Informatics for peptide retention properties in proteomic LC-MS

Proteomics

Volumn 8, Issue 4, 2008, Pages 787-798

  Shinoda, Kosaku ^a,b   Sugimoto, Masahiro ^a,c   Tomita, Masaru ^a,b   Ishihama, Yasushi ^a,d  

^a KEIO UNIVERSITY   (Japan)

^b Keio University and Human Metabolome Technologies Inc   (Japan)

^c MITSUBISHI SPACE SOFTWARE CO LTD   (Japan)

^d JAPAN SCIENCE AND TECHNOLOGY AGENCY   (Japan)

Author keywords

Bioinformatics; Liquid chromatography tandem mass spectrometry; Neural networks; Peptide; QSRR

Indexed keywords

PEPTIDE; PROTEOME;

ALGORITHM; AMINO ACID COMPOSITION; ARTIFICIAL NEURAL NETWORK; BIOINFORMATICS; HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; ION EXCHANGE CHROMATOGRAPHY; LIQUID CHROMATOGRAPHY; MASS SPECTROMETRY; PRIORITY JOURNAL; PROTEOMICS; REVIEW; TANDEM MASS SPECTROMETRY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CHROMATOGRAPHY, HIGH PRESSURE LIQUID; COMPUTATIONAL BIOLOGY; NEURAL NETWORKS (COMPUTER); PEPTIDES; PROTEOMICS; TANDEM MASS SPECTROMETRY;

EID: 40649126964     PISSN: 16159853     EISSN: 16159861     Source Type: Journal    
DOI: 10.1002/pmic.200700692     Document Type: Review

References (108)

