19 12 乔 连 生 1, 郭 亦 然 2 1*, 张 燕 玲 ( 1. 北 京 中 医 药 大 学 中 药 信 息 工 程 研 究 室, 北 京 100102; 2. 四 川 大 学 生 命 科 学 学 院, 成 都 610046) Drugbank 36 8 62 10 72 39 R287 doi 10. 11653 /syfj2013120310 A 1005-9903 2013 12-0310-05 20130119 002 2011-SYSKFKT-04 2011ZX09401-028 Tel 010-13683677151 E-mail 2284607267@ qq. com * Tel 010-84738620 E-mail collean_zhang@ 163. com 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 檶 4 1. M. 7. 5 2011 12. 2. S. 1994 112. 3. S. 1 1 2002 330. 4. 4 J. 2010 16 18 220. 5. J. 2009 15 3 80. 310
Fragment and Similarity based on Virtual Screening for Anti-asthma Traditional Chinese Medicine QIAO Lian-sheng 1 GUO Yi-ran 2 ZHANG Yan-ling 1* 1. Beijing University of Chinese Medicine Research Center of Traditional Chinese Medicine information Engineering Beijing 100102 China 2. Sichuan University Faculty of Life Science Chengdu 610064 China Abstract Objective To obtain new potential active traditional Chinese medicine TCM compounds for the prevention and treatment of asthma. Method The drugs with anti-asthma activity were collected from Drugbank database. Two virtual screening methods active fragment-based and structural similarity-based were used respectively for the screening of potential active TCM compounds from traditional Chinse medicine database TCMD 2009. Combined the principles and methods of TCM the source TCM of the hit compounds were then traced for the formulating prescription against asthma. Result Thirty-six drugs with the action on eight targets were obtained from the Drugbank database. Seventy-two potential active compounds which come from 39 commonly used TCM were hit by virtual screening. Some of the source TCM have been demonstrated to have the anti-asthma effect such as ephedra pinellia asarum ginger earthworm licorice ophiopogon aster evodia and so on. The principle of clearing lung-heat and removing phlegm for lowering adverse qi is always used for the treatment of thermal asthma while the warming lung for dispelling cold and removing phlegm is for cold asthma. Two prescriptions were discussed for thermal and cold asthma respectively. The prescription consisted by ephedra pinellia ginger licorice was discussed for cold asthma while the prescription of ephedra earthworm licorice for thermal asthma. Conclusion The results showed that the application of fragment-based search and similarity search methods are feasible for the discovery of Chinese medicine. Key words asthma fragment search similarity search bronchial asthma T 4 β 2 1-2 5 Drugbank 0. 5% ~ 6% 3 1 1. 1 Drugbank 1 36 β 2 311
19 12 1 Drugbank DB β 2 DB01001 DB00938 DB00816 DB00221 DB05039 DB00983 DB00668 DB00852 DB01364 DB01407 DB01408 DB01408 DB01274 salmeterol salbutamol orciprenaline isoetharine indacaterol formoterol epinephrine pseudoephedrine ephedrine clenbuterol bitolterol bambuterol arformoterol DB00651 dyphylline DB00277 theophylline DB00824 enprofylline DB01114 chlorpheniramine DB00427 triprolidine DB00792 tripelennamine DB08800 chloropyramine DB01407 cetirizine DB00920 ketotifen ICS DB00394 beclometasone dipropionate DB01222 budesonide DB00635 prednisone DB00764 mometasone DB00741 hydrocortisone DB00588 fluticasone propionate DB00180 flunisolide DB01380 cortisone acetate DB00744 DB00549 DB00471 DB00587 zileuton zafirlukast montelukast cinalukast DB00356 chlorzoxazone DB03852 eucalyptol Drugbank H 1 1 2. 2 β 2 ATP 2. 1 3 camp 3 H1 72 39 TCMD traditional Chinese medicine 4 database 2009 23 033 312 1. 2 6 Tanimoto Tanimoto 0 ~ 1 0 1 > 0. 5 7 Cytoscape ChemViz Tanimoto 1. 3 13 β 2 β 8 C 11 D 17 9 H1 N C O 10 2 2. 1 > 0. 5 22 2 1 2 2 34
2 No. 1 eucalyptol 1 8-1 3 3- -2-2. 2. 2 1 2 1 3 1 4-0. 5 4 hydrocortisone 15α 20β- -Δ4- -3-0. 516 5 isoetharine 0. 514 6 pseudoephedrine 1 7 D- 1 8 D- 0. 68 9 0. 68 10 0. 52 3 β 8 2 2 2 1 1 1 1 1 1 1 1 1 1 1 2 1 1 4 5 1 1 1 1 4 2 1 1 1 1 1 1 7 1 1 1 1 1 2 /% /% 62 34 10 27 10 13 60 69 11 12 5 13-15 16-19 20 21 4 11 30 38 5 3 10 6 313
19 12 5 No. 1 β 2 β 1 2 β 2 β 1 3 4 5 α-1 6 β 2 β 1 7 H 1 8 camp- 3' 5'- 9 β 2 β 1 H1 M. 2007 460. 11. D. 12. M. 1. J. 1984. 2012 10 16 63. 13. 2. J. 2010 28 9 35. J. 2012 25 7 718. 14. 3. C. J. 2006 5 5 63. 15. 2009 43. J. 2007 13 5 105. 4. 16. 34 J. 2009 40 4 289. J. 2004 32 3 293. 5 Takigawa Ichigaku Tsuda Koji Mamitsuka Hiroshi. 17. Mining significant substructure pairs for interpreting J. 2012 18 8 285. polypharmacology in drug-target network J. PLOS ONE 2011 2 6 3. 6 John D Holliday Peter Willett Hua Xiang. Interactions between weighting scheme and similarity cofficient in similarty-based virtual screening J. Inter Chemoinformatics Chem Engineering IJCCE 2012 2 2 28. 7 John B Taylor David J Triggle. Comprehensive medicinal chemistry II volume 4 Computer-assisted drug design M. Elsevier 2006 174. 8. β 2 J. 2004 14 3 187. 21. - 9. M. 2008 304. 2009 11. 18. J. 2007 32 1 3. 19. 86 J. 2012 18 15 238. 20. J. 2009 16 3 94. D. 2008. 10. 314