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QSAR analysis of immune recognition for triazine herbicides based on immunoassay data for polyclonal and monoclonal antibodies

A common task in the immunodetection of structurally close compounds is to analyze the selectivity of immune recognition; it is required to understand the regularities of immune recognition and to elucidate the basic structural elements which provide it. Triazines are compounds of particular interes...

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Autores principales: Buglak, Andrey A., Zherdev, Anatoly V., Lei, Hong-Tao, Dzantiev, Boris B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6447172/
https://www.ncbi.nlm.nih.gov/pubmed/30943259
http://dx.doi.org/10.1371/journal.pone.0214879
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author Buglak, Andrey A.
Zherdev, Anatoly V.
Lei, Hong-Tao
Dzantiev, Boris B.
author_facet Buglak, Andrey A.
Zherdev, Anatoly V.
Lei, Hong-Tao
Dzantiev, Boris B.
author_sort Buglak, Andrey A.
collection PubMed
description A common task in the immunodetection of structurally close compounds is to analyze the selectivity of immune recognition; it is required to understand the regularities of immune recognition and to elucidate the basic structural elements which provide it. Triazines are compounds of particular interest for such research due to their high variability and the necessity of their monitoring to provide safety for agricultural products and foodstuffs. We evaluated the binding of 20 triazines with polyclonal (pAb) and monoclonal (mAb) antibodies obtained using atrazine as the immunogenic hapten. A total of over 3000 descriptors were used in the quantitative structure-activity relationship (QSAR) analysis of binding activities (pIC(50)). A comparison of the two enzyme immunoassay systems showed that the system with pAb is much easier to describe using 2D QSAR methodology, while the system with mAb can be described using the 3D QSAR CoMFA. Thus, for the 3D QSAR model of the polyclonal antibodies, the main statistical parameter q(2) (‘leave-many-out’) is equal to 0.498, and for monoclonal antibodies, q(2) is equal to 0.566. Obviously, in the case of pAb, we deal with several targets, while in the case of mAb the target is one, and therefore it is easier to describe it using specific fields of molecular interactions distributed in space.
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spelling pubmed-64471722019-04-17 QSAR analysis of immune recognition for triazine herbicides based on immunoassay data for polyclonal and monoclonal antibodies Buglak, Andrey A. Zherdev, Anatoly V. Lei, Hong-Tao Dzantiev, Boris B. PLoS One Research Article A common task in the immunodetection of structurally close compounds is to analyze the selectivity of immune recognition; it is required to understand the regularities of immune recognition and to elucidate the basic structural elements which provide it. Triazines are compounds of particular interest for such research due to their high variability and the necessity of their monitoring to provide safety for agricultural products and foodstuffs. We evaluated the binding of 20 triazines with polyclonal (pAb) and monoclonal (mAb) antibodies obtained using atrazine as the immunogenic hapten. A total of over 3000 descriptors were used in the quantitative structure-activity relationship (QSAR) analysis of binding activities (pIC(50)). A comparison of the two enzyme immunoassay systems showed that the system with pAb is much easier to describe using 2D QSAR methodology, while the system with mAb can be described using the 3D QSAR CoMFA. Thus, for the 3D QSAR model of the polyclonal antibodies, the main statistical parameter q(2) (‘leave-many-out’) is equal to 0.498, and for monoclonal antibodies, q(2) is equal to 0.566. Obviously, in the case of pAb, we deal with several targets, while in the case of mAb the target is one, and therefore it is easier to describe it using specific fields of molecular interactions distributed in space. Public Library of Science 2019-04-03 /pmc/articles/PMC6447172/ /pubmed/30943259 http://dx.doi.org/10.1371/journal.pone.0214879 Text en © 2019 Buglak et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Buglak, Andrey A.
Zherdev, Anatoly V.
Lei, Hong-Tao
Dzantiev, Boris B.
QSAR analysis of immune recognition for triazine herbicides based on immunoassay data for polyclonal and monoclonal antibodies
title QSAR analysis of immune recognition for triazine herbicides based on immunoassay data for polyclonal and monoclonal antibodies
title_full QSAR analysis of immune recognition for triazine herbicides based on immunoassay data for polyclonal and monoclonal antibodies
title_fullStr QSAR analysis of immune recognition for triazine herbicides based on immunoassay data for polyclonal and monoclonal antibodies
title_full_unstemmed QSAR analysis of immune recognition for triazine herbicides based on immunoassay data for polyclonal and monoclonal antibodies
title_short QSAR analysis of immune recognition for triazine herbicides based on immunoassay data for polyclonal and monoclonal antibodies
title_sort qsar analysis of immune recognition for triazine herbicides based on immunoassay data for polyclonal and monoclonal antibodies
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6447172/
https://www.ncbi.nlm.nih.gov/pubmed/30943259
http://dx.doi.org/10.1371/journal.pone.0214879
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