Immunoassay for phenylurea herbicides: application of molecular modeling and quantitative structure-activity relationship analysis on an antigen-antibody interaction study

Establishment of an indirect competitive immunoassay for the detection of dicamba based on a highly specific nanobody

Dicamba, a traditional highly effective and low toxicity herbicide, has gained new life with the development of dicamba-tolerant transgenic crops in recent years. However, dicamba is highly volatile and therefore easy to cause drift damage to sensitive crops. The development of efficient and sensitive detection methods is essential for monitoring of trace dicamba in the environment. Nanobody-based immunoassay plays an important role in on-site detection of pesticides. However, now rapid and sensitive immunoassay methods based on nanobody for dicamba detection were lacking. In this study, the nanobodies specifically recognizing dicamba were successfully obtained by immunising camels and phage display library construction, and then an indirect competitive immunoassay based on Nb-242 was constructed with IC50 of 0.93 μg/mL and a linear range of 0.11-8.01 μg/mL. Nb-242 had good specificity with no cross-reactivities against the dicamba analogs other than 2,3,6-trichlorobenzoic acid and the developed immnoassay had a good correlation with the standard HPLC in the spike-recovery studies. Finally, the key amino acid Ala 123, Tyr 55, Tyr 59 and Arg 72 of Nb-242 that specifically recognizing and binding with dicamba were identified by homologous modeling and molecular docking, laying an important foundation for further structural modification of Nb-242. This study has important guiding significance for constructing immunoassay method of dicamba based on nanobody and provides a sensitive, specific, and reliable detection method that is suitable for the detection of dicamba in the environment.

A comprehensive evaluation of liposome/water partition coefficient prediction models based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method: Challenges from different descriptor dimension reduction methods and machine learning algorithms

The liposome/water partition coefficient (K_lip/w) is a key parameter to evaluate the bioaccumulation potential of pollutants. Considering that it is difficult to determine the K_lip/w values of all pollutants through experiments, researchers gradually developed models to predict it. However, there is currently no research on how to comprehensively evaluate prediction models and recommend a compelling optimal modeling method. To remedy the defect of single parameters in a traditional model comparison, the TOPSIS evaluation method, based on entropy weight, was first proposed. We use this method to comprehensively evaluate models from multiple angles in this study. Thirty QSPR models, including 3 descriptor dimension reduction methods and 10 algorithms (belonging to 4 tribes), were used to predict K_lip/w and verify the effectiveness of the comprehensive assessment method. The results showed that RF (descriptor dimension reduction method), symbolism (tribes) and RF (algorithm) exhibited significant advantages in establishing the K_lip/w value prediction model. At present, the application of TOPSIS in environmental model evaluations is almost absent. We hope that the proposed TOPSIS evaluation method can be applied to more chemical datasets and provide a more systematic and comprehensive basis for the application of the QSPR model in environmental studies and other fields.

Multiple machine learning algorithms assisted QSPR models for aqueous solubility: Comprehensive assessment with CRITIC-TOPSIS

As an essential environmental property, the aqueous solubility quantifies the hydrophobicity of a compound. It could be further utilized to evaluate the ecological risk and toxicity of organic pollutants. Concerned about the proliferation of organic contaminants in water and the associated technical burden, researchers have developed QSPR models to predict aqueous solubility. However, there are no standard procedures or best practices on how to comprehensively evaluate models. Hence, the CRITIC-TOPSIS comprehensive assessment method was first-ever proposed according to a variety of statistical parameters in the environmental model research field. 39 models based on 13 ML algorithms (belonged to 4 tribes) and 3 descriptor screening methods, were developed to calculate aqueous solubility values (log K_ws) for organic chemicals reliably and verify the effectiveness of the comprehensive assessment method. The evaluations were carried out for exhibiting better predictive accuracy and external competitiveness of the MLR-1, XGB-1, DNN-1, and kNN-1 models in contrast to other prediction models in each tribe. Further, XGB model based on SRM (XGB-1, C = 0.599) was selected as an optimal pathway for prediction of aqueous solubility. We hope that the proposed comprehensive evaluation approach could act as a promising tool for selecting the optimum environmental property prediction methods.

