CN113033606A - Method for screening heterologous competitive antigen for improving immunodetection sensitivity - Google Patents
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Abstract
The invention provides a method for screening a heterologous competitive antigen for improving the sensitivity of immunodetection, which comprises the following steps: calculating corresponding molecular descriptors of the enrofloxacin analogue, and performing principal component analysis on the molecular descriptors to obtain a principal component analysis result; respectively measuring the obtained semi-inhibitory concentration of the enrofloxacin and the semi-inhibitory concentration of each enrofloxacin analogue, and further calculating the cross reaction rate of each enrofloxacin analogue; establishing a mathematical model and carrying out classification learning according to the principal component analysis result and the corresponding cross reaction rate of each enrofloxacin analogue so as to obtain a classification learning result; according to the classification learning result, molecular docking is carried out by utilizing the quinolone molecular cross-member and lysine to obtain conformation, and the conformation is optimized to obtain minimum energy conformation so as to determine the optimal heterogeneous competitive antigen. The invention is convenient for screening and determining the heterogenous competitive antigen capable of improving the sensitivity of the immunodetection, and has good application prospect.
Description
Technical Field
The invention relates to the technical field of medicine detection, in particular to a method for screening a heterologous competitive antigen for improving the sensitivity of immunodetection.
Background
The design and synthesis of antigen and the preparation of high-sensitivity antibody are the core of immunoassay, and the specificity and affinity of the antibody to the antigen directly determine the accuracy and sensitivity of the detection method. In most immunoassays, the target analyte, or a small modification, is typically conjugated to a different carrier protein as a hapten to form an immunogen and an envelope. In this case, the coatingen has the same structure as the hapten in the immunogen, so the coatingen is called homologous coating, and the corresponding hapten is a homologous competitor. While the coating antigen structurally different from the hapten in the immunogen is called heterologous coating, the corresponding hapten is a heterologous competitor.
At present, the foreign coating is reported in documents at home and abroad to improve the sensitivity of the immunoassay method, but not all the foreign coatings can improve the sensitivity of the immunoassay. Heterologous coating can significantly improve sensitivity only when the affinity of the antibody is poor, and cannot significantly improve immunoassay sensitivity when the affinity of the antibody is high. In addition, the improvement degree of different heterologous coatings on the immunoassay sensitivity is quite different, and some heterologous coatings can be improved by dozens of times, even dozens of times, and some heterologous coatings can only be improved by several times. Therefore, if the best competitor can be designed for a specific antibody, so that the sensitivity of immunoassay is maximally improved by a heterogeneous competition mode, and the originally 'unqualified' antibody is changed into a 'qualified' antibody, the qualification rate of the antibody is greatly improved, and the cost for preparing the antibody is saved.
However, in the prior art, a method for screening heterologous competitive antigens capable of effectively improving the sensitivity of immunoassay is lacked, and practical application is limited to a certain extent.
Disclosure of Invention
The invention aims to solve the problem that the practical application is limited to a certain extent because a method for screening a heterologous competitive antigen capable of effectively improving the sensitivity of immunoassay is lacked in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a method for screening a heterologous competitive antigen for improving the sensitivity of immunodetection, which comprises the following steps:
the method comprises the following steps: calculating corresponding molecular descriptors of the enrofloxacin analogue, and performing principal component analysis on the molecular descriptors to obtain a principal component analysis result, wherein the enrofloxacin analogue is prepared from enrofloxacin and quinolone molecular cross products;
step two: respectively measuring the obtained semi-inhibitory concentration of the enrofloxacin and the semi-inhibitory concentration of each enrofloxacin analogue by adopting an indirect competitive ELISA method, and calculating the cross reaction rate of each enrofloxacin analogue according to the semi-inhibitory concentration of the enrofloxacin and the semi-inhibitory concentration of the enrofloxacin analogue;
step three: establishing a mathematical model and performing classification learning according to the principal component analysis result and the corresponding cross reaction rate of each enrofloxacin analogue to obtain a classification learning result, wherein the principal component analysis result comprises a molecular descriptor in the quinolone molecular cross-member;
step four: according to the classification learning result, performing molecular docking by using each quinolone molecular cross-member and lysine to obtain a conformation, and performing configuration optimization on the conformation to obtain a minimum energy conformation so as to determine the optimal heterogeneous competitive antigen.
