Novel approaches to predict the retention of histidine-containing peptides in immobilized metal-affinity chromatography

Proteomics

Volumn 8, Issue 11, 2008, Pages 2185-2195

  Du, Hongying ^a   Zhang, Xiaoyun ^a   Wang, Jie ^a   Yao, Xiaojun ^a   Hu, Zhide ^a  

^a LANZHOU UNIVERSITY   (China)

Author keywords

Immobilized metal affinity chromatography; Local lazy regression; Peptides; Projection pursuit regression; Quantitative structure retention relationship

Indexed keywords

HISTIDINE; IMIDAZOLE; PEPTIDE DERIVATIVE;

ARTICLE; IMMOBILIZED METAL AFFINITY CHROMATOGRAPHY; MATHEMATICAL MODEL; PEPTIDE ANALYSIS; PREDICTION; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEOMICS; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

CHROMATOGRAPHY; CHROMATOGRAPHY, AFFINITY; DATABASES, PROTEIN; HISTIDINE; IMIDAZOLES; METALS; MODELS, STATISTICAL; PEPTIDES; PRINCIPAL COMPONENT ANALYSIS; PROTEINS; PROTEOME; PROTEOMICS; REGRESSION ANALYSIS; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 46049096405     PISSN: 16159853     EISSN: 16159861     Source Type: Journal    
DOI: 10.1002/pmic.200700788     Document Type: Article

References (42)