Development of monoclonal antibodies for the detection of diuron in water and sugarcane and their application in immunochromatographic strips

Diuron (DR) as a chemical herbicide persists in soil and water for a long time and causes extensive harm to humans. We have produced an excellent monoclonal antibody (mAb) with the ability to sensitively and specifically recognize DR in water and sugarcane samples. The semi-inhibitory concentration (IC₅₀) of this mAb was 0.28 ng mL^-1, and the limit of detection (LOD) was 0.07 ng mL^-1. Based on the mAb, an immunochromatographic assay (ICA) strip was developed. The visual detection limits of the strip assay were estimated, and the critical values of DR in water and sugarcane samples were determined to be 5 and 10 ng mL^-1, respectively, when assessed by the naked eye. The ICA strip was validated by HPLC-MS for water and sugarcane samples which had been spiked with DR. This ICA strip could be a useful tool for in-site and rapid detection of DR in water and sugarcane samples.

Progress in Immunoassays of Toxic Alkaloids in Plant-Derived Medicines: A Review

Plants are the cradle of the traditional medicine system, assuaging human or animal diseases, and promoting health for thousands of years. However, many plant-derived medicines contain toxic alkaloids of varying degrees of toxicity that pose a direct or indirect threat to human and animal health through accidental ingestion, misuse of plant materials, or through the food chain. Thus, rapid, easy, and sensitive methods are needed to effectively screen these toxic alkaloids to guarantee the safety of plant-derived medicines. Antibodies, due to their inherent specificity and high affinity, have been used as a variety of analytical tools and techniques. This review describes the antigen synthesis and antibody preparation of the common toxic alkaloids in plant-derived medicines and discusses the advances of antibody-based immunoassays in the screening and detection of toxic alkaloids in plants or other related matrices. Finally, the limitations and prospects of immunoassays for toxic alkaloids are discussed.

Improved molecular softness of tadalafil hapten enhancing antibody performance in immunoassay: Evidence from computational chemistry

The tadalafil-like compounds have appeared recently as adulterants in drinks and healthcare dietary supplements sourced from medicinal and edible food, which may cause illness and even death. In this work, the rationality of haptens was explored by computational chemistry and molecular simulation theories such as frontier molecular orbital (FMO)-based softness (S), three-dimensional (3D) structure, surface electrostatic potential (ESP), and lipophilic potential (LP). An antiserum from hapten H5 with the highest softness and maintaining the appropriate three-dimensional (3D) structure showed the optimal immunoassay performance, indicating an increasing softness was a critical factor for effective hapten. Based on the antibody induced by hapten H5, an indirect competitive enzyme-linked immunosorbent assay (icELISA) method for detecting multiple tadalafil-like adulterants was established. The icELISA showed a limit of detection (LOD), 50% inhibition concentration (IC₅₀ ), and a working range of 0.004-0.396, 0.89-4.27, and 0.094-16.71 ng/ml for tadalafil, amino tadalafil, acetamino tadalafil, nortadalafil, and N-desmethyl ent-tadalafil, respectively. The spiked recoveries of tadalafil-like adulterants in samples ranged from 84.9% to 116.2%. The results of the icELISA and HPLC-MS/MS methods had a good correlation for real samples with the R² of 0.9955. Specially, this work not only provided a convenient immunoassay method for measuring tadalafil-like adulterants in spirit drinks and dietary supplements in group-screening manner, but also suggested that softness was likely to be a general theory for rational hapten design. PRACTICAL APPLICATION: Rapid monitoring of tadalafil-like adulterants in food samples is very necessary and important for consumers, regulatory agencies, and the food industry.

