Modeling the LPS neutralization activity of anti-endotoxins

Molecules

Volumn 14, Issue 5, 2009, Pages 1869-1888

  Thippakorn, Chadinee ^a   Suksrichavalit, Thummaruk ^a   Nantasenamat, Chanin ^a   Tantimongcolwat, Tanawut ^a   Isarankura Na Ayudhya, Chartchalerm ^a   Naenna, Thanakorn ^a   Prachayasittikul, Virapong ^a  

^a MAHIDOL UNIVERSITY   (Thailand)

Author keywords

Anti endotoxin; Artificial neural network; Endotoxin; Lipopolysaccharide; QSAR

Indexed keywords

BACTERIUM ANTIBODY; CYTOKINE; ENDOTOXIN; LIPOPOLYSACCHARIDE;

AGED; ARTICLE; CHEMICAL STRUCTURE; CHEMISTRY; CHILD; GRAM NEGATIVE BACTERIUM; HUMAN; IMMUNOLOGY; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

AGED; ANTIBODIES, BACTERIAL; CHILD; CYTOKINES; ENDOTOXINS; GRAM-NEGATIVE BACTERIA; HUMANS; LIPOPOLYSACCHARIDES; MODELS, MOLECULAR; MOLECULAR STRUCTURE; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 67249130797     PISSN: None     EISSN: 14203049     Source Type: Journal    
DOI: 10.3390/molecules14051869     Document Type: Article

References (44)

