Large-scale identification of potential drug targets based on the topological features of human protein-protein interaction network

Analytica Chimica Acta

Volumn 871, Issue , 2015, Pages 18-27

  Li, Zhan Chao ^a   Zhong, Wen Qian ^a   Liu, Zhi Qing ^a   Huang, Meng Hua ^a   Xie, Yun ^a   Dai, Zong ^b   Zou, Xiao Yong ^b  

^a GUANGDONG PHARMACEUTICAL UNIVERSITY   (China)

^b SUN YAT SEN UNIVERSITY   (China)

Author keywords

Drug targets; Graph theory; Protein protein interaction network; Random forests; Topology

Indexed keywords

DECISION TREES; DIAGNOSIS; DRUG INTERACTIONS; GRAPH THEORY; RANDOM FORESTS; TOPOLOGY;

DRUG TARGETS; MINIMUM REDUNDANCY-MAXIMUM RELEVANCES; MOLECULAR MECHANISM; OPTIC NEUROPATHIES; POTENTIAL DRUG TARGETS; PRIMARY STRUCTURES; PROTEIN-PROTEIN INTERACTION NETWORKS; TOPOLOGICAL FEATURES;

PROTEINS;

CELL NUCLEUS RECEPTOR; ENZYME; G PROTEIN COUPLED RECEPTOR; GLYCOSIDASE; ION CHANNEL; DRUG; PROTEIN;

ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; CONTROLLED STUDY; DRUG TARGETING; GRAPH THEORY; HUMAN; INTERMETHOD COMPARISON; MINIMUM REDUNDANCY MAXIMUM RELEVANCE; NEEDLEMAN WUNSCH ALGORITHM; PRIORITY JOURNAL; PROTEIN PROTEIN INTERACTION; PROTEIN TARGETING; RANDOM FOREST; RANDOM SAMPLE; RANK SUM TEST; SCORING MATRIX; THEORY; CHEMICAL DATABASE; CHEMISTRY; DRUG DEVELOPMENT; PROCEDURES; PROTEIN ANALYSIS; PROTEIN CONFORMATION; PROTEIN DATABASE; THEORETICAL MODEL;

ALGORITHMS; DATABASES, CHEMICAL; DATABASES, PROTEIN; DRUG DISCOVERY; HUMANS; MODELS, THEORETICAL; PHARMACEUTICAL PREPARATIONS; PROTEIN CONFORMATION; PROTEIN INTERACTION MAPPING; PROTEIN INTERACTION MAPS; PROTEINS;

EID: 84926420881     PISSN: 00032670     EISSN: 18734324     Source Type: Journal    
DOI: 10.1016/j.aca.2015.02.032     Document Type: Article

References (44)