1. Knight, C. A., Paper chromatography of some lower peptides. J. Biol. Chem. 1951, 190, 753-756.
2. Pardee, A. B., Calculations on paper chromatography of peptides. J. Biol. Chem. 1951, 190, 757-762.
3. Mant, C. T., Burke, T. W., Black, J. A., Hodges, R. S., Effect of peptide chain length on peptide retention behaviour in reversed-phase chromatography. J. Chromatogr. 1988, 458, 193-205.
4. Meek, J. L., Prediction of peptide retention times in high-pressure liquid chromatography on the basis of amino acid composition. Proc. Natl. Acad. Sci. USA 1980, 77, 1632-1636.
5. Wilce, M. C., Aguilar, M. I., Hearn, M. T., High-performance liquid chromatography of amino acids, peptides and proteins. CXXII. Application of experimentally derived retention coefficients to the prediction of peptide retention times: studies with myohemerythrin. J. Chromatogr. 1993, 632, 11-18.
6. Yoshida, T., Calculation of peptide retention coefficients in normal-phase liquid chromatography. J. Chromatogr. A 1998, 808, 105-112.
7. Yoshida, T., Okada, T., Prediction of peptide retention times in normal-phase liquid chromatography with only a single gradient run. J. Chromatogr. A 1999, 841, 19-32.
8. [81 Petritis, K., Kangas, L. J., Ferguson, P. L., Anderson, G. A. et al., Use of artificial neural networks for the accurate prediction of peptide liquid chromatography elution times in proteome analyses. Anal. Chem. 2003, 75, 1039-1048.
9. Hearn, M. T., Aguilar, M. I., High-performance liquid chromatography of amino acids, peptides and proteins. LXIX. Evaluation of retention and bandwidth relationships of myosin-related peptides separated by gradient elution reversed-phase high-performance liquid chromatography. J. Chromatogr. 1987, 392, 33-49.
10. Petritis, K., Brussaux, S., Guenu, S., Elfakire, C., Dreux, M., Ion-pair reversed-phase liquid chromatography-electrospray mass spectrometry for the analysis of underivatized small peptides. J. Chromatogr. A 2002, 957, 173-185.
11. Terabe, S., Konaka, R., Inouye, K., Separation of some polypeptide hormones by high-performance liquid chromatography. J. Chromatogr. 1979, 172, 163-177.
12. 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.
13. Aires-de-Sousa, J., Hemmer, M. C., Gasteiger, J., Prediction of 1H NMR chemical shifts using neural networks. Anal. Chem. 2002, 74, 80-90.
14. Havel, J., Breadmore, M., Macka, M., Haddad, P. R., Artificial neural networks for computer-aided modelling and optimisation in micellar electrokinetic chromatography. J. Chromatogr. A 1999, 850, 345-353.
15. Jalali-Heravi, M., Fatemi, M. H., Prediction of thermal conductivity detection response factors using an artificial neural network. J. Chromatogr. A 2000, 897, 227-235.
16. Jalali-Heravi, M., Fatemi, M. H., Artificial neural network modeling of Kovats retention indices for noncyclic and monocyclic terpenes. J. Chromatogr. A 2001, 915, 177-183.
17. Jalali-Heravi, M., Garkani-Nejad, Z., Prediction of electrophoretic mobilities of sulfonamides in capillary zone electrophoresis using artificial neural networks. J. Chromatogr. A 2001, 927, 211-218.
18. Jalali-Heravi, M., Garkani-Nejad, Z., Prediction of electrophoretic mobilities of alkyl- and alkenylpyridines in capillary electrophoresis using artificial neural networks. J. Chromatogr. A 2002, 971, 207-215.
19. Malovana, S., Frias-Garcia, S.,Havel, J., Artificial neural networks for modeling electrophoretic mobilities of inorganic cations and organic cationic oximes used as antidote contra nerve paralytic chemical weapons. Electrophoresis 2002, 23, 1815-1821.
20. Muzikar, M., Havel, J., Macka, M., Capillary electrophoresis determinations of trace concentrations of inorganic ions in large excess of chloride: soft modelling using artificial neural networks for optimisation of electrolyte composition. Electrophoresis 2003, 24, 2252-2258.
21. Ruggieri, F., D'Archivio, A. A., Carlucci, G., Mazzeo, P., Application of artificial neural networks for prediction of retention factors of triazine herbicides in reversed-phase liquid chromatography. J. Chromatogr. A 2005, 1076, 163-169.
