WO2022166129A1 - Screening method for heterologous competitive antigen for use in improvement of immunodetection sensitivity - Google Patents

Screening method for heterologous competitive antigen for use in improvement of immunodetection sensitivity Download PDF

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WO2022166129A1
WO2022166129A1 PCT/CN2021/108338 CN2021108338W WO2022166129A1 WO 2022166129 A1 WO2022166129 A1 WO 2022166129A1 CN 2021108338 W CN2021108338 W CN 2021108338W WO 2022166129 A1 WO2022166129 A1 WO 2022166129A1
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enrofloxacin
molecular
sensitivity
heterologous
inhibitory concentration
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French (fr)
Chinese (zh)
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郭德斌
苏婷
郭振
彭娟
毛春财
李慧
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江西煌上煌集团食品股份有限公司
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    • G—PHYSICS
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    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/577—Immunoassay; Biospecific binding assay; Materials therefor involving monoclonal antibodies binding reaction mechanisms characterised by the use of monoclonal antibodies; monoclonal antibodies per se are classified with their corresponding antigens
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/58—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving labelled substances
    • G01N33/581—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving labelled substances with enzyme label (including co-enzymes, co-factors, enzyme inhibitors or substrates)
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
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    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

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  • the invention relates to the technical field of medical detection, in particular to a screening method for heterologous competing antigens for improving the sensitivity of immune detection.
  • the design and synthesis of antigens and the preparation of highly sensitive antibodies are the core of immunoassay methods.
  • the specificity and affinity of antibodies to antigens directly determine the accuracy and sensitivity of detection methods.
  • the target analyte or a slightly modified hapten is generally coupled with different carrier proteins to form immunogens and coating elements. Since the coating original in this case has the same structure as the hapten in the immunogen, it is called a homologous coating, and the corresponding hapten is a homologous competitor. However, the coating that is structurally different from the hapten in the immunogen is called a heterologous coating, and the corresponding hapten is a heterologous competitor.
  • heterologous coatings can improve the sensitivity of immunoassay methods, but not all heterologous coatings can improve the sensitivity of immunoassays.
  • Heterologous coating can significantly improve the sensitivity only when the antibody affinity is poor, while when the antibody affinity is high, the heterologous coating does not significantly improve the sensitivity of the immunoassay.
  • the degree of improvement of the sensitivity of immunoassays by different heterologous coatings is quite different, some can increase ten times or even dozens of times, and some can only improve several times.
  • the purpose of the present invention is to solve the problem that in the prior art, there is a lack of a screening method for heterologous competing antigens that can effectively improve the sensitivity of immune detection, which limits the practical application to a certain extent.
  • the present invention proposes a screening method for a heterologous competing antigen for improving the sensitivity of immune detection, wherein the method comprises the following steps:
  • Step 1 Calculate the corresponding molecular descriptors of the enrofloxacin analogs, and perform principal component analysis on the molecular descriptors to obtain the principal component analysis results, wherein the enrofloxacin analogs are composed of enrofloxacin and enrofloxacin. Preparation of quinolone molecular cross products;
  • Step 2 The semi-inhibitory concentration of enrofloxacin and the semi-inhibitory concentration of each enrofloxacin analog are determined by indirect competitive ELISA, and the semi-inhibitory concentration of enrofloxacin and the enrofloxacin The half-inhibitory concentration of the star analog was calculated to obtain the cross-reaction rate of each of the enrofloxacin analogs;
  • Step 3 According to the principal component analysis result and the corresponding cross-reaction rate of each of the enrofloxacin analogs, establish a mathematical model and perform classification learning to obtain a classification learning result, wherein the principal component analysis result includes all the Molecular descriptors in the described quinolone molecular intersections;
  • Step 4 According to the classification learning result, use each of the quinolone molecular crossovers and lysine to perform molecular docking to obtain a conformation, and optimize the conformation to obtain a minimum energy conformation to determine the best heterologous competing antigen.
  • a method for screening heterologous competing antigens for improving the sensitivity of immune detection proposed by the present invention, firstly performs molecular descriptor calculation on enrofloxacin analogs, and then performs principal component analysis on the molecular descriptors to obtain the principal component analysis result, Then, the cross-reaction rate of enrofloxacin analogs was determined and calculated by indirect competitive ELISA method; then based on the results of principal component analysis and the cross-reaction rate of enrofloxacin analogs, a mathematical model was established and classified learning was performed to obtain the classification learning results.
  • the molecular docking of the quinolone molecular cross and lysine to obtain the conformation the conformation is optimized to obtain the minimum energy conformation, and finally the best heterologous competition antigen is determined.
  • the invention is convenient for screening and determining the heterologous competing antigen which can improve the sensitivity of immune detection, and has good application prospect.
  • the molecular descriptors include:
  • the method for screening heterologous competitive antigens for improving the sensitivity of immunodetection wherein, in the second step, the half-inhibitory concentration of the enrofloxacin and the half-inhibitory concentration of the enrofloxacin analog
  • the determination method includes the following steps:
  • the enrofloxacin coating is sealed with a plate, and then pre-prepared enrofloxacin, quinolone molecular crossovers and standard products are added, wherein the quinolone molecular crossovers include balofloxacin, besifloxacin, Cinofloxacin, Clinifloxacin, Darfloxacin, Froxacin, Gemifloxacin, Lomefloxacin, Marbofloxacin, Moxifloxacin, Nalidixic acid, Norfloxacin, Orbifloxacin, Oxaquinic acid , pefloxacin, pululifloxacin, pipemidic acid, pazufloxacin, sarafloxacin, sitafloxacin, and sparfloxacin, including florfenicol, sulfamethazine, and tetracycline ;
  • the half-inhibitory concentration of enrofloxacin and the half-inhibitory concentration of enrofloxacin analogs are determined according to the inhibition standard curve.
  • CR% is the cross-reaction rate of the enrofloxacin analogs
  • the IC50 of ENR is the half inhibitory concentration of the enrofloxacin
  • the IC50 of the analogs is the half-inhibitory concentration of the enrofloxacin analogs. inhibitory concentration.
  • the method for screening heterologous competing antigens for improving the sensitivity of immune detection wherein, in the step 3, the quinolone molecule crossovers for classification learning include:
  • the third step includes:
  • the molecular descriptors of each of the quinolone molecular crossovers and the corresponding cross-reaction rates of each of the enrofloxacin analogs are respectively subjected to classification learning, wherein the software for the classification learning operation is MATLAB.
  • the cross-reaction rate of the enrofloxacin analog is greater than 0.07, it means that the quinolone molecular cross-compound can be captured by the antibody in the enzyme-linked immunosorbent assay system.
  • the method for screening heterologous competing antigens for improving the sensitivity of immune detection wherein, in the step 3, after the classification learning result is obtained, the method further includes the following steps:
  • the molecular descriptor of the test sample is machine-learned to obtain the corresponding machine CR value
  • the actual CR value of the test sample is obtained by measuring the test sample by enzyme-linked immunosorbent assay
  • the accuracy of machine learning is calculated according to the machine CR value and the actual CR value of the test sample, so as to evaluate the classification learning result.
  • the screening method for heterologous competitive antigens for improving the sensitivity of immunodetection wherein, when the conformation is optimized, the set force field for minimizing molecular mechanics is MMFF94x, and the cutoff value for non-bonding interaction is MMFF94x.
  • the software for configuration optimization is Gaussian software.
  • Fig. 1 is the principle block diagram of the screening method of the heterologous competition antigen proposed for improving the sensitivity of immunodetection proposed by the present invention
  • Fig. 2 is the flow chart of the screening method of the heterologous competition antigen proposed by the present invention for improving the sensitivity of immunodetection;
  • Figure 3 shows the distribution of electrostatic potential near different quinolone antigenic determinants in the present invention.