Prediction models with multiple machine learning algorithms for POPs: The calculation of PDMS-air partition coefficient from molecular descriptor

Polydimethylsiloxane-air partition coefficient (K_PDMS-air) is a key parameter for passive sampling to measure POPs concentrations. In this study, 13 QSPR models were developed to predict K_PDMS-air, with two descriptor selection methods (MLR and RF) and seven algorithms (MLR, LASSO, ANN, SVM, kNN, RF and GBDT). All models were based on a data set of 244 POPs from 13 different categories. The diverse model evaluation parameters calculated from training and test set were used for internal and external verification. Notably, the R_adj², Q_BOOT² and Q_ext² are 0.995, 0.980 and 0.951 respectively for GBDT model, showing remarkable superiority in fitting, robustness and predictability compared with other models. The discovery that molecular size, branches and types of the bonds were the main internal factors affecting the partition process was revealed by mechanism explanation. Different from the existing QSPR models based on single category compounds, the models developed herein considered multiple classes compounds, so that its application domain was more comprehensive. Therefore, the obtained models can fill the data gap of missing experimental K_PDMS-air values for compounds in the application range, and help researchers better understand the distribution behavior of POPs from the perspective of molecular structure.

Changing Cross-Reactivity for Different Immunoassays Using the Same Antibodies: Theoretical Description and Experimental Confirmation

Many applications of immunoassays involve the possible presence of structurally similar compounds that bind with antibodies, but with different affinities. In this regard, an important characteristic of an immunoassay is its cross-reactivity: the possibility of detecting various compounds in comparison with a certain standard. Based on cross-reactivity, analytical systems are assessed as either high-selective (responding strictly to a specific compound) or low-selective (responding to a number of similar compounds). The present study demonstrates that cross-reactivity is not an intrinsic characteristic of antibodies but can vary for different formats of competitive immunoassays using the same antibodies. Assays with sensitive detection of markers and, accordingly, implementation at low concentrations of antibodies and modified (competing) antigens are characterized by lower cross-reactivities and are, thus, more specific than assays requiring high concentrations of markers and interacting reagents. This effect was confirmed by both mathematical modeling and experimental comparison of an enzyme immunoassay and a fluorescence polarization immunoassay of sulfonamides and fluoroquinolones. Thus, shifting to lower concentrations of reagents decreases cross-reactivities by up to five-fold. Moreover, the cross-reactivities are changed even in the same assay format by varying the ratio of immunoreactants’ concentrations and shifting from the kinetic or equilibrium mode of the antigen-antibody reaction. The described patterns demonstrate the possibility of modulating immunodetection selectivity without searching for new binding reactants.

A stable and sensitive enzyme-linked immunosorbent assay (ELISA) for the determination of metsulfuron-methyl residues in foods

A hapten of metsulfuron-methyl was successfully designed and synthesized from 2-methylester-phenylsulfonamide and succinic anhydride, and the polyclonal antibody against metsulfuron-methyl was prepared by immunization procedure with the hapten-bovine serum albumin conjugate. A stable and sensitive direct competitive enzyme-linked immunosorbent assay (dcELISA) method had been developed under the optimal conditions. The sensitivity (IC₅₀ ) was 37.03 ± 1.87 µg/L, and the detection line (IC₁₅ ) was 1.57 ± 0.11 µg/L. Rice, wheat, oat, flaxseed, milk, and water were chosen to study the recovery test and the recovery rates were 83.11%-117.44% . The matrix effect was eliminated by a simple dilution of the sample extracts. The results from dcELISA were well agreement with the results from HPLC-MS. It was indicated that the developed method had good accuracy and stability. It could be applied for the detection of metsulfuron-methyl residues. It was worth mentioning that the antibody could recognize metsulfuron-methyl and tribenuron-methyl with cross-reactivities of 100% and 49.72%, respectively. In order to understand the cross-reactivity, molecular modeling including molecular alignment and electrostatic potential surfaces were introduced. It was found that the special group of metsulfuron-methyl played an important role, especially on C3 position of the phenyl group. PRACTICAL APPLICATION: A stable, sensitive, and low-cost dc ELISA method had been developed with good accuracy and applied in the determination of metsulfuron-methyl in foods. Molecular simulation was introduced to understand the specificity between the antibody and the analyst. It was a good method to study the cross-reactivity between the antibody and the analyst or analogue.

