Development of a broad-specificity antibody-based immunoassay for triazines in ginger and the quantitative structure-activity relationship study of cross-reactive molecules by molecular modeling

Liu, Congmin; Dou, Xiaobing; Zhang, Lei; Kong, Weijun; Liu, Wu; Duan, Yaping; Yang, Meihua

doi:10.1016/j.aca.2018.01.042

Cited by 18 publications

(9 citation statements)

References 29 publications

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“…In contrast, immunoassays such as enzyme-linked immunosorbent assay (ELISA) and immunochromatographic assay (ICA), are time saving, convenient and economic, and they have been widely applied to food quality control [9][10][11][12][13][14][15], disease diagnosis [16][17][18][19][20][21][22][23][24][25] and environmental pollutant analysis [26][27][28][29][30]. Several immunoassays have also been developed for the determination of triazines (Table 1) [31][32][33][34][35][36][37]. These methods are all based on the use of a specific antibody (monoclonal antibody, polyclonal antibody, or antisera) against antigen.…”

Section: Introductionmentioning

confidence: 99%

Multiple detection of 15 triazine herbicides by gold nanoparticle based-paper sensor

Guo

Zhao

et al. 2022

Nano Res.

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Triazine herbicides have been widely used in agriculture, but their residues can harm the environment and human health. To help monitor these, we have developed an effective immunochromatographic strip test that can simultaneously detect 15 different triazines in grain samples (including ametryn, cyprazine, atraton, prometon, prometryn, atrazine, propazine, terbuthylazine, simetryn, trietazine, secbumeton, simazine, desmetryn, terbumeton and simetone). Based on our optimization procedure, the visual limit of detection (vLOD) for these triazines was found to be 2–10 ng/mL in assay buffer, and 0.02−0.1 mg/kg in grain samples. Four different grain matrices including corn, brown rice, wheat, and sorghum were studied and the test results showed no significant differences between the 15 triazines analyzed using this method. This test is simple, convenient, rapid, and low-cost, and could be an effective tool for primary screening of triazine residues in grain samples. Electronic Supplementary Material Supplementary material (UV–vis spectra of 15 nm-GNP; K2CO3 usage; cross reactivity; strip images for spiked rice, wheat and sorghum samples, UPLC-MS/MS parameters; gray values for strip optimization) is available in the online version of this article at 10.1007/s12274-022-4164-2.

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Section: Introductionmentioning

confidence: 99%

Multiple detection of 15 triazine herbicides by gold nanoparticle based-paper sensor

Guo

Zhao

et al. 2022

Nano Res.

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“…The immunoassay determination of triazines is much easier, less expensive, highly sensitive and rapid. Due to its selectivity and simplicity, the immunoassay has been used to detect a wide variety of environmental pollutants including triazines [8–10]. However, the significant variety of triazines and their derivatives makes the detailed analysis of the regulations of their immune recognition necessary.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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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“…Through QSPR studies, the relationship between the structure and activity of compounds can be mined [1,2]. The QSPR model can be used to predict the activity of unknown materials and discover key influencing factors of the activity of related substances, such as groups or substituents determining the activity of the molecular structure [3,4]. Nowadays, QSPR has been applied in the fields of computer science, chemistry, materials science, medicine science, and life sciences [5,6].…”

Section: Introductionmentioning

confidence: 99%

A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network

Zhang

Liu

et al. 2018

Applied Sciences

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Featured Application:The proposed hybrid intelligent model can be applied in engineering design, material performance prediction, numerical calculation, and the prediction of physical and chemical properties.Abstract: A quantitative structure-property relationship (QSPR) model is proposed to explore the relationship between the pKa of various compounds and their structures. Through QSPR studies, the relationship between the structure and properties can be obtained. In this study, a novel chaos-enhanced accelerated particle swarm algorithm (CAPSO) is adopted to screen molecular descriptors and optimize the weights of back propagation artificial neural network (BP ANN). Then, the QSPR model based on CAPSO and BP ANN is proposed and named the CAPSO BP ANN model. The prediction experiment showed that the CAPSO algorithm was a reliable method for screening molecular descriptors. The five molecular descriptors obtained by the CAPSO algorithm could well characterize the molecular structure of each compound in pKa prediction. The experimental results also showed that the CAPSO BP ANN model exhibited good performance in predicting the pKa values of various compounds. The absolute mean relative error, root mean square error, and square correlation coefficient are respectively 0.5364, 0.0632, and 0.9438, indicating the high prediction accuracy. The proposed hybrid intelligent model can be applied in engineering design and the prediction of physical and chemical properties.The establishment of the QSPR model mainly involves the following steps: acquisition of experimental data, construction and optimization of the molecular structure, calculation and screening of molecular descriptors, establishment and verification of the model, etc. First of all, the variable selection is important in many fields, such as spectroscopy [7,8], QSPR [9,10], and other fields [11,12]. The selection of molecular descriptors largely determines the quality of the QSPR model [13][14][15]. The step of molecular descriptor screening aims to reflect more structural information so that there is no noise in the descriptors. Many methods have been developed to screen molecular descriptors and can be mainly divided into two categories [16][17][18]. The first category includes the common methods, such as Akaike information criterion (AIC), Bayesian information criterion (BIC), and forward/backward/bi-directional stepwise multiple linear regression (MLR). The second includes the modern search algorithms, such as genetic algorithm (GA), simulated annealing algorithm (SA), ant colony algorithm (AC), particle swarm optimization (PSO), and other swarm intelligence algorithms [7,11,[19][20][21]. The common methods are the most simple and efficient, but their overall performances are low in complex nonlinear problems. The modern search algorithms based on the optimization strategy have obvious advantages and can search for optimal variables and deal with complex large data points. The model establishment is important in the QSPR study and commonly used QSPR model...

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Development of a broad-specificity antibody-based immunoassay for triazines in ginger and the quantitative structure-activity relationship study of cross-reactive molecules by molecular modeling

Cited by 18 publications

References 29 publications

Multiple detection of 15 triazine herbicides by gold nanoparticle based-paper sensor

Multiple detection of 15 triazine herbicides by gold nanoparticle based-paper sensor

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

A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network

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