Review of QSPR modeling of mobilities of peptides in capillary zone electrophoresis

Journal of Liquid Chromatography and Related Technologies

Volumn 31, Issue 11-12, 2008, Pages 1808-1822

  Liu, K P ^a   Xia, B B ^a   Zhang, X Y ^a  

^a LANZHOU UNIVERSITY   (China)

Author keywords

CZE; Mobility; Peptide; QSPR; Review

Indexed keywords

AMINES; CAPILLARY ELECTROPHORESIS; EIGENVALUES AND EIGENFUNCTIONS; ELECTROPHORESIS; STRUCTURAL PROPERTIES;

CAPILLARY ZONE ELECTROPHORESIS (CZE); CURRENT TRENDS; NON-LINEAR METHODS; STRUCTURE PROPERTY RELATIONSHIPS;

ELECTROPHORETIC MOBILITY;

PEPTIDE;

BACK PROPAGATION NEURAL NETWORK; CAPILLARY ZONE ELECTROPHORESIS; CHEMOMETRICS; ELECTROPHORETIC MOBILITY; LINEAR DISCRIMINANT ANALYSIS; MOLECULAR MECHANICS; MULTIPLE LINEAR REGRESSION ANALYSIS; PRINCIPLE COMPONENT REGRESSION; QUANTITATIVE STRUCTURE PROPERTY RELATION; RADIAL BASIS FUNCTION NEURAL NETWORK; REVIEW; STATISTICAL ANALYSIS; SUPPORT VECTOR MACHINE;

EID: 45849088054     PISSN: 10826076     EISSN: 1520572X     Source Type: Journal    
DOI: 10.1080/10826070802129001     Document Type: Review

References (46)