  • the present invention proposes a method for screening heterologous competing antigens for improving the sensitivity of immunodetection, wherein the method comprises the following steps:
  • molecular descriptors include:
  • principal component analysis was performed on the molecular descriptors of the enrofloxacin analogs to obtain principal component analysis results.
  • the principal component analysis method is a statistical method of dimensionality reduction. With the help of an orthogonal transformation, the original random vector whose components are related is converted into a new random vector whose components are not related.
  • the covariance matrix of random vectors is transformed into a diagonal matrix, which is geometrically expressed as transforming the original coordinate system into a new orthogonal coordinate system, so that it points to the p orthogonal directions where the sample points are most spread; Carry out dimensionality reduction processing, so that it can be converted into a low-dimensional variable system with a higher precision, and then further convert the low-dimensional system into a one-dimensional system by constructing an appropriate value function.
  • the quinolone molecular crosses include balofloxacin (BAL), besifloxacin (BES), cinofloxacin (CIN), clinifloxacin (CLI), danofloxacin (DAN), floroxacin (FLE) ), gemifloxacin (GEM), lomefloxacin (LOM), marbofloxacin (MAR), moxifloxacin (MOX), nalidixic acid (NAL), norfloxacin (NOR), orbifloxacin (ORB), oxoquinic acid (OXO), pefloxacin (PEF), pululifloxacin (PRU), pipemidic acid (PIP), pazufloxacin (PAZ), sarafloxacin (SAR), theta Floxacin (SIT) and sparfloxacin (SPA).
  • BAL balofloxacin
  • BES besifloxacin
  • CIN clinifloxacin
  • CLI danof
  • CR% is the cross-reaction rate of enrofloxacin analogs
  • the IC50 of ENR is the half-inhibitory concentration of enrofloxacin
  • the IC50 of the analog is the half-inhibitory concentration of enrofloxacin analogs.
  • the semi-inhibitory concentration of enrofloxacin and the corresponding semi-inhibitory concentration of each enrofloxacin analog can be determined. Then, according to the half-inhibitory concentration of enrofloxacin and the corresponding half-inhibitory concentration of each enrofloxacin analog, the actual CR value corresponding to each enrofloxacin analog is calculated.
  • the quinolone molecule crossovers for classification learning include:
  • Enrofloxacin (ENR), balofloxacin (PEF), pipemidic acid (PIP), norfloxacin (NOR), danofloxacin (DAN), floroxacin (FLE), balofloxacin ( BAL), Besifloxacin (BES), Cinofloxacin (CIN), Clinifloxacin (CLI), Gemifloxacin (GEM), Lomefloxacin (LOM), Marbofloxacin (MAR), Moxifloxacin ( MOX), nalidixic acid (NAL), orbifloxacin (ORB), oxoquinic acid (OXO), and pazufloxacin (PAZ).
  • the above-mentioned classification learning result also needs to be evaluated.
  • use the test sample to evaluate the classification learning result includes the following steps:
  • test samples include PRL, sarafloxacin (SAR), sitafloxacin (SIT), sparfloxacin (SPA), florfenicol (FLO), Sulfamethazine (SMZ) and Tetracycline (TET).
  • S104 uses each of the quinolone molecular crossovers and lysine to perform molecular docking to obtain a conformation, and optimize the conformation to obtain a minimum energy conformation, so as to determine the best heterologous competing antigen.
  • the force field of molecular mechanics minimization is set to MMFF94x, and the cutoff value of non-bonding interaction is set to
  • Gaussian 09 software the conformation after the initial minimization was further determined to achieve more precise geometric optimization and frequency analysis at the HF/6-31G(d) level, finally obtaining the minimum energy conformation of all condensation products.
  • atomic point charges and electrostatic potentials were calculated at the same level using Gaussian 09 software and observed using GaussView 5.0 software to finalize the optimal heterologous competing antigen.
  • a method for screening heterologous competing antigens for improving the sensitivity of immune detection proposed by the present invention, firstly performs molecular descriptor calculation on enrofloxacin analogs, and then performs principal component analysis on the molecular descriptors to obtain the principal component analysis result, Then, the cross-reaction rate of enrofloxacin analogs was determined and calculated by indirect competitive ELISA method; then based on the results of principal component analysis and the cross-reaction rate of enrofloxacin analogs, a mathematical model was established and classified learning was performed to obtain the classification learning results.
  • the invention is convenient for screening and determining the heterologous competing antigen which can improve the sensitivity of immune detection, and has good application prospect.

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Abstract

A screening method for a heterologous competitive antigen for use in improvement of immunodetection sensitivity. The method comprises: performing corresponding molecular descriptor calculation on enrofloxacin analogues, and performing principal component analysis on molecular descriptors to obtain a principal component analysis result (S101); respectively performing measurement to obtain the half maximal inhibitory concentration of enrofloxacin and the half maximal inhibitory concentration of each enrofloxacin analogue, so as to calculate a cross-reactivity rate of each enrofloxacin analogue (S102); according to the principal component analysis result and the cross-reactivity rate of the corresponding enrofloxacin analogue, establishing a mathematical model and performing classification learning to obtain a classification learning result (S103); and according to the classification learning result, performing molecular docking by using a quinolone molecular cross object and lysine, so as to obtain a conformation, and performing configuration optimization to obtain a minimum energy conformation, so as to determine an optimal heterologous competitive antigen (S104). The method facilitates the screening and determining of the heterologous competitive antigen capable of improving immunodetection sensitivity, and has a good application prospect.

Description

一种用于提高免疫检测灵敏度的异源竞争抗原的筛选方法A screening method for heterologous competing antigens for improving the sensitivity of immunodetection 技术领域technical field
本发明涉及医药检测技术领域，特别涉及一种用于提高免疫检测灵敏度的异源竞争抗原的筛选方法。The invention relates to the technical field of medical detection, in particular to a screening method for heterologous competing antigens for improving the sensitivity of immune detection.
背景技术Background technique
抗原的设计合成与高灵敏抗体的制备是免疫分析方法的核心，抗体对抗原的特异性以及亲和性，直接决定了检测方法的准确性以及灵敏度。在大部分免疫分析方法中，一般是将目标待测物或稍加改造后作为半抗原与不同的载体蛋白偶联形成免疫原和包被原。由于此种情况下的包被原与免疫原中的半抗原结构相同，故称同源包被，对应的半抗原为同源竞争原。而把与免疫原中的半抗原结构不同的包被原称为异源包被，对应的半抗原为异源竞争原。The design and synthesis of antigens and the preparation of highly sensitive antibodies are the core of immunoassay methods. The specificity and affinity of antibodies to antigens directly determine the accuracy and sensitivity of detection methods. In most immunoassay methods, the target analyte or a slightly modified hapten is generally coupled with different carrier proteins to form immunogens and coating elements. Since the coating original in this case has the same structure as the hapten in the immunogen, it is called a homologous coating, and the corresponding hapten is a homologous competitor. However, the coating that is structurally different from the hapten in the immunogen is called a heterologous coating, and the corresponding hapten is a heterologous competitor.