1. Brown D., Superti-Furga G. Rediscovering the sweet spot in drug discovery. Drug Discov. Today 2003, 8:1067-1077.
2. Hopkins A.L., Groom C.R. The druggable genome. Nat. Rev. Drug Discov. 2002, 1:727-730.
3. Zhu S., Okuno Y., Tsujimoto G., Mamitsuka H. A probabilistic model for mining implicit chemical compound-gene relations from literature. Bioinformatics 2005, 21:ii245-ii251.
4. Altman R.B., Bergman C.M., Blake J., Blaschke C., Cohen A., Gannon F., Grivell L., Hahn U., Hersh W., Hirschman L., Jensen L.J., Krallinger M., Mons B., O'Donoghue S.I., Peitsch M.C., Rebholz-Schuhmann D., Shatkay H., Valencia A. Text mining for biology-the way forward: opinions from leading scientists. Genome Biol. 2008, 9:S7.
5. Li Q., Lai L. Prediction of potential drug targets based on simple sequence properties. BMC Bioinf. 2007, 8:353.
6. Bakheet T.M., Doig A.J. Properties and identification of human protein drug targets. Bioinformatics 2009, 25:451-457.
7. Bakheet T.M., Doig A.J. Properties and identification of antibiotic drug targets. BMC Bioinf. 2010, 11:195.
8. Huang C., Zhang R., Chen Z., Jiang Y., Shang Z., Sun P., Zhang X., Li X. Predict potential drug targets from the ion channel proteins based on SVM. J. Theor. Biol. 2010, 262:750-760.
9. Volkamer A., Kuhn D., Grombacher T., Rippmann F., Rarey M. Combining global and local measure for structure-based druggability predictions. J. Chem. Inf. Model. 2012, 52:360-372.
10. Perola E., Herman L., Weiss J. Development of a rule-based method for the assessment of protein druggability. J. Chem. Inf. Model. 2012, 52:1027-1038.
11. Schmidtke P., Barril X. Understanding and predicting durggability. A high-throughput method for detection of drug binding sites. J. Med. Chem. 2010, 53:5858-5867.
12. 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.
13. Krasowski A., Muthas D., Sarkar A., Schmitt S., Brenk R. DrugPred: a structure-based approach to predict protein druggability developed using an extensive nonredundant data set. J. Chem. Inf. Model. 2011, 51:2829-2842.
14. Zhao S., Li S. Network-based relating pharmacological and genomic spaces for drug target identification. PLoS One 2010, 5:e11764.
15. Keiser M.J., Roth B.L., Armbruster B.N., Ernsberger P., Irwin J.J., Shoichet B.K. Relating protein pharmacology by ligand chemistry. Nat. Biotechnol. 2007, 25:197-206.
16. Keiser M.J., Setola V., Irwin J.J., Laggner C., Abbas A.I., Hufeisen S.J., Jensen N.H., Kuijer M.B., Matos R.C., Tran T.B., Whaley R., Glennon R.A., Hert J., Thomas K.L., Edwards D.D., Shoichet B.K., Roth B.L. Predicting new molecular targets for known drugs. Nature 2009, 462:175-181.
17. Takarabe M., Kotera M., Nishimura Y., Goto G., Yamanishi Y. Drug target prediction using adverse event report systems: a pharmacogenomics approach. Bioinformatics 2012, 28:i611-i618.
18. Zhu M., Gao L., Liu X., Liu Z., Xu C., Yan Y., Walker E., Jiang W., Su B., Chen X., Lin H. The analysis of the drug-targets based on the topological properties in the human protein-protein interaction network. J. Drug Target. 2009, 17:524-532.
19. Wang C., Jiang W., Li W., Lian B., Chen X., Hua L., Lin H., Li D., Li X., Liu Z. Topological properties of the drug targets regulated by microRNA in human protein-protein interaction network. J. Drug Target. 2011, 19:354-364.
20. Kotlyar M., Fortney K., Jurisica I. Network-based characterization of drug-regulated genes, drug targets and toxicity. Methods 2012, 57:499-507.
21. Yao L., Rzhetsky A. Quantitative systems-level determinants of human genes targets by successful drugs. Genome Res. 2008, 18:206-213.
22. Hwang W.C., Zhang A., Ramanathan M. Identification of information flow-modulating drug targets: a novel bridging paradigm for drug discovery. Clin. Pharmacol. Ther. 2008, 84:563-572.
23. Hase T., Tanaka H., Suzuki Y., Nakagawa S., Kitano H. Structure of protein interaction networks and their implications on drug design. PLoS Comput. Biol. 2009, 5:e1000550.
24. Schaefer M.H., Fontaine J.F., Vinayagam A., Porras P., Wanker E.E., Andrade-Navarro M.A. HIPPIE: integrating protein interaction networks with experiment based quality scores. PLoS One 2012, 7:e31826.
25. Law V., Knox C., Djoumbou Y., Jewison T., Guo A.C., Liu Y., Maciejewski A., Arndt D., Wilson M., Neveu V., Tang A., Gabriel G., Ly C., Adamjee S., Dame Z.T., Han B., Zhou Y., Wishart D.S. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 2014, 42:D1091-D1097.