1. Wolters, D.A.; Washburn, M.P.; Yates, J.R. An automated multidimensional protein identification technology for shotgun proteomics. Anal. Chem. 2001, 73, 5683-5690.
2. Kasicka, V. Recent advances in capillary electrophoresis and capillary electrochromatography of peptides. Electrophoresis 2003, 24, 4013-4046.
3. Tessier, B.; Blanchard, F.; Vanderesse, R.; Harscoat, C.; Marc, I. Applicability of predictive models to the peptide mobility analysis by capillary electrophoresis-electrospray mass spectrometry. J. Chromatogr A 2004, 1024, 255-266.
4. Kuhn, R.; Hoffstetter-Kuhn, S. Capillary Electrophoresis - Principle and Practice, Springer-Verlag: Berlin, 1993.
5. Fatemi, M.H.; Goudarzi, N. Quantitative structure property relationship study of the electrophoretic mobilities of some benzoic acids derivatives in different carrier electrolyte compositions. Electrophoresis 2005, 26, 2968-2973.
6. Yao, X.J.; Liu, M.C.; Zhang, X.Y.; Hu, Z.D.; Fan, B.T. Radial basis function network-based quantitative structure-property relationship for the prediction of Henry's law constant. Anal. Chim. Acta 2002, 462, 101-117.
7. Xue, C.X.; Zhang, R.S.; Liu, H.X.; Yao, X.J.; Liu, M.C.; Hu, Z.D.; Fan, B.T. QSAR models for the prediction of binding affinities to human serum albumin using the heuristic method and a support vector machine. J. Chem. Inf. Comp. Sci. 2004, 44, 1693-1700.
8. Luan, F.; Ma, W.P.; Zhang, H.X.; Zhang, X.Y.; Liu, M.C.; Hu, Z.D.; Fan, B.T., Prediction of pK(a) for neutral and basic drugs based on radial basis function neural networks and the heuristic method. Pharmaceut. Res. 2005, 22, 1454-1460.
9. Katritzky, A.R.; Lobanov, V.S.; Karelson, M. QSPR: The Correlation and quantitative prediction of chemical and physical properties from structure. Chem. Soc. Rev. 1995, 279-287.
10. Karelson, M.; Maran, U.; Wang, Y.; Katritzky, A.R. QSPR and QSAR models derived using large descriptor spaces. A review of CODESSA applications. Collect. Czech. Chem. Commun. 1999, 64, 1551-1571.
11. Katritzky, A.R.; Maran, U.; Lobanov, V.S.; Karelson, M. Structurally diverse quantitative structure-property relationship correlations of technologically relevant physical properties. J. Chem. Inf. Comput. Sci. 2000, 40, 1-18.
12. Chemdraw 2006; Cambridge Soft Corporation, 2006.
13. MDL ISIS Draw 2.5; MDL Information Systems, Inc., 2002.
14. HyperChem 4.0; Hypercube, Inc., 1994.
15. Gaussian 98, Revision A.7; Gaussian Inc., 1998.
16. Sybyl 7.2; Tripos Associates, Inc., 2003.
17. MOPAC 2000; Fujitsu Limited, 1999.
18. Xia, B.B.; Ma, W.P.; Zhang, X.Y.; Fan, B.T. Quantitative structure-retention relationships for organic pollutants in biopartitioning micellar chromatography. Anal. Chim. Acta 2007, 598, (1), 12-18.
19. Dragon 5.4, Talete srl, Milan, Italy, 2006.
20. Turner, J.V.; Glass, B.D.; Agatonovic-Kustrin, S. Prediction of drug bioavailability based on molecular structure. Anal. Chim. Acta 2003, 485, 89-102.
21. Zhang, D.R. QSPR studies of PCBs by the combination of genetic algorithms and PLS analysis. Comput. Chem. 2001, 25, 197-204.
22. Oblak, M.; Randic, M.; Solmajer, T. Quantitative structure-activity relationship of flavonoid analogues. 3. Inhibition of p56(lck) protein tyrosine kinase. J. Chem. Inf. Comp. Sci. 2000, 40, (4), 994-1001.
23. Deconinck, E.; Coomans, D.; Heyden, Y.V. Exploration of linear modelling techniques and their combination with multivariate adaptive regression splines to predict gastro-intestinal absorption of drugs. J. Pharmaceut. Biomed. 2007, 43, 119-130.
24. Tantishaiyakul, V.; Worakul, N.; Wongpoowarak, W. Prediction of solubility parameters using partial least square regression. Int. J. Pharm. 2006, 325, 8-14.
25. Katritzky, A.R.; Gordeeva, V. Traditional topological induces vs electronic, geometrical, and combined molecular descriptors in QSAR QSPR research. J. Chem. Inf. Comput. Sci. 1993, 33, 835-857.
26. Kachigan, S.K. Statistical Analysis, Radius Press: New York, 1986.
27. Garg, P.; Verma, J. In silico prediction of blood brain barrier permeability: An artificial neural network model. J. Chem. Inf. Comp. Sci. 2006, 46, 289-297.
28. Li, Q.F.; Yao X.J.; Chen X.G.; Liu, M.C.; Zhang, R.S.; Zhang, X.Y.; Hu, Z.D. Application of artificial neural networks for the simultaneous determination of a mixture of fluorescent dyes by synchronous fluorescence. Analyst 2000, 125, 2049-2053.
29. Xue, C.X.; Liu, H.X.; Yao, X.J.; Liu, M.C.; Hu, Z.D.; Fan, B.T. Study of quantitative structure-mobility relationship of carboxylic and sulphonic acids in capillary electrophoresis. J. Chromatogr. A 2004, 1048, 233-243.
30. Ma, W.P.; Zhang, X.Y.; Luan, F.; Zhang, H.X.; Zhang, R.S.; Liu, M.C.; Hu, Z.D.; Fang, B.T. Support vector machine and the heuristic method to predict the solubility of hydrocarbons in electrolyte. J. Phys. Chem. A 2005, 109, 3485-3492.
31. Maggiora, G.M.; Elrod, D.W.; Trenary, R.G. Computational neural networks as nodel-free mapping devices. J. Chem. Inf. Comput. Sci. 1992, 32, 732-741.
32. Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn. 1995, 20, 273-297.
33. Wang, W.J.; Xu, Z.B.; Lu, W.Z.; Zhang, X.Y. Neurocomputing 2003, 55, 643-663.
34. Cifuentes, A.; Poppe, H. Behavior of peptides in capillary electrophoresis: Effect of peptide charge, mass and structure. Electrophoresis 1997, 18, 2362-2376.
35. Sanz-Nebot, V.; Benavente, F.; Barbosa, J. Liquid chromatography-mass spectrometry and capillary electrophoresis combined approach for separation and characterization of multicomponent peptide mixtures - Application to crude products of leuprolide synthesis. J. Chromatogr A 2002, 950, 99-111.
36. Offord, R.E. Nature (London) 1966, 211, 591-593.
37. Cifuentes, A.; Poppe, H. Simulation and optimization of peptide separation by capillary electrophoresis. J. Chromatogr A 1994, 680, 321-340.
38. Rickard, E.C.; Strohl, M.M.; Nielsen, R.G. Correlation of electrophoretic mobilities from capillary electrophoresis with physicochemical properties of proteins and peptides. Anal. Biochem. 1991, 197, 197-207.
39. Janini, G.M.; Metral, C.J.; Issaq, H.J.; Muschik, G.M. Peptide mobility and peptide mapping in capillary zone electrophoresis - Experimental determination and theoretical simulation. J. Chromatogr A 1999, 848, 417-433.
40. Solinova, V.; Kasicka, V.; Koval, D.; Hlavacek, J. Separation and investigation of structure-mobility relationships of insect oostatic peptides by capillary zone electrophoresis. Electrophoresis 2004, 25, 2299-2308.
41. Solinova, V.; Kasicka, V.; Sazelova, P.; Barth, T.; Miksik I. Separation and investigation of structure-mobility relationship of gonadotropin-releasing hormones by capillary zone electrophoresis in conventional and isoelectric acidic background electrolytes. J. Chromatogr A 2007, 1155, 146-153.
42. Jalali-Heravi, M.; Shen, Y.; Hassanisadi, M.; Khaledi, M.G. Prediction of electrophoretic mobilities of peptides in capillary zone electrophoresis by quantitative structure-mobility relationships using the offord model and artificial neural networks. Electrophoresis 2005, 26, 1874-1885.
43. Jalali-Heravi, M.; Shen, Y.; Hassanisadi, M.; Khaledi, M.G. Artificial neural network modeling of peptide mobility and peptide mapping in capillary zone electrophoresis. J Chromatogr A 2005, 1096, 58-68.
44. Ma, W.P.; Luan, F.; Zhang, H.X.; Zhang, X.Y.; Liu, M.C.; Hu, Z.D.; Fan, B.T. Accurate quantitative structure-property relationship model of mobilities of peptides in capillary zone electrophoresis. Analyst 2006, 131, 1254-1260.
45. Liu, H.X.; Yao, X.J.; Xue, C.X.; Zhang, R.S.; Liu, M.C.; Hu, Z.D.; Fan, B.T. Study of quantitative structure-mobility relationship of the peptides based on the structural descriptors and support vector machines. Anal. Chim. Acta 2005, 542, 249-259.
46. Yu, K.; Cheng, Y.Y. Machine learning techniques for the prediction of the peptide mobility in capillary zone electrophoresis. Talanta 2007, 71, 676-682.