细菌代谢物浓度的化学信息学预测及在杀菌剂发现中的应用
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摘要
随着基因组、转录组、蛋白质组等各种“组学”研究计划的蓬勃开展,生命科学进入了“组学”时代。代谢组学(metabolomics)作为一门对体内各种代谢途径、代谢产物进行高通量和系统分析的学科,是继基因组学、转录组学、蛋白质组学后系统生物学的另一重要研究领域。代谢组学以代谢物(基本上是化学小分子)为研究对象,代谢物的种类和浓度变化被视作生物系统对遗传变异或环境扰动的最终响应,因此代谢组比基因组和蛋白质组更接近生物表型。代谢组学作为系统生物学的重要分支,其研究的重点是细胞内代谢物种类与浓度的定性和定量分析以及代谢网络的构建和模拟。以往代谢组学的研究仅仅局限在高通量实验技术上,近年来,基于生物信息学工具对代谢组分析的方法得到了快速发展,为杀菌剂及靶标的发现提供了强有力的技术支撑。
首先,我们从KEGG数据库中收集了E. coli的代谢物及代谢反应方向等数据,重构代谢网络,计算代谢物的物理化学性质。通过相关性分析,我们发现代谢物浓度与代谢网络的拓扑性质及其物理化学性质(极性、分子体积和水溶性)有较强的相关性。为此,我们分别建立了基于物理化学性质、网络连接度的代谢物浓度的线性回归预测模型和支持向量回归(SVR)模型。随后运用逐步回归的方法得到了代谢物浓度与7个描述符之间的线性回归方程；并利用递归特征消除法筛选到了1个网络拓扑参数和10个与极性、水溶性等相关的物理化学性质。通过与线性回归及网络热力学方法的对比,我们发现特征筛选后的SVR代谢物浓度模型的预测结果更准确,这个结果与我们的预期相符,即大肠杆菌的代谢物浓度主要与代谢网络连接度及代谢物的极性和水溶性有关。
其次,我们把基于SVR的代谢物浓度预测模型应用于其它动、植物致病菌。基于代谢物的10个物理化学性质和代谢网络参数的共11个特征,预测出这些致病菌的代谢物浓度,并构建了代谢物浓度预测网站。该网站提供了胡萝卜软腐果胶杆菌、金黄色葡萄球菌、结核分枝杆菌等10种细菌的代谢物浓度查询服务,同时也可用于其它细菌的代谢物浓度预测。
另外,我们提出了利用代谢物浓度在杀菌剂发现中的一个新规则：靶标底物浓度小于0.5mM,且竞争性抑制剂水溶解度应该超过底物浓度的100倍。利用此规则,我们很好地解释了前人一些有趣的研究现象。
最后,我们借助代谢物浓度发现了3个有研发潜力的黄酮类杀菌剂靶标。同时我们发现位于黄酮化合物小分子上的吡喃环3位上的没食子酸或者糖苷集团对FrdA、PyrD、FabI的结合有比较大的影响。对3种靶标蛋白的生物功能的分析显示与已经报道的黄酮杀菌剂的杀菌机制相一致。结合代谢物浓度判别规则,发现12种黄酮化合物的水溶解度与代谢物底物浓度相比普遍都具有较高的水溶解度。因此,这3个靶标是非常有潜力的杀菌剂靶标。
本文利用支持向量回归的方法结合代谢网络建立了细菌代谢物浓度预测模型,为从代谢水平研究药物靶标提供了新的思路和判别规则,并成功地应用在黄酮类杀菌剂靶标的筛选上。
With the development of omics, such as genomes, transcriptomes, and protomes, life science has entered the era of omics. Metabolomics is a subject that analyzes all pathways and metabolites in a high-throughput manner, which is another important research field in system biology after genomics, transcriptomics and proteomics. Metabolites are the end products of the cellular control process and the research object of metabolomics. The types and concentration changes comprise the final response of biological systems for genetic variation or disturbance in the environment. Therefore, the metabolome is closer to the biological phenotype than the genome and protome. Metabolomic research focuses on the qualitative and quantitative analyses of metabolite types and concentrations, as well as the reconstruction or simulation on metabolite networks. In the past, the metabolomic studies were limited to high-throughput experimental technologies. However, metabolite network analyses have rapidly developed based on the bioinformatics tools and have provided strong technical support for the discovery of antimicrobial drug targets.
First, we collected metabolites of E.coli from the KEGG database, reconstructed metabolic networks, and computed the physico-chemical properties. By correlation analysis, we determined that metabolite concentrations and their physico-chemical properties (i.e., polarity, molecular weight, and solubility) have a strong correlation. The linear prediction model and support vector regression (SVR) model were constructed based on networks degree and84physico-chemical properties. We obtained the linear regression equation through stepwise regression. Ten physico-chemical properties and the degree were screened by recursive feature elimination (RFE) method. Compared with the linear regression and network thermodynamic methods, the prediction results of the SVR model with RFE were more accurate, consistent with our expectations. In particular, the metabolite concentrations of E.coli are related to the metabolic network degree, polarity, and water solubility.
Second, we applied the SVR model of E.coli to animal and plant pathogens. A concentration prediction website was built based on the10physico-chemical properties and network degree. The prediction concentration of10bacteria types, such as Pectobacterium carotovorum, Staphylococcus aureus, and M. tuberculosis can be queried from this website. Other bacteria metabolite concentrations can also be predicted on this website.
Third, we proposed a useful criterion for selecting antimicrobial targets based on metabolite concentration. Use this criterion, we clearly explained previous studies of interesting phenomena.
Finally, we identified three flavonoid targets by metabolite concentrations. Docking results reveal that substitution of galloyl or glycosides at position3of the heterocyclic pyrane ring in flavonoids enhances the binding affinity to three targets(i.e., FrdA, PyrD, and FabI). The biological functions of the three targets are consistent with the reported antibacterial mechanisms of flavonoids. Based on the combined metabolite concentration discrimination criterion, the water solubility of the18flavonoids was generally higher than the metabolite substrates concentration. This result shows that flavonoids have good antibacterial activity. Thus, the three targets can be potential antibacterial targets.
Our study provided a new idea and criterion for studying target screening at the metabolic level, which were successfully applied in screening flavonoid targets.