Versatile in silico modeling of partition coefficients of organic compounds in polydimethylsiloxane using linear and nonlinear methods

Environmental fate, behavior and effects of hazardous organic compounds have recently received great attention in diverse environmental phases, including water, atmosphere, soil and sediment. Considering polydimethylsiloxane (PDMS) fibers were validated for the wide application in the determination of partition behavior in passive sampling, in this work, several in silico models were established to predict PDMS-water (K_PDMS-w), PDMS-air (K_PDMS-a) and PDMS-seawater partition coefficients (K_PDMS-sw) of diverse chemicals. This is an attempt to combine conventional linear method and popular nonlinear algorithm for the estimation of partition coefficients between PDMS and different environmental media. All of the developed models showed satisfactory goodness-of-fit with high adjusted correlation coefficient (R²_adj) and were validated to be robust, stable and predictable by various internal and external validation techniques, deriving a wide series of statistical checks. Moreover, it was found that hydrophobicity, polarizability, charge distribution and molecular size of compounds contributed significantly to the model development by interpreting the selected descriptors. Based on the broad applicability domains (ADs), the current study provides suitable tools to fill the experimental data gap for other compounds and to help researchers better understand the mechanistic basis of adsorption behavior of PDMS.

QSPR study on the polyacrylate-water partition coefficients of hydrophobic organic compounds

The partition coefficient is essential for the analysis of organic chemicals using solid-phase microextraction (SPME) techniques. In this study, a quantitative structure-property relationship (QSPR) model was developed with chemical descriptors for the prediction of the polyacrylate (PA)-water partition coefficient (K_PA-w). The major variables influencing K_PA-w in the QSPR model were CrippenlogP (crippen octanal-water partition coefficient), RNCG (relative negative charge-most negative charge/total negative charge), VE2_Dzv (average coefficient sum of the last eigenvector from the Barysz matrix/weighted by van der Waals volume), and ATSC4v (centred Broto-Moreau autocorrelation-lag 4/weighted by van der Waals volume). The relative determination coefficient (R²) and cross-validation coefficient (Q²) were 0.898 and 0.858, respectively, which implied that the model had excellent robustness. Mechanistic interpretation suggested that the factors affecting the partitioning process between PA and water are the hydrophobicity, relative negative charge, and van der Waals volume of a chemical. The results of this study provide a good tool for predicting the log K_PA-w values of diverse hydrophobic organic compounds (HOCs) within the applicability domain to reduce experimental costs and the time required for innovation.

Real-time forecasting of pesticide concentrations in soil

Forecasting pesticide residues in soils in real time is essential for agronomic purposes, to manage phytotoxic effects, and in catchments to manage surface and ground water quality. This has not been possible in the past due to both modelling and measurement constraints. Here, the analytical transient probability distribution (pdf) of pesticide concentrations is derived. The pdf results from the random ways in which rain events occur after pesticide application. First-order degradation kinetics and linear equilibrium sorption are assumed. The analytical pdfs allow understanding of the relative contributions that climate (mean storm depth and mean rainfall event frequency) and chemical (sorption and degradation) properties have on the variability of soil concentrations into the future. We demonstrated the two uncertain reaction parameters can be constrained using Bayesian methods. An approach to a Bayesian informed forecast is then presented. With the use of new rapid tests capable of providing quantitative measurements of soil concentrations in the field, real-time forecasting of future pesticide concentrations now looks possible for the first time. Such an approach offers new means to manage crops, soils and water quality, and may be extended to other classes of pesticides for ecological risk assessment purposes.

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 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 (pIC₅₀). 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 q² (‘leave-many-out’) is equal to 0.498, and for monoclonal antibodies, q² 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.