The invention provides a method for screening heterologous competitive antigens for improving the sensitivity of immunodetection, which comprises the steps of firstly carrying out molecular descriptor calculation on an enrofloxacin analogue, then carrying out principal component analysis on the molecular descriptor to obtain a principal component analysis result, and then measuring and calculating the cross reaction rate of the enrofloxacin analogue by adopting an indirect competitive ELISA method; and establishing a mathematical model according to the main component analysis result and the cross reaction rate of the enrofloxacin analogue, carrying out classification learning to obtain a classification learning result, carrying out molecular docking on the quinolone molecular cross-member and the lysine to obtain a conformation according to the classification learning result, carrying out configuration optimization to obtain a minimum energy conformation, and finally determining the optimal heterogeneous competitive antigen. The invention is convenient for screening and determining the heterogenous competitive antigen capable of improving the sensitivity of the immunodetection, and has good application prospect.
The method for screening heterologous competitive antigens for improving the immunodetection sensitivity, wherein in the step one, the molecular descriptor comprises:
symbols and terms, physical attributes, Hull theory descriptors, subdivided surface regions, atom and bond counts, connectivity and Kappa shape indices, adjacency and distance matrix descriptors, pharmacophore feature descriptors, and charge descriptors.
The method for screening the heterologous competitive antigen for improving the immunodetection sensitivity comprises the following steps of:
firstly, carrying out plate-coating sealing on enrofloxacin coating source, and then adding prepared enrofloxacin, quinolone molecular cross-linked substances and standard substances, wherein the quinolone molecular cross-linked substances comprise balofloxacin, besifloxacin, cinoxacin, clinafloxacin, danofloxacin, fleroxacin, gemifloxacin, lomefloxacin, marbofloxacin, moxifloxacin, nalidixic acid, norfloxacin, orbifloxacin, oxolinic acid, pefloxacin, pralifloxacin, pipemidic acid, pazufloxacin, sarafloxacin, sitafloxacin and sparfloxacin, and the standard substances comprise florfenicol, sulfamethazine and tetracycline;
respectively adding 50ul of standard substance and 50ul of anti-enrofloxacin monoclonal antibody diluent into each hole, and continuing adding the enzyme-labeled secondary antibody, developing, terminating and measuring the light absorption value after finishing the steps so as to establish and obtain an inhibition standard curve;
and determining the semi-inhibitory concentration of the enrofloxacin and the semi-inhibitory concentration of the enrofloxacin analogue according to the inhibition standard curve.
The screening method of the heterologous competitive antigen for improving the immunodetection sensitivity comprises the following steps of:
CR% (% ENR IC)50IC of/analogue50)×100
Wherein CR% is the cross reaction rate of the enrofloxacin analogue and the IC of ENR50Is the semi-inhibitory concentration of the enrofloxacin, IC of the analogue50Is the half inhibitory concentration of the enrofloxacin analogue.
The method for screening heterologous competitive antigens for improving the sensitivity of immunodetection, wherein in the third step, the quinolone molecule cross-product for classification learning comprises:
enrofloxacin, balofloxacin, pipemidic acid, norfloxacin, danofloxacin, fleroxacin, balofloxacin, besifloxacin, cinoxacin, clinafloxacin, gemifloxacin, lomefloxacin, marbofloxacin, moxifloxacin, nalidixic acid, orbifloxacin, oxolinic acid, and pazufloxacin;
the third step comprises:
and respectively carrying out classification learning on the molecular descriptors of the quinolone molecular cross-members and the corresponding cross-reaction rates of the enrofloxacin analogues, wherein the software for carrying out classification learning operation is MATLAB.
The method for screening a heterologous competitive antigen for improving the sensitivity of immunodetection, wherein, in the step three;
in the classification learning, when the cross reaction rate of the enrofloxacin analogue is less than 0.01, the quinolone molecular cross-member can not be identified by an antibody in an enzyme-linked immunosorbent assay system;
when the cross-reactivity rate of the enrofloxacin analogue is more than 0.07, the quinolone molecular cross-member can be captured by an antibody in an enzyme-linked immunosorbent assay system.