首先,我们从KEGG数据库中收集了E. coli的代谢物及代谢反应方向等数据,重构代谢网络,计算代谢物的物理化学性质。通过相关性分析,我们发现代谢物浓度与代谢网络的拓扑性质及其物理化学性质(极性、分子体积和水溶性)有较强的相关性。为此,我们分别建立了基于物理化学性质、网络连接度的代谢物浓度的线性回归预测模型和支持向量回归(SVR)模型。随后运用逐步回归的方法得到了代谢物浓度与7个描述符之间的线性回归方程；并利用递归特征消除法筛选到了1个网络拓扑参数和10个与极性、水溶性等相关的物理化学性质。通过与线性回归及网络热力学方法的对比,我们发现特征筛选后的SVR代谢物浓度模型的预测结果更准确,这个结果与我们的预期相符,即大肠杆菌的代谢物浓度主要与代谢网络连接度及代谢物的极性和水溶性有关。
其次,我们把基于SVR的代谢物浓度预测模型应用于其它动、植物致病菌。基于代谢物的10个物理化学性质和代谢网络参数的共11个特征,预测出这些致病菌的代谢物浓度,并构建了代谢物浓度预测网站。该网站提供了胡萝卜软腐果胶杆菌、金黄色葡萄球菌、结核分枝杆菌等10种细菌的代谢物浓度查询服务,同时也可用于其它细菌的代谢物浓度预测。
另外,我们提出了利用代谢物浓度在杀菌剂发现中的一个新规则：靶标底物浓度小于0.5mM,且竞争性抑制剂水溶解度应该超过底物浓度的100倍。利用此规则,我们很好地解释了前人一些有趣的研究现象。
最后,我们借助代谢物浓度发现了3个有研发潜力的黄酮类杀菌剂靶标。同时我们发现位于黄酮化合物小分子上的吡喃环3位上的没食子酸或者糖苷集团对FrdA、PyrD、FabI的结合有比较大的影响。对3种靶标蛋白的生物功能的分析显示与已经报道的黄酮杀菌剂的杀菌机制相一致。结合代谢物浓度判别规则,发现12种黄酮化合物的水溶解度与代谢物底物浓度相比普遍都具有较高的水溶解度。因此,这3个靶标是非常有潜力的杀菌剂靶标。
本文利用支持向量回归的方法结合代谢网络建立了细菌代谢物浓度预测模型,为从代谢水平研究药物靶标提供了新的思路和判别规则,并成功地应用在黄酮类杀菌剂靶标的筛选上。
With the development of omics, such as genomes, transcriptomes, and protomes, life science has entered the era of omics. Metabolomics is a subject that analyzes all pathways and metabolites in a high-throughput manner, which is another important research field in system biology after genomics, transcriptomics and proteomics. Metabolites are the end products of the cellular control process and the research object of metabolomics. The types and concentration changes comprise the final response of biological systems for genetic variation or disturbance in the environment. Therefore, the metabolome is closer to the biological phenotype than the genome and protome. Metabolomic research focuses on the qualitative and quantitative analyses of metabolite types and concentrations, as well as the reconstruction or simulation on metabolite networks. In the past, the metabolomic studies were limited to high-throughput experimental technologies. However, metabolite network analyses have rapidly developed based on the bioinformatics tools and have provided strong technical support for the discovery of antimicrobial drug targets.
First, we collected metabolites of E.coli from the KEGG database, reconstructed metabolic networks, and computed the physico-chemical properties. By correlation analysis, we determined that metabolite concentrations and their physico-chemical properties (i.e., polarity, molecular weight, and solubility) have a strong correlation. The linear prediction model and support vector regression (SVR) model were constructed based on networks degree and84physico-chemical properties. We obtained the linear regression equation through stepwise regression. Ten physico-chemical properties and the degree were screened by recursive feature elimination (RFE) method. Compared with the linear regression and network thermodynamic methods, the prediction results of the SVR model with RFE were more accurate, consistent with our expectations. In particular, the metabolite concentrations of E.coli are related to the metabolic network degree, polarity, and water solubility.
Second, we applied the SVR model of E.coli to animal and plant pathogens. A concentration prediction website was built based on the10physico-chemical properties and network degree. The prediction concentration of10bacteria types, such as Pectobacterium carotovorum, Staphylococcus aureus, and M. tuberculosis can be queried from this website. Other bacteria metabolite concentrations can also be predicted on this website.
Third, we proposed a useful criterion for selecting antimicrobial targets based on metabolite concentration. Use this criterion, we clearly explained previous studies of interesting phenomena.
Finally, we identified three flavonoid targets by metabolite concentrations. Docking results reveal that substitution of galloyl or glycosides at position3of the heterocyclic pyrane ring in flavonoids enhances the binding affinity to three targets(i.e., FrdA, PyrD, and FabI). The biological functions of the three targets are consistent with the reported antibacterial mechanisms of flavonoids. Based on the combined metabolite concentration discrimination criterion, the water solubility of the18flavonoids was generally higher than the metabolite substrates concentration. This result shows that flavonoids have good antibacterial activity. Thus, the three targets can be potential antibacterial targets.
Our study provided a new idea and criterion for studying target screening at the metabolic level, which were successfully applied in screening flavonoid targets.
引文
1.邓世果,吴干华,杨会杰.代谢网络的重构.上海理工大学学报,2012,34：511-516.
2.李叶,唐浩国,刘建学.黄酮类化合物抑菌作用的研究进展.农产品加工(学刊),2008,12：53-55.
3. 肖枝洪,朱强,苏理云,魏正元,袁中尚.多元数据分析及其R实现.北京：科学出版社,2013.
4. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. JMol Biol,1990,215:403-410.
5. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res,1997,25:3389-3402.
6. Anderson WF. Structural genomics and drug discovery for infectious diseases. Infect Disord Drug Targets,2009,9:507-517.
7. Anishetty S, Pulimi M, Pennathur G. Potential drug targets in Mycobacterium tuberculosis through metabolic pathway analysis. Comput Biol Chem, 2005, 29: 368-378.
8. Arakawa K, Yamada Y, Shinoda K. GEM System: automatic prototyping of cell-wide metabolic pathway models from genomes. BMC Bioinformatics,2006, 7: 168.
9. Bakheet TM, Doig AJ. Properties and indentification of human protein drug targets. Bioinformatics.2009, 25:7-451.
10. Bakheet TM, Doig AJ. Properties and identification of antibiotic drug targets. BMC Bioinformatics, 2010, 11:195.
11. Barabasi AL, Albert R. Emergence of scaling in random networks. Science, 1999, 286:509-512.
12. Bar-Even A, Noor E, Flamholz A, Buescher JM, Milo R. Hydrophobicity and charge shape cellular metabolite concentrations. PLoS Comput Biol,2011,7:e1002166.
13. Barh D, Tiwari S, Jain N, Ali A, Santos AR, Misra AN, Azevedo V, Kumar A. In silico subtractive genomics for target identification in human bacterial pathogens. Drug Dev Res,2011,72:162-177.
14. Batagelj V, Mrvar A. Pajek-analysis and visualization of large networks. Chapter in Graph Drawing Software. Berlin: Springer,2003,77-103.
15. Becker SA, Feist AM, Mo ML, Hannum G, Palsson B?, Herrgard MJ. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox Nat Protoc,2007,2:727-738.
16. Bennett BD, Kimball EH, Gao M, Osterhout R, Van Dien SJ, Rabinowitz JD. Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli. Nat Chem Biol,2009,5:593-599.
17. Bernard FX, Sable S, Cameron B, Provost J, Desnottes, JF, Crouzet J, Blanche F. Glycosylated flavones as selective inhibitors of topoisomerase Ⅳ. Antimicrob Agents Chemother,1997,41:992-998.
18. Bielaszewska M, Mellmann A, Zhang W, Kock R, Fruth A, Bauwens A, Peters G, Karch H. Characterisation of the Escherichia coli strain associated with an outbreak of haemolytic uraemic syndrome in Germany,2011:a microbiological study. Lancet Infect Dis,2011,11:671-676.
19. Brotz-Oesterhelt H, Bandow JE, Labischinski H. Bacterial proteomics and its role in antibacterial drug discovery. Mass Spectrom Rev,2005,24:549-565.
20. Bruccoleri RE, Dougherty TJ, Davison DB. Concordance analysis of microbial genomes. Nucleic Acids Res,1998,26:4482-4486.
21. Bogoyevitch MA, Barr RK, Ketterman AJ. Peptide inhibitors of protein kinases-discovery, characterisation and use. Biochim Biophys Acta, 2005, 1754: 79-99.
