期刊论文详细信息
BMC Systems Biology
Antibacterial mechanisms identified through structural systems pharmacology
Bernhard O Palsson1  Philip E Bourne3  Lei Xie2  Roger L Chang4 
[1] Department of Bioengineering, University of California San Diego, La Jolla, CA 92093-0412, USA;The Graduate Center, The City University of New York, New York, NY 10065, USA;San Diego Supercomputer Center, University of California San Diego, La Jolla, CA USA;Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
关键词: Escherichia coli;    Ligand binding;    Metabolic model;    Antibacterial;    Structural systems pharmacology;   
Others  :  1142067
DOI  :  10.1186/1752-0509-7-102
 received in 2013-07-02, accepted in 2013-10-07,  发布年份 2013
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【 摘 要 】

Background

The growing discipline of structural systems pharmacology is applied prospectively in this study to predict pharmacological outcomes of antibacterial compounds in Escherichia coli K12. This work builds upon previously established methods for structural prediction of ligand binding pockets on protein molecules and utilizes and expands upon the previously developed genome scale model of metabolism integrated with protein structures (GEM-PRO) for E. coli, structurally accounting for protein complexes. Carefully selected case studies are demonstrated to display the potential for this structural systems pharmacology framework in discovery and development of antibacterial compounds.

Results

The prediction framework for antibacterial activity of compounds was validated for a control set of well-studied compounds, recapitulating experimentally-determined protein binding interactions and deleterious growth phenotypes resulting from these interactions. The antibacterial activity of fosfomycin, sulfathiazole, and trimethoprim were accurately predicted, and as a negative control glucose was found to have no predicted antibacterial activity. Previously uncharacterized mechanisms of action were predicted for compounds with known antibacterial properties, including (1-hydroxyheptane-1,1-diyl)bis(phosphonic acid) and cholesteryl oleate. Five candidate inhibitors were predicted for a desirable target protein without any known inhibitors, tryptophan synthase β subunit (TrpB). In addition to the predictions presented, this effort also included significant expansion of the previously developed GEM-PRO to account for physiological assemblies of protein complex structures with activities included in the E. coli K12 metabolic network.

Conclusions

The structural systems pharmacology framework presented in this study was shown to be effective in the prediction of molecular mechanisms of antibacterial compounds. The study provides a promising proof of principle for such an approach to antibacterial development and raises specific molecular and systemic hypotheses about antibacterials that are amenable to experimental testing. This framework, and perhaps also the specific predictions of antibacterials, is extensible to developing antibacterial treatments for pathogenic E. coli and other bacterial pathogens.