1. Hurley, J.C. Antibiotic-induced release of endotoxin: a reappraisal. Clin. Infect. Dis. 1992, 15, 840-854.
2. 2.Masoud,H.;Altman,E.;Richards,J.C.;Lam,J.S. Generalstrategyforstructuralanalysisoftheoligosaccharideregionoflipooli gosaccharides. StructureoftheoligosaccharidecomponentofPseudomonasaeruginosaIATSse rotype06mutantR5rough-typelipopolysaccharide.Biochemistry1994,33,10568-10578. (Pubitemid24292276)
3. Munford, R.S. Severe sepsis and septic shock: the role of gram-negative bacteremia. Annu. Rev. Pathol. 2006, 1, 467-496.
4. 4. Ulevitch, R.J.; Tobias, P.S. Recognition of gram-negative bacteria and endotoxin by the innate immune system. Curr. Opin. Immunol. 1999, 11, 19-22. (Pubitemid 29082331)
5. 5.Zhang,F.X.;Kirschning,C.J.;Mancinelli,R.;Xu,X.P.;Jin,Y.;Faure,E. ;Mantovani,A.;Rothe,M.;Muzio,M.;Arditi,M. Bacteriallipopolysaccharideactivatesnuclearfactor-kappaBthroughinterleukin- 1signalingmediatorsinculturedhumandermalendothelialcellsandmono nuclearphagocytes.J.Biol.Chem.1999,274,7611-7614.(Pubitemid129518142)
6. 6.Tsiotou,A.G.;Sakorafas,G.H.;Anagnostopoulos,G.;Bramis,J. Septicshock;currentpathogenicconceptsfromaclinicalperspective.Med.Sci.Monit. 2005,11,RA76-RA85.(Pubitemid40445995)
7. 7. Muhle, S.A.; Tam, J.P. Design of Gram-negative selective antimicrobial peptides. Biochemistry 2001, 40, 5777-5785. (Pubitemid 32440882)
8. 8. Khownium, K.; Wood, S.J.; Miller, K.A.; Balakrishna, R.; Nguyen, T.B.; Kimbrell, M.R.; Georg, G.I.; David, S.A. Novel endotoxin-sequestering compounds with terephthalaldehyde-bisguanylhydrazone scaffolds. Bioorg. Med. Chem. Lett. 2006, 16, 1305-1308. (Pubitemid 43143011)
9. Martin, G.S.; Mannino, D.M.; Eaton, S.; Moss, M. The epidemiology of sepsis in the United States from 1979 through 2000. New Engl. J. Med. 2003, 348, 1546-1554.
10. 10. Moss, M.; Martin, G.S. A global perspective on the epidemiology of sepsis. Intens. Care Med. 2004, 30, 527-529. (Pubitemid 38530087)
11. Siegel, J.P. Antiendotoxin antibodies. Ann. Intern. Med. 1995, 122, 315-316.
12. 12. David, S.A. Towards a rational development of anti-endotoxin agents: novel approaches to sequestration of bacterial endotoxins with small molecules. J. Mol. Recognit. 2001, 14, 370-387. (Pubitemid 34085333)
13. 13.David,S.A.;Silverstein,R.;Amura,C.R.;Kielian,T.;Morrison,D.C. Lipopolyamines: novelantiendotoxincompoundsthatreducemortalityinexperimentalsepsiscausedbygram- negativebacteria.Antimicrob.AgentsChemother.1999,43,912-919.(Pubitemid29165757)
14. Hase, S.; Rietschel, E.T. Isolation and analysis of the lipid A backbone. Lipid A structure of lipopolysaccharides from various bacterial groups. Eur. J. Biochem. 1976, 63, 101-107.
15. 15.Kim,K.S.;Kang,J.H.;Cross,A.S.;Kaufman,B.;Zollinger,W.;Sadoff,J. Functionalactivitiesofmonoclonalantibodies- theOsidechainofEscherichiacolilipopolysaccharidesinvitroandinvivo.J.Infect.Dis. 1988,157,47-53.(Pubitemid18037450)
16. 16. Raetz, C.R.; Whitfield, C. Lipopolysaccharide endotoxins. Annu. Rev. Biochem. 2002, 71, 635-700. (Pubitemid 34800232)
17. 17. Vaarala, O.; Vaara, M.; Palosuo, T. Effective inhibition of cardiolipin-binding antibodies in gramnegative infections by bacterial lipopolysaccharide. Scand. J. Immunol. 1988, 28, 607-612. (Pubitemid 19035717)
18. Zahringer, U.; Lindner, B.; Rietschel, E.T. Molecular structure of lipid A, the endotoxic center of bacterial lipopolysaccharides. Adv. Carbohydr. Chem. Biochem. 1994, 50, 211-276.
19. 19. Gozalbes, R.; Brun-Pascaud, M.; Garcia-Domenech, R.; Galvez, J.; Girard, P.M.; Doucet, J.P.; Derouin, F. Prediction of quino lone activity against Mycobacterium avium by molecular topology and virtual computational screening. Antimicrob. Agents Chemother. 2000, 44, 2764-2770. (Pubitemid 30737697)
20. 20. Nantasenamat, C.; Isarankura-Na-Ayudhya, C.; Naenna, T.; Prachayasittikul, V. Quantitative structure-imprinting factor relationship of molecularly imprinted polymers. Biosens. Bioelectron. 2007, 22, 3309-3317. (Pubitemid 46754891)
21. Nantasenamat, C.; Isarankura-Na-Ayudhya, C.; Naenna, T.; Prachayasittikul, V. Prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine. J. Mol. Graph. Model. 2008, 27, 188-196.