22. Boser BE, Guyon IM and Vapnik VN. A training algorithm for optimal margin classifiers.5th Annual ACM Workshop on COLT,1992,144-152.
23. Bumann D and Valdivia RH. Identification of host-induced pathogen genes by differential fluorescence induction reporter systems. Nat Protoc,2007,2: 770-777.
24. Bush K. Antibacterial drug discovery in the 21st century. Clin Microbiol Infect, 2004, 4:10-17.
25. Caspi R, Foerster H, Fulcher CA, Kaipa P, Krummenacker M, Latendresse M, Paley S, Rhee SY, Shearer AG, Tissier C, Walk TC, Zhang P, Karp PD. The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res,2008,36:D623-D631.
26. Caturla N, Vera-Samper E, Villalain J, Mateo CR, Micol V. The relationship between the antioxidant and the antibacterial properties of galloylated catechins and the structure of phospholipid model membranes. Free Radical Bio Med,2003,34: 648-662.
27. Chahrour O, Cairns D, Omran Z. Small molecule kinase inhibitors as anti-cancer therapeutics. Mini Rev Med Chem,2012,12:399-411.
28. Cortes C and Vapnik V. Support vector networks. Mach Learn,1995,20:273-295.
29. Cushnie TP, Lamb AJ. Antimicrobial activity of flavonoids. Int J Antimicrob Agents, 2005,26:343-56.
30. Cushnie TP, Lamb AJ. Recent advances in understanding the antibacterial properties of flavonoids. Int J Antimicrob Agents,2011,38:99-107.
31. Dehbi M, Moeck G, Arhin FF, Bauda P, Berqeron D, Kwan T, Liu J, McCarty J, Dubow M, Pelletier J. Inhibition of transcription in Staphylococcus aureus by a primary sigma factor-binding polypeptide from phage Gl. J Bacteriol,2009,191: 3763-3771.
32. Di L, Fish PV, Mano T. Bridging solubility between drug discovery and development. Drug Discov Today,2012,17:486-495.
33. Dobson PD, Patel Y, Kell DB.'Metabolite-likeness' as a criterion in the design and selection of pharmaceutical drug libraries. Drug Discov Today,2009,14:31-40.
34. Dundas J, Ouyang Z, Tseng J, Binkowski A, Turpaz Y, Liang J. CASTp:computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucl Acids Res,2006,34:W116-W118.
35. Fang X, Wallqvist A, Reifman J. Development and analysis of an in vivo-compatible metabolic network of Mycobacterium tuberculosis. BMC Syst Biol,2010,4:160.
36. Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson B?. A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol,2007,3:121.
37. Fell D. Understanding the Control of Metabolism. London: Porland Press, 1996.
38. Fendt SM, Buescher JM, Rudroff F, Picotti P, Zamboni N, Sauer U. Tradeoff between enzyme and metabolite efficiency maintains metabolic homeostasis upon perturbations in enzyme capacity. Mol Syst Biol,2010,6: 356.
39. Filutowicz M, Jonczyk P. The gyrB gene product functions in both initiation and chain polymerization of Escherichia coli chromosome replication: suppression of the initiation deficiency in gyrB-ts mutants by a class of rpoB mutations. Mol Gen Genet, 1983,191:282-287.
40. Fischer HP, Freiberg C. Applications of transcriptional profiling in antibiotics discovery and development. Prog Drug Res,2007,64: 23-47.
41. Fleischmann RD, Adams MD, White O, Clayton RA, et al. Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science, 1999, 269: 496-512.
42. Freiberg C, Brotz-Oesterhelt H, Labischinski H. The impact of transcriptome and proteome analyses on antibiotic drug discovery. Curr Opin Microbiol, 2004, 7: 451-459.
43. Freiberg C, Brotz-Oesterhelt H. Functional genomics in antibacterial drug discovery. Drug Discov Today,2005,10: 927-935.
44. Friedman M. Overview of antibacterial, antitoxin, antiviral, and antifungal activities of tea flavonoids and teas. Mol Nutr Food Res,2007,51:116-134.
45. Furlanello C, Serafini M, Merler S, Jurman G. An accelerated procedure for recursive feature ranking on microarray data. Neural Netw, 2003,16:641-648.
46. Gastiger J, Engel T. Chemoinformatics. Wiley-VCH Verlag GmbH & Co. KGaA. 2004,203-504.
47. Goldsmith P, Fenton H, Morris-Stiff G, Ahmad N, Fisher J, Prasad KR. Metabonomics:a useful tool for the future surgeon. JSurg Res,2010,160:122-132.
48. Goodacre R, Vaidyanathan S, Dunn W B, Harrigan G G, Kell D B. Metabolomics by numbers:acquiring and understanding global metabolite data. Trends Biotechnol, 2004,22:245-252.
49. Gordon NC, Wareham DW. Antimicrobial activity of the green tea polyphenol (-)-epigallocatechin-3-gallate (EGCG) against clinical isolates of Stenotrophomonas maltophilia. Int J Antimicrob Agents,2010,36:129-131.
50. Guyon I,Weston J, Barnhill S. Gene selection for cancer classification using support vector machine. Mach Learn,2002,46,389-422.
51. Haraquchi H, Tanimoto K, Tamura Y, Mizutani K, Kinoshita T. Mode of antibacterial action of retrochalcones from Glycyrrhiza inflata. Phytochemistry, 1998,48: 125-129.
52. Heath R, White S, Rock C. Inhibitors of fatty acid synthesis as antimicrobial chemotherapeutics. Appl Microbiol Biotechnol,2002,58:695-703.
53. Henry CS, DeJongh M, Best AA, Frybarqer PM, Linsay B, Stevens RL High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol,2010,28:977-982.
54. Hernandez I, Alegre L, Van Breusegem F, Munne-Bosch S. How relevant are flavonoids as antioxidants in plants? Trends Plant Sci,2009, 14:125-132.
55. Hormozdiari F, Salari R, Bafna V, Sahinalp SC. Protein-protein interaction network evaluation for identifying potential drug targets. JComput Biol,2010,17:669-684.
56. Huss M, Holme P. Currency and commodity metabolites:their identification and relation to the modularity of metabolic networks. IET Syst Biol,2007,1:280-285
57. Ikigai H, Nakae T, Hara Y, Shimamura T. Bactericidal catechins damage the lipid bilayer. Biochim Biophys Acta,1993,1147:132-136.
58. Ioerger TR, Sacchettini JC. Structural genomics approach to drug discovery for Mycobacterium tuberculosis. Curr Opin Microbiol,2009,12:318-325.
59. Jeong H, Tombor B, Albert R,Oltvai ZN, Barabasi AL. The large-scale organization of metabolic networks. Nature,2000,407:651-654.
60. Jiang YY, Kong DX, Qin T, Zhang HY. How does oxygen rise drive evolution? Clues from oxygen-dependent biosynthesis of nuclear receptor ligands. Biochem Biophys Res Commun,391:1158-1160.
61. Jones D. The antibacterial lead discovery challenge. Nat Rev Drug Discov,2010,9: 751-752.
62. Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y. KEGG for linking genomes to life and the environment. Nucleic Acids Res,2008,36: D480-D484.
63. Kim PJ, Lee DY, Kim TY, Lee KH, Jeong H, Lee SY, Park S. Metabolite essentiality elucidates robustness of Escherichia coli metabolism. Proc Natl Acad Sci USA,2007, 104:13638-13642.
