Artificial neural networks for qualitative and quantitative analysis of target proteins with polymerized liposome vesicles

    Authors

    M. Santos; S. Nadi; H. C. Goicoechea; M. K. HaIdar; A. D. Campiglia;S. Mallik

    Comments

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    Abbreviated Journal Title

    Anal. Biochem.

    Keywords

    luminescence; lanthanide ions; polymerized liposomes; carbonic; anhydrase; gamma-globulins; human serum albumin; partial least squares; artificial neural network; PARTIAL LEAST-SQUARES; MULTIVARIATE CALIBRATION; CAPILLARY-ELECTROPHORESIS; LIPID-MEMBRANES; GAMMA-GLOBULIN; SERUM-ALBUMIN; BINDING; MICROARRAYS; REGRESSION; IONS; Biochemical Research Methods; Biochemistry & Molecular Biology; Chemistry, Analytical

    Abstract

    We investigate the feasibility of using the luminescence response of polymerized liposomes incorporating ethylenediaminetetraacetate europium(III) (EDTA-Eu3+) for monitoring protein concentrations in aqueous media. Quantitative analysis is based on the linear relationship between the luminescence enhancement of the lanthanide ion and protein concentration. Analytical figures of merit are presented for carbonic anhydrase, human serum albumin, gamma-globulins, and thermolysin. Qualitative analysis is based on the luminescence lifetime of the liposome sensor. This parameter, which follows well-behaved single exponential decays and provides characteristic values for each of the four studied proteins, demonstrates the selective potential for protein identification. Then partial least squares-1 and artificial neural networks are compared toward the quantitative and qualitative analysis of human serum albumin and carbonic anhydrase in binary mixtures without previous separation at the concentration levels found in aqueous humor. (c) 2006 Published by Elsevier Inc.

    Journal Title

    Analytical Biochemistry

    Volume

    361

    Issue/Number

    1

    Publication Date

    1-1-2007

    Document Type

    Article

    Language

    English

    First Page

    109

    Last Page

    119

    WOS Identifier

    WOS:000243572300012

    ISSN

    0003-2697

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