Predict drug-protein interaction in cellular networking

Current Topics in Medicinal Chemistry

Volumn 13, Issue 14, 2013, Pages 1707-1712

  Xiao, Xuan ^a,b,d   Min, Jian Liang ^a   Wang, Pu ^a   Chou, Kuo Chen ^c,d  

^a JINGDEZHEN CERAMIC INSTITUTE   (China)

^b Zhejiang Textile and Fashion College   (China)

^c KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

^d GORDON LIFE SCIENCE INSTITUTE   (United States)

Author keywords

G protein couple receptors (GPCRs); ICDI PseFpt; IGPCR Drug; Molecular fingerprints; Protein channels; Pseudo amino acid composition

Indexed keywords

G PROTEIN COUPLED RECEPTOR; ION CHANNEL;

AMINO ACID COMPOSITION; CELLULAR NETWORKING; CELLULAR PARAMETERS; DRUG PROTEIN BINDING; HUMAN; ICDI PSEFPT DATABASE; IGPCR DRUG INTERACTION DATABASE; MOLECULAR FINGERPRINT; MOLECULAR MODEL; PROTEIN CHANNEL; PROTEIN STRUCTURE; REFERENCE DATABASE; REVIEW; STRUCTURE ANALYSIS;

CELL COMMUNICATION; COMPUTATIONAL BIOLOGY; DRUG DESIGN; HUMANS; MODELS, MOLECULAR; PHARMACEUTICAL PREPARATIONS; RECEPTORS, G-PROTEIN-COUPLED;

EID: 84887868490     PISSN: 15680266     EISSN: 18734294     Source Type: Journal    
DOI: 10.2174/15680266113139990121     Document Type: Review

References (64)

