Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction

PLoS Computational Biology

Volumn 12, Issue 11, 2016, Pages

  Coelho, Edgar D ^a   Arrais, Joel P ^b   Oliveira, José Luís ^a  

^a UNIVERSITY OF AVEIRO   (Portugal)

^b UNIVERSITY OF COIMBRA   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); DECISION TREES; DRUG INTERACTIONS; FORECASTING; PIPELINES; TENSORS;

CLASSIFICATION MODELS; COMPUTATIONAL MODELLING; DRUG DISCOVERY; DRUG REPOSITIONING; DRUG TARGETS; DRUG-TARGET INTERACTIONS; INTERACTION PREDICTION; INTERACTOME; MOLECULAR FUNCTION; TIME-CONSUMING TASKS;

LIGANDS;

ARTICLE; CLASSIFICATION; COMPUTATIONAL FLUID DYNAMICS; CONTROLLED STUDY; DRUG REPOSITIONING; DRUG TARGETING; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MOLECULAR DOCKING; PREDICTION; PREDICTIVE VALUE; PROCESS MODEL; PROTEIN SYNTHESIS; CHEMICAL MODEL; CHEMISTRY; COMPUTER SIMULATION; DRUG DEVELOPMENT; PRECLINICAL STUDY; PROCEDURES; PROTEIN ANALYSIS;

ANTIINFECTIVE AGENT; BACTERIAL PROTEIN;

ANTI-BACTERIAL AGENTS; BACTERIAL PROTEINS; COMPUTER SIMULATION; DRUG DISCOVERY; DRUG EVALUATION, PRECLINICAL; DRUG REPOSITIONING; MODELS, CHEMICAL; PROTEIN INTERACTION MAPPING;

EID: 84999751697     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1005219     Document Type: Article

References (76)