目前国内外均有文献报道过异源包被可以提高免疫分析方法的灵敏度，但并不是所有的异源包被都能提高免疫分析的灵敏度。只有当抗体亲和力较差时异源包被才能显著提高灵敏度，而当抗体亲和力较高时，异源包被并不能显著提高免疫分析灵敏度。除此之外，不同的异源包被对免疫分析灵敏度的提高程度是截然不同的，有的能提高十几倍，甚至几十倍，有的只能提高几倍。因此，如果对于确定的抗体，若能设计出它的最佳竞争原，使得通过异源竞争的模式最大化地提高免疫分析的灵敏度，将原本“不合格”的抗体变成“合格”的抗体，那么将大大提高抗体的合格率，节省抗体制备的成本。At present, it has been reported in the literature that heterologous coatings can improve the sensitivity of immunoassay methods, but not all heterologous coatings can improve the sensitivity of immunoassays. Heterologous coating can significantly improve the sensitivity only when the antibody affinity is poor, while when the antibody affinity is high, the heterologous coating does not significantly improve the sensitivity of the immunoassay. In addition, the degree of improvement of the sensitivity of immunoassays by different heterologous coatings is quite different, some can increase ten times or even dozens of times, and some can only improve several times. Therefore, if for a certain antibody, if its optimal competitor can be designed, the sensitivity of immunoassay can be maximized through the mode of heterologous competition, and the original "unqualified" antibody can be turned into a "qualified" antibody. , then the qualification rate of the antibody will be greatly improved and the cost of antibody preparation will be saved.
然而，现有技术中，缺乏一种能够有效提高免疫检测灵敏度的异源竞争抗原的筛选方法，在一定程度上限制了实际应用。However, in the prior art, there is a lack of a screening method for heterologous competing antigens that can effectively improve the sensitivity of immune detection, which limits the practical application to a certain extent.
发明内容SUMMARY OF THE INVENTION
本发明的目的是为了解决现有技术中，缺乏一种能够有效提高免疫检测灵敏度的异源竞争抗原的筛选方法，在一定程度上限制了实际应用的问题。The purpose of the present invention is to solve the problem that in the prior art, there is a lack of a screening method for heterologous competing antigens that can effectively improve the sensitivity of immune detection, which limits the practical application to a certain extent.
为了实现上述目的，本发明采用了如下技术方案：In order to achieve the above object, the present invention adopts the following technical solutions:
本发明提出一种用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，其中，所述方法包括如下步骤：The present invention proposes a screening method for a heterologous competing antigen for improving the sensitivity of immune detection, wherein the method comprises the following steps:
步骤一：对恩诺沙星类似物进行对应分子描述符计算，并对所述分子描述符进行主成分分析以得到主成分分析结果，其中所述恩诺沙星类似物由恩诺沙星与喹诺酮分子交叉物制得；Step 1: Calculate the corresponding molecular descriptors of the enrofloxacin analogs, and perform principal component analysis on the molecular descriptors to obtain the principal component analysis results, wherein the enrofloxacin analogs are composed of enrofloxacin and enrofloxacin. Preparation of quinolone molecular cross products;
步骤二：采用间接竞争ELISA法分别测定得到恩诺沙星的半抑制浓度以及各个恩诺沙星类似物的半抑制浓度，并根据所述恩诺沙星的半抑制浓度以及所述恩诺沙星类似物的半抑制浓度计算得到各个所述恩诺沙星类似物的交叉反应率；Step 2: The semi-inhibitory concentration of enrofloxacin and the semi-inhibitory concentration of each enrofloxacin analog are determined by indirect competitive ELISA, and the semi-inhibitory concentration of enrofloxacin and the enrofloxacin The half-inhibitory concentration of the star analog was calculated to obtain the cross-reaction rate of each of the enrofloxacin analogs;
步骤三：根据所述主成分分析结果以及对应的各所述恩诺沙星类似物的交叉反应率，建立数学模型并进行分类学习，以得到分类学习结果，其中所述主成分分析结果包括所述喹诺酮分子交叉物中的分子描述符；Step 3: According to the principal component analysis result and the corresponding cross-reaction rate of each of the enrofloxacin analogs, establish a mathematical model and perform classification learning to obtain a classification learning result, wherein the principal component analysis result includes all the Molecular descriptors in the described quinolone molecular intersections;
步骤四：根据所述分类学习结果，利用各所述喹诺酮分子交叉物及赖氨酸进行分子对接以得到构象，并对构象进行构型优化得到最小能量构象，以确定最佳异源竞争抗原。Step 4: According to the classification learning result, use each of the quinolone molecular crossovers and lysine to perform molecular docking to obtain a conformation, and optimize the conformation to obtain a minimum energy conformation to determine the best heterologous competing antigen.
本发明提出的一种用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，首先对恩诺沙星类似物进行分子描述符计算，再对分子描述符进行主成分分析得到主成分分析结果，然后采用间接竞争ELISA法测定计算得到恩诺沙星类似物的交叉反应率；再根据主成分分析结果以及恩诺沙星类似物的交叉反应率，建立数学模型并进行分类学习以得到分类学习结果，最后根据分类学习结果，将喹诺酮分子交叉物及赖氨酸进行分子对接以得到构象，进行构型优化以得到最小能量构象，最终确定最佳异源竞争抗原。本发明便于筛选确定能提高免疫检测灵敏度的异源竞争抗原，具有良好的应用前景。A method for screening heterologous competing antigens for improving the sensitivity of immune detection proposed by the present invention, firstly performs molecular descriptor calculation on enrofloxacin analogs, and then performs principal component analysis on the molecular descriptors to obtain the principal component analysis result, Then, the cross-reaction rate of enrofloxacin analogs was determined and calculated by indirect competitive ELISA method; then based on the results of principal component analysis and the cross-reaction rate of enrofloxacin analogs, a mathematical model was established and classified learning was performed to obtain the classification learning results. , and finally, according to the classification learning results, the molecular docking of the quinolone molecular cross and lysine to obtain the conformation, the conformation is optimized to obtain the minimum energy conformation, and finally the best heterologous competition antigen is determined. The invention is convenient for screening and determining the heterologous competing antigen which can improve the sensitivity of immune detection, and has good application prospect.
所述用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，其中，在所述步骤一中，所述分子描述符包括：In the method for screening heterologous competing antigens for improving the sensitivity of immunodetection, in the step 1, the molecular descriptors include:
符号与术语、物理属性、休克尔理论描述符、细分表面区域、原子计数和键计数、连接性和Kappa形状指数、邻接和距离矩阵描述符、药效团特征描述 符以及电荷描述符。Notation and terminology, physical properties, Huckel theory descriptors, subdivided surface area, atomic and bond counts, connectivity and Kappa shape indices, adjacency and distance matrix descriptors, pharmacophore feature descriptors, and charge descriptors.
所述用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，其中，在所述步骤二中，所述恩诺沙星的半抑制浓度以及所述恩诺沙星类似物的半抑制浓度的测定方法包括如下步骤：The method for screening heterologous competitive antigens for improving the sensitivity of immunodetection, wherein, in the second step, the half-inhibitory concentration of the enrofloxacin and the half-inhibitory concentration of the enrofloxacin analog The determination method includes the following steps:
首先对恩诺沙星包被原进行包板封闭，然后加入预先制备好的恩诺沙星、喹诺酮分子交叉物以及标准品，其中所述喹诺酮分子交叉物包括巴洛沙星、贝西沙星、西诺沙星、克林沙星、达氟沙星、弗罗沙星、吉米沙星、洛美沙星、马波沙星、莫西沙星、萘啶酮酸、诺氟沙星、奥比沙星、恶喹酸、培氟沙星、普鲁利沙星、吡哌酸、帕珠沙星、沙拉沙星、西塔沙星以及司帕沙星，所述标准品包括氟苯尼考、磺胺二甲基嘧啶以及四环素；First, the enrofloxacin coating is sealed with a plate, and then pre-prepared enrofloxacin, quinolone molecular crossovers and standard products are added, wherein the quinolone molecular crossovers include balofloxacin, besifloxacin, Cinofloxacin, Clinifloxacin, Darfloxacin, Froxacin, Gemifloxacin, Lomefloxacin, Marbofloxacin, Moxifloxacin, Nalidixic acid, Norfloxacin, Orbifloxacin, Oxaquinic acid , pefloxacin, pululifloxacin, pipemidic acid, pazufloxacin, sarafloxacin, sitafloxacin, and sparfloxacin, including florfenicol, sulfamethazine, and tetracycline ;
每孔分别加入50ul的标准品以及50ul的抗恩诺沙星单克隆抗体稀释液，完成后继续加入酶标二抗、显色、终止以及测吸光值步骤，以建立得到抑制标准曲线；Add 50ul of standard substance and 50ul of anti-enrofloxacin monoclonal antibody diluent to each well. After completion, continue to add enzyme-labeled secondary antibody, develop color, stop and measure absorbance to establish an inhibition standard curve;
根据所述抑制标准曲线确定得到所述恩诺沙星的半抑制浓度以及恩诺沙星类似物的半抑制浓度。The half-inhibitory concentration of enrofloxacin and the half-inhibitory concentration of enrofloxacin analogs are determined according to the inhibition standard curve.