64. Kim TY, Kim HU, Lee SY. Metabolite-centric approaches for the discovery of antibacterials using genome-scale metabolic networks. Metab Eng, 2010, 12: 105-111.
65. Kim HU, Kim SY, Jeong H, Kim TY, Kim JJ, Choy HE, Yi KY, Rhee JH, Lee SY. Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery. Mol Syst Biol,2011,7: 460.
66. Kirst HA. Antibiotics versus resistant bacteria: a continual challenge. Expert Opin Ther Pat,2010,20:1271-1272.
67. Klamt S, Saez-Rodriguez J, Gilles ED. Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Syst Biol,2007,1:2.
68. Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, Djoumbou Y, Eisner R, Guo AC, Wishart DS. DrugBank 3.0:a comprehensive resource for' omics' research on drugs. Nucleic Acids Res.,2011,39: D1035-D1041.
69. Kohavi R and John GH. Wrappers for feature subset selection. Artif Intell, 1997, 97: 273-324.
70. Koul A, Arnoult E, Lounis N, Guillemont J, Andries K. The challenge of new drug discovery for tuberculosis. Nature,2011,469:483-490.
71. Kumarasamy KK, et al. Emergence of a new antibiotic resistance mechanism in India, Pakistan, and the UK: a molecular, biological, and epidemiological study. Lancet Infect Dis,2010,10: 597-602.
72. Kummel A, Panke S, Heinemann M. Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol Syst Biol, 2006, 2:2006.0034.
73. Kushwaha SK, Shakya M. Protein interaction network analysis--approach for potential drug target identification in Mycobacterium tuberculosis. J Theor Biol, 2010, 262:284-294.
74. Lamichhane G, Freundlich JS, Ekins S, Wickramaratne N, Nolan ST, Bishai WR. Essential Metabolites of Mycobacterium tuberculosis and Their Mimics. MBio, 2011, 2:e00301-e00310.
75. Lancet. Urgently needed: new antibiotics. Lancet,2009,374:1868.
76. Langley P. Selection of relevant features in machine learning[C]. Proc of AAAI Fall Symposium on Relevance. New Orleans,1994,1-5.
77. Leach AR, Gillet VJ. An Introduction of Chemoinformatics. The Netherlands. Kluwer Academic Publishers.2003,53-76.
78. Lee DY, Yun H, Park S, Lee SY. MetaFluxNet: the management of metabolic reaction information and quantitative metabolic flux analysis. Bioinformatics, 2003,19: 2144-2146.
79. Lee DS, Burd H, Liu J, Almaas E, Wiest O, Barabasi AL, Oltvai ZN, Kapatral V. Comparative genome-scale metabolic reconstruction and flux balance analysis of multiple Staphylococcus aureus genomes identify novel antimicrobial drug targets. J Bacteriol,2009,191:4015-4024.
80. Li H, Ung CY, Yap CW, Xue Y, Li ZR, Cao ZW, Chen YZ. Prediction of genotoxicity of chemical compounds by statistical learning methods. Chem Res Toxicol, 2005, 18: 1071-1080.
81. Liu J, Dehbi M, Moeck G, Arhin F, Bauda P, Bergeron D, Callejo M, Ferretti V, Ha N, Kwan T, McCarty J, Srikumar R, Williams D, Wu JJ, Gros P, Pelletier J, DuBow M. Antimicrobial drug discovery through bacteriophage genomics. Nat Biotechnol, 2004, 22:185-191.
82. Lu MF, Xiao ZT, Zhang HY. Where do health benefits of flavonoids come from? Insights from flavonoid targets and their evolutionary history. Biochem Biophys Res Commun,2013,434:701-704.
83. Lu W, Bennett BD, Rabinowitz JD. Analytical strategies for LC-MS-based targeted metabolomics. J Chromatogr B Analyt Technol Biomed Life Sci,2008,871:236-242.
84. Ma H, Zeng AP. Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics,2003,19: 270-277.
85. Mori A, Nishino C, Enoki N, Tawata S. Antibacterial activity and mode of action of plant flavonoids against Proteus vulgaris and Staphylococcus aureus, Phytochemistry. 1987,26: 2231-2234.
86. Morya VK, Varun D, Mecarty SD, Raghuvir S. In silico Analysis of Metabolic Pathways for Identification of Putative Drug Targets for Staphylococcus aureus. J Comput Sci Syst Biol, 2010,3:62-69.
87. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem,1998,19:1639-1662.
88. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew Rk, Goodsell DS, Olson AJ. AutoDock4 and AutoDockTools4:Automated docking with selective receptor flexibility. J Comput Chem,2009,30:2785-2791.
89. Murray CW, Baxter CA, Frenkel AD. The sensitivity of the results of molecular docking to induced fit effects: application to thrombin, thermolysin and neuraminidase. J Comput Aided Mol Des,1999,13:547-62.
90. Nagaraj NS, Singh OV. Using genomics to develop novel antibacterial therapeutics. Crit Rev Microbiol,2010,36:340-348.
91. Naray-Szabo G and Nagy P. Electrostatic lock-and-key model for the study of biological isosterism: role of structural water in the binding of basic pancreatic trypsin inhibitor to beta-trypsin. Enzyme,1986,36:44-53.
92. Ng WL, Kazmierczak KM, Robertson GT, Gilmour R, Winkler ME. Transcriptional regulation and signature patterns revealed by microarray analyses of Streptococcus pneumoniae R6 challenged with sublethal concentrations of translation inhibitors. J Bacteril,2003,185:359-370.
93. Nicholson JK, Lindon JC, Holmes E.'Metabonomics':understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica,1999,29:1181-1189.
94. Oberhardt MA, Palsson B?, Papin JA. Applications of genome-scale metabolic reconstructions. Mol Syst Biol, 2009, 5:320.
95. Oliveira AP, Nielsen J, Forster J. Modeling Lactococcus lactis using a genome-scale flux model. BMC Microbiol, 2005,5:39.
96. Otsuka N, Liu MH, Shiota S, Ogawa W, Kuroda T, Hatano T, Tsuchiya T. Anti-methicillin resistant Staphylococcus aureus (MRSA) compounds isolated from Laurus nobilis. Biol Pharm Bull,2008,31:1794-1797.
97. Payne DJ, Gwynn MN, Holmes DJ, Pompliano DL. Drugs for bad bugs:confronting the challenges of antibacterial discovery. Nat Rev Drug Discov,2007,6:29-40.
98. Perumal D, Samal A, Sakharkar KR, Sakharkar MK. Targeting multiple targets in Pseudomonas aeruginosa PAO1 using flux balance analysis of a reconstructed genome-scale metabolic network. J Drug Target, 2011,19:1-13.
99. Pinney JW, Shirley MW, McConkey GA, Westhead DR. metaSHARK:software for automated metabolic network prediction from DNA sequence and its application to the genomes of Plasmodium falciparum and Eimeria tenella. Nucleic Acids Res, 2005, 33:1399-1409.
100.Plaimas K, Eils R, Konig R. Identifying essential genes in bacterial metabolic networks with machine learning methods. BMC Syst Biol,2010,4:56.
101.Plaper A, Golob M, Hafner I, Oblak M, Solmajer T, Jerala R. Characterization of quercetin binding site on DNA gyrase. Biochem Biophys Res Commun, 2003,306: 530-536.
102.Pohjanen E, Thysell E, Lindberg J, Schuppe-Koistinen I, Moritz T, Jonsson P, Antti H. Statistical multivariate metabolite profiling for aiding biomarker pattern detection and mechanistic interpretations in GC/MS based metabolomics. Metabolomics, 2006, 2:257-268.