1. ECDC (2015) Annual Epidemiological Report 2014—Antimicrobial Resistance and Healthcare-Associated Infections. Stockholm: European Centre for Disease Prevention and Control.
2. Roca I, Akova M, Baquero F, Carlet J, Cavaleri M, et al. (2015) The global threat of antimicrobial resistance: science for intervention. New Microbes and New Infections6: 22–29. 26029375
3. Kolář M, Urbánek K, Látal T, (2001) Antibiotic selective pressure and development of bacterial resistance. International Journal of Antimicrobial Agents17: 357–363. 11337221
4. Haag NL, Velk KK, Wu C, (2012) Potential Antibacterial Targets in Bacterial Central Metabolism. Int J Adv Life Sci4: 21–32. 24151543
5. Dudley JT, Deshpande T, Butte AJ, (2011) Exploiting drug-disease relationships for computational drug repositioning. Brief Bioinform12: 303–311. doi: 10.1093/bib/bbr01321690101
6. Ashburn TT, Thor KB, (2004) Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov3: 673–683. doi: 10.1038/nrd146815286734
7. Dudley JT, Sirota M, Shenoy M, Pai RK, Roedder S, et al. (2011) Computational Repositioning of the Anticonvulsant Topiramate for Inflammatory Bowel Disease. Science Translational Medicine3: 96ra76. doi: 10.1126/scitranslmed.300264821849664
8. Dudley JT, Deshpande T, Butte AJ, (2011) Exploiting drug–disease relationships for computational drug repositioning. Briefings in Bioinformatics.
9. Ekins S, Williams AJ, Krasowski MD, Freundlich JS, (2011) In silico repositioning of approved drugs for rare and neglected diseases. Drug Discovery Today16: 298–310. doi: 10.1016/j.drudis.2011.02.01621376136
10. Cheng F, Zhou Y, Li J, Li W, Liu G, et al. (2012) Prediction of chemical-protein interactions: multitarget-QSAR versus computational chemogenomic methods. Mol Biosyst8: 2373–2384. doi: 10.1039/c2mb25110h22751809
11. Raje N, Anderson K, (1999) Thalidomide—A Revival Story. New England Journal of Medicine341: 1606–1609. doi: 10.1056/NEJM19991118341211010564693
12. Cheng F, Liu C, Jiang J, Lu W, Li W, et al. (2012) Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference. PLoS Comput Biol8: e1002503. doi: 10.1371/journal.pcbi.100250322589709
13. Yang F, Xu J, Zeng J, (2014) Drug-target interaction prediction by integrating chemical, genomic, functional and pharmacological data. Pac Symp Biocomput: 148–159. 24297542
14. Lewis K, (2013) Platforms for antibiotic discovery. Nat Rev Drug Discov12: 371–387. doi: 10.1038/nrd397523629505
15. Nzila A, Ma Z, Chibale K, (2011) Drug repositioning in the treatment of malaria and TB. Future Med Chem3: 1413–1426. doi: 10.4155/fmc.11.9521879845
16. Iwata H, Sawada R, Mizutani S, Yamanishi Y, (2015) Systematic Drug Repositioning for a Wide Range of Diseases with Integrative Analyses of Phenotypic and Molecular Data. Journal of Chemical Information and Modeling55: 446–459. doi: 10.1021/ci500670q25602292
17. Berenstein AJ, Magariños MP, Chernomoretz A, Agüero F, (2016) A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases. PLoS Negl Trop Dis10: e0004300. doi: 10.1371/journal.pntd.000430026735851
18. Savoia D, (2016) New Antimicrobial Approaches: Reuse of Old Drugs. Curr Drug Targets17: 731–738. 26245476
19. Chong CR, Sullivan DJ, Jr. (2007) New uses for old drugs. Nature448: 645–646. doi: 10.1038/448645a17687303
20. Chopra I, (2007) Bacterial RNA polymerase: a promising target for the discovery of new antimicrobial agents. Curr Opin Investig Drugs8: 600–607. 17668362
21. Bai H, Zhou Y, Hou Z, Xue X, Meng J, et al. (2011) Targeting bacterial RNA polymerase: promises for future antisense antibiotics development. Infect Disord Drug Targets11: 175–187. 21470098
22. Poehlsgaard J, Douthwaite S, (2005) The bacterial ribosome as a target for antibiotics. Nat Rev Micro3: 870–881.
23. Kohanski MA, Dwyer DJ, Collins JJ, (2010) How antibiotics kill bacteria: from targets to networks. Nat Rev Micro8: 423–435.
24. Wilson DN, (2014) Ribosome-targeting antibiotics and mechanisms of bacterial resistance. Nat Rev Micro12: 35–48.
25. Shoji S, Walker SE, Fredrick K, (2009) Ribosomal Translocation: One Step Closer to the Molecular Mechanism. ACS Chemical Biology4: 93–107. doi: 10.1021/cb800294619173642
26. Borg A, Holm M, Shiroyama I, Hauryliuk V, Pavlov M, et al. (2015) Fusidic Acid Targets Elongation Factor G in Several Stages of Translocation on the Bacterial Ribosome. Journal of Biological Chemistry290: 3440–3454. doi: 10.1074/jbc.M114.61160825451927