1. Knowles, J.; Gromo, G. A guide to drug discovery: Target selection in drug discovery. Nat Rev Drug Discov, 2003, 2, 63-69.
2. He, Z.; Zhang, J.; Shi, X. H.; Hu, L. L.; Kong, X.; Cai, Y. D.; Chou, K. C. Predicting drug-target interaction networks based on functional groups and biological features. PLoS ONE, 2010, 5, e9603.
3. Johnson, D. E.; Wolfgang, G. H. Predicting human safety: screening and computational approaches. Drug Discov Today, 2000, 5, 445-454.
4. Sirois, S.; Hatzakis, G. E.; Wei, D. Q.; Du, Q. S.; Chou, K. C. Assessment of chemical libraries for their druggability. Computational Biology & Chemistry, 2005, 29, 55-67.
5. Chou, K. C.; Wei, D. Q.; Du, Q. S.; Sirois, S.; Zhong, W. Z. Review: Progress in computational approach to drug development against SARS. Current Medicinal Chemistry, 2006, 13, 3263-3270.
6. Wang, J. F.; Wei, D. Q.; Li, L.; Zheng, S. Y.; Li, Y. X.; Chou, K. C. 3D structure modeling of cytochrome P450 2C19 and its implication for personalized drug design. Biochem Biophys Res Commun (Corrigendum: ibid, 2007, Vol. 357, 330), 2007, 355, 513-519.
7. Wang, J. F.; Wei, D. Q.; Chen, C.; Li, Y.; Chou, K. C. Molecular modeling of two CYP2C19 SNPs and its implications for personalized drug design. Protein & Peptide Letters, 2008, 15, 27-32.
8. Wang, J. F.; Wei, D. Q.; Li, L.; Chou, K. C. Review: Pharmacogenomics and personalized use of drugs. Current Topics of Medicinal Chemistry, 2008, 8, 1573-1579.
9. Cheng, A. C.; Coleman, R. G.; Smyth, K. T.; Cao, Q.; Soulard, P.; Caffrey, D. R.; Salzberg, A. C.; Huang, E. S. Structure-based maximal affinity model predicts small-molecule druggability. Nat Biotechnol, 2007, 25, 71-75.
10. Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol, 1996, 261, 470-489.
11. Chou, K. C. Review: Structural bioinformatics and its impact to biomedical science. Current Medicinal Chemistry, 2004, 11, 2105-2134.
12. Chou, K. C.; Wei, D. Q.; Zhong, W. Z. 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, 2003, 308, 148-151.
13. Zhu, S.; Okuno, Y.; Tsujimoto, G.; Mamitsuka, H. A probabilistic model for mining implicit 'chemical compound-gene' relations from literature. Bioinformatics, 2005, 21 Suppl 2, ii245-251.
14. Yamanishi, Y.; Araki, M.; Gutteridge, A.; Honda, W.; Kanehisa, M. Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics, 2008, 24, i232-240.
15. Gan, Y. R.; Huang, H.; Huang, Y. D.; Rao, C. M.; Zhao, Y.; Liu, J. S.; Wu, L.; Wei, D. Q. Synthesis and activity of an octapeptide inhibitor designed for SARS coronavirus main proteinase. Peptides, 2006, 27, 622-625.
16. Chou, K. C. A vectorized sequence-coupling model for predicting HIV protease cleavage sites in proteins. J. Biol. Chem, 1993, 268, 16938-16948.
17. Du, Q. S.; Sun, H.; Chou, K. C. Inhibitor design for SARS coronavirus main protease based on "distorted key theory. " Medicinal Chemistry, 2007, 3, 1-6.
18. Chou, K. C. Review: Prediction of human immunodeficiency virus protease cleavage sites in proteins. Anal. Biochem, 1996, 233, 1-14.
19. Nagamine, N.; Sakakibara, Y. Statistical prediction of protein chemical interactions based on chemical structure and mass spectrometry data. Bioinformatics, 2007, 23, 2004-2012.
20. Nagamine, N.; Shirakawa, T.; Minato, Y.; Torii, K.; Kobayashi, H.; Imoto, M.; Sakakibara, Y. Integrating statistical predictions and experimental verifications for enhancing protein-chemical interaction predictions in virtual screening. PLoS Comput Biol, 2009, 5, e1000397.
21. Vina, D.; Uriarte, E.; Orallo, F.; Gonzalez-Diaz, H. Alignment-free prediction of a drug-target complex network based on parameters of drug connectivity and protein sequence of receptors. Mol Pharm, 2009, 6, 825-835.
22. Chou, K. C. Prediction of G-protein-coupled receptor classes. Journal of Proteome Research, 2005, 4, 1413-1418.
23. Chou, K. C. Coupling interaction between thromboxane A2 receptor and alpha-13 subunit of guanine nucleotide-binding protein. Journal of Proteome Research, 2005, 4, 1681-1686.
24. Heuss, C.; Gerber, U. G-protein-independent signaling by Gprotein-coupled receptors. Trends Neurosci, 2000, 23, 469-475.
25. Milligan, G.; White, J. H. Protein-protein interactions at G-proteincoupled receptors. Trends Pharmacol. Sci, 2001, 22, 513-518.
26. Hall, R. A.; Lefkowitz, R. J. Regulation of G protein-coupled receptor signaling by scaffold proteins. Circ. Res, 2002, 91, 672-680.