103.Reo NV. NMR-based metabolomics. Drug Chem Toxicol, 2002,25:375-382.
104.Rice-Evans C. Flavonoids and isoflavones: absorption, metabolism, and bioactivity. Free Radic Biol Med,2004,36:827-828.
105.Roemer T, Boone C. Systems-level antimicrobial drug and drug synergy discovery. Nat Chem Biol,2013,9:222-231.
106.Scheer M, Grote A, Chang A, Schomburg I, Munaretto C, Rother M, Sohngen C, Stelzer M, Thiele J, Schomburg D. BRENDA, the enzyme information system in 2011. Nucleic Acids Res,2011,39: D670-D676.
107. Schilling CH, Edwards JS, Palsson BO. Toward metabolic phenomics: Analysis of genomic data using flux balances. Biotechnol Prog,1999,15:288-295.
108. Schuster S, Dandekar T, Fell DA. Detection of elementary flux modes in biochemical networks:A promising tool for pathway analysis and metabolic engineering. Trends Biotechnol,1999,17:53-60.
109. Schuster S, Fell DA, Dandekar T. A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nat Biotechnol,2000,18:326-332.
110.Shaw KJ, Morrow BJ. Transcriptional profiling and drug discovery. Curr Opin Pharmacol,2003,3,508-512.
111.Shen Y, Liu J, Estiu G, Isin B, Ahn YY, Lee DS, Barabasi AL, Kapatral V, Wiest O, Oltvai ZN. Blueprint for antimicrobial hit discovery targeting metabolic networks. Proc Natl Acad Sci USA,2010,107:1082-1087.
112.Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. Cytoscape 2.8:new features for data integration and network visualization. Bioinformatics, 2011,27:431-432.
113.Stanton TB. A call for antibiotic alternatives research. Trends Microbiol, 2013, 21:111-113.
114.Steinsiek S, Frixel S, Stagge S, Bettenbrock K. Characterization of E. coli MG1655 and frdA and sdhC mutants at various aerobiosis levels. J Biotechnol, 2011,154: 35-45.
115.Tasdemir D, Lack G, Brun R, Ruedi P, Scapozza L, Perozzo R. Inhibition of Plasmodium falciparum fatty acid biosynthesis:evaluation of FabG, FabZ, and FabI as drug targets for flavonoids. JMed Chem,2006,49:3345-3353.
116.Thiele I, Palsson B?. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc,2010,5:93-121.
117.Todeschini R, Consonni V. Handbook of Molecular Descriptors. Weinheim. Germany. Wiley-VCH.2000.
118.Tsuchiya H and Iinuma M. Reduction of membrane fluidity by antibacterial sophoraflavanone G isolated from Sophora exigua. Phytomedicine,2000,7:161-165.
119.van den Boogaard J, Kibiki GS, Kisanga ER, Boeree MJ, Aarnoutse RE. New drugs against tuberculosis:problems, progress, and evaluation of agents in clinical development. Antimicrob Agents Chemother,2009,53:849-862.
120.Vapnik V and Lerner A. Pattern recognition using generalized portrait method. Automat Rem Contr,1963,24:774-780.
121.Vapnik V. The Nature of Statistical Learning Theory. Springer. New York.1995.
122.Varner J, Ramdrishna D. Metabolic engineering from a cybernetic perspective.1. Theoretical preliminaries. Biotechnol Prog,1999, 15:407-425.
123.Wickramasinghe SR, Inglis KA, Urch JE, Miiller S, van Aalten DM, Fairlamb AH. Kinetic, inhibition and structural studies on 3-oxoacyl-ACP reductase from Plasmodium falciparum, a key enzyme in fatty acid biosynthesis. Biochem J, 2006, 393:447-57.
124.World Health Organization. Global tuberculosis report 2013,2013.
125.Wu D, Kong Y, Han C, Chen J, Hu L, Jiang H, Shen X. D-Alanine:D-alanine ligase as a new target for the flavonoids quercetin and apigenin. Int J Antimicrob Agents, 2008,32:421-426.
126.Xiong M, Zhang H.-Y. Finding Hsp90 inhibitors by drug repurposing: the power of chemical genetics. Drug Discov Today,2012,17: 531-533.
127.Xue Y, Li H, Ung CY, Yap CW, Chen YZ. Classifiction of a Diverse set of Tetrahymena pyriformis Toxicity Chemical compounds from molecular descriptors by statistical learning methods. Chem Res Toxicol.2006,19:1030-1039.
128.Yeh I, Hanekamp T, Tsoka S, Karp PD, Altman RB. Computational analysis of Plasmodium falciparum metabolism: organizing genomic information to facilitate drug discovery. Genome Res,2004,14:917-924.
129.Yu H, Yang J, Wang W, Han J. Discovering Compact and Highly Discriminative Feature or Feature Combinations of Drug Actives Using Support Vector Machines. Proc IEEE Comput Soc Bioinform Conf. Standford.2003.220-228.
130.Zamboni N, Kummel A, Panke S, Heinemann M. anNET: a tool for network-embedded thermodynamic analysis of quantitative metabolome data. BMC Bioinformatics,2008,9:199.
131.Zhang YM, Rock CO. Evaluation of epigallocatechin gallate and related plant polyphenols as inhibitors of the FabG and FabI reductases of bacterial type II fatty-acid synthase. JBiol Chem,2004,279:30994-31001.
132.Zhu F, Shi Z, Qin C, Tao L, Liu X, Xu F, Zhang L, Song Y, Liu X, Zhang J, Han B, Zhang P, Chen Y. Therapeutic target database update 2012:a resource for facilitating targetoriented drug discovery. Nucleic Acids Res,2012,40: D1128-D1136.
2.李叶,唐浩国,刘建学.黄酮类化合物抑菌作用的研究进展.农产品加工(学刊),2008,12：53-55.
3. 肖枝洪,朱强,苏理云,魏正元,袁中尚.多元数据分析及其R实现.北京：科学出版社,2013.
4. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. JMol Biol,1990,215:403-410.
5. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res,1997,25:3389-3402.
6. Anderson WF. Structural genomics and drug discovery for infectious diseases. Infect Disord Drug Targets,2009,9:507-517.
7. Anishetty S, Pulimi M, Pennathur G. Potential drug targets in Mycobacterium tuberculosis through metabolic pathway analysis. Comput Biol Chem, 2005, 29: 368-378.
8. Arakawa K, Yamada Y, Shinoda K. GEM System: automatic prototyping of cell-wide metabolic pathway models from genomes. BMC Bioinformatics,2006, 7: 168.
9. Bakheet TM, Doig AJ. Properties and indentification of human protein drug targets. Bioinformatics.2009, 25:7-451.
10. Bakheet TM, Doig AJ. Properties and identification of antibiotic drug targets. BMC Bioinformatics, 2010, 11:195.
11. Barabasi AL, Albert R. Emergence of scaling in random networks. Science, 1999, 286:509-512.
12. Bar-Even A, Noor E, Flamholz A, Buescher JM, Milo R. Hydrophobicity and charge shape cellular metabolite concentrations. PLoS Comput Biol,2011,7:e1002166.
13. Barh D, Tiwari S, Jain N, Ali A, Santos AR, Misra AN, Azevedo V, Kumar A. In silico subtractive genomics for target identification in human bacterial pathogens. Drug Dev Res,2011,72:162-177.