Production and characterization of a monoclonal antibody for Pefloxacin and mechanism study of antibody recognition

In this report, an artificial antigen (PFLX–BSA: Pefloxacin connected bovine serum albumin) was successfully prepared. The monoclonal antibody against pefloxacin was produced and characterized using a direct competitive ELISA. The linear range of detection was 0.115–6.564 µg/L. The limit of detection defined as IC15 was 0.170 ± 0.05 µg/L and the IC50 was 0.902 ± 0.03 µg/L. The antibody variable region genes were amplified, assembled, and sequenced. A three–dimensional structural model of the variable region was constructed to study the mechanism of antibody recognition using molecular docking analysis. Three predicted essential amino acids, Thr53, Arg97 of heavy chain and Thr52 of light chain, were mutated to verify the theoretical model. Three mutants lost binding activity significantly against pefloxacin as predicted. These may provide useful insights for studying antigen–antibody interaction mechanisms to improve antibody affinity maturation in vitro.

Application of quantitative structure-activity relationship analysis on an antibody and alternariol-like compounds interaction study

The antigen-antibody interaction determines the sensitivity and specificity of competitive immunoassay for hapten detection. In this paper, the specificity of a monoclonal antibody against alternariol-like compounds was evaluated through indirect competitive ELISA. The results showed that the antibody had cross-reactivity with 33 compounds with the binding affinity (expressed by IC₅₀ ) ranging from 9.4 ng/mL to 12.0 μg/mL. All the 33 compounds contained a common moiety and similar substituents. To understand how this common moiety and substituents affected the recognition ability of the antibody, a three-dimensional quantitative structure-activity relationship (3D-QSAR) between the antibody and the 33 alternariol-like compounds was constructed using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. The q² values of the CoMFA and CoMSIA models were 0.785 and 0.782, respectively, and the r² values were 0.911 and 0.988, respectively, indicating that the models had good predictive ability. The results of 3D-QSAR showed that the most important factor affecting antibody recognition was the hydrogen bond mainly formed by the hydroxyl group of alternariol, followed by the hydrophobic force mainly formed by the methyl group. This study provides a reference for the design of new hapten and the mechanisms for antibody recognition.

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

In the present study, molecular modeling and principle component analysis (PCA) were used to select appropriate haptens for group detection of triazine herbicides. Four new structures together with three reported triazine derivatives were chosen for the screening of immunizing and coating haptens. A total of 31 triazines coupled with a 3D-QSAR methodology were employed to investigate the relationship between antigen-antibody recognition and molecular structures, the results of which revealed that the antibodies may recognize triazines from the side of molecules with the distinguishing atom and a steric volume matching with the spatial structure of antibodies. Finally, a broad-specificity heterologous immunoassay was developed for determining 10 triazine herbicides in ginger, where the detection limits were 2.5-15.1 μg kg-1 and recoveries were 67.9-102.6%. This study may broaden insight into triazine-antibody interactions and benefit designing novel performance-enhanced antibodies. The developed immunoassay can be further used for triazine detection in other complicated matrices.

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

Based on the structural features of fluometuron, an immunizing hapten was synthesized and conjugated to bovine serum albumin as an immunogen to prepare a polyclonal antibody. However, the resultant antibody indicated cross-reactivity with 6 structurally similar phenylurea herbicides, with binding activities (expressed by IC₅₀ values) ranging from 1.67 µg/L to 42.71 µg/L. All 6 phenylurea herbicides contain a common moiety and three different substitutes. To understand how these three different chemical groups affect the antibody-phenylurea recognition activity, quantum chemistry, using density function theory (DFT) at the B3LYP/6-311++ G(d,p) level of theory, was employed to optimize all phenylurea structures, followed by determination of the 3D conformations of these molecules, pharmacophore analysis, and molecular electrostatic potential (ESP) analysis. The molecular modeling results confirmed that the geometry configuration, pharmacophore features and electron distribution in the substituents were related to the antibody binding activity. Spearman correlation analysis further elucidated that the geometrical and electrostatic properties on the van der Waals (vdW) surface of the substituents played a critical role in the antibody-phenylurea recognition process.