所述用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，其中，所述恩诺沙星类似物的交叉反应率的计算公式为：Said method for screening heterologous competing antigens for improving the sensitivity of immunodetection, wherein the formula for calculating the cross-reaction rate of the enrofloxacin analogs is:
CR％＝(ENR的IC 50/类似物的IC 50)×100 CR%=( IC50 of ENR/ IC50 of analog)×100
其中，CR％为所述恩诺沙星类似物的交叉反应率，ENR的IC 50为所述恩诺沙星的半抑制浓度，类似物的IC 50为所述恩诺沙星类似物的半抑制浓度。 Wherein, CR% is the cross-reaction rate of the enrofloxacin analogs, the IC50 of ENR is the half inhibitory concentration of the enrofloxacin, and the IC50 of the analogs is the half-inhibitory concentration of the enrofloxacin analogs. inhibitory concentration.
所述用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，其中，在所述步骤三中，进行分类学习的所述喹诺酮分子交叉物包括：The method for screening heterologous competing antigens for improving the sensitivity of immune detection, wherein, in the step 3, the quinolone molecule crossovers for classification learning include:
恩诺沙星、巴洛沙星、吡哌酸、诺氟沙星、达氟沙星、弗罗沙星、巴洛沙星、贝西沙星、西诺沙星、克林沙星、吉米沙星、洛美沙星、马波沙星、莫西沙星、萘啶酮酸、奥比沙星、恶喹酸以及帕珠沙星；Enrofloxacin, balofloxacin, pipemidic acid, norfloxacin, danofloxacin, frofloxacin, balofloxacin, besifloxacin, cinofloxacin, clinifloxacin, gemifloxacin, lomeza Star, marbofloxacin, moxifloxacin, nalidixic acid, orbifloxacin, oxquinic acid and pazufloxacin;
所述步骤三包括：The third step includes:
对各所述喹诺酮分子交叉物的分子描述符及对应的各所述恩诺沙星类似物的交叉反应率分别进行分类学习，其中进行分类学习操作的软件为MATLAB。The molecular descriptors of each of the quinolone molecular crossovers and the corresponding cross-reaction rates of each of the enrofloxacin analogs are respectively subjected to classification learning, wherein the software for the classification learning operation is MATLAB.
所述用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，其中，在所述步骤三中；The method for screening heterologous competing antigens for improving the sensitivity of immunodetection, wherein, in the third step;
在进行分类学习中，当所述恩诺沙星类似物的交叉反应率小于0.01，表示所述喹诺酮分子交叉物不能在酶联免疫吸附剂测定体系中被抗体识别；In the classification learning, when the cross-reaction rate of the enrofloxacin analog is less than 0.01, it means that the quinolone molecular cross-compound cannot be recognized by the antibody in the enzyme-linked immunosorbent assay system;
当所述恩诺沙星类似物的交叉反应率大于0.07，表示所述喹诺酮分子交叉物能在酶联免疫吸附剂测定体系中被抗体捕获。When the cross-reaction rate of the enrofloxacin analog is greater than 0.07, it means that the quinolone molecular cross-compound can be captured by the antibody in the enzyme-linked immunosorbent assay system.
所述用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，其中，在所述步骤三中，在得到了所述分类学习结果之后，所述方法还包括如下步骤：The method for screening heterologous competing antigens for improving the sensitivity of immune detection, wherein, in the step 3, after the classification learning result is obtained, the method further includes the following steps:
利用测试样本对所述分类学习结果进行评价，其中进行评价的方法包括如下步骤：Use test samples to evaluate the classification learning results, wherein the evaluation method includes the following steps:
将测试样本的分子描述符经机器学习后得到对应的机器CR值；The molecular descriptor of the test sample is machine-learned to obtain the corresponding machine CR value;
将测试样本通过酶联免疫吸附法测定得到测试样本的实际CR值；The actual CR value of the test sample is obtained by measuring the test sample by enzyme-linked immunosorbent assay;
根据所述机器CR值以及所述测试样本的实际CR值计算得到机器学习的准确度，以对所述分类学习结果进行评价。The accuracy of machine learning is calculated according to the machine CR value and the actual CR value of the test sample, so as to evaluate the classification learning result.
所述用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，其中，所述机器学习的准确度的计算公式表示为：The method for screening heterologous competitive antigens for improving the sensitivity of immune detection, wherein, the calculation formula of the accuracy of the machine learning is expressed as:
Figure PCTCN2021108338-appb-000001
Figure PCTCN2021108338-appb-000001
所述用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，其中，在对构象进行构型优化时，设置的分子力学最小化的力场为MMFF94x，非键合相互作用的截止值为
Figure PCTCN2021108338-appb-000002
进行构型优化的软件为Gaussian软件。 The screening method for heterologous competitive antigens for improving the sensitivity of immunodetection, wherein, when the conformation is optimized, the set force field for minimizing molecular mechanics is MMFF94x, and the cutoff value for non-bonding interaction is MMFF94x.
Figure PCTCN2021108338-appb-000002
The software for configuration optimization is Gaussian software.
本公开的其他特征和优点将在随后的说明书中阐述，或者，部分特征和优点可以从说明书推知或毫无疑义地确定，或者通过实施本公开的上述技术即可得知。Additional features and advantages of the present disclosure will be set forth in the description that follows, or some may be inferred or unambiguously determined from the description, or may be learned by practicing the above-described techniques of the present disclosure.
为使本发明的上述目的、特征和优点能更明显易懂，下文特举较佳实施例，并配合所附附图，作详细说明如下。In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below, and are described in detail as follows in conjunction with the accompanying drawings.
附图说明Description of drawings
图1为本发明提出的用于提高免疫检测灵敏度的异源竞争抗原的筛选方法的原理框图；Fig. 1 is the principle block diagram of the screening method of the heterologous competition antigen proposed for improving the sensitivity of immunodetection proposed by the present invention;
图2为本发明提出的用于提高免疫检测灵敏度的异源竞争抗原的筛选方法的流程图；Fig. 2 is the flow chart of the screening method of the heterologous competition antigen proposed by the present invention for improving the sensitivity of immunodetection;
图3为本发明中不同喹诺酮类抗原决定簇附近的静电势分布情况。Figure 3 shows the distribution of electrostatic potential near different quinolone antigenic determinants in the present invention.
具体实施方式Detailed ways
为了便于理解本发明，下面将参照相关附图对本发明进行更全面的描述。附图中给出了本发明的首选实施例。但是，本发明可以以许多不同的形式来实现，并不限于本文所描述的实施例。相反地，提供这些实施例的目的是使对本发明的公开内容更加透彻全面。In order to facilitate understanding of the present invention, the present invention will be described more fully hereinafter with reference to the related drawings. Preferred embodiments of the invention are shown in the drawings. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
除非另有定义，本文所使用的所有的技术和科学术语与属于本发明的技术领域的技术人员通常理解的含义相同。本文中在本发明的说明书中所使用的术语只是为了描述具体的实施例的目的，不是旨在于限制本发明。本文所使用的术语“及/或”包括一个或多个相关的所列项目的任意的和所有的组合。Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terms used herein in the description of the present invention are for the purpose of describing specific embodiments only, and are not intended to limit the present invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
现有技术中，缺乏一种能够有效提高免疫检测灵敏度的异源竞争抗原的筛选方法，在一定程度上限制了实际应用。In the prior art, there is a lack of a screening method for heterologous competing antigens that can effectively improve the sensitivity of immune detection, which limits the practical application to a certain extent.