27. Lundstrom, K. Latest development in drug discovery on G proteincoupled receptors. Curr Protein Pept Sci, 2006, 7, 465-470.
28. Overington, J. P.; Al-Lazikani, B.; Hopkins, A. L. How many drug targets are there? Nat Rev Drug Discov, 2006, 5, 993-996.
29. Chou, K. C. Insights from modelling three-dimensional structures of the human potassium and sodium channels. Journal of Proteome Research, 2004, 3, 856-861.
30. Schnell, J. R.; Chou, J. J. Structure and mechanism of the M2 proton channel of influenza A virus. Nature, 2008, 451, 591-595.
31. OuYang, B.; Xie, S.; Berardi, M. J.; Zhao, X. M.; Dev, J.; Yu, W.; Sun, B.; Chou, J. J. Unusual architecture of the p7 channel fromhepatitis C virus Nature 2013, 498, 521-525.
32. 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. Comm, 2008, 377, 1243-1247.
33. 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.
34. Oxenoid, K.; Chou, J. J. The structure of phospholamban pentamer reveals a channel-like architecture in membranes. Proc. Natl. Acad. Sci. U. S. A, 2005, 102, 10870-10875.
35. Gabashvili, I. S.; Sokolowski, B. H.; Morton, C. C.; Giersch, A. B. Ion channel gene expression in the inner ear. J Assoc Res Otolaryngol, 2007, 8, 305-328.
36. Chou, K. C. Molecular therapeutic target for type-2 diabetes. Journal of Proteome Research, 2004, 3, 1284-1288.
37. Kaczorowski, G. J.; McManus, O. B.; Priest, B. T.; Garcia, M. L. Ion channels as drug targets: the next GPCRs. J Gen Physiol, 2008, 131, 399-405.
38. Xiao, X.; Min, J. L.; Wang, P.; Chou, K. C. iGPCR-Drug: A web server for predicting interaction between GPCRs and drugs in cellular networking. PLoS ONE, 2013, in press.
39. Xiao, X.; Min, J. L.; Wang, P.; Chou, K. C. iICD-PseFpt: Identify the ion channel-drug interaction in cellular networking with PseAAC and molecular fingerprints. Journal of Thoretical Biology, 2013, in press.
40. Chou, K. C. Some remarks on protein attribute prediction and pseudo amino acid composition (50th Anniversary Year Review). J. Theor. Biol, 2011, 273, 236-247.
41. Chou, K. C. Prediction of protein cellular attributes using pseudo amino acid composition. PROTEINS: Structure, Function, and Genetics (Erratum: ibid., 2001, Vol. 44, 60), 2001, 43, 246-255.
42. Chou, K. C. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics, 2005, 21, 10-19.
43. Deng, J. L. Introduction to Grey System Theory. The Journal of Grey System, 1989, 1-24.
44. Chou, K. C.; Shen, H. B. Review: recent advances in developing web-servers for predicting protein attributes (doi: 10. 4236/ns. 2009. 12011). Natural Science, 2009, 2, 63-92 (openly accessible at http://www. scirp. org/journal/NS/).
45. Lin, S. X.; Lapointe, J. Theoretical and experimental biology in one. J. Biomedical Science and Engineering (JBiSE), 2013, 6, 435-442 (open accessible at http://www. scirp. org/journal/jbise/, doi: 410. 4236/jbise. 2013. 64054).
46. Chou, K. C. Some Remarks on Predicting Multi-Label Attributes in Molecular Biosystems. Molecular Biosystems, 2013, 9, 1092-1100.
47. Gonzalez-Diaz, H.; Prado-Prado, F.; Garcia-Mera, X.; Alonso, N.; Abeijon, P.; Caamano, O.; Yanez, M.; Munteanu, C. R.; Pazos, A.; Auxiliadora Dea-Ayuela, M.; Teresa Gomez-Munoz, M.; Magdalena Garijo, M.; Sansano, J.; Ubeira, F. M., MIND-BEST: Web Server for Drugs and Target Discovery; Design, Synthesis, and Assay of MAO-B Inhibitors and Theoretical-Experimental Study of G3PDH Protein from Trichomonas gallinae. Journal of Proteome Research, 2011, 10, (4), 1698-1718.
48. Gonzalez-Diaz, H.; Prado-Prado, F.; Sobarzo-Sanchez, E.; Haddad, M.; Chevalley, S. M.; Valentin, A.; Quetin-Leclercq, J.; Dea-Ayuela, M. A.; Teresa Gomez-Munos, M.; Munteanu, C. R.; Jose Torres-Labandeira, J.; Garcia-Mera, X.; Tapia, R. A.; Ubeira, F. M., NL MIND-BEST: A web server for ligands and proteins discovery-Theoretic-experimental study of proteins of Giardia lamblia and new compounds active against Plasmodium falciparum. Journal of Theoretical Biology, 2011, 276, (1), 229-249.
49. Gonzalez-Diaz, H., Editorial: QSAR/QSPR Models as Enabling Technologies for Drug & Targets Discovery in: Medicinal Chemistry, Microbiology-Parasitology, Neurosciences, Bioinformatics, Proteomics and Other Biomedical Sciences. Curr Top Med Chem, 2012, 12, (8), 799-801.