14. Batagelj V, Mrvar A. Pajek-analysis and visualization of large networks. Chapter in Graph Drawing Software. Berlin: Springer,2003,77-103.
15. Becker SA, Feist AM, Mo ML, Hannum G, Palsson B?, Herrgard MJ. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox Nat Protoc,2007,2:727-738.
16. Bennett BD, Kimball EH, Gao M, Osterhout R, Van Dien SJ, Rabinowitz JD. Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli. Nat Chem Biol,2009,5:593-599.
17. Bernard FX, Sable S, Cameron B, Provost J, Desnottes, JF, Crouzet J, Blanche F. Glycosylated flavones as selective inhibitors of topoisomerase Ⅳ. Antimicrob Agents Chemother,1997,41:992-998.
18. Bielaszewska M, Mellmann A, Zhang W, Kock R, Fruth A, Bauwens A, Peters G, Karch H. Characterisation of the Escherichia coli strain associated with an outbreak of haemolytic uraemic syndrome in Germany,2011:a microbiological study. Lancet Infect Dis,2011,11:671-676.
19. Brotz-Oesterhelt H, Bandow JE, Labischinski H. Bacterial proteomics and its role in antibacterial drug discovery. Mass Spectrom Rev,2005,24:549-565.
20. Bruccoleri RE, Dougherty TJ, Davison DB. Concordance analysis of microbial genomes. Nucleic Acids Res,1998,26:4482-4486.
21. Bogoyevitch MA, Barr RK, Ketterman AJ. Peptide inhibitors of protein kinases-discovery, characterisation and use. Biochim Biophys Acta, 2005, 1754: 79-99.
22. Boser BE, Guyon IM and Vapnik VN. A training algorithm for optimal margin classifiers.5th Annual ACM Workshop on COLT,1992,144-152.
23. Bumann D and Valdivia RH. Identification of host-induced pathogen genes by differential fluorescence induction reporter systems. Nat Protoc,2007,2: 770-777.
24. Bush K. Antibacterial drug discovery in the 21st century. Clin Microbiol Infect, 2004, 4:10-17.
25. Caspi R, Foerster H, Fulcher CA, Kaipa P, Krummenacker M, Latendresse M, Paley S, Rhee SY, Shearer AG, Tissier C, Walk TC, Zhang P, Karp PD. The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res,2008,36:D623-D631.
26. Caturla N, Vera-Samper E, Villalain J, Mateo CR, Micol V. The relationship between the antioxidant and the antibacterial properties of galloylated catechins and the structure of phospholipid model membranes. Free Radical Bio Med,2003,34: 648-662.
27. Chahrour O, Cairns D, Omran Z. Small molecule kinase inhibitors as anti-cancer therapeutics. Mini Rev Med Chem,2012,12:399-411.
28. Cortes C and Vapnik V. Support vector networks. Mach Learn,1995,20:273-295.
29. Cushnie TP, Lamb AJ. Antimicrobial activity of flavonoids. Int J Antimicrob Agents, 2005,26:343-56.
30. Cushnie TP, Lamb AJ. Recent advances in understanding the antibacterial properties of flavonoids. Int J Antimicrob Agents,2011,38:99-107.
31. Dehbi M, Moeck G, Arhin FF, Bauda P, Berqeron D, Kwan T, Liu J, McCarty J, Dubow M, Pelletier J. Inhibition of transcription in Staphylococcus aureus by a primary sigma factor-binding polypeptide from phage Gl. J Bacteriol,2009,191: 3763-3771.
32. Di L, Fish PV, Mano T. Bridging solubility between drug discovery and development. Drug Discov Today,2012,17:486-495.
33. Dobson PD, Patel Y, Kell DB.'Metabolite-likeness' as a criterion in the design and selection of pharmaceutical drug libraries. Drug Discov Today,2009,14:31-40.
34. Dundas J, Ouyang Z, Tseng J, Binkowski A, Turpaz Y, Liang J. CASTp:computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucl Acids Res,2006,34:W116-W118.
35. Fang X, Wallqvist A, Reifman J. Development and analysis of an in vivo-compatible metabolic network of Mycobacterium tuberculosis. BMC Syst Biol,2010,4:160.
36. Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson B?. A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol,2007,3:121.
37. Fell D. Understanding the Control of Metabolism. London: Porland Press, 1996.
38. Fendt SM, Buescher JM, Rudroff F, Picotti P, Zamboni N, Sauer U. Tradeoff between enzyme and metabolite efficiency maintains metabolic homeostasis upon perturbations in enzyme capacity. Mol Syst Biol,2010,6: 356.
39. Filutowicz M, Jonczyk P. The gyrB gene product functions in both initiation and chain polymerization of Escherichia coli chromosome replication: suppression of the initiation deficiency in gyrB-ts mutants by a class of rpoB mutations. Mol Gen Genet, 1983,191:282-287.
40. Fischer HP, Freiberg C. Applications of transcriptional profiling in antibiotics discovery and development. Prog Drug Res,2007,64: 23-47.
41. Fleischmann RD, Adams MD, White O, Clayton RA, et al. Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science, 1999, 269: 496-512.
42. Freiberg C, Brotz-Oesterhelt H, Labischinski H. The impact of transcriptome and proteome analyses on antibiotic drug discovery. Curr Opin Microbiol, 2004, 7: 451-459.
43. Freiberg C, Brotz-Oesterhelt H. Functional genomics in antibacterial drug discovery. Drug Discov Today,2005,10: 927-935.
44. Friedman M. Overview of antibacterial, antitoxin, antiviral, and antifungal activities of tea flavonoids and teas. Mol Nutr Food Res,2007,51:116-134.
45. Furlanello C, Serafini M, Merler S, Jurman G. An accelerated procedure for recursive feature ranking on microarray data. Neural Netw, 2003,16:641-648.
46. Gastiger J, Engel T. Chemoinformatics. Wiley-VCH Verlag GmbH & Co. KGaA. 2004,203-504.
47. Goldsmith P, Fenton H, Morris-Stiff G, Ahmad N, Fisher J, Prasad KR. Metabonomics:a useful tool for the future surgeon. JSurg Res,2010,160:122-132.
48. Goodacre R, Vaidyanathan S, Dunn W B, Harrigan G G, Kell D B. Metabolomics by numbers:acquiring and understanding global metabolite data. Trends Biotechnol, 2004,22:245-252.
49. Gordon NC, Wareham DW. Antimicrobial activity of the green tea polyphenol (-)-epigallocatechin-3-gallate (EGCG) against clinical isolates of Stenotrophomonas maltophilia. Int J Antimicrob Agents,2010,36:129-131.
50. Guyon I,Weston J, Barnhill S. Gene selection for cancer classification using support vector machine. Mach Learn,2002,46,389-422.
51. Haraquchi H, Tanimoto K, Tamura Y, Mizutani K, Kinoshita T. Mode of antibacterial action of retrochalcones from Glycyrrhiza inflata. Phytochemistry, 1998,48: 125-129.
52. Heath R, White S, Rock C. Inhibitors of fatty acid synthesis as antimicrobial chemotherapeutics. Appl Microbiol Biotechnol,2002,58:695-703.
53. Henry CS, DeJongh M, Best AA, Frybarqer PM, Linsay B, Stevens RL High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol,2010,28:977-982.
54. Hernandez I, Alegre L, Van Breusegem F, Munne-Bosch S. How relevant are flavonoids as antioxidants in plants? Trends Plant Sci,2009, 14:125-132.