为了解决这一技术问题，本发明提出一种用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，其中，所述方法包括如下步骤：In order to solve this technical problem, the present invention proposes a method for screening heterologous competing antigens for improving the sensitivity of immunodetection, wherein the method comprises the following steps:
S101，对恩诺沙星类似物进行对应分子描述符计算，并对所述分子描述符进行主成分分析以得到主成分分析结果，其中所述恩诺沙星类似物由恩诺沙星与喹诺酮分子交叉物制得。S101, perform corresponding molecular descriptor calculation on the enrofloxacin analog, and perform principal component analysis on the molecular descriptor to obtain a principal component analysis result, wherein the enrofloxacin analog is composed of enrofloxacin and quinolone Molecular crossover obtained.
在本步骤中，使用MOE 2016.10软件对恩诺沙星类似物进行对应分子描述符计算。具体的，每个训练样本均有204个分子描述符。具体的，分子描述符包括：In this step, the corresponding molecular descriptors of enrofloxacin analogs were calculated using MOE 2016.10 software. Specifically, each training sample has 204 molecular descriptors. Specifically, molecular descriptors include:
(a)符号与术语；(b)物理属性；(c)Hückel(休克尔)理论描述符；(d)细分表面区域；(e)原子计数和键计数；(f)Kier连接性和Kappa形状指数；(g) 邻接和距离矩阵描述符；(h)药效团特征描述符；(i)电荷描述符。(a) symbols and terminology; (b) physical properties; (c) Hückel theoretical descriptors; (d) subdivided surface area; (e) atomic and bond counts; (f) Kier connectivity and Kappa shape index; (g) adjacency and distance matrix descriptors; (h) pharmacophore feature descriptors; (i) charge descriptors.
进一步的，对恩诺沙星类似物的分子描述符进行主成分分析以得到主成分分析结果。具体的，主成分分析法是一种降维的统计方法，借助于一个正交变换，将其分量相关的原随机向量转化成其分量不相关的新随机向量，这在代数上表现为将原随机向量的协方差阵变换成对角形阵，在几何上表现为将原坐标系变换成新的正交坐标系，使之指向样本点散布最开的p个正交方向；然后对多维变量***进行降维处理，使之能以一个较高的精度转换成低维变量***，再通过构造适当的价值函数，进一步把低维***转化成一维***。Further, principal component analysis was performed on the molecular descriptors of the enrofloxacin analogs to obtain principal component analysis results. Specifically, the principal component analysis method is a statistical method of dimensionality reduction. With the help of an orthogonal transformation, the original random vector whose components are related is converted into a new random vector whose components are not related. The covariance matrix of random vectors is transformed into a diagonal matrix, which is geometrically expressed as transforming the original coordinate system into a new orthogonal coordinate system, so that it points to the p orthogonal directions where the sample points are most spread; Carry out dimensionality reduction processing, so that it can be converted into a low-dimensional variable system with a higher precision, and then further convert the low-dimensional system into a one-dimensional system by constructing an appropriate value function.
在本实施例中，对训练样本的分子描述符的主成分分析程序在MATLAB 2015a(The MathWorks，Inc.，USA)软件上进行。在此需要特别指出的是，上述的恩诺沙星类似物由恩诺沙星与喹诺酮分子交叉物制得。In this example, the principal component analysis procedure for the molecular descriptors of the training samples was performed on MATLAB 2015a (The MathWorks, Inc., USA) software. It should be specially pointed out here that the above-mentioned enrofloxacin analogs are prepared from the molecular crosses of enrofloxacin and quinolone.
S102，采用间接竞争ELISA法分别测定得到恩诺沙星的半抑制浓度以及各个恩诺沙星类似物的半抑制浓度，并根据所述恩诺沙星的半抑制浓度以及所述恩诺沙星类似物的半抑制浓度计算得到各个所述恩诺沙星类似物的交叉反应率。S102, adopt the indirect competitive ELISA method to obtain the semi-inhibitory concentration of enrofloxacin and the semi-inhibitory concentration of each enrofloxacin analog respectively, and according to the semi-inhibitory concentration of enrofloxacin and the enrofloxacin The half-inhibitory concentration of the analogs was calculated to obtain the cross-reactivity rate for each of the enrofloxacin analogs.
在本步骤中，制备恩诺沙星类似物的交叉反应率的步骤如下所述：In this step, the steps of preparing the cross-reactivity ratio of enrofloxacin analogs are as follows:
(1)、首先对恩诺沙星包被原进行包板封闭，然后加入预先制备好的恩诺沙星(ENR)、喹诺酮分子交叉物以及标准品。(1) First, seal the enrofloxacin coating, and then add the pre-prepared enrofloxacin (ENR), quinolone molecular crossovers and standard products.
其中，所述喹诺酮分子交叉物包括巴洛沙星(BAL)、贝西沙星(BES)、西诺沙星(CIN)、克林沙星(CLI)、达氟沙星(DAN)、弗罗沙星(FLE)、吉米沙星(GEM)、洛美沙星(LOM)、马波沙星(MAR)、莫西沙星(MOX)、萘啶酮酸(NAL)、诺氟沙星(NOR)、奥比沙星(ORB)、恶喹酸(OXO)、培氟沙星(PEF)、普鲁利沙星(PRU)、吡哌酸(PIP)、帕珠沙星(PAZ)、沙拉沙星(SAR)、西塔沙星(SIT)以及司帕沙星(SPA)。上述的标准品包括氟苯尼考(FLO)、磺胺二甲基嘧啶(SMZ)以及四环素(TET)。Wherein, the quinolone molecular crosses include balofloxacin (BAL), besifloxacin (BES), cinofloxacin (CIN), clinifloxacin (CLI), danofloxacin (DAN), floroxacin (FLE) ), gemifloxacin (GEM), lomefloxacin (LOM), marbofloxacin (MAR), moxifloxacin (MOX), nalidixic acid (NAL), norfloxacin (NOR), orbifloxacin (ORB), oxoquinic acid (OXO), pefloxacin (PEF), pululifloxacin (PRU), pipemidic acid (PIP), pazufloxacin (PAZ), sarafloxacin (SAR), theta Floxacin (SIT) and sparfloxacin (SPA). The aforementioned standards include florfenicol (FLO), sulfamethazine (SMZ), and tetracycline (TET).
(2)、每孔分别加入50ul的标准品以及50ul的抗恩诺沙星单克隆抗体稀释液，完成后继续加入酶标二抗、显色、终止以及测吸光值步骤，以建立得到抑制标准曲线。(2) Add 50ul of standard substance and 50ul of anti-enrofloxacin monoclonal antibody diluent to each well. After completion, continue to add enzyme-labeled secondary antibody, develop color, stop and measure absorbance to establish the inhibition standard. curve.
(3)、根据所述抑制标准曲线确定得到所述恩诺沙星的半抑制浓度以及恩诺沙星类似物的半抑制浓度。(3), determine the half-inhibitory concentration of enrofloxacin and the half-inhibitory concentration of enrofloxacin analogs according to the inhibition standard curve.