Doses of Immunogen Contribute to Specificity Spectrums of Antibodies against Aflatoxin

Research about antibody specificity spectra was conducted to develop single-specific antibodies or broad-specific antibodies. Aflatoxins, as one class of high-toxicity mycotoxins, were selected as the research targets to investigate the effect of the immunogen dose on antibody specificity spectra. For this aim, 16 monoclonal antibodies were induced by low or high doses of aflatoxin B₁-BSA, and 34 monoclonal antibodies were induced by low or high doses of aflatoxin M₁-BSA. The specificities of the antibodies induced, whether by aflatoxin B₁ conjugate or aflatoxin M₁ conjugate, indicated that the low dose of the immunogen induced a narrow spectrum of antibody specificity, while the high dose of the immunogen showed an advantage to form a broad spectrum of antibody specificity. Therefore, this report provides important information for the development of new antibodies against small molecules like aflatoxins.

Crystal Structure of the Fab Fragment of an Anti-ofloxacin Antibody and Exploration of Its Specific Binding

The limited knowledge on the mechanism of interactions between small contaminants and the corresponding antibodies greatly inhibits the development of enzyme-linked immunosorbent assay methods. In this study, the crystal structure of a Fab fragment specific for ofloxacin was obtained. On the basis of the crystal characteristics, the modeling of the interactions between ofloxacin and the Fab revealed that TYR31 and HIS99 of the heavy chain and MET20 and GLN79 of the light chain formed a hydrophobic region and that SER52 and ALA97 of the heavy chain and TYR35 of the light chain formed a salt bridge and two hydrogen bonds for specific binding. The key roles of SER52 and ALA97 were further confirmed by site-directed mutation. A specificity analysis using 14 ofloxacin analogues indicates that the length of the bond formed between the piperazine ring and the antibody plays key roles in specific recognition. This work helps to clarify the mechanisms through which antibodies recognize small molecules and improve immune detection methods.

Quantitative structure-activity relationship correlation between molecular structure and the Rayleigh enantiomeric enrichment factor

It was recently demonstrated that under environmentally relevant conditions the Rayleigh equation is valid to describe the enantiomeric enrichment - conversion relationship, yielding a proportional constant called the enantiomeric enrichment factor, εER. In the present study we demonstrate a quantitative structure-activity relationship model (QSAR) that describes well the dependence of εER on molecular structure. The enantiomeric enrichment factor can be predicted by the linear Hansch model, which correlates biological activity with physicochemical properties. Enantioselective hydrolysis of sixteen derivatives of 2-(phenoxy)propionate (PPMs) have been analyzed during enzymatic degradation by lipases from Pseudomonas fluorescens (PFL), Pseudomonas cepacia (PCL), and Candida rugosa (CRL). In all cases the QSAR relationships were significant with R(2) values of 0.90-0.93, and showed high predictive abilities with internal and external validations providing QLOO(2) values of 0.85-0.87 and QExt(2) values of 0.8-0.91. Moreover, it is demonstrated that this model enables differentiation between enzymes with different binding site shapes. The enantioselectivity of PFL and PCL was dictated by electronic properties, whereas the enantioselectivity of CRL was determined by lipophilicity and steric factors. The predictive ability of the QSAR model demonstrated in the present study may serve as a helpful tool in environmental studies, assisting in source tracking of unstudied chiral compounds belonging to a well-studied homologous series.

Stereospecific recognition and quantitative structure–activity relationship between antibodies and enantiomers: ofloxacin as a model hapten

In this study, ofloxacin stereoisomers were chosen as a simple model to investigate the stereospecific recognition of chiral haptens and antibodies.

