686.
46. Goto S, Nishioka T, Kanehisa M (1998) LIGAND: chemical database for enzyme reactions. Bioinformatics 14: 591-599.
47. Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, et al. (2006) From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res 34: D354-357.
48. Chou KC, Cai YD, Zhong WZ (2006) Predicting networking couples for metabolic pathways of Arabidopsis. EXCLI Journal (Experimental and Clinical Sciences International Online Journal for Advances in Science) 5: 55-65.
49. Altschul SF (1997) Evaluating the statistical significance of multiple distinct local alignments. In: Suhai S, ed. Theoretical and Computational Methods in Genome Research. New York: Plenum. pp 1-14.
50. Chou KC (1995) A novel approach to predicting protein structural classes in a (20-1)-D amino acid composition space. Proteins: Structure, Function & Genetics 21: 319-344.
51. Chen C, Chen L, Zou X, Cai P (2009) Prediction of protein secondary structure content by using the concept of Chou's pseudo amino acid composition and support vector machine. Protein & Peptide Letters 16: 27-31.
52. Georgiou DN, Karakasidis TE, Nieto JJ, Torres A (2009) Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition. Journal of Theoretical Biology 257: 17-26.
53. Jiang X, Wei R, Zhang TL, Gu Q (2008) Using the concept of Chou's pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy. Protein & Peptide Letters 15: 392-396.
54. Li FM, Li QZ (2008) Predicting protein subcellular location using Chou's pseudo amino acid composition and improved hybrid approach. Protein & Peptide Letters 15: 612-616.
55. Lin H (2008) The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition. Journal of Theoretical Biology 252: 350-356.
56. Lin H, Ding H, Feng-Biao Guo FB, Zhang AY, Huang J (2008) Predicting subcellular localization of mycobacterial proteins by using Chou's pseudo amino acid composition. Protein & Peptide Letters 15: 739-744.
57. Lin H, Wang H, Ding H, Chen YL, Li QZ (2009) Prediction of Subcellular Localization of Apoptosis Protein Using Chou's Pseudo Amino Acid Composition. Acta Biotheor 57: 321-330.
58. Qiu JD, Huang JH, Liang RP, Lu XQ (2009) Prediction of G-protein-coupled receptor classes based on the concept of Chou's pseudo amino acid composition: an approach from discrete wavelet transform. Analytical Biochemistry 390: 68-73.
59. Zeng YH, Guo YZ, Xiao RQ, Yang L, Yu LZ, et al. (2009) Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. Journal of Theoretical Biology 259: 366-372.
60. Zhang GY, Fang BS (2008) Predicting the cofactors of oxidoreductases based on amino acid composition distribution and Chou's amphiphilic pseudo amino acid composition. Journal of Theoretical Biology 253: 310-315.
61. Zhang GY, Li HC, Fang BS (2008) Predicting lipase types by improved Chou's pseudo-amino acid composition. Protein & Peptide Letters 15: 1132-1137.
62. Zhou XB, Chen C, Li ZC, Zou XY (2007) Using Chou's amphiphilic pseudoamino acid composition and support vector machine for prediction of enzyme subfamily classes. Journal of Theoretical Biology 248: 546-551.
63. Ding YS, Zhang TL (2008) Using Chou's pseudo amino acid composition to predict subcellular localization of apoptosis proteins: an approach with immune genetic algorithm-based ensemble classifier. Pattern Recognition Letters 29: 1887-1892.
64. Ding H, Luo L, Lin H (2009) Prediction of cell wall lytic enzymes using Chou's amphiphilic pseudo amino acid composition. Protein & Peptide Letters 16: 351-355.
65. Gonzalez-Diaz H, Vilar S, Santana L, Uriarte E (2007) Medicinal chemistry and bioinformatics - current trends in drugs discovery with networks topological indices. Curr Top Med Chem 10: 1015-1029.
66. Gonzalez-Diaz H, Gonzalez-Diaz Y, Santana L, Ubeira FM, Uriarte E (2008) Proteomics, networks, and connectivity indices. Proteomics 8: 750-778.