22. Sugimoto, M., Kikuchi, S., Arita, M., Soga, T. et al., Large-scale prediction of cationic metabolite identity and migration time in capillary electrophoresis mass spectrometry using artificial neural networks. Anal. Chem. 2005, 77, 78-84.
23. Tham, S. Y., Agatonovic-Kustrin, S., Application of the artificial neural network in quantitative structure-gradient elution retention relationship of phenylthiocarbamyl amino acids derivatives. J. Pharm. Biomed. Anal. 2002, 28, 581-590.
24. Petritis, K., Kangas, L. J., Yan, B., Monroe, M. E. et al., Improved peptide elution time prediction for reversed-phase liquid chromatography-MS by incorporating peptide sequence information. Anal. Chem. 2006, 78, 5026-5039.
25. Shinoda, K., Sugimoto, M., Yachie, N., Sugiyama, N. et al., Prediction of liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome using artificial neural networks. J. Proteome Res. 2006, 5, 3312-3317.
26. Liu, H. X., Xue, C. X., Zhang, R. S., Yao, X. J. et al., Quantitative prediction of logk of peptides in high-performance liquid chromatography based on molecular descriptors by using the heuristic method and support vector machine. J. Chem. Inf. Comput. Sci. 2004, 44, 1979-1986.
27. O'Hare, M. J., Nice, E. C., Hydrophobic high-performance liquid chromatography of hormonal polypeptides and proteins on alkylsilane-bonded silica. J. Chromatogr. 1979, 171, 209-226.
28. Su, S.-J., Grego, B., Niven, B., Hearn, M. T. W., Analysis of group retention contributions for peptides separated by reversed phase high performance liquid chromatography. J. Liquid Chromatogr. Rel. Technol. 1981, 4, 1745-1764.
29. Blondelle, S. E., Buttner, K., Houghten, R. A., Evaluation of peptide-peptide interactions using reversed-phase high-performance liquid chromatography. J. Chromatogr. 1992, 625, 199-206.
30. Blondelle, S. E., Ostresh, J. M., Houghten, R. A., Perez-Paya, E., Induced conformational states of amphipathic peptides in aqueous/lipid environments. Biophys. J. 1995, 68, 351-359.
31. Buttner, K., Pinilla, C., Appel, J. R., Houghten, R. A., Anomalous reversed-phase high-performance liquid chromatographic behavior of synthetic peptides related to antigenic helper T cell sites. J. Chromatogr. 1992, 625, 191-198.
32. Chen, Y., Mant, C. T., Hodges, R. S., Temperature selectivity effects in reversed-phase liquid chromatography due to conformation differences between helical and non-helical peptides. J. Chromatogr. A 2003, 1010, 45-61.
33. Rothemund, S., Krause, E., Beyermann, M., Dathe, M. et al., Recognition of alpha-helical peptide structures using high-performance liquid chromatographic retention data for D-amino acid analogues: influence of peptide amphipathicity and of stationary phase hydrophobicity. J. Chromatogr. A 1995, 689, 219-226.
34. Sereda, T. J., Mant, C. T., Sonnichsen, F. D., Hodges, R. S., Reversed-phase chromatography of synthetic amphipathic alpha-helical peptides as a model for ligand/receptor interactions. Effect of changing hydrophobic environment on the relative hydrophilicity/hydrophobicity of amino acid side-chains. J. Chromatogr. A 1994, 676, 139-153.
35. Steer, D. L., Thompson, P. E., Blondelle, S. E., Houghten, R. A., Aguilar, M. I., Comparison of the binding of alpha-helical and beta-sheet peptides to a hydrophobic surface. J. Pept. Res. 1998, 51, 401-412.
36. Su, J. Y., Hodges, R. S., Kay, C. M., Effect of chain length on the formation and stability of synthetic alpha-helical coiled coils. Biochemistry 1994, 33, 15501-15510.
37. Wieprecht, T., Rothemund, S., Bienert, M., Krause, E., Role of helix formation for the retention of peptides in reversed-phase high-performance liquid chromatography. J. Chromatogr. A 2001, 912, 1-12.
38. Wimley, W. C., Creamer, T. P., White, S. H., Solvation energies of amino acid side chains and backbone in a family of host-guest pentapeptides. Biochemistry 1996, 35, 5109-5124.