The method for screening the heterologous competitive antigen for improving the immunodetection sensitivity, wherein in the third step, after the classification learning result is obtained, the method further comprises the following steps:
and evaluating the classification learning result by using the test sample, wherein the evaluation method comprises the following steps:
obtaining a corresponding machine CR value after the molecular descriptor of the test sample is subjected to machine learning;
measuring the test sample by an enzyme-linked immunosorbent assay to obtain the actual CR value of the test sample;
and calculating the accuracy of machine learning according to the CR value of the machine and the actual CR value of the test sample so as to evaluate the classification learning result.
The method for screening the heterologous competitive antigen for improving the immunodetection sensitivity is characterized in that the calculation formula of the accuracy of machine learning is as follows:
the method for screening the heterologous competitive antigen for improving the immunodetection sensitivity is characterized in that when the conformation is optimized, the set force field with minimized molecular mechanics is MMFF94x, and the cut-off value of non-bonding interaction is
The software for carrying out configuration optimization is Gaussian software.
Additional features and advantages of the disclosure will be set forth in the description which follows, or in part may be learned by the practice of the above-described techniques of the disclosure, or may be learned by practice of the disclosure.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
FIG. 1 is a schematic block diagram of the method for screening a heterologous competitive antigen for improved sensitivity of immunodetection according to the present invention;
FIG. 2 is a flow chart of the method for screening a heterologous competitive antigen for improved sensitivity of immunodetection according to the present invention;
FIG. 3 shows the distribution of electrostatic potentials around different quinolone epitopes in the present invention.
Detailed Description
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present invention are shown in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, 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 terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
In the prior art, a method for screening a heterologous competitive antigen capable of effectively improving the sensitivity of immunodetection is lacked, and practical application is limited to a certain extent.
In order to solve the technical problem, the invention provides a method for screening a heterologous competitive antigen for improving the sensitivity of immunodetection, wherein the method comprises the following steps:
s101, calculating corresponding molecular descriptors of the enrofloxacin analogue, and performing principal component analysis on the molecular descriptors to obtain a principal component analysis result, wherein the enrofloxacin analogue is prepared from enrofloxacin and quinolone molecular cross-products.
In this step, the MOE 2016.10 software was used to perform the corresponding molecular descriptor calculation for the enrofloxacin analogue. Specifically, each training sample has 204 molecular descriptors. Specifically, the molecular descriptors include:
(a) symbols and terms; (b) a physical property; (c) huckel theoretical descriptors; (d) subdividing a surface area; (e) atom count and bond count; (f) kier connectivity and Kappa shape index; (g) adjacency and distance matrix descriptors; (h) a pharmacophore characteristic descriptor; (i) a charge descriptor.
Further, performing principal component analysis on the molecular descriptors of the enrofloxacin analogue to obtain a principal component analysis result. Specifically, the principal component analysis is a dimension-reduction statistical method, which converts an original random vector whose components are related to each other into a new random vector whose components are not related to each other by means of an orthogonal transformation, which is algebraically represented by transforming a covariance matrix of the original random vector into a diagonal matrix, and geometrically represented by transforming an original coordinate system into a new orthogonal coordinate system, so that the new orthogonal coordinate system points to p orthogonal directions in which sample points are most spread; then, the multidimensional variable system is subjected to dimensionality reduction treatment, so that the multidimensional variable system can be converted into a low-dimensional variable system with higher precision, and the low-dimensional variable system is further converted into a one-dimensional system by constructing a proper value function.
In this example, a principal component analysis program of molecular descriptors on training samples was performed on MATLAB2015a (The Math Works, inc., USA) software. It is specifically noted that the enrofloxacin analogue is prepared by crossing enrofloxacin and quinolone molecules.
S102, respectively measuring the obtained semi-inhibitory concentration of the enrofloxacin and the semi-inhibitory concentration of each enrofloxacin analogue by adopting an indirect competitive ELISA method, and calculating the cross reaction rate of each enrofloxacin analogue according to the semi-inhibitory concentration of the enrofloxacin and the semi-inhibitory concentration of the enrofloxacin analogue.
In this step, the step of preparing the cross-reactivity of the enrofloxacin analogue is as follows:
(1) firstly, carrying out plate-wrapping sealing on the enrofloxacin coating source, and then adding the prepared Enrofloxacin (ENR), the quinolone molecular cross-product and the standard substance.