恩诺沙星类似物的交叉反应率的计算公式为：The formula for calculating the cross-reactivity rate of enrofloxacin analogs is:
CR％＝(ENR的IC 50/类似物的IC 50)×100 CR%=( IC50 of ENR/ IC50 of analog)×100
其中，CR％为恩诺沙星类似物的交叉反应率，ENR的IC 50为恩诺沙星的半抑制浓度，类似物的IC 50为恩诺沙星类似物的半抑制浓度。 Among them, CR% is the cross-reaction rate of enrofloxacin analogs, the IC50 of ENR is the half-inhibitory concentration of enrofloxacin, and the IC50 of the analog is the half-inhibitory concentration of enrofloxacin analogs.
在此需要指出的是，利用上述步骤S102，可测定得到恩诺沙星的半抑制浓度以及各个恩诺沙星类似物对应的半抑制浓度。然后再根据上述的恩诺沙星的半抑制浓度以及各个恩诺沙星类似物对应的半抑制浓度，计算得到各恩诺沙星类似物对应的实际CR值。It should be pointed out here that, by using the above step S102, the semi-inhibitory concentration of enrofloxacin and the corresponding semi-inhibitory concentration of each enrofloxacin analog can be determined. Then, according to the half-inhibitory concentration of enrofloxacin and the corresponding half-inhibitory concentration of each enrofloxacin analog, the actual CR value corresponding to each enrofloxacin analog is calculated.
S103，根据所述主成分分析结果以及对应的各所述恩诺沙星类似物的交叉反应率，建立数学模型并进行分类学习，以得到分类学习结果，其中所述主成分分析结果包括所述喹诺酮分子交叉物中的分子描述符。S103, according to the principal component analysis result and the corresponding cross-reaction rate of each of the enrofloxacin analogs, establish a mathematical model and perform classification learning to obtain a classification learning result, wherein the principal component analysis result includes the Molecular descriptors in quinolone molecular intersections.
在本步骤中，对各喹诺酮分子交叉物的分子描述符及对应的各所述恩诺沙星类似物的交叉反应率分别进行分类学习，其中进行分类学习操作的软件为MATLAB 2015a。In this step, the molecular descriptor of each quinolone molecular crossover and the corresponding cross-reaction rate of each of the enrofloxacin analogs are classified and learned respectively, and the software for the classification learning operation is MATLAB 2015a.
具体的，进行分类学习的所述喹诺酮分子交叉物包括：Specifically, the quinolone molecule crossovers for classification learning include:
恩诺沙星(ENR)、巴洛沙星(PEF)、吡哌酸(PIP)、诺氟沙星(NOR)、达氟沙星(DAN)、弗罗沙星(FLE)、巴洛沙星(BAL)、贝西沙星(BES)、西诺沙星(CIN)、克林沙星(CLI)、吉米沙星(GEM)、洛美沙星(LOM)、马波沙星(MAR)、莫西沙星(MOX)、萘啶酮酸(NAL)、奥比沙星(ORB)、恶喹酸(OXO)以及帕珠沙星(PAZ)。Enrofloxacin (ENR), balofloxacin (PEF), pipemidic acid (PIP), norfloxacin (NOR), danofloxacin (DAN), floroxacin (FLE), balofloxacin ( BAL), Besifloxacin (BES), Cinofloxacin (CIN), Clinifloxacin (CLI), Gemifloxacin (GEM), Lomefloxacin (LOM), Marbofloxacin (MAR), Moxifloxacin ( MOX), nalidixic acid (NAL), orbifloxacin (ORB), oxoquinic acid (OXO), and pazufloxacin (PAZ).
在进行分类学习中，当恩诺沙星类似物的交叉反应率小于0.01，表示喹诺酮分子交叉物不能在酶联免疫吸附剂测定体系中被抗体识别。当恩诺沙星类似物的交叉反应率大于0.07，表示喹诺酮分子交叉物能在酶联免疫吸附剂测定体系中被抗体捕获。In the classification learning, when the cross-reaction rate of the enrofloxacin analogs is less than 0.01, it means that the quinolone molecular crossovers cannot be recognized by the antibody in the enzyme-linked immunosorbent assay system. When the cross-reaction rate of the enrofloxacin analog was greater than 0.07, it indicated that the quinolone molecular cross-compound could be captured by the antibody in the enzyme-linked immunosorbent assay system.
作为补充的，在得到了上述的分类学习结果之后，还需要对上述的分类学习结果进行评价。具体的，利用测试样本对所述分类学习结果进行评价，其中 进行评价的方法包括如下步骤：As a supplement, after the above-mentioned classification learning result is obtained, the above-mentioned classification learning result also needs to be evaluated. Specifically, use the test sample to evaluate the classification learning result, and the method for evaluating includes the following steps:
(1)、将测试样本的分子描述符经机器学习后得到对应的机器CR值；(1) The molecular descriptor of the test sample is machine-learned to obtain the corresponding machine CR value;
(2)、将测试样本通过酶联免疫吸附法测定得到测试样本的实际CR值；(2), measure the test sample by enzyme-linked immunosorbent assay to obtain the actual CR value of the test sample;
(3)、根据所述机器CR值以及所述测试样本的实际CR值计算得到机器学习的准确度，以对所述分类学习结果进行评价。(3) Calculate the accuracy of machine learning according to the machine CR value and the actual CR value of the test sample, so as to evaluate the classification learning result.
在此需要指出的是，在本实施例中，上述的测试样本包括PRL、沙拉沙星(SAR)、西塔沙星(SIT)、司帕沙星(SPA)、氟苯尼考(FLO)、磺胺二甲基嘧啶(SMZ)以及四环素(TET)。It should be pointed out that, in this embodiment, the above-mentioned test samples include PRL, sarafloxacin (SAR), sitafloxacin (SIT), sparfloxacin (SPA), florfenicol (FLO), Sulfamethazine (SMZ) and Tetracycline (TET).
具体的，机器学习的准确度的计算公式表示为：Specifically, the calculation formula of the accuracy of machine learning is expressed as:
Figure PCTCN2021108338-appb-000003
Figure PCTCN2021108338-appb-000003
S104，根据所述分类学习结果，利用各所述喹诺酮分子交叉物及赖氨酸进行分子对接以得到构象，并对构象进行构型优化得到最小能量构象，以确定最佳异源竞争抗原。S104, according to the classification learning result, use each of the quinolone molecular crossovers and lysine to perform molecular docking to obtain a conformation, and optimize the conformation to obtain a minimum energy conformation, so as to determine the best heterologous competing antigen.
进一步的，在本步骤中，使用分子操作环境(MOE)2016.10软件对喹诺酮分子交叉物及赖氨酸进行分子对接，以阐明单克隆抗体和异源包被抗原之间的识别能力。Further, in this step, molecular docking of quinolone molecular crossover and lysine was carried out using Molecular Operating Environment (MOE) 2016.10 software to clarify the recognition ability between monoclonal antibodies and heterologous coated antigens.
在进行构型优化时，具体的，设置分子力学最小化的力场为MMFF94x，非键合相互作用的截止值为
Figure PCTCN2021108338-appb-000004
通过使用Gaussian 09软件，进一步确定初步最小化之后的构象以在HF/6-31G(d)水平上实现更精确的几何优化和频率分析，最终获得所有缩合产物的最小能量构象。此外，使用Gaussian 09软件在相同水平计算原子点电荷和静电势，并使用GaussView 5.0软件进行观察，以最终确定得到最佳异源竞争抗原。 When performing configuration optimization, specifically, the force field of molecular mechanics minimization is set to MMFF94x, and the cutoff value of non-bonding interaction is set to
Figure PCTCN2021108338-appb-000004
By using Gaussian 09 software, the conformation after the initial minimization was further determined to achieve more precise geometric optimization and frequency analysis at the HF/6-31G(d) level, finally obtaining the minimum energy conformation of all condensation products. In addition, atomic point charges and electrostatic potentials were calculated at the same level using Gaussian 09 software and observed using GaussView 5.0 software to finalize the optimal heterologous competing antigen.