39. Yu, Y. B., Wagschal, K. C., Mant, C. T., Hodges, R. S., Trapping the monomeric alpha-helical state during unfolding of coiled-coils by reversed-phase liquid chromatography. J. Chromatogr. A 2000, 890, 81-94.
40. Zhou, N. E., Mant, C. T., Hodges, R. S., Effect of preferred binding domains on peptide retention behavior in reversed-phase chromatography: amphipathic alpha-helices. Pept. Res. 1990, 3, 8-20.
41. Krokhin, O. V., Craig, R., Spicer, V., Ens, W. et al., An improved model for prediction of retention times of tryptic peptides in ion pair reversed-phase HPLC: its application to protein peptide mapping by off-line HPLC-MALDI MS. Mol. Cell. Proteomics 2004, 3, 908-919.
42. 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.
43. Baczek, T., Wiczling, P., Marszall, 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.
44. Makrodimitris, K., Fernandez, E. J., Woolf, T. B., O'Connell, J. P., Mesoscopic simulation of adsorption of peptides in a hydrophobic chromatography system. Anal. Chem. 2005, 77, 1243-1252.
45. Al-Shahib, A., Breitling, R., Gilbert, D., Feature selection and the class imbalance problem in predicting protein function from sequence. Appl. Bioinformatics 2005, 4, 195-203.
46. Al-Shahib, A., Breitling, R., Gilbert, D., FrankSum: new feature selection method for protein function prediction. Int. J. Neural Syst. 2005, 15, 259-275.
47. Li, L., Jiang, W., Li, X., Moser, K. L. et al., A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset. Genomics 2005, 85, 16-23.
48. Sindhwani, V., Rakshit, S., Deodhare, D., Erdogmus, D. et al., Feature selection in MLPs and SVMs based on maximum output information. Neural Networks, IEEE Transactions on 2004, 15, 937-948.
49. Xue, Y, Li, Z. R., Yap, C. W., Sun, L. Z. et al., Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents. J. Chem. Inf. Comput. Sci. 2004, 44, 1630-1638.
50. Zhang, X., Lu, X., Shi, Q., Xu, X. Q. et al., Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data. BMC Bioinformatics 2006, 7, 197.
51. Furlanello, C., Serafini, M., Merler, S., Jurman, G., An accelerated procedure for recursive feature ranking on microarray data. Neural Netw. 2003, 16, 641-648.
52. Berrueta, L. A., Alonso-Salces, R. M., Heberger, K., Supervised pattern recognition in food analysis. J. Chromatogr. A, in press.
53. Jaitly, N., Monroe, M. E., Petyuk, V. A., Clauss, T. R. et al., Robust algorithm for alignment of liquid chromatography-mass spectrometry analyses in an accurate mass and time tag data analysis pipeline. Anal. Chem. 2006, 78, 7397-7409.
54. Norbeck, A. D., Monroe, M. E., Adkins, J. N., Anderson, K. K. et al., The utility of accurate mass and LC elution time information in the analysis of complex proteomes. J. Am. Soc. Mass Spectrom. 2005, 16, 1239-1249.
55. Rumelhart, D. E., Hinton, G. E., Williams, R. J., Learning representations by back-propagating errors. Nature 1986, 323, 533-536.
56. Funahashi, K., On the approximate realization of continuous mappings by neural networks. Neural Netw. 1989, 2, 183-192.
57. Vapnik, V. N., Statistical learning theory, Wiley, New York 1998.
58. Vapnik, V., The nature of statistical learning theory, Springer-Verlag, New York 1995.
59. Cortes, C., Vapnik, V., Support-vector networks. Machine Learning 1995, 20, 273-297.
60. Bernhard, E. B., Isabelle, M. G., Vladimir, N. V., Proceedings of the fifth annual workshop on Computational learning theory, ACM Press, Pittsburgh, PA, USA 1992.
61. Burbidge, R., Trotter, M., Buxton, B., Holden, S., Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput. Chem. 2001, 26, 5-14.
62. Ding, C. H., Dubchak, I., Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 2001, 17, 349-358.
63. Karchin, R., Karplus, K., Haussler, D., Classifying G-protein coupled receptors with support vector machines. Bioinformatics 2002, 18, 147-159.
64. Czerminski, R., Yasri, A., Hartsough, D., Use of support vector machine in pattern classification: Application to QSAR studies. Quant. Struct.-Act. Relat. 2001, 20, 227-240.