Wherein the quinolone molecular cross-over includes Balofloxacin (BAL), Besifloxacin (BES), Cinoxacin (CIN), Clinafloxacin (CLI), Danofloxacin (DAN), Fleroxacin (FLE), Gemifloxacin (GEM), Lomefloxacin (LOM), Marbofloxacin (MAR), Moxifloxacin (MOX), Nalidixic Acid (NAL), Norfloxacin (NOR), Orbifloxacin (ORB), oxolinic acid (OXO), Pefloxacin (PEF), Prulifloxacin (PRU), pipemidic acid (PIP), Pazufloxacin (PAZ), Sarafloxacin (SAR), Sitafloxacin (SIT) and Sparfloxacin (SPA). Such standards include Florfenicol (FLO), sulfadimethy pyrimidine (SMZ), and tetracycline (TET).
(2) And respectively adding 50ul of standard substance and S0ul anti-enrofloxacin monoclonal antibody diluent into each hole, and then continuing adding the enzyme-labeled secondary antibody, developing, terminating and measuring the light absorption value to establish an inhibition standard curve.
(3) And determining the semi-inhibitory concentration of the enrofloxacin and the semi-inhibitory concentration of the enrofloxacin analogue according to the inhibition standard curve.
The cross reaction rate of the enrofloxacin analogue is calculated by the following formula:
CR% (% ENR IC)50IC of/analogue50)×100
Wherein CR% is the cross reaction rate of enrofloxacin analogue and IC of ENR50Is the semi-inhibitory concentration of enrofloxacin, IC of the analogue50Is the semi-inhibitory concentration of the enrofloxacin analogue.
It should be noted that, in the step S102, the semi-inhibitory concentration of enrofloxacin and the corresponding semi-inhibitory concentration of each enrofloxacin analogue can be determined. And then calculating to obtain the actual CR value corresponding to each enrofloxacin analogue according to the semi-inhibitory concentration of the enrofloxacin and the semi-inhibitory concentration corresponding to each enrofloxacin analogue.
S103, establishing a mathematical model and performing classification learning according to the principal component analysis result and the corresponding cross reaction rate of each enrofloxacin analogue to obtain a classification learning result, wherein the principal component analysis result comprises the molecular descriptors in the quinolone molecular cross-members.
In this step, the molecular descriptors of each quinolone molecular cross-product and the corresponding cross-reactivity of each enrofloxacin analogue are separately classified and learned, wherein the software for performing the classification learning operation is MATLAB2015 a.
Specifically, the quinolone molecular cross-member subjected to classification learning comprises:
enrofloxacin (ENR), balofloxacin (PEF), pipemidic acid (PIP), Norfloxacin (NOR), Danofloxacin (DAN), Fleroxacin (FLE), Balofloxacin (BAL), Besifloxacin (BES), Cinoxacin (CIN), Clinafloxacin (CLI), Gemifloxacin (GEM), Lomefloxacin (LOM), Marbofloxacin (MAR), Moxifloxacin (MOX), Nalidixic Acid (NAL), Orbifloxacin (ORB), oxolinic acid (OXO), and Pazufloxacin (PAZ).
In the classification learning, when the cross reaction rate of the enrofloxacin analogue is less than 0.01, the quinolone molecular cross-member can not be identified by the antibody in an enzyme-linked immunosorbent assay system. When the cross reaction rate of the enrofloxacin analogue is more than 0.07, the quinolone molecular cross-member can be captured by the antibody in an enzyme-linked immunosorbent assay system.
Additionally, after the above classification learning result is obtained, the above classification learning result needs to be evaluated. Specifically, the classification learning result is evaluated by using a test sample, wherein the evaluation method comprises the following steps:
(1) obtaining a corresponding machine CR value after the molecular descriptor of the test sample is subjected to machine learning;
(2) determining the test sample by an enzyme-linked immunosorbent assay to obtain the actual CR value of the test sample;
(3) and calculating the accuracy of machine learning according to the CR value of the machine and the actual CR value of the test sample so as to evaluate the classification learning result.
It should be noted that, in the present embodiment, the test samples include PRL, Sarafloxacin (SAR), Sitafloxacin (SIT), Sparfloxacin (SPA), Florfenicol (FLO), Sulfadimetrazine (SMZ), and tetracycline (TET).