本发明提出的一种用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，首先对恩诺沙星类似物进行分子描述符计算，再对分子描述符进行主成分分析得到主成分分析结果，然后采用间接竞争ELISA法测定计算得到恩诺沙星类似物的交叉反应率；再根据主成分分析结果以及恩诺沙星类似物的交叉反应率，建立数学模型并进行分类学习以得到分类学习结果，最后根据分类学习结果， 将喹诺酮分子交叉物及赖氨酸进行分子对接以得到构象，进行构型优化以得到最小能量构象，最终确定最佳异源竞争抗原。本发明便于筛选确定能提高免疫检测灵敏度的异源竞争抗原，具有良好的应用前景。A method for screening heterologous competing antigens for improving the sensitivity of immune detection proposed by the present invention, firstly performs molecular descriptor calculation on enrofloxacin analogs, and then performs principal component analysis on the molecular descriptors to obtain the principal component analysis result, Then, the cross-reaction rate of enrofloxacin analogs was determined and calculated by indirect competitive ELISA method; then based on the results of principal component analysis and the cross-reaction rate of enrofloxacin analogs, a mathematical model was established and classified learning was performed to obtain the classification learning results. , and finally, according to the classification learning results, molecular docking of quinolone molecular crossover and lysine to obtain the conformation, and configuration optimization to obtain the minimum energy conformation, and finally determine the best heterologous competition antigen. The invention is convenient for screening and determining the heterologous competing antigen which can improve the sensitivity of immune detection, and has good application prospect.
最后应说明的是：以上所述实施例，仅为本发明的具体实施方式，用以说明本发明的技术方案，而非对其限制，本发明的保护范围并不局限于此，尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，其依然可以对前述实施例所记载的技术方案进行修改或可轻易想到变化，或者对其中部分技术特征进行等同替换；而这些修改、变化或者替换，并不使相应技术方案的本质脱离本发明实施例技术方案的精神和范围，都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应以所述权利要求的保护范围为准。Finally, it should be noted that the above-mentioned embodiments are only specific implementations of the present invention, and are used to illustrate the technical solutions of the present invention, but not to limit them. The protection scope of the present invention is not limited thereto, although referring to the foregoing The embodiment has been described in detail the present invention, and those of ordinary skill in the art should understand that: any person skilled in the art is within the technical scope disclosed by the present invention, and he can still modify the technical solutions described in the foregoing embodiments. Or can easily think of changes, or equivalently replace some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be covered in the present invention. within the scope of protection. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims (9)

  1. 一种用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，其特征在于，所述方法包括如下步骤：A screening method for a heterologous competing antigen for improving the sensitivity of immune detection, characterized in that the method comprises the steps:
    步骤一：对恩诺沙星类似物进行对应分子描述符计算，并对所述分子描述符进行主成分分析以得到主成分分析结果，其中所述恩诺沙星类似物由恩诺沙星与喹诺酮分子交叉物制得；Step 1: Calculate the corresponding molecular descriptors of the enrofloxacin analogs, and perform principal component analysis on the molecular descriptors to obtain the principal component analysis results, wherein the enrofloxacin analogs are composed of enrofloxacin and enrofloxacin. Preparation of quinolone molecular cross products;
    步骤二：采用间接竞争ELISA法分别测定得到恩诺沙星的半抑制浓度以及各个恩诺沙星类似物的半抑制浓度，并根据所述恩诺沙星的半抑制浓度以及所述恩诺沙星类似物的半抑制浓度计算得到各个所述恩诺沙星类似物的交叉反应率；Step 2: The semi-inhibitory concentration of enrofloxacin and the semi-inhibitory concentration of each enrofloxacin analog are determined by indirect competitive ELISA, and the semi-inhibitory concentration of enrofloxacin and the enrofloxacin The half-inhibitory concentration of the star analog was calculated to obtain the cross-reaction rate of each of the enrofloxacin analogs;
    步骤三：根据所述主成分分析结果以及对应的各所述恩诺沙星类似物的交叉反应率，建立数学模型并进行分类学习，以得到分类学习结果，其中所述主成分分析结果包括所述喹诺酮分子交叉物中的分子描述符；Step 3: According to the principal component analysis result and the corresponding cross-reaction rate of each of the enrofloxacin analogs, establish a mathematical model and perform classification learning to obtain a classification learning result, wherein the principal component analysis result includes all the Molecular descriptors in the described quinolone molecular intersections;
    步骤四：根据所述分类学习结果，利用各所述喹诺酮分子交叉物及赖氨酸进行分子对接以得到构象，并对构象进行构型优化得到最小能量构象，以确定最佳异源竞争抗原。Step 4: According to the classification learning result, use each of the quinolone molecular crossovers and lysine to perform molecular docking to obtain a conformation, and optimize the conformation to obtain a minimum energy conformation to determine the best heterologous competing antigen.
  2. 根据权利要求1所述的用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，其特征在于，在所述步骤一中，所述分子描述符包括：The method for screening heterologous competing antigens for improving the sensitivity of immunodetection according to claim 1, wherein in the step 1, the molecular descriptors include:
    符号与术语、物理属性、休克尔理论描述符、细分表面区域、原子计数和键计数、连接性和Kappa形状指数、邻接和距离矩阵描述符、药效团特征描述符以及电荷描述符。Notation and terminology, physical properties, Huckel theory descriptors, subdivision surface area, atomic and bond counts, connectivity and Kappa shape indices, adjacency and distance matrix descriptors, pharmacophore feature descriptors, and charge descriptors.
  3. 根据权利要求2所述的用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，其特征在于，在所述步骤二中，所述恩诺沙星的半抑制浓度以及所述恩诺沙星类似物的半抑制浓度的测定方法包括如下步骤：The method for screening heterologous competitive antigens for improving immunodetection sensitivity according to claim 2, wherein in the second step, the half inhibitory concentration of enrofloxacin and the enrofloxacin The method for determining the half-inhibitory concentration of an analog includes the following steps:
    首先对恩诺沙星包被原进行包板封闭，然后加入预先制备好的恩诺沙星、喹诺酮分子交叉物以及标准品，其中所述喹诺酮分子交叉物包括巴洛沙星、贝西沙星、西诺沙星、克林沙星、达氟沙星、弗罗沙星、吉米沙星、洛美沙星、马波沙星、莫西沙星、萘啶酮酸、诺氟沙星、奥比沙星、恶喹酸、培氟沙星、普鲁利沙星、吡哌酸、帕珠沙星、沙拉沙星、西塔沙星以及司帕沙星，所述标 准品包括氟苯尼考、磺胺二甲基嘧啶以及四环素；First, the enrofloxacin coating is sealed with a plate, and then pre-prepared enrofloxacin, quinolone molecular crossovers and standard products are added, wherein the quinolone molecular crossovers include balofloxacin, besifloxacin, Cinofloxacin, Clinifloxacin, Darfloxacin, Froxacin, Gemifloxacin, Lomefloxacin, Marbofloxacin, Moxifloxacin, Nalidixic acid, Norfloxacin, Orbifloxacin, Oxaquinic acid , pefloxacin, pululifloxacin, pipemidic acid, pazufloxacin, sarafloxacin, sitafloxacin, and sparfloxacin, including florfenicol, sulfamethazine, and tetracycline ;
    每孔分别加入50ul的标准品以及50ul的抗恩诺沙星单克隆抗体稀释液，完成后继续加入酶标二抗、显色、终止以及测吸光值步骤，以建立得到抑制标准曲线；Add 50ul of standard substance and 50ul of anti-enrofloxacin monoclonal antibody diluent to each well. After completion, continue to add enzyme-labeled secondary antibody, develop color, stop and measure absorbance to establish an inhibition standard curve;
    根据所述抑制标准曲线确定得到所述恩诺沙星的半抑制浓度以及恩诺沙星类似物的半抑制浓度。The half-inhibitory concentration of enrofloxacin and the half-inhibitory concentration of enrofloxacin analogs are determined according to the inhibition standard curve.