65. Song, M., Breneman, C. M., Bi, J., Sukumar, N. et al., Prediction of protein retention times in anion-exchange chromatography systems using support vector regression. J. Chem. Inf. Comput. Sci. 2002, 42, 1347-1357.
66. Ladiwala, A., Xia, F., Luo, Q., Breneman, C. M., Cramer, S. M., Investigation of protein retention and selectivity in HIC systerns using quantitative structure retention relationship models. Biotechnol. Bioeng. 2006, 93, 836-850.
67. Noble, W. S., What is a support vector machine? Nat. Biotechnol. 2006, 24, 1565-1567.
68. Holland, J. H., Adaption in natural and artificial systems: An introductory analysis with applications to Biology, control and artificial intelligence. University of Michigan Press 1975.
69. Holland, J. H., Adaptation in natural and artificial systems, MIT Press, Cambridge, MA, USA 1992.
70. Goldberg, D. E., Genetic algorithms in search, optimization, and machine learning, Addison-Wesley Pub. Co., Reading, MA, USA 1989.
71. Leardi, R., Genetic algorithms in chemistry. J. Chromatogr. A 2007, 1158, 226-233
72. Stone, M., Cross-validatory choice and assessment of statistical predictions. Jo. Roy. Statist. Soc. Series B (Methodological) 1974, 36, 111-147.
73. Lachenbruch, P. A., Mickey, M. R., Estimation of error rates in discriminant analysis. Technometrics 1968, 10, 1-11.
74. Shao, J., Linear model selection by cross-validation. J. Am. Statist. Assoc. 1993, 88, 486-494.
75. Altman, N., Leger, C., On the optimality of prediction-based selection criteria and the convergence rates of estimators. J. Roy. Statist. Soc. Series B (Methodological) 1997, 59, 205-216.
76. Witten, I. H., Frank, E., Data mining: Practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, San Mateo, CA 1999.
77. Jonathan, P., Krzanowski, W. J., McCarthy, W. V., On the use of cross-validation to assess performance in multivariate prediction. Statistics Computing 2000, 10, 209-229.
78. Ganeshanandam, S., Krzanowski, W. J., On selecting variables and assessing their performance in linear discriminant analysis. Aust. J. Stat. 1989, 31, 433-447.
79. Krzanowski, W. J., Selection of variables, and assessment of their performance, in mixed-variable discriminant analysis. Comput. Statist. Data Analysis 1995, 19, 419-431.
80. Bellman, R., Adaptive control processes: a guided tour, Princeton University Press, Princeton 1961.
81. Palmblad, M., Ramstrom, M., Markides, K. E., Hakansson, P., Bergquist, J., Prediction of chromatographic retention and protein identification in liquid chromatography/mass spectrometry. Anal. Chem. 2002, 74, 5826-5830.
82. Strittmatter, E. F., Ferguson, P. L., Tang, K., Smith, R. D., Proteome analyses using accurate mass and elution time peptide tags with capillary LC time-of-flight mass spectrometry. J. Am. Soc. Mass Spectrom. 2003, 14, 980-991.
83. Smith, R. D., Anderson, G. A., Lipton, M. S., Pasa-Tolic, L. et al., An accurate mass tag strategy for quantitative and high-throughput proteome measurements. Proteomics 2002, 2, 513-523.
84. Lipton, M. S., Pasa-Tolic, L., Anderson, G. A., Anderson, D. J. et al., Global analysis of the Deinococcus radiodurans proteome by using accurate mass tags. Proc. Natl. Acad. Sci. USA 2002, 99, 11049-11054.
85. Conrads, T. P., Anderson, G. A., Veenstra, T. D., Pasa-Tolic, L., Smith, R. D., Utility of accurate mass tags for proteome-wide protein identification. Anal. Chem. 2000, 72, 3349-3354.
86. Le Bihan, T., Robinson, M. D., Stewart, II, Figeys, D., Definition and characterization of a "trypsinosome" from specific peptide characteristics by nano-HPLC-MS/MS and in silico analysis of complex protein mixtures. J. Proteome Res 2004, 3, 1138-1148.
87. Kawakami, T., Tateishi, K., Yamano, Y., Ishikawa, T. et al., Protein identification from product ion spectra of peptides validated by correlation between measured and predicted elution times in liquid chromatography/mass spectrometry. Proteomics 2005, 5, 856-864.