26. 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. U. S. A. 1995, 92:8700-8704.
27. Cao D.S., Zhou G.H., Liu S., Zhang L.X., Xu Q.S., He M., Liang Y.Z. Large-scale prediction of human kinase-inhibitor interactions using protein sequences and molecular topological structures. Anal. Chim. Acta 2013, 792:10-18.
28. Guo J., Rao N., Liu G., Yang Y., Wang G. Predicting protein folding rates using the concept of Chou's pseudo amino acid composition. J. Comput. Chem. 2011, 32:1612-1617.
29. Cao D.S., Liu S., Xu Q.S., Lu H.M., Huang J.H., Hu Q.N., Liang Y.Z. Large-scale prediction of drug-target interactions using protein sequence and drug topological structures. Anal. Chim. Acta 2012, 752:1-10.
30. Khan A., Majid A., Hayat M. CE-PLoc: an ensemble classifier for predicting protein subcellular locations by fusing different modes of pseudo amino acid composition. Comput. Biol. Chem. 2011, 35:218-229.
31. Lin Z., Vicente C.M., Dai L., Lu H.M., Huang J.H., Ji H., Wang D.S., Yi L.Z., Liang Y.Z. Exploring metabolic syndrome serum profiling based on gas chromatography mass spectrometry and random forest models. Anal. Chim. Acta 2014, 827:22-27.
32. Hao M., Wang Y., Bryant S.H. An efficient algorithm coupled with synthetic minority over-sampling technique to classify imbalanced Pubchem BioAssay data. Anal. Chim. Acta 2014, 806:117-127.
33. Li Z.C., Lai Y.H., Chen L.L., Zhou X., Dai Z., Zou X.Y. Identification of human protein complexes from local sub-graphs of protein-protein interaction network based on random forest with topological structure features. Anal. Chim. Acta 2012, 718:32-41.
34. Scott I.M., Lin W., Liakata M., Wood J.E., Vermeer C.P., Allaway D., Ward J.L., Draper J., Beale M.H., Corol D.I., Baker J.M., King R.D. Merits of random forests emerge in evaluation of chemometric classifiers by external validation. Anal. Chim. Acta 2013, 801:22-33.
35. Leo B. Random forests. Mach. Learn. 2001, 45:5-32.
36. Peng H., Long F., Ding C. Feature selection based on mutual information: criteria of max-dependency max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27:1226-1238.
37. Fu L., Niu B., Zhu Z., Wu S., Li W. CD-HIT: accelerated fro clustering the next-generation sequencing data. Bioinformatics 2012, 28:3150-3152.
38. Gunther S., Kuhn M., Dunkel M., Campillos M., Senger C., Petsalaki E., Ahmed J., Gewiess E.G.A., Jensen L.J., Schneider R., Skoblo R., Russell R.B., Bourne P.E., Bork P., Preissner R. SuperTarget and Matador: resources for exploring drug-target relationships. Nucleic Acids Res. 2008, 36:D919-D922.
39. Pawson A.J., Sharman J.L., Benson H.E., Faccenda E., Alexander S.P., Buneman O.P., Davenport A.P., McGrath J.C., Peters J.A., Southan C., Spedding M., Yu W., Harmar A.J. NC-IUPHAR. The IUPHAR/BPS guide to PHARMACOLOGY: an expert-driven knowledgebase of drug targets and their ligands. Nucleic Acids Res. 2014, 42:D1098-D1106.
40. Schwartz I., Silva L.R., Leistner S., Todeschini L.A., Burin M.G., Pina-Neto J.M., Islam R.M., Shah G.N., Sly W.S., Giugliani R. Mucopolysaccharidosis VII: clinical, biochemical and molecular investigation of a Brazilian family. Clin. Genet. 2003, 64:172-175.
41. Storch S., Wittenstein B., Islam R., Ullrich K., Sly W.S., Braulke T. Mutational analysis in longest known survivor of mucopolysaccharidosis type VII. Hum. Genet. 2003, 112:190-194.
42. Salomon O., Rosenberg N., Steinberg D.M., Huna-Baron R., Moisseive J., Dardik R., Goldan O., Kurtz S., Ifrah A., Seligsohn U. Nonarteritic anterior ischemic optic neurophathy is associated with a specific platelet polymorphism located on the glycoprotein lbalpha gene. Ophthalmology 2004, 111:184-188.
43. Savoia A., Balduini C.L., Savino M., Noris P., Del Vecchio M., Perrotta S., Belletti S., Poggi V., Iolascon A. Autosomal dominant macrothrombocytopenia in Italy is most frequently a type of heterozygous Bernard-Soulier syndrome. Blood 2001, 97:1330-1335.
44. Matsubara Y., Murata M., Sugita K., Ikeda Y. Identification of a novel point mutation in platelet glycoprotein lbalpha Gly to Ser at residue 233, in a Japanses family with platelet-type von Willebrand disease. J. Thromb. Haemost. 2003, 1:2198-2205.