1. Porath, J., Carlsson, J., Olsson, I., Belfrage, G., Metal chelate affinity chromatography, a new approach to protein fractionation. Nature 1975, 258, 598-599.
2. Waugh, D. S., Making the most of aYnity tags. Trends Biotechnol. 2005, 23, 316-320.
3. Chaga, G. S., Twenty-five years of immobilized metal ion affinity chromatography: Past, present and future. J. Biochem. Biophys. Meth. 2001, 49, 313-334.
4. Gaberc-Porekar, V., Menart, V., Perspectives of immobilized-metal affinity chromatography. J. Biochem. Biophys. Meth. 2001, 49, 335-360.
5. Zachariou, M., Immobilized metal ion affinity chromatography of proteins. Methods Mol. Biol. 2004, 251, 89-102.
6. Posewitz, M. C., Tempst, P., Immobilized gallium (III) affinity chromatography of phosphopeptides. Anal. Chem. 1999, 71, 2883-2892.
7. Hanora, A., Bernaudat, F., Plieva, F. M., Dainiak, M. B. et al., Screening of peptide affinity tags using immobilised metal affinity chromatography in 96-well plate format. J. Chromatogr. A 2005, 1087, 38-44.
8. Hochuli, E., Dobeli, H., Schacher, A., New metal chelate adsorbent selective for proteins and peptides containing neighbouring histidine residues. J. Chromatogr. 1987, 411, 177-184.
9. Smith, M. C., Furman, T. C., Ingolia, T. D., Pidgeon, C., Chelating peptide immobilized metal ion affinity chromatography. A new concept in affinity chromatography for recombinant proteins. J. Biol. Chem. 1988, 263, 7211-7215.
10. Mohanty, A. K., Wiener, M. C., Membrane protein expression and production: Effects of polyhistidine tag length and position. Protein Expres. Purif. 2004, 33, 311-325.
11. Kaliszan, R., Straten, M. A. v., Markuszewski, M., Cramers, C. A., Claessens, H. A., Molecular mechanism of retention in reversed-phase high-performance liquid chromatography and classification of modern stationary phases by using quantitative structure-retention relationships. J. Chromatogr. A 1999, 855, 455-486.
12. Golmohammadi, H., Fatemi, M. H., Artificial neural network prediction of retention factors of some benzene derivatives and heterocyclic compounds in micellar electrokinetic chromatography. Electrophoresis 2005, 26, 3438-3444.
13. Krokhin, O. V., Sequence-specific retention calculator algorithm for peptide retention prediction in ion-pair RP-HPLC: Application to 300- and 100-Å pore size C18 sorbents. Anal. Chem. 2006, 78, 7785-7795.
14. Morris, D. L. Jr., Sutton, J. N., Harper, R. G., Timperman, A. T., Reversed-Phase HPLC separation of human serum employing a novel saw-tooth gradient: Toward multidimensional proteome analysis. J. Proteome Res. 2004, 3, 1149-1154.
15. Baczek, T., Wiczling, P., Marsza, M., Heyden, Y. V., Kaliszan, R., Prediction of peptide retention at different HPLC conditions from multiple linear regression models. J. Proteome Res. 2005, 4, 555-563.
16. Baczek, T., Bucinski, A., Ivanov, A. R., Kaliszan, R., Artificial neural network analysis for evaluation of peptide MS/MS spectra in proteomics. Anal. Chem. 2004, 76, 1726-1732.
17. Kaliszan, R., Baczek, T., Cimochowska, A., Juszczyk, P. et al., Prediction of high-performance liquid chromatography retention of peptides with the use of quantitative structure-retention relationships. Proteomics 2005, 5, 409-415.
18. Shinoda, K., Sugimoto, M., Tomita, M., Ishihama, Y., Informatics for peptide retention properties in proteomic LC-MS. Proteomics 2008, 8, 787-798.
19. Hellberg, S., Eriksson, L., Jonsson, J., Lindgren, F. et al., Minimum analogue peptide sets (MAPS) for quantitative structure-activity relationships. Int. J. Pept. Protein Res. 1991, 37, 414-424.
20. Put, R., Heyden, Y. V., The evaluation of two-step multivariate adaptive regression splines for chromatographic retention prediction of peptides. Proteomics 2007, 7, 1664-1677.
21. Kermani, B. G., Kozlov, I., Melnyk, P., Zhao, C. et al., Using support vector machine regression to model the retention of peptides in immobilized metal-affinity chromatography. Sens. Actuators B Chem. 2007, 125, 149-157.
22. Kennard, R. W., Stone, L. A., Computer aided design of experiments. Technometrics 1969, 11, 137-148.
23. Katritzky, A. R., Lobanov, V. S., Karelson, M., CODESSA Version 2.0 Reference Manual. University of Florida, Gainesville, Florida, 1995-1997.
24. 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, 24, 279-287.
25. Katritzky, A. R., Lobanov, V. S., Karelson, M., CODESSA: Training Manual. University of Florida, Gainesville, Florida 1995.
26. Friedman, J. H., Stuetzle, W., Projection pursuit regression. J. Am. Stat. Assoc. 1981, 76, 817-823.
27. Huber, P. J., Projection pursuit (with discussion). Ann. Statist. 1985, 13, 435-475.
28. Diaconis, P., Shahshahani, M., On nonlinear functions of linear combinations. SIAM J. Sci. Statist. Comput. 1984, 5, 175-191.
29. Du, Y. P., Liang, Y. Z., Yun, D., Data mining for seeking an accurate quantitative relationship between molecular structure and GC retention indices of alkenes by projection pursuit. J. Chem. Inf. Comput. Sci. 2002, 42, 1283-1292.
30. Liu, H. X., Yao, X. J., Liu, M. C., Hu, Z. D., Fan, B. T., Prediction of gas-phase reduced ion mobility constants (K0) based on the multiple linear regression and projection pursuit regression. Talanta 2007, 71, 258-263.
31. Tropsha, A., Gramatica, P., Gombar, V. K., The importance of being earnest validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb. Sci. 2003, 22, 69-77.
32. Aha, D. W., Lazy learning: Special issue editorial. Artif. Intell. Rev. 1997, 11, 7-10.
33. Atkeson, C. G., Moore, A. W., Schaal, S., Locally weighted learning. Artif. Intell. Rev. 1997, 11, 11-73.
34. Cleveland, W. S., Devlin, S. J., Grosse, S. J., Regression by local fitting: Methods, properties and computational algorithms. J. Econometrics 1988, 37, 87-114.
35. Birattari, M., Bontempi, G., Bersini, H., Lazy learning meets the recursive least squares algorithm, Proceedings of the 1998 conference on Advances in neutral information processing systems II, Adv. Neural Inf. Process. Syst. 1999, 11, 375.
36. Bontempi, G., Birattari, M., Bersini, H., Lazy learning for modeling and control design. Int. J. Control 1999, 72, 643-658.
37. Zhang, S. X., Golbraikh, A., Oloff, S., Kohn, H., Tropsha, A., A novel automated lazy learning QSAR (ALL-QSAR) approach: Method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models. J. Chem. Inf. Model. 2006, 46, 1984-1995.
38. Guha, R., Dutta, D., Jurs, P. C., Chen, T., Local lazy regression: Making use of the neighborhood to improve QSAR predictions. J. Chem. Inf. Model. 2006, 46, 1836-1847.
39. Pan, T. H., Li, S. Y., Cai, W. J., Lazy learning-based online identification and adaptive PID control: A case study for CSTR process. Ind. Eng. Chem. Res. 2007, 46, 472-480.
40. Venables, W. N., Smith, D. M., the R Development Core Team, An Introduction to R, Network Theory Ltd., 2004.
41. Birattari, M., Bontempi, G., The Lazy Package for R, Technical report IRIDIA-ULB, TR/IRIDIA/2003-38.
42. Zefirov, N. S., Kirpichenok, M. A., Izmailov, F. F., Trofimov, M. I., Scheme for the calculation of the electronegativities of atoms in a molecule in the framework of sanderson's principle. Dokl. Akad. Nauk SSSR 1987, 296, 883-887.