88. Varnum, S. M., Covington, C. C., Woodbury, R. L., Petritis, K. et al., Proteomic characterization of nipple aspirate fluid: identification of potential biomarkers of breast cancer. Breast Cancer Res. Treat. 2003, 80, 87-97.
89. Strittmatter, E. F., Kangas, L. J., Petritis, K., Mottaz, H. M. et al., Application of peptide LC retention time information in a discriminant function for peptide identification by tandem mass spectrometry. J. Proteome Res. 2004, 3, 760-769.
90. Qian, W. J., Liu, T., Monroe, M. E., Strittmatter, E. F. et al., Probability-based evaluation of peptide and protein identifications from tandem mass spectrometry and SEQUEST analysis: the human proteome. J. Proteome Res. 2005, 4, 53-62.
91. Nielsen, N.-P. V., Carstensen, J. M., Smedsgaard, J., Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping. J. Chromatogr. A 1998, 805, 17-35.
92. van Nederkassel, A. M., Daszykowski, M., Eilers, P. H., Heyden, Y. V., A comparison of three algorithms for chromatograms alignment. J. Chromatogr. A 2006, 1118, 199-210.
93. Callister, S. J., Dominguez, M. A., Nicora, C. D., Zeng, X. et al., Application of the accurate mass and time tag approach to the proteome analysis of sub-cellular fractions obtained from Rhodobacter sphaeroides 2.4.1. Aerobic and photosynthetic cell cultures. J. Proteome Res. 2006, 5, 1940-1947.
94. Jennifer, S.D., Zimmer, M. E. M. W.-J. Q. R. D. S., Advances in proteomics data analysis and display using an accurate mass and time tag approach. Mass Spectrom. Rev. 2006, 25, 450-482.
95. Radulovic, D., Jelveh, S., Ryu, S., Hamilton, T. G. et al., Informatics platform for global proteomic profiling and biomarker discovery using liquid chromatography-tandem mass spectrometry. Mol. Cell. Proteomics 2004, 3, 984-997.
96. Listgarten, J., Neal, R. M., Roweis, S. T., Emili, A., Multiple alignment of continuous time series. MIT Press, Cambridge, MA 2005.
97. Prakash, A., Mallick, P., Whiteaker, J., Zhang, H. et al., Signal maps for mass spectrometry-based comparative proteomics. Mol. Cell. Proteomics 2006, 5, 423-432.
98. Bylund, D., Danielsson, R., Malmquist, G., Markides, K. E., Chromatographic alignment by warping and dynamic programming as a pre-processing tool for PARAFAC modelling of liquid chromatography-mass spectrometry data. J. Chromatogr. A 2002, 961, 237-244.
99. Stadalius, M. A., Gold, H. S., Snyder, L. R., Optimization model for the gradient elution separation of peptide mixtures by reversed-phase high-performance liquid chromatography : Verification of retention relationships. J. Chromatogr. A 1984, 296, 31-59.
100. Ishihama, Y., Method for detection of peptide sequence based on chromatography retention time. Patent: W02007/013701 A1.
101. Ishihama, Y., Oda, Y., Tabata, T., Kawai, T. et al., Probability enhancement in protein identification by parent ion related parameters. 3rd Japan Human Proteome Organization Conference/JHUPO, Yokohama, Japan 2005, P1-15.
102. Shinoda, K., Yachie, N., Masuda, T., Sugiyama, N. et al., HybGFS: a hybrid method for genome-fingerprint scanning. BMC Bioinformatics 2006, 7, 479.
103. Brunner, E., Ahrens, C. H., Mohanty, S.,Baetschmann, H. et al., A high-quality catalog of the Drosophila melanogaster proteome. Nat. Biotechnol.2007, 25, 576-583.
104. Craig, R., Cortens, J. P., Beavis, R. C., The use of proteotypic peptide libraries for protein identification. Rapid Commun. Mass Spectrom. 2005, 19, 1844-1850.
105. Kuster, B., Schirle, M., Mallick, P., Aebersold, R., Scoring proteomes with proteotypic peptide probes. Nat. Rev. Mol. Cell. Biol. 2005, 6, 577-583.
106. Ishihama, Y., Wei, F. Y., Aoshima, K., Sato, T. et al., Enhancement of the efficiency of phosphoproteomic identification by removing phosphates after phosphopeptide enrichment. J. Proteome Res. 2007, 6, 1139-1144.
107. Steen, H., Jebanathirajah, J. A., Rush, J., Morrice, N., Kirschner, M. W., Phosphorylation analysis by mass spectrometry: myths, facts, and the consequences for qualitative and quantitative measurements. Mol. Cell. Proteomics 2006, 5, 172-181.
108. 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.