Specifically, the calculation formula of the accuracy of machine learning is represented as:
s104, according to the classification learning result, performing molecular docking by using each quinolone molecular cross-member and lysine to obtain a conformation, and performing conformation optimization on the conformation to obtain a minimum energy conformation so as to determine the optimal heterogeneous competitive antigen.
Further, in this step, molecular docking of the quinolone molecular cross-over and lysine was performed using molecular environment of operation (MOE)2016.10 software to elucidate the recognition ability between the monoclonal antibody and the heterologous envelope antigen.
In configuration optimization, specifically, MMFF94x is set as the force field for minimizing molecular mechanics, and the cutoff value for non-bonding interaction is set as
The conformation after the preliminary minimization was further determined by using Gaussian 09 software to achieve more precise geometric optimization and frequency analysis at 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 Gauss View 5.0 software to finalize the optimal heterologous competitor antigen.
The invention provides a method for screening heterologous competitive antigens for improving the sensitivity of immunodetection, which comprises the steps of firstly carrying out molecular descriptor calculation on an enrofloxacin analogue, then carrying out principal component analysis on the molecular descriptor to obtain a principal component analysis result, and then measuring and calculating the cross reaction rate of the enrofloxacin analogue by adopting an indirect competitive ELISA method; and establishing a mathematical model according to the main component analysis result and the cross reaction rate of the enrofloxacin analogue, carrying out classification learning to obtain a classification learning result, carrying out molecular docking on the quinolone molecular cross-member and the lysine to obtain a conformation according to the classification learning result, carrying out configuration optimization to obtain a minimum energy conformation, and finally determining the optimal heterogeneous competitive antigen. The invention is convenient for screening and determining the heterogenous competitive antigen capable of improving the sensitivity of the immunodetection, and has good application prospect.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (9)
1. A method for screening for a heterologous competing antigen for improved sensitivity of an immunoassay, said method comprising the steps of:
the method comprises the following steps: calculating corresponding molecular descriptors of the enrofloxacin analogue, and performing principal component analysis on the molecular descriptors to obtain a principal component analysis result, wherein the enrofloxacin analogue is prepared from enrofloxacin and quinolone molecular cross products;
step two: respectively measuring the obtained semi-inhibitory concentration of the enrofloxacin and the semi-inhibitory concentration of each enrofloxacin analogue by adopting an indirect competitive ELISA method, and calculating the cross reaction rate of each enrofloxacin analogue according to the semi-inhibitory concentration of the enrofloxacin and the semi-inhibitory concentration of the enrofloxacin analogue;
step three: establishing a mathematical model and performing classification learning according to the principal component analysis result and the corresponding cross reaction rate of each enrofloxacin analogue to obtain a classification learning result, wherein the principal component analysis result comprises a molecular descriptor in the quinolone molecular cross-member;
step four: according to the classification learning result, performing molecular docking by using each quinolone molecular cross-member and lysine to obtain a conformation, and performing configuration optimization on the conformation to obtain a minimum energy conformation so as to determine the optimal heterogeneous competitive antigen.
2. The method for screening heterogeneous competitive antigens for enhanced immunodetection sensitivity according to claim 1, wherein in the first step, the molecular descriptor comprises:
symbols and terms, physical attributes, Hull theory descriptors, subdivided surface regions, atom and bond counts, connectivity and Kappa shape indices, adjacency and distance matrix descriptors, pharmacophore feature descriptors, and charge descriptors.
3. The method for screening the heterologous competitive antigen for improving the immunodetection sensitivity according to claim 2, wherein in the second step, the method for measuring the semi-inhibitory concentration of the enrofloxacin and the semi-inhibitory concentration of the enrofloxacin analogue comprises the following steps:
firstly, carrying out plate-coating sealing on enrofloxacin coating source, and then adding prepared enrofloxacin, quinolone molecular cross-linked substances and standard substances, wherein the quinolone molecular cross-linked substances comprise balofloxacin, besifloxacin, cinoxacin, clinafloxacin, danofloxacin, fleroxacin, gemifloxacin, lomefloxacin, marbofloxacin, moxifloxacin, nalidixic acid, norfloxacin, orbifloxacin, oxolinic acid, pefloxacin, pralifloxacin, pipemidic acid, pazufloxacin, sarafloxacin, sitafloxacin and sparfloxacin, and the standard substances comprise florfenicol, sulfamethazine and tetracycline;
respectively adding 50ul of standard substance and 50ul of anti-enrofloxacin monoclonal antibody diluent into each hole, and continuing adding the enzyme-labeled secondary antibody, developing, terminating and measuring the light absorption value after finishing the steps so as to establish and obtain an inhibition standard curve;
and determining the semi-inhibitory concentration of the enrofloxacin and the semi-inhibitory concentration of the enrofloxacin analogue according to the inhibition standard curve.