27. Siboo IR, Chambers HF, Sullam PM, (2005) Role of SraP, a Serine-Rich Surface Protein of Staphylococcus aureus, in Binding to Human Platelets. Infection and Immunity73: 2273–2280. doi: 10.1128/IAI.73.4.2273-2280.200515784571
28. Siboo IR, Chaffin DO, Rubens CE, Sullam PM, (2008) Characterization of the Accessory Sec System of Staphylococcus aureus. Journal of Bacteriology190: 6188–6196. doi: 10.1128/JB.00300-0818621893
29. Duarte-Pereira S, Silva SS, Azevedo L, Castro L, Amorim A, et al. (2014) NAMPT and NAPRT1: novel polymorphisms and distribution of variants between normal tissues and tumor samples. Scientific Reports4: 6311. doi: 10.1038/srep0631125201160
30. Zhang Y, (2008) I-TASSER server for protein 3D structure prediction. BMC Bioinformatics9: 1–8.
31. Roy A, Kucukural A, Zhang Y, (2010) I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc5: 725–738. doi: 10.1038/nprot.2010.520360767
32. Yang J, Yan R, Roy A, Xu D, Poisson J, et al. (2015) The I-TASSER Suite: protein structure and function prediction. Nat Meth12: 7–8.
33. Wiederstein M, Sippl MJ, (2007) ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res35: W407–410. doi: 10.1093/nar/gkm29017517781
34. Sippl MJ, (1995) Knowledge-based potentials for proteins. Current Opinion in Structural Biology5: 229–235. 7648326
35. Grosdidier A, Zoete V, Michielin O, (2011) SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Research39: W270–W277. doi: 10.1093/nar/gkr36621624888
36. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, et al. (2009) AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility. Journal of computational chemistry30: 2785–2791. doi: 10.1002/jcc.2125619399780
37. Schuster FL, Mandel N, (1984) Phenothiazine compounds inhibit in vitro growth of pathogenic free-living amoebae. Antimicrobial Agents and Chemotherapy25: 109–112. 6703673
38. Spengler G, Miczák A, Hajdú E, Kawase M, Amaral L, et al. (2003) Enhancement of plasmid curing by 9-aminoacridine and two phenothiazines in the presence of proton pump inhibitor 1-(2-benzoxazolyl)-3,3,3-trifluoro-2-propanone. International Journal of Antimicrobial Agents22: 223–227. 13678825
39. Rani Basu L, Mazumdar K, Kumar Dutta N, Karak P, Dastidar SG, (2005) Antibacterial property of the antipsychotic agent prochlorperazine, and its synergism with methdilazine. Microbiological Research160: 95–100. 15782943
40. Bergman P, Linde C, Pütsep K, Pohanka A, Normark S, et al. (2011) Studies on the Antibacterial Effects of Statins—In Vitro and In Vivo. PLoS ONE6: e24394. doi: 10.1371/journal.pone.002439421912631
41. Masadeh M, Mhaidat N, Alzoubi K, Al-azzam S, Alnasser Z, (2012) Antibacterial activity of statins: a comparative study of Atorvastatin, Simvastatin, and Rosuvastatin. Annals of Clinical Microbiology and Antimicrobials11: 1–5.
42. Ryssel H, Kloeters O, Germann G, Schäfer T, Wiedemann G, et al. The antimicrobial effect of acetic acid—An alternative to common local antiseptics?Burns35: 695–700.
43. Halstead FD, Rauf M, Moiemen NS, Bamford A, Wearn CM, et al. (2015) The Antibacterial Activity of Acetic Acid against Biofilm-Producing Pathogens of Relevance to Burns Patients. PLoS ONE10: e0136190. doi: 10.1371/journal.pone.013619026352256
44. Tomás-Vert F, Pérez-Giménez F, Salabert-Salvador MT, Garcı et al. (2000) Artificial neural network applied to the discrimination of antibacterial activity by topological methods. Journal of Molecular Structure: THEOCHEM504: 249–259.
45. Murcia-Soler M, Pérez-Giménez F, García-March FJ, Salabert-Salvador MT, Díaz-Villanueva W, et al. (2003) Discrimination and selection of new potential antibacterial compounds using simple topological descriptors. Journal of Molecular Graphics and Modelling21: 375–390. 12543136
46. Bjarnsholt T, Alhede M, Jensen PO, Nielsen AK, Johansen HK, et al. (2015) Antibiofilm Properties of Acetic Acid. Adv Wound Care (New Rochelle)4: 363–372.
47. Huang CB, Alimova Y, Myers TM, Ebersole JL, (2011) Short- and medium-chain fatty acids exhibit antimicrobial activity for oral microorganisms. Archives of Oral Biology56: 650–654. doi: 10.1016/j.archoralbio.2011.01.01121333271
48. Breiman L, (2001) Random Forests. Machine Learning45: 5–32.
49. Estrada E, Rodríguez-Velázquez JA, (2005) Subgraph centrality in complex networks. Physical Review E71: 056103.
50. Yu H, Kim PM, Sprecher E, Trifonov V, Gerstein M, (2007) The Importance of Bottlenecks in Protein Networks: Correlation with Gene Essentiality and Expression Dynamics. PLoS Comput Biol3: e59. doi: 10.1371/journal.pcbi.003005917447836