50. Prado-Prado, F.; Garcia-Mera, X.; Escobar, M.; Alonso, N.; Caamano, O.; Yanez, M.; Gonzalez-Diaz, H., 3D MI-DRAGON: New Model for the Reconstruction of US FDA Drug-Target Network and Theoretical-Experimental Studies of Inhibitors of Rasagiline Derivatives for AChE. Current Topics in Medicinal Chemistry, 2012, 12, (16), 1843-1865.
51. Riera-Fernandez, I.; Martin-Romalde, R.; Prado-Prado, F. J.; Escobar, M.; Munteanu, C. R.; Concu, R.; Duardo-Sanchez, A.; Gonzalez-Diaz, H., From QSAR models of Drugs to Complex Networks: State-of-Art Review and Introduction of New Markov-Spectral Moments Indices. Current Topics in Medicinal Chemistry, 2012.
52. Tenorio-Borroto, E.; Penuelas Rivas, C. G.; Vasquez Chagoyan, J. C.; Castanedo, N.; Prado-Prado, F. J.; Garcia-Mera, X.; Gonzalez-Diaz, H., ANN multiplexing model of drugs effect on macrophages; theoretical and flow cytometry study on the cytotoxicity of the anti-microbial drug G1 in spleen. Bioorganic & Medicinal Chemistry, 2012, 20, (20), 6181-6194.
53. Tenorio-Borroto, E.; Penuelas-Rivas, C. G.; Vasquez-Chagoyan, J. C.; Prado-Pradoa, F. J.; Garcia-Mera, X.; Gonzalez-Diaz, H., Immunotoxicity, Flow Cytometry, and Chemoinformatics: Review, Bibliometric Analysis, and New QSAR Model of Drug Effects Over Macrophages. Current Topics in Medicinal Chemistry, 2012, 12, (16), 1815-1833.
54. Garcia, I.; Fall, Y.; Gomez, G.; Gonzalez-Diaz, H., First computational chemistry multi-target model for anti-Alzheimer, antiparasitic, anti-fungi, and anti-bacterial activity of GSK-3 inhibitors in vitro, in vivo, and in different cellular lines. Molecular Diversity, 2011, 15, (2), 561-567.
55. Marzaro, G.; Chilin, A.; Guiotto, A.; Uriarte, E.; Brun, P.; Castagliuolo, I.; Tonus, F.; Gonzalez-Diaz, H., Using the TOPS-MODE approach to fit multi-target QSAR models for tyrosine kinases inhibitors. European Journal of Medicinal Chemistry, 2011, 46, (6), 2185-2192.
56. Prado-Prado, F.; Garcia-Mera, X.; Escobar, M.; Sobarzo-Sanchez, E.; Yanez, M.; Riera-Fernandez, P.; Gonzalez-Diaz, H., 2D MIDRAGON: a new predictor for protein-ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins. European Journal of Medicinal Chemistry, 2011, 46, (12), 5838-5851.
57. Prado-Prado, F. J.; Garcia, I.; Garcia-Mera, X.; Gonzalez-Diaz, H., Entropy multi-target QSAR model for prediction of antiviral drug complex networks. Chemometrics and Intelligent Laboratory Systems, 2011, 107, (2), 227-233.
58. Prado-Prado, F.; Garcia-Mera, X.; Abeijon, P.; Alonso, N.; Caamano, O.; Yanez, M.; Garate, T.; Mezo, M.; Gonzalez-Warleta, M.; Muino, L.; Ubeira, F. M.; Gonzalez-Diaz, H., Using entropy of drug and protein graphs to predict FDA drug-target network: Theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica. European Journal of Medicinal Chemistry, 2011, 46, (4), 1074-1094.
59. Gonzalez-Diaz, H.; Romaris, F.; Duardo-Sanchez, A.; Perez-Montoto, L. G.; Prado-Prado, F.; Patlewicz, G.; Ubeira, F. M., Predicting drugs and proteins in parasite infections with topological indices of complex networks: theoretical backgrounds, applications, and legal issues. Curr Pharm Des, 2010, 16, (24), 2737-2764.
60. Gonzalez-Diaz, H., QSAR and Complex Networks in Pharmaceutical Design, Microbiology, Parasitology, Toxicology, Cancer and Neurosciences. Current Pharmaceutical Design, 2010, 16, (24), 2598-U2524.
61. Prado-Prado, F. J.; Garcia-Mera, X.; Gonzalez-Diaz, H., Multitarget spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. Bioorganic & Medicinal Chemistry, 2010, 18, (6), 2225-2231.
62. Prado-Prado, F. J.; Ubeira, F. M.; Borges, F.; Gonzalez-Diaz, H., Unified QSAR & Network-Based Computational Chemistry Approach to Antimicrobials. II. Multiple Distance and Triadic Census Analysis of Antiparasitic Drugs Complex Networks. Journal of Computational Chemistry, 2010, 31, (1), 164-173.
63. Gonzalez-Diaz, H.; Arrasate, S.; Sotomayor, N.; Lete, E.; Munteanu, C. R.; Pazos, A.; Besada-Porto, L.; Ruso, J. M., MIANN Models in Medicinal, Physical and Organic Chemistry. Curr Top Med Chem, 2013, 13, (5), 619-641.
64. Luan, F.; Cordeiro, M. N.; Alonso, N.; Garcia-Mera, X.; Caamano, O.; Romero-Duran, F. J.; Yanez, M.; Gonzalez-Diaz, H., TOPSMODE model of multiplexing neuroprotective effects of drugs and experimental-theoretic study of new 1, 3-rasagiline derivatives potentially useful in neurodegenerative diseases. Bioorg Med Chem, 2013, 21, (7), 1870-1879.