  4. 根据权利要求3所述的用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，其特征在于，所述恩诺沙星类似物的交叉反应率的计算公式为：The method for screening a heterologous competitive antigen for improving immunodetection sensitivity according to claim 3, wherein the formula for calculating the cross-reaction rate of the enrofloxacin analog is:
    CR％＝(ENR的IC 50/类似物的IC 50)×100 CR%=( IC50 of ENR/ IC50 of analog)×100
    其中，CR％为所述恩诺沙星类似物的交叉反应率，ENR的IC 50为所述恩诺沙星的半抑制浓度，类似物的IC 50为所述恩诺沙星类似物的半抑制浓度。 Wherein, CR% is the cross-reaction rate of the enrofloxacin analogs, the IC50 of ENR is the half inhibitory concentration of the enrofloxacin, and the IC50 of the analogs is the half-inhibitory concentration of the enrofloxacin analogs. inhibitory concentration.
  5. 根据权利要求2所述的用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，其特征在于，在所述步骤三中，进行分类学习的所述喹诺酮分子交叉物包括：The method for screening heterologous competitive antigens for improving the sensitivity of immune detection according to claim 2, characterized in that, in the step 3, the quinolone molecule crossovers for classification and learning include:
    恩诺沙星、巴洛沙星、吡哌酸、诺氟沙星、达氟沙星、弗罗沙星、巴洛沙星、贝西沙星、西诺沙星、克林沙星、吉米沙星、洛美沙星、马波沙星、莫西沙星、萘啶酮酸、奥比沙星、恶喹酸以及帕珠沙星；Enrofloxacin, balofloxacin, pipemidic acid, norfloxacin, danofloxacin, frofloxacin, balofloxacin, besifloxacin, cinofloxacin, clinifloxacin, gemifloxacin, lomeza Star, marbofloxacin, moxifloxacin, nalidixic acid, orbifloxacin, oxquinic acid and pazufloxacin;
    所述步骤三包括：The third step includes:
    对各所述喹诺酮分子交叉物的分子描述符及对应的各所述恩诺沙星类似物的交叉反应率分别进行分类学习，其中进行分类学习操作的软件为MATLAB。The molecular descriptors of each of the quinolone molecular crossovers and the corresponding cross-reaction rates of each of the enrofloxacin analogs are respectively subjected to classification learning, wherein the software for the classification learning operation is MATLAB.
  6. 根据权利要求5所述的用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，其特征在于，在所述步骤三中；The method for screening heterologous competing antigens for improving the sensitivity of immunodetection according to claim 5, characterized in that, in the step 3;
    在进行分类学习中，当所述恩诺沙星类似物的交叉反应率小于0.01，表示所述喹诺酮分子交叉物不能在酶联免疫吸附剂测定体系中被抗体识别；In the classification learning, when the cross-reaction rate of the enrofloxacin analog is less than 0.01, it means that the quinolone molecular cross-compound cannot be recognized by the antibody in the enzyme-linked immunosorbent assay system;
    当所述恩诺沙星类似物的交叉反应率大于0.07，表示所述喹诺酮分子交叉物能在酶联免疫吸附剂测定体系中被抗体捕获。When the cross-reaction rate of the enrofloxacin analog is greater than 0.07, it means that the quinolone molecular cross-compound can be captured by the antibody in the enzyme-linked immunosorbent assay system.
  7. 根据权利要求2所述的用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，其特征在于，在所述步骤三中，在得到了所述分类学习结果之后，所述 方法还包括如下步骤：The method for screening heterologous competing antigens for improving the sensitivity of immune detection according to claim 2, wherein in the step 3, after the classification learning result is obtained, the method further comprises the following steps :
    利用测试样本对所述分类学习结果进行评价，其中进行评价的方法包括如下步骤：Use test samples to evaluate the classification learning results, wherein the evaluation method includes the following steps:
    将测试样本的分子描述符经机器学习后得到对应的机器CR值；The molecular descriptor of the test sample is machine-learned to obtain the corresponding machine CR value;
    将测试样本通过酶联免疫吸附法测定得到测试样本的实际CR值；The actual CR value of the test sample is obtained by measuring the test sample by enzyme-linked immunosorbent assay;
    根据所述机器CR值以及所述测试样本的实际CR值计算得到机器学习的准确度，以对所述分类学习结果进行评价。The accuracy of machine learning is calculated according to the machine CR value and the actual CR value of the test sample, so as to evaluate the classification learning result.
  8. 根据权利要求7所述的用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，其特征在于，所述机器学习的准确度的计算公式表示为：The method for screening heterologous competing antigens for improving the sensitivity of immune detection according to claim 7, wherein the calculation formula of the accuracy of the machine learning is expressed as:
    Figure PCTCN2021108338-appb-100001
    Figure PCTCN2021108338-appb-100001
  9. 根据权利要求2所述的用于提高免疫检测灵敏度的异源竞争抗原的筛选方法，其特征在于，在对构象进行构型优化时，设置的分子力学最小化的力场为MMFF94x，非键合相互作用的截止值为
    Figure PCTCN2021108338-appb-100002
    进行构型优化的软件为Gaussian软件。     The method for screening heterologous competing antigens for improving the sensitivity of immunodetection according to claim 2, wherein when the conformation is optimized, the set force field for minimizing molecular mechanics is MMFF94x, and the non-bonding force field is MMFF94x. The cutoff for the interaction is
    Figure PCTCN2021108338-appb-100002
    The software for configuration optimization is Gaussian software.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070156343A1 (en) * 2003-08-28 2007-07-05 Anwar Rayan Stochastic method to determine, in silico, the drug like character of molecules
CN102708269A (en) * 2011-10-24 2012-10-03 西北师范大学 Method for predicting inhibiting concentration of inhibitor of cytosolic phospholipase A2alpha based on support vector machine
CN110890137A (en) * 2019-11-18 2020-03-17 上海尔云信息科技有限公司 Modeling method, device and application of compound toxicity prediction model
CN111402967A (en) * 2020-03-12 2020-07-10 中南大学 Method for improving virtual screening capability of docking software based on machine learning algorithm
CN113033606A (en) * 2021-02-08 2021-06-25 江西煌上煌集团食品股份有限公司 Method for screening heterologous competitive antigen for improving immunodetection sensitivity

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070156343A1 (en) * 2003-08-28 2007-07-05 Anwar Rayan Stochastic method to determine, in silico, the drug like character of molecules
CN102708269A (en) * 2011-10-24 2012-10-03 西北师范大学 Method for predicting inhibiting concentration of inhibitor of cytosolic phospholipase A2alpha based on support vector machine
CN110890137A (en) * 2019-11-18 2020-03-17 上海尔云信息科技有限公司 Modeling method, device and application of compound toxicity prediction model
CN111402967A (en) * 2020-03-12 2020-07-10 中南大学 Method for improving virtual screening capability of docking software based on machine learning algorithm
CN113033606A (en) * 2021-02-08 2021-06-25 江西煌上煌集团食品股份有限公司 Method for screening heterologous competitive antigen for improving immunodetection sensitivity

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
HU SONG: "Novel Labels and Heterologous Competitive Systems for Improving Sensitivity of Immunoassay", MASTER THESIS, TIANJIN POLYTECHNIC UNIVERSITY, CN, no. 1, 15 January 2021 (2021-01-15), CN , XP055955441, ISSN: 1674-0246 *

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