4. The method for screening the heterologous competitive antigen for improving the immunodetection sensitivity according to claim 3, wherein the calculation formula of the cross reaction rate of the enrofloxacin analogue is as follows:
CR% (% ENR IC)50IC of/analogue50)×100
Wherein CR% is the cross reaction rate of the enrofloxacin analogue and the IC of ENR50Is the semi-inhibitory concentration of the enrofloxacin, IC of the analogue50Is the half inhibitory concentration of the enrofloxacin analogue.
5. The method for screening heterogeneous competitive antigens for enhanced immunodetection sensitivity according to claim 2, wherein in the third step, the quinolone molecule cross-member for class learning comprises:
enrofloxacin, balofloxacin, pipemidic acid, norfloxacin, danofloxacin, fleroxacin, balofloxacin, besifloxacin, cinoxacin, clinafloxacin, gemifloxacin, lomefloxacin, marbofloxacin, moxifloxacin, nalidixic acid, orbifloxacin, oxolinic acid, and pazufloxacin;
the third step comprises:
and respectively carrying out classification learning on the molecular descriptors of the quinolone molecular cross-members and the corresponding cross-reaction rates of the enrofloxacin analogues, wherein the software for carrying out classification learning operation is MATLAB.
6. The method for screening heterologous competitive antigens for improved immunodetection sensitivity according to claim 5, wherein in step three;
in the classification learning, when the cross reaction rate of the enrofloxacin analogue is less than 0.01, the quinolone molecular cross-member can not be identified by an antibody in an enzyme-linked immunosorbent assay system;
when the cross-reactivity rate of the enrofloxacin analogue is more than 0.07, the quinolone molecular cross-member can be captured by an antibody in an enzyme-linked immunosorbent assay system.
7. The method for screening heterogeneous competitive antigens for enhanced immunodetection sensitivity according to claim 2, wherein in the third step, after obtaining the classification learning result, the method further comprises the following steps:
and evaluating the classification learning result by using the test sample, wherein the evaluation method comprises the following steps:
obtaining a corresponding machine CR value after the molecular descriptor of the test sample is subjected to machine learning;
measuring the test sample by an enzyme-linked immunosorbent assay to obtain the actual CR value of the test sample;
and calculating the accuracy of machine learning according to the CR value of the machine and the actual CR value of the test sample so as to evaluate the classification learning result.
8. The method of claim 7, wherein the accuracy of machine learning is calculated as:
9. the method of claim 2, wherein the conformational configuration is optimized by setting a force field for MMFF94x that minimizes molecular mechanics and a cut-off value for non-binding interactions is set as
The software for carrying out configuration optimization is G alpha ussian software.
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| WO2005022111A2 (en) * | 2003-08-28 | 2005-03-10 | Yissum Research Development Company Of The Hebrew University Of Jerusalem | A stochastic method to determine, in silico, the drug like character of molecules |
| CN102708269B (en) * | 2011-10-24 | 2016-05-25 | 西北师范大学 | Based on SVM prediction cytosolic phospholipase A2The method of the inhibition concentration of alpha inhibitor |
| 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 |
| CN111402967B (en) * | 2020-03-12 | 2023-04-07 | 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 |
-
2021
- 2021-02-08 CN CN202110180909.XA patent/CN113033606A/en active Pending
- 2021-07-26 WO PCT/CN2021/108338 patent/WO2022166129A1/en active Application Filing
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022166129A1 (en) * | 2021-02-08 | 2022-08-11 | 江西煌上煌集团食品股份有限公司 | Screening method for heterologous competitive antigen for use in improvement of immunodetection sensitivity |
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