51. Grosdidier A, Zoete V, Michielin O, (2011) Fast docking using the CHARMM force field with EADock DSS. Journal of Computational Chemistry32: 2149–2159. doi: 10.1002/jcc.2179721541955
52. Brooks BR, Brooks CL, 3rdMackerell AD, Jr.Nilsson L, Petrella RJ, et al. (2009) CHARMM: the biomolecular simulation program. J Comput Chem30: 1545–1614. doi: 10.1002/jcc.2128719444816
53. Haberthur U, Caflisch A, (2008) FACTS: Fast analytical continuum treatment of solvation. J Comput Chem29: 701–715. doi: 10.1002/jcc.2083217918282
54. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, et al. (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res34: D668–672. doi: 10.1093/nar/gkj06716381955
55. 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. Bioinformatics24: i232–i240. doi: 10.1093/bioinformatics/btn16218586719
56. Kanehisa M, Goto S, (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res28: 27–30. 10592173
57. Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M, et al. (2014) Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res42: D199–205. doi: 10.1093/nar/gkt107624214961
58. Schomburg I, Hofmann O, Baensch C, Chang A, Schomburg D, (2000) Enzyme data and metabolic information: BRENDA, a resource for research in biology, biochemistry, and medicine. Gene Function & Disease1: 109–118.
59. Hecker N, Ahmed J, von Eichborn J, Dunkel M, Macha K, et al. (2012) SuperTarget goes quantitative: update on drug-target interactions. Nucleic Acids Res40: D1113–1117. doi: 10.1093/nar/gkr91222067455
60. Bleakley K, Yamanishi Y, (2009) Supervised prediction of drug–target interactions using bipartite local models. Bioinformatics25: 2397–2403. doi: 10.1093/bioinformatics/btp43319605421
61. He Z, Zhang J, Shi X-H, Hu L-L, Kong X, et al. (2010) Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features. PLoS ONE5: e9603. doi: 10.1371/journal.pone.000960320300175
62. Yamanishi Y, Kotera M, Kanehisa M, Goto S, (2010) Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework. Bioinformatics26: i246–i254. doi: 10.1093/bioinformatics/btq17620529913
63. Chen X, Liu M-X, Yan G-Y, (2012) Drug-target interaction prediction by random walk on the heterogeneous network. Molecular BioSystems8: 1970–1978. doi: 10.1039/c2mb00002d22538619
64. Consortium TU (2015) UniProt: a hub for protein information. Nucleic Acids Research43: D204–D212. doi: 10.1093/nar/gku98925348405
65. Chen X, Liu M, Gilson MK, (2001) BindingDB: a web-accessible molecular recognition database. Comb Chem High Throughput Screen4: 719–725. 11812264
66. Yang J, Roy A, Zhang Y, (2013) BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions. Nucleic Acids Research41: D1096–D1103. doi: 10.1093/nar/gks96623087378
67. Fu G, Ding Y, Seal A, Chen B, Sun Y, et al. (2016) Predicting drug target interactions using meta-path-based semantic network analysis. BMC Bioinformatics17: 1–10.
68. O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, et al. (2011) Open Babel: An open chemical toolbox. Journal of cheminformatics3: 1.
69. Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, et al. (2009) STRING 8—a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res37: D412–416. doi: 10.1093/nar/gkn76018940858
70. Estrada E, Hatano N, (2008) Communicability in complex networks. Physical Review E77: 036111.
71. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, et al. (2011) Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research12: 2825–2830.
72. Larrañaga P, Calvo B, Santana R, Bielza C, Galdiano J, et al. (2006) Machine learning in bioinformatics. Briefings in Bioinformatics7: 86–112. 16761367
73. Touw WG, Bayjanov JR, Overmars L, Backus L, Boekhorst J, et al. (2013) Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?Briefings in Bioinformatics14: 315–326. doi: 10.1093/bib/bbs03422786785
74. Kotsiantis SB, Zaharakis I, Pintelas P (2007) Supervised machine learning: A review of classification techniques.
75. Bleeker SE, Moll HA, Steyerberg EW, Donders ART, Derksen-Lubsen G, et al. (2003) External validation is necessary in prediction research:: A clinical example. Journal of Clinical Epidemiology56: 826–832. 14505766
76. Simon R, (2005) Roadmap for developing and validating therapeutically relevant genomic classifiers. J Clin Oncol23: 7332–7341. doi: 10.1200/JCO.2005.02.871216145063