55. Hormozdiari F, Salari R, Bafna V, Sahinalp SC. Protein-protein interaction network evaluation for identifying potential drug targets. JComput Biol,2010,17:669-684.
56. Huss M, Holme P. Currency and commodity metabolites:their identification and relation to the modularity of metabolic networks. IET Syst Biol,2007,1:280-285
57. Ikigai H, Nakae T, Hara Y, Shimamura T. Bactericidal catechins damage the lipid bilayer. Biochim Biophys Acta,1993,1147:132-136.
58. Ioerger TR, Sacchettini JC. Structural genomics approach to drug discovery for Mycobacterium tuberculosis. Curr Opin Microbiol,2009,12:318-325.
59. Jeong H, Tombor B, Albert R,Oltvai ZN, Barabasi AL. The large-scale organization of metabolic networks. Nature,2000,407:651-654.
60. Jiang YY, Kong DX, Qin T, Zhang HY. How does oxygen rise drive evolution? Clues from oxygen-dependent biosynthesis of nuclear receptor ligands. Biochem Biophys Res Commun,391:1158-1160.
61. Jones D. The antibacterial lead discovery challenge. Nat Rev Drug Discov,2010,9: 751-752.
62. Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y. KEGG for linking genomes to life and the environment. Nucleic Acids Res,2008,36: D480-D484.
63. Kim PJ, Lee DY, Kim TY, Lee KH, Jeong H, Lee SY, Park S. Metabolite essentiality elucidates robustness of Escherichia coli metabolism. Proc Natl Acad Sci USA,2007, 104:13638-13642.
64. Kim TY, Kim HU, Lee SY. Metabolite-centric approaches for the discovery of antibacterials using genome-scale metabolic networks. Metab Eng, 2010, 12: 105-111.
65. Kim HU, Kim SY, Jeong H, Kim TY, Kim JJ, Choy HE, Yi KY, Rhee JH, Lee SY. Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery. Mol Syst Biol,2011,7: 460.
66. Kirst HA. Antibiotics versus resistant bacteria: a continual challenge. Expert Opin Ther Pat,2010,20:1271-1272.
67. Klamt S, Saez-Rodriguez J, Gilles ED. Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Syst Biol,2007,1:2.
68. Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, Djoumbou Y, Eisner R, Guo AC, Wishart DS. DrugBank 3.0:a comprehensive resource for' omics' research on drugs. Nucleic Acids Res.,2011,39: D1035-D1041.
69. Kohavi R and John GH. Wrappers for feature subset selection. Artif Intell, 1997, 97: 273-324.
70. Koul A, Arnoult E, Lounis N, Guillemont J, Andries K. The challenge of new drug discovery for tuberculosis. Nature,2011,469:483-490.
71. Kumarasamy KK, et al. Emergence of a new antibiotic resistance mechanism in India, Pakistan, and the UK: a molecular, biological, and epidemiological study. Lancet Infect Dis,2010,10: 597-602.
72. Kummel A, Panke S, Heinemann M. Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol Syst Biol, 2006, 2:2006.0034.
73. Kushwaha SK, Shakya M. Protein interaction network analysis--approach for potential drug target identification in Mycobacterium tuberculosis. J Theor Biol, 2010, 262:284-294.
74. Lamichhane G, Freundlich JS, Ekins S, Wickramaratne N, Nolan ST, Bishai WR. Essential Metabolites of Mycobacterium tuberculosis and Their Mimics. MBio, 2011, 2:e00301-e00310.
75. Lancet. Urgently needed: new antibiotics. Lancet,2009,374:1868.
76. Langley P. Selection of relevant features in machine learning[C]. Proc of AAAI Fall Symposium on Relevance. New Orleans,1994,1-5.
77. Leach AR, Gillet VJ. An Introduction of Chemoinformatics. The Netherlands. Kluwer Academic Publishers.2003,53-76.
78. Lee DY, Yun H, Park S, Lee SY. MetaFluxNet: the management of metabolic reaction information and quantitative metabolic flux analysis. Bioinformatics, 2003,19: 2144-2146.
79. Lee DS, Burd H, Liu J, Almaas E, Wiest O, Barabasi AL, Oltvai ZN, Kapatral V. Comparative genome-scale metabolic reconstruction and flux balance analysis of multiple Staphylococcus aureus genomes identify novel antimicrobial drug targets. J Bacteriol,2009,191:4015-4024.
80. Li H, Ung CY, Yap CW, Xue Y, Li ZR, Cao ZW, Chen YZ. Prediction of genotoxicity of chemical compounds by statistical learning methods. Chem Res Toxicol, 2005, 18: 1071-1080.
81. Liu J, Dehbi M, Moeck G, Arhin F, Bauda P, Bergeron D, Callejo M, Ferretti V, Ha N, Kwan T, McCarty J, Srikumar R, Williams D, Wu JJ, Gros P, Pelletier J, DuBow M. Antimicrobial drug discovery through bacteriophage genomics. Nat Biotechnol, 2004, 22:185-191.
82. Lu MF, Xiao ZT, Zhang HY. Where do health benefits of flavonoids come from? Insights from flavonoid targets and their evolutionary history. Biochem Biophys Res Commun,2013,434:701-704.
83. Lu W, Bennett BD, Rabinowitz JD. Analytical strategies for LC-MS-based targeted metabolomics. J Chromatogr B Analyt Technol Biomed Life Sci,2008,871:236-242.
84. Ma H, Zeng AP. Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics,2003,19: 270-277.
85. Mori A, Nishino C, Enoki N, Tawata S. Antibacterial activity and mode of action of plant flavonoids against Proteus vulgaris and Staphylococcus aureus, Phytochemistry. 1987,26: 2231-2234.
86. Morya VK, Varun D, Mecarty SD, Raghuvir S. In silico Analysis of Metabolic Pathways for Identification of Putative Drug Targets for Staphylococcus aureus. J Comput Sci Syst Biol, 2010,3:62-69.
87. Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem,1998,19:1639-1662.
88. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew Rk, Goodsell DS, Olson AJ. AutoDock4 and AutoDockTools4:Automated docking with selective receptor flexibility. J Comput Chem,2009,30:2785-2791.
89. Murray CW, Baxter CA, Frenkel AD. The sensitivity of the results of molecular docking to induced fit effects: application to thrombin, thermolysin and neuraminidase. J Comput Aided Mol Des,1999,13:547-62.
90. Nagaraj NS, Singh OV. Using genomics to develop novel antibacterial therapeutics. Crit Rev Microbiol,2010,36:340-348.
91. Naray-Szabo G and Nagy P. Electrostatic lock-and-key model for the study of biological isosterism: role of structural water in the binding of basic pancreatic trypsin inhibitor to beta-trypsin. Enzyme,1986,36:44-53.
92. Ng WL, Kazmierczak KM, Robertson GT, Gilmour R, Winkler ME. Transcriptional regulation and signature patterns revealed by microarray analyses of Streptococcus pneumoniae R6 challenged with sublethal concentrations of translation inhibitors. J Bacteril,2003,185:359-370.
93. Nicholson JK, Lindon JC, Holmes E.'Metabonomics':understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica,1999,29:1181-1189.
94. Oberhardt MA, Palsson B?, Papin JA. Applications of genome-scale metabolic reconstructions. Mol Syst Biol, 2009, 5:320.