【 授权许可】

   
2013 Chang et al.; licensee BioMed Central Ltd.

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【 参考文献 】
  • [1]Tan H, Ge X, Xie L: Structural systems pharmacology: a new frontier in discovering novel drug targets. Curr Drug Targets 2013, 14:952-958.
  • [2]Xie L, Bourne PE: Detecting evolutionary relationships across existing fold space, using sequence order-independent profile-profile alignments. Proc Nat Acad Sci USA 2008, 105:5441-5446.
  • [3]Xie L, Li J, Bourne PE: Drug discovery using chemical systems biology: identification of the protein-ligand binding network to explain the side effects of CETP inhibitors. PLoS Comput Biol 2009, 5:e1000387.
  • [4]Ren J, Xie L, Li WW, Bourne PE: SMAP-WS: a parallel web service for structural proteome-wide ligand-binding site comparison. Nucleic Acids Res 2010, 38:W441-W444.
  • [5]Kinnings SL, Liu N, Buchmeier N, Tonge PJ, Xie L, Bourne PE: Drug discovery using chemical systems biology: repositioning the safe medicine Comtan to treat multi-drug and extensively drug resistant tuberculosis. PLoS Comput Biol 2009, 5:e1000423.
  • [6]Chang RL, Xie L, Bourne PE, Palsson BO: Drug off-target effects predicted using structural analysis in the context of a metabolic network model. PLoS Comput Biol 2010, 6:e1000938.
  • [7]Kinnings SL, Xie L, Fung KH, Jackson RM, Bourne PE: The Mycobacterium tuberculosis drugome and its polypharmacological implications. PLoS Comput Biol 2010, 6:e1000976.
  • [8]Ho Sui SJ, Lo R, Fernandes AR, Caulfield MD, Lerman JA, Xie L, Bourne PE, Baillie DL, Brinkman FS: Raloxifene attenuates Pseudomonas aeruginosa pyocyanin production and virulence. Int J Antimicrob Agents 2012, 40:246-251.
  • [9]Chang RL, Andrews K, Kim D, Li Z, Godzik A, Palsson BO: Structural systems biology evaluation of metabolic thermotolerance in Escherichia coli. Science 2013, 340:1220-1223.
  • [10]Orth JD, Conrad TM, Na J, Lerman JA, Nam H, Feist AM, Palsson BO: A comprehensive genome-scale reconstruction of Escherichia coli metabolism–2011. Molecular Syst Biol 2011, 7:535.
  • [11]Keseler IM, Collado-Vides J, Santos-Zavaleta A, Peralta-Gil M, Gama-Castro S, Muniz-Rascado L, Bonavides-Martinez C, Paley S, Krummenacker M, Altman T, et al.: EcoCyc: a comprehensive database of Escherichia coli biology. Nucleic Acids Res 2011, 39:D583-D590.
  • [12]Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE: The protein data bank. Nucleic Acids Res 2000, 28:235-242.
  • [13]Krissinel E, Henrick K: Inference of macromolecular assemblies from crystalline state. J Molecular Biol 2007, 372:774-797.
  • [14]Li M, Smith CJ, Walker MT, Smith TJ: Novel inhibitors complexed with glutamate dehydrogenase: allosteric regulation by control of protein dynamics. J Biolog Chem 2009, 284:22988-23000.
  • [15]Zhang QC, Petrey D, Deng L, Qiang L, Shi Y, Thu CA, Bisikirska B, Lefebvre C, Accili D, Hunter T, et al.: Structure-based prediction of protein-protein interactions on a genome-wide scale. Nature 2012, 490:556-560.
  • [16]Glazer DS, Radmer RJ, Altman RB: Improving structure-based function prediction using molecular dynamics. Structure 2009, 17:919-929.
  • [17]Lahti JL, Tang GW, Capriotti E, Liu T, Altman RB: Bioinformatics and variability in drug response: a protein structural perspective. J Royal Soc Int 2012, 9:1409-1437.
  • [18]Zhang Y, Thiele I, Weekes D, Li Z, Jaroszewski L, Ginalski K, Deacon AM, Wooley J, Lesley SA, Wilson IA, et al.: Three-dimensional structural view of the central metabolic network of Thermotoga maritima. Science 2009, 325:1544-1549.
  • [19]Wang X, Wei X, Thijssen B, Das J, Lipkin SM, Yu H: Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nat Biotechnol 2012, 30:159-164.
  • [20]Cheng TM, Goehring L, Jeffery L, Lu YE, Hayles J, Novak B, Bates PA: A structural systems biology approach for quantifying the systemic consequences of missense mutations in proteins. PLoS Comput Biol 2012, 8:e1002738.
  • [21]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.
  • [22]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.
  • [23]Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000, 28:27-30.
  • [24]Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, et al.: DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res 2011, 39:D1035-D1041.
  • [25]Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP: ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 2012, 40:D1100-D1107.
  • [26]Weininger D: SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inform Com Sci 1988, 28:31-36.
  • [27]Cao Y, Jiang T, Girke T: Accelerated similarity searching and clustering of large compound sets by geometric embedding and locality sensitive hashing. Bioinformatics 2010, 26:953-959.
  • [28]Chen GS, Segel IH: Purification and properties of glycogen phosphorylase from Escherichia coli. Arch Biochem Biophys 1968, 127:175-186.
  • [29]Lipinski CA, Lombardo F, Dominy BW, Feeney PJ: Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Delivery Rev 2001, 46:3-26.
  • [30]Grosdidier A, Zoete V, Michielin O: SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res 2011, 39:W270-W277.
  • [31]Schellenberger J, Que R, Fleming RM, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, et al.: Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protocols 2011, 6:1290-1307.
  • [32]Orth JD, Thiele I, Palsson BO: What is flux balance analysis? Nat Biotechnol 2010, 28:245-248.
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