Computer-aided molecular modeling study on antibody recognition of small molecules: an immunoassay for triazine herbicides

Most immunoassays for determination of small molecules are still designed on the basis of the "trial and error" method, due to the lack of understanding of antibody recognition. In the present study, we developed a heterologous indirect competitive enzyme-linked immunosorbent assay for determination of triazine herbicides, with limits of detection for 11 triazines ranging from 0.05 to 29.4 μg/L. Mechanisms of the antigen-antibody interaction were studied by computer-aided molecular modeling (CAMM)-based quantitative structure-activity relationship analyses. Co-effects of the analytes' substructural hydrophobic, electrostatic, and steric fields on antibody recognition were further revealed. Hydrophobicity of the antigens was demonstrated to have the most important impact. Even less exposed substituents provided hydrophobic force to the antigen-antibody interaction. Dislocated orientation of analyte functional groups could lead to steric hindrance and hydrophobic misleading of antibody recognition. This may happen even when the antigens contained the same substituent as the hapten. Frontier orbital energies also affect the reaction significantly. This study highlights of the power of CAMM-based analyses, providing insights into antibody recognition of small molecules.

From Hansch-Fujita Analysis to AFMoC: A Road to Structure-Based QSAR

Since the pioneering effort of Hansch and Fujita, quantitative structure-activity relationships (QSAR) have proved valuable in optimizing lead structures. Enriching classical 3D-QSAR analysis, which exploits the three-dimensional structure of ligands, with structural information of the target has helped to improve the interpretability of the derived models and to increase their predictive power. One such method is the Adaption of Fields for Molecular Comparison (AFMoC) approach where protein-specifically adapted knowledge-based pair-potentials are tailored to one particular protein by considering additional structural and energetic information about ligands. Here, we summarize applications of AFMoC, describe recent developments, and provide an outlook on how to improve the method.

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

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous contaminants found in the environment. Immunoassays represent useful analytical methods to complement traditional analytical procedures for PAHs. Cross-reactivity (CR) is a very useful character to evaluate the extent of cross-reaction of a cross-reactant in immunoreactions and immunoassays. The quantitative relationships between the molecular properties and the CR of PAHs were established by stepwise multiple linear regression, principal component regression and partial least square regression, using the data of two commercial enzyme-linked immunosorbent assay (ELISA) kits. The objective is to find the most important molecular properties that affect the CR, and predict the CR by multiple regression methods. The results show that the physicochemical, electronic and topological properties of the PAH molecules have an integrated effect on the CR properties for the two ELISAs, among which molar solubility (S_m) and valence molecular connectivity index (³χ^v) are the most important factors. The obtained regression equations for Ris^C kit are all statistically significant (p < 0.005) and show satisfactory ability for predicting CR values, while equations for RaPID kit are all not significant (p > 0.05) and not suitable for predicting. It is probably because that the Ris^C immunoassay employs a monoclonal antibody, while the RaPID kit is based on polyclonal antibody. Considering the important effect of solubility on the CR values, cross-reaction potential (CRP) is calculated and used as a complement of CR for evaluation of cross-reactions in immunoassays. Only the compounds with both high CR and high CRP can cause intense cross-reactions in immunoassays.

Development of a highly sensitive and specific immunoassay for determining chrysoidine, a banned dye, in soybean milk film

A highly specific and sensitive indirect competitive enzyme-linked immunosorbent assay (icELISA）was developed for the first time for the detection of chrysoidine, a dye banned in soybean milk film. Two haptens with different spacer arms were synthesized to produce antibodies. Both homologous and heterologous immunoassay formats were compared to enhance the icELISA sensitivity. The heterologous icELISA exhibited better performance, with an IC(50) (50% inhibitory concentration) of 0.33 ng/mL, a limit of detection (LOD, 10% inhibitory concentration) of 0.04 ng/mL, and a limit of quantitation (LOQ, 20%-80% inhibitory concentration) from 0.09 to 4.9 ng/mL. The developed icELISA was high sensitive and specific, and was applied to determine chrysoidine in fortified soybean milk film samples. The results were in good agreement with that obtained by high-performance liquid chromatography (HPLC) analyses.