95. Oliveira AP, Nielsen J, Forster J. Modeling Lactococcus lactis using a genome-scale flux model. BMC Microbiol, 2005,5:39.
96. Otsuka N, Liu MH, Shiota S, Ogawa W, Kuroda T, Hatano T, Tsuchiya T. Anti-methicillin resistant Staphylococcus aureus (MRSA) compounds isolated from Laurus nobilis. Biol Pharm Bull,2008,31:1794-1797.
97. Payne DJ, Gwynn MN, Holmes DJ, Pompliano DL. Drugs for bad bugs:confronting the challenges of antibacterial discovery. Nat Rev Drug Discov,2007,6:29-40.
98. Perumal D, Samal A, Sakharkar KR, Sakharkar MK. Targeting multiple targets in Pseudomonas aeruginosa PAO1 using flux balance analysis of a reconstructed genome-scale metabolic network. J Drug Target, 2011,19:1-13.
99. Pinney JW, Shirley MW, McConkey GA, Westhead DR. metaSHARK:software for automated metabolic network prediction from DNA sequence and its application to the genomes of Plasmodium falciparum and Eimeria tenella. Nucleic Acids Res, 2005, 33:1399-1409.
100.Plaimas K, Eils R, Konig R. Identifying essential genes in bacterial metabolic networks with machine learning methods. BMC Syst Biol,2010,4:56.
101.Plaper A, Golob M, Hafner I, Oblak M, Solmajer T, Jerala R. Characterization of quercetin binding site on DNA gyrase. Biochem Biophys Res Commun, 2003,306: 530-536.
102.Pohjanen E, Thysell E, Lindberg J, Schuppe-Koistinen I, Moritz T, Jonsson P, Antti H. Statistical multivariate metabolite profiling for aiding biomarker pattern detection and mechanistic interpretations in GC/MS based metabolomics. Metabolomics, 2006, 2:257-268.
103.Reo NV. NMR-based metabolomics. Drug Chem Toxicol, 2002,25:375-382.
104.Rice-Evans C. Flavonoids and isoflavones: absorption, metabolism, and bioactivity. Free Radic Biol Med,2004,36:827-828.
105.Roemer T, Boone C. Systems-level antimicrobial drug and drug synergy discovery. Nat Chem Biol,2013,9:222-231.
106.Scheer M, Grote A, Chang A, Schomburg I, Munaretto C, Rother M, Sohngen C, Stelzer M, Thiele J, Schomburg D. BRENDA, the enzyme information system in 2011. Nucleic Acids Res,2011,39: D670-D676.
107. Schilling CH, Edwards JS, Palsson BO. Toward metabolic phenomics: Analysis of genomic data using flux balances. Biotechnol Prog,1999,15:288-295.
108. Schuster S, Dandekar T, Fell DA. Detection of elementary flux modes in biochemical networks:A promising tool for pathway analysis and metabolic engineering. Trends Biotechnol,1999,17:53-60.
109. Schuster S, Fell DA, Dandekar T. A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nat Biotechnol,2000,18:326-332.
110.Shaw KJ, Morrow BJ. Transcriptional profiling and drug discovery. Curr Opin Pharmacol,2003,3,508-512.
111.Shen Y, Liu J, Estiu G, Isin B, Ahn YY, Lee DS, Barabasi AL, Kapatral V, Wiest O, Oltvai ZN. Blueprint for antimicrobial hit discovery targeting metabolic networks. Proc Natl Acad Sci USA,2010,107:1082-1087.
112.Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. Cytoscape 2.8:new features for data integration and network visualization. Bioinformatics, 2011,27:431-432.
113.Stanton TB. A call for antibiotic alternatives research. Trends Microbiol, 2013, 21:111-113.
114.Steinsiek S, Frixel S, Stagge S, Bettenbrock K. Characterization of E. coli MG1655 and frdA and sdhC mutants at various aerobiosis levels. J Biotechnol, 2011,154: 35-45.
115.Tasdemir D, Lack G, Brun R, Ruedi P, Scapozza L, Perozzo R. Inhibition of Plasmodium falciparum fatty acid biosynthesis:evaluation of FabG, FabZ, and FabI as drug targets for flavonoids. JMed Chem,2006,49:3345-3353.
116.Thiele I, Palsson B?. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc,2010,5:93-121.
117.Todeschini R, Consonni V. Handbook of Molecular Descriptors. Weinheim. Germany. Wiley-VCH.2000.
118.Tsuchiya H and Iinuma M. Reduction of membrane fluidity by antibacterial sophoraflavanone G isolated from Sophora exigua. Phytomedicine,2000,7:161-165.
119.van den Boogaard J, Kibiki GS, Kisanga ER, Boeree MJ, Aarnoutse RE. New drugs against tuberculosis:problems, progress, and evaluation of agents in clinical development. Antimicrob Agents Chemother,2009,53:849-862.
120.Vapnik V and Lerner A. Pattern recognition using generalized portrait method. Automat Rem Contr,1963,24:774-780.
121.Vapnik V. The Nature of Statistical Learning Theory. Springer. New York.1995.
122.Varner J, Ramdrishna D. Metabolic engineering from a cybernetic perspective.1. Theoretical preliminaries. Biotechnol Prog,1999, 15:407-425.
123.Wickramasinghe SR, Inglis KA, Urch JE, Miiller S, van Aalten DM, Fairlamb AH. Kinetic, inhibition and structural studies on 3-oxoacyl-ACP reductase from Plasmodium falciparum, a key enzyme in fatty acid biosynthesis. Biochem J, 2006, 393:447-57.
124.World Health Organization. Global tuberculosis report 2013,2013.
125.Wu D, Kong Y, Han C, Chen J, Hu L, Jiang H, Shen X. D-Alanine:D-alanine ligase as a new target for the flavonoids quercetin and apigenin. Int J Antimicrob Agents, 2008,32:421-426.
126.Xiong M, Zhang H.-Y. Finding Hsp90 inhibitors by drug repurposing: the power of chemical genetics. Drug Discov Today,2012,17: 531-533.
127.Xue Y, Li H, Ung CY, Yap CW, Chen YZ. Classifiction of a Diverse set of Tetrahymena pyriformis Toxicity Chemical compounds from molecular descriptors by statistical learning methods. Chem Res Toxicol.2006,19:1030-1039.
128.Yeh I, Hanekamp T, Tsoka S, Karp PD, Altman RB. Computational analysis of Plasmodium falciparum metabolism: organizing genomic information to facilitate drug discovery. Genome Res,2004,14:917-924.
129.Yu H, Yang J, Wang W, Han J. Discovering Compact and Highly Discriminative Feature or Feature Combinations of Drug Actives Using Support Vector Machines. Proc IEEE Comput Soc Bioinform Conf. Standford.2003.220-228.
130.Zamboni N, Kummel A, Panke S, Heinemann M. anNET: a tool for network-embedded thermodynamic analysis of quantitative metabolome data. BMC Bioinformatics,2008,9:199.
131.Zhang YM, Rock CO. Evaluation of epigallocatechin gallate and related plant polyphenols as inhibitors of the FabG and FabI reductases of bacterial type II fatty-acid synthase. JBiol Chem,2004,279:30994-31001.
132.Zhu F, Shi Z, Qin C, Tao L, Liu X, Xu F, Zhang L, Song Y, Liu X, Zhang J, Han B, Zhang P, Chen Y. Therapeutic target database update 2012:a resource for facilitating targetoriented drug discovery. Nucleic Acids Res,2012,40: D1128-D1136.