Literature DB >> 20449576

Predicting the cross-reactivities of polycyclic aromatic hydrocarbons in ELISA by regression analysis and CoMFA methods.

Yan-Feng Zhang1, Yi Ma, Zhi-Xian Gao, Shu-Gui Dai.   

Abstract

Immunoassays have been regarded as a possible alternative or supplement for measuring polycyclic aromatic hydrocarbons (PAHs) in the environment. Since there are too many potential cross-reactants for PAH immunoassays, it is difficult to determine all the cross-reactivities (CRs) by experimental tests. The relationship between CR and the physical-chemical properties of PAHs and related compounds was investigated using the CR data from a commercial enzyme-linked immunosorbent assay (ELISA) kit test. Two quantitative structure-activity relationship (QSAR) techniques, regression analysis and comparative molecular field analysis (CoMFA), were applied for predicting the CR of PAHs in this ELISA kit. Parabolic regression indicates that the CRs are significantly correlated with the logarithm of the partition coefficient for the octanol-water system (log K(ow)) (r(2) = 0.643, n = 23, P < 0.0001), suggesting that hydrophobic interactions play an important role in the antigen-antibody binding and the cross-reactions in this ELISA test. The CoMFA model obtained shows that the CRs of the PAHs are correlated with the 3D structure of the molecules (r(cv)(2) = 0.663, r(2) = 0.873, F(4,32) = 55.086). The contributions of the steric and electrostatic fields to CR were 40.4 and 59.6%, respectively. Both of the QSAR models satisfactorily predict the CR in this PAH immunoassay kit, and help in understanding the mechanisms of antigen-antibody interaction.

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Year:  2010        PMID: 20449576     DOI: 10.1007/s00216-010-3785-6

Source DB:  PubMed          Journal:  Anal Bioanal Chem        ISSN: 1618-2642            Impact factor:   4.142


  2 in total

1.  Investigating the quantitative structure-activity relationships for antibody recognition of two immunoassays for polycyclic aromatic hydrocarbons by multiple regression methods.

Authors:  Yan-Feng Zhang; Li Zhang; Zhi-Xian Gao; Shu-Gui Dai
Journal:  Sensors (Basel)       Date:  2012-07-09       Impact factor: 3.576

2.  Substructure-activity relationship studies on antibody recognition for phenylurea compounds using competitive immunoassay and computational chemistry.

Authors:  Fuyuan Zhang; Bing Liu; Guozhen Liu; Yan Zhang; Junping Wang; Shuo Wang
Journal:  Sci Rep       Date:  2018-02-15       Impact factor: 4.379

  2 in total

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