22. 22. Nantasenamat, C.; Isarankura-Na-Ayudhya, C.; Tansila, N.; Naenna, T.; Prachayasittikul, V. Prediction of GFP spectral properties using artificial neural network. J. Comput. Chem. 2007, 28, 1275-1289. (Pubitemid 46653001)
23. 23. Nantasenamat, C.; Naenna, T.; Isarankura-Na-Ayudhya, C.; Prachayasittikul, V. Quantitative prediction of imprinting factor of molecularly imprinted polymers by artificial neural network. J. Comput. Aid. Mol. Des. 2005, 19, 509-524. (Pubitemid 43150057)
24. Nantasenamat, C.; Naenna, T.; Isarankura-Na-Ayudhya, C.; Prachayasittikul, V. Recognition of DNA Splice Junction via Machine Learning Approaches. Excli J. 2005, 4, 114-129.
25. 25. Nantasenamat, C.; Piacham, T.; Tantimongcolwat, T.; Naenna, T.; Isarankura-Na-Ayudhya, C.; Prachayasittikul, V. QSAR model of the quorum-quenching N-acyl-homoserine lactone lactonase activity. J. Biol. Syst. 2008, 16, 279-293. (Pubitemid 351799306)
26. Worachartcheewan, A.; Nantasenamat, C.; Naenna, T.; Isarankura-Na- Ayudhya, C.; Prachayasittikul, V. Modeling the activity of furin inhibitors using artificial neural network. Eur. J. Med. Chem. 2008, 44, 1664-1673.
27. 27. Burns, M.R.; Wood, S.J.; Miller, K.A.; Nguyen, T.; Cromer, J.R.; David, S.A. Lysine-spermine conjugates: hydrophobic polyamine amides as potent lipopolysaccharide séquestrants. Bioorg. Med. Chem. 2005, 13, 2523-2536. (Pubitemid 40341656)
28. 28. Frecer, V.; Ho, B.; Ding, J.L. De Novo Design of Potent Antimicrobial Peptides Antimicrob. Agents Chemother. 2004, 48, 3349-3357. (Pubitemid 39129330)
29. 29. Liu, D.; DeGrado, W.F. De novo design, synthesis, and characterization of antimicrobial betapeptides. J. Am. Chem. Soc. 2001, 123, 7553-7559. (Pubitemid 32899391)
30. 30. Wakefield, D.H.; Klein, J.J.; Wolff, J.A.; Rozema, D.B. Membrane Activity and Transfection Ability of Amphipathic Polycations as a Function of Alkyl Group Size. Bioconjugate Chem. 2005, 16, 1204-1208. (Pubitemid 41368122)
31. 31. Burns, M.R.; Jenkins, S.A.; Wood, SJ.; Miller, K.; David, S.A. Structure-activity relationships in lipopolysaccharide neutralizers: design, synthesis, and biological evaluation of a 540-membered amphipathic bisamide library. J. Comb. Chem. 2006, 8, 32-43. (Pubitemid 43166079)
32. nd Ed.; Wiley-VCH: Weinheim, Germany, 1999.
33. 33. Bak, A.; Polanski, J. A 4D-QSAR study on anti-HIV HEPT analogues. Bioorg. Med. Chem. 2006, 14, 273-279. (Pubitemid 41635489)
34. Loukas, Y.L. Artificial neural networks in liquid chromatography: efficient and improved quantitative structure-retention relationship models. J. Chromatogr. A 2000, 904, 119-129.
35. 35. Zhang, R.; Yan, A.; Liu, M.; Liu, H.; Hu, Z. Application of artificial neural networks for prediction of the retention indices of alkylbenzenes. Chemometr. Intell. Lab. Syst. 1999, 45, 113-120. (Pubitemid 29087035)
36. 36. Agatonovic-Kustrin, S.; Zecevic, M.; Zivanovic, L. Use of ANN modelling in structure-retention relationships of diuretics in RP-HPLC J. Pharm. Biomed. Anal. 1999, 21, 95-103. (Pubitemid 29430146)
37. Niculescu, S.P. Artificial neural networks and genetic algorithms in QSAR. J. Mol. Struct. (Theochem) 2003, 622, 71-83.
38. NIST/SEMATECH. E-Handbook of Statistical Methods, http://www.itl.nist. gov/div898/handbook/.
39. 39. Miller, K.A.; Suresh Kumar, E.V.; Wood, S.J.; Cromer, J.R.; Datta, A.; David, S.A. Lipopolysaccharide sequestrants: structural correlates of activity and toxicity in novel acylhomospermines. J. Med. Chem. 2005, 48, 2589-2599. (Pubitemid 40516448)
40. MarvinSketch, Version 5.2. ChemAxon: Budapest, Hungary; http://www.chemaxon.com/product/msketch.html.
41. RECON, Version 5.5. Rensselaer Polytechnic Institute: Troy, New York, U.S.A.; http://www.drugmining.com/files/RECON/recondoc/WinRecon.html.
42. 42. Krasowski, M.D.; Hong, X.; Hopfinger, A.J.; Harrison, N.L. 4D-QSAR analysis of a set of propofol analogues: mapping binding sites for an anesthetic phenol on the GABA(A) receptor. J. Med. Chem. 2002, 45, 3210-3221. (Pubitemid 34774272)
43. Whitley, D.C.; Ford, M.G.; Livingstone, D.J. Unsupervised forward selection: a method for eliminating redundant variables. J. Chem. Inf. Comput. Sci. 2000, 40, 1160-1168.
44. Witten, I.; Frank, E. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann: San Francisco, CA, USA, 2000.