67. Pan YX, Zhang ZZ, Guo ZM, Feng GY, Huang ZD, et al. (2003) Application of pseudo amino acid composition for predicting protein subcellular location: stochastic signal processing approach. Journal of Protein Chemistry 22: 395-402.
68. Wang M, Yang J, Liu GP, Xu ZJ, Chou KC (2004) Weighted-support vector machines for predicting membrane protein types based on pseudo amino acid composition. Protein Engineering, Design, and Selection 17: 509-516.
69. Wang M, Yang J, Xu ZJ, Chou KC (2005) SLLE for predicting membrane protein types. Journal of Theoretical Biology 232: 7-15.
70. Xiao X, Shao SH, Huang ZD, Chou KC (2006) Using pseudo amino acid composition to predict protein structural classes: approached with complexity measure factor. Journal of Computational Chemistry 27: 478-482.
71. Gao Y, Shao SH, Xiao X, Ding YS, Huang YS, et al. (2005) Using pseudo amino acid composition to predict protein subcellular location: approached with Lyapunov index, Bessel function, and Chebyshev filter. Amino Acids 28: 373-376.
72. Xiao X, Shao S, Ding Y, Huang Z, Chen X, et al. (2005) Using cellular automata to generate Image representation for biological sequences. Amino Acids 28: 29-35.
73. Xiao X, Shao SH, Ding YS, Huang ZD, Chou KC (2006) Using cellular automata images and pseudo amino acid composition to predict protein subcellular location. Amino Acids 30: 49-54.
74. Diao Y, Ma D, Wen Z, Yin J, Xiang J, et al. (2008) Using pseudo amino acid composition to predict transmembrane regions in protein: cellular automata and Lempel-Ziv complexity. Amino Acids 34: 111-117.
75. Lin H, Li QZ (2007) Using Pseudo Amino Acid Composition to Predict Protein Structural Class: Approached by Incorporating 400 Dipeptide Components. Journal of Computational Chemistry 28: 1463-1466.
76. Xiao X, Wang P, Chou KC (2008) Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image. Journal of Theoretical Biology 254: 691-696.
77. Xiao X, Lin WZ, Chou KC (2008) Using grey dynamic modeling and pseudo amino acid composition to predict protein structural classes. Journal of Computational Chemistry 29: 2018-2024.
78. Cai YD, Chou KC (2006) Predicting membrane protein type by functional domain composition and pseudo amino acid composition. Journal of Theoretical Biology 238: 395-400.
79. Chou KC, Shen HB (2006) Hum-PLoc: A novel ensemble classifier for predicting human protein subcellular localization. Biochem Biophys Res Commun 347: 150-157.
80. Chou KC, Shen HB (2007) Large-scale plant protein subcellular location prediction. Journal of Cellular Biochemistry 100: 665-678.
81. Wang T, Yang J, Shen HB, Chou KC (2008) Predicting membrane protein types by the LLDA algorithm. Protein & Peptide Letters 15: 915-921.
82. Chou KC, Shen HB (2006) Predicting eukaryotic protein subcellular location by fusing optimized evidence-theoretic K-nearest neighbor classifiers. Journal of Proteome Research 5: 1888-1897.
83. Chou KC (2009) Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Current Proteomics 6: 262-274.
84. Dubchak I, Muchnik I, Mayor C, Dralyuk I, Kim SH (1999) Recognition of a protein fold in the context of the Structural Classification of Proteins (SCOP) classification. PROTEINS: Structure, Function, and Genetics 35: 401-407.
85. Chothia C, Finkelstein AV (1990) The classification and origins of protein folding patterns. Annu Rev Biochem 59: 1007-1039.
86. Frishman D, Argos P (1997) Seventy-five percent accuracy in protein secondary structure prediction. Proteins 27: 329-335.
87. Chou KC, Zhang CT (1994) Predicting protein folding types by distance functions that make allowances for amino acid interactions. Journal of Biological Chemistry 269: 22014-22020.
88. Keller JM, Gray MR, Givens JA (1985) A fuzzy k-nearest neighbours algorithm. IEEE Trans Syst Man Cybern 15: 580-585.
89. Mardia KV, Kent JT, Bibby JM (1979) Multivariate Analysis: Chapter 11 Discriminant Analysis; Chapter 12 Multivariate analysis of variance; Chapter 13 cluster analysis (pp. 322-381). London: Academic Press. pp 322-381.
90. Mahalanobis PC (1936) On the generalized distance in statistics. Proc Natl Inst Sci India 2: 49-55.
91. Pillai KCS (1985) Mahalanobis D2. In: Kotz S, Johnson NL, eds. Encyclopedia of Statistical Sciences. New York: John Wiley & Sons, This reference also presents a brief biography of Mahalanobis who was a man of great originality and who made considerable contributions to statistics. pp 176-181.
92. Chou KC, Zhang CT (1995) Review: Prediction of protein structural classes. Critical Reviews in Biochemistry and Molecular Biology 30: 275-349.
93. Chou KC, Shen HB (2007) Review: Recent progresses in protein subcellular location prediction. Analytical Biochemistry 370: 1-16.
94. Zhou GP (1998) An intriguing controversy over protein structural class prediction. Journal of Protein Chemistry 17: 729-738.
95. Zhou GP, Assa-Munt N (2001) Some insights into protein structural class prediction. PROTEINS: Structure, Function, and Genetics 44: 57-59.
96. Zhou GP, Doctor K (2003) Subcellular location prediction of apoptosis proteins. PROTEINS: Structure, Function, and Genetics 50: 44-48.
97. Lin JH (2006) CYP induction-mediated drug interactions: in vitro assessment and clinical implications. Pharm Res 23: 1089-1116.
98. Beresford AP (1993) CYP1A1: friend or foe? Drug Metab Rev 25: 503-517.
99. Pelkonen O, Turpeinen M, Hakkola J, Honkakoski P, Hukkanen J, et al. (2008) Inhibition and induction of human cytochrome P450 enzymes: current status. Arch Toxicol 82: 667-715.
100. Baudin B (2000) Angiotensin I-converting enzyme gene polymorphism and drug response. Clin Chem Lab Med 38: 853-856.
101. Faulon JL, Misra M, Martin S, Sale K, Sapra R (2008) Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor. Bioinformatics 24: 225-233.
102. Cai CZ, Han LY, Ji ZL, Chen X, Chen YZ (2003) SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res 31: 3692-3697.
103. Bockaert J, Pin JP (1999) Molecular tinkering of G protein-coupled receptors: an evolutionary success. Embo J 18: 1723-1729.
104. Avlani VA, Gregory KJ, Morton CJ, Parker MW, Sexton PM, et al. (2007) Critical role for the second extracellular loop in the binding of both orthosteric and allosteric G protein-coupled receptor ligands. J Biol Chem 282: 25677-25686.
105. Huber T, Menon S, Sakmar TP (2008) Structural basis for ligand binding and specificity in adrenergic receptors: implications for GPCR-targeted drug discovery. Biochemistry 47: 11013-11023.
106. Okuno Y, Tamon A, Yabuuchi H, Niijima S, Minowa Y, et al. (2008) GLIDA: GPCR - ligand database for chemical genomics drug discovery - database and tools update. Nucleic Acids Res 36: D907-912.
107. Wei H, Wang CH, Du QS, Meng J, Chou KC (2009) Investigation into adamantane-based M2 inhibitors with FB-QSAR. Medicinal Chemistry 5: 305-317.
108. Huang RB, Du QS, Wang CH, Chou KC (2008) An in-depth analysis of the biological functional studies based on the NMR M2 channel structure of influenza A virus. Biochem Biophys Res Comm 377: 1243-1247.
109. Camerino DC, Tricarico D, Desaphy JF (2007) Ion channel pharmacology. Neurotherapeutics 4: 184-198.
110. Moore JT, Collins JL, Pearce KH (2006) The nuclear receptor superfamily and drug discovery. ChemMedChem 1: 504-523.