Oxidized low-density lipoprotein detection method based on 4E6 antibody

Abstract

The invention discloses a 4E6 antibody-based oxidized low-density lipoprotein detection method, which comprises: obtaining a model constructed by a target 4E6 monoclonal antibody sequence, carrying out docking analysis on the interaction of antigen binding regions, designing a mutant prediction influence, selecting an optimal combination to construct a new sequence, and carrying out expression purification to obtain the oxidized low-density lipoprotein. The optimization effect is verified by measuring and combining kinetic parameters; a dendritic-linear polymer modified structure on the surface of a latex microsphere is designed and optimized based on a computational material science method, and the modified latex microsphere is synthesized and coupled with an optimized 4E6 monoclonal antibody to form a detection probe; and establishing an oxidized low-density lipoprotein standard curve through a plurality of groups of standard substance solutions, and determining a detection limit and a quantitation limit so as to calculate the sample concentration. According to the scheme, the antibody recognition capability and the signal amplification effect can be improved, and the detection sensitivity and accuracy are improved.

Description

Technical Field

[0001] The present application relates to the field of computational chemistry, and in particular to a method for detecting oxidized low-density lipoprotein based on 4E6 antibodies. Background Art

[0002] Oxidized low-density lipoprotein (ox-LDL) plays a key role in the development and progression of atherosclerosis, and its detection has become an important indicator for assessing cardiovascular disease risk. ox-LDL is formed from low-density lipoprotein (LDL) under conditions of oxidative stress and is highly atherogenic. Detection of ox-LDL not only provides early warning of cardiovascular disease risk but also guides clinical treatment, making it crucial for the prevention and management of cardiovascular disease. Currently, numerous laboratory methods exist for the detection of ox-LDL, including conjugated diene assays, thiobarbituric acid reaction assays, agarose electrophoresis, and enzyme-linked immunosorbent assays (ELISAs). These methods utilize specific antibodies to recognize specific epitopes on ox-LDL, followed by quantitative detection through enzyme labeling or radioisotope labeling. However, these traditional technologies have some problems: first, the operation is complicated and requires professional personnel and equipment; second, the detection time is long, which makes it difficult to meet the needs of rapid diagnosis; third, the sensitivity and specificity need to be improved, especially in the detection of low-concentration ox-LDL; finally, the cost is high, which is not conducive to large-scale screening and clinical application.

[0003] To address these issues, researchers have developed a variety of improved methods. For example, they have developed biosensors based on nanomaterials, leveraging the unique optical or electrochemical properties of nanoparticles to enhance detection signals; introduced microfluidics technology to enable automated sample processing and rapid analysis; and some researchers have attempted to use aptamers instead of antibodies to improve detection stability and reproducibility. However, while these methods have shown some improvement, they still have some drawbacks. First, while nanobiosensors are highly sensitive, their preparation process is complex and costly, making large-scale production difficult; while microfluidics technology is highly automated, it has strict equipment requirements and is not easily promoted to primary healthcare institutions; and while aptamers are stable, their specificity and affinity are generally inferior to those of high-quality monoclonal antibodies. Furthermore, most of these methods fail to fully consider the complexity and diversity of ox-LDL molecules, making it difficult to accurately distinguish and quantify LDL with different degrees of oxidation.

[0004] Therefore, there is an urgent need for a technical solution that can improve the antibody recognition ability and signal amplification effect. Summary of the Invention

[0005] To address the shortcomings of the existing technology, the present invention provides a method for detecting oxidized low-density lipoprotein (ODL) based on the 4E6 antibody. Immunoturbidimetric technology, based on colorimetry, has rapidly developed and is widely used in fully automated biochemical analyzers at major medical institutions. It enables rapid, sensitive, and automated detection, meeting clinical needs. This application addresses technical issues such as the lack of specificity and sensitivity of the existing technology.

[0006] The present application provides an embodiment of a method for detecting oxidized low-density lipoprotein based on a 4E6 antibody, comprising: obtaining a model constructed using a target 4E6 monoclonal antibody sequence, docking and analyzing interactions within the antigen-binding region, designing mutants to predict their effects, selecting optimal combinations to construct new sequences, expressing and purifying the sequences, and verifying the optimization effect by measuring binding kinetic parameters; designing and optimizing a dendritic-linear polymer modification structure on the surface of latex microspheres based on computational materials science methods, synthesizing the modified particles, and coupling the modified particles with the optimized 4E6 monoclonal antibody to form a detection probe; mixing an enriched oxidized low-density lipoprotein sample with the detection probe under target conditions, and measuring the optical signal value of the solution turbidity change before and after the reaction using an optical detection device to calculate the turbidity change based on the optical signal value; establishing an oxidized low-density lipoprotein standard curve using multiple sets of standard solutions, and determining the detection limit and quantification limit to calculate the sample concentration.

[0007] In one possible implementation, a model constructed using a target 4E6 monoclonal antibody sequence is obtained, docking is performed to analyze interactions within the antigen-binding region, mutants are designed to predict their effects, optimal combinations are selected to construct new sequences, and expression and purification are performed to verify the optimization effect by measuring binding kinetic parameters. The method includes: obtaining amino acid sequence information of the target 4E6 monoclonal antibody and constructing a corresponding three-dimensional structural model using homology modeling; molecular docking is performed between the antigen-binding region of the antibody and the target antigen to determine their interaction; based on the docking results, multiple mutants are designed, and the effects of the mutations on the antibody-antigen interaction are predicted using a preset calculation method, including: calculating changes in binding free energy, analyzing complex stability through molecular dynamics simulation, and calculating changes in solvent accessible surface area; based on the calculation results, target mutation combinations are selected to design new antibody sequences; the optimized antibodies are expressed and purified using a protein expression system; and the binding kinetic parameters of the optimized antibodies and the target antigen are measured using a surface plasmon resonance method, and compared with those before optimization.

[0008] In one possible implementation, a dendritic-linear polymer modification structure on the surface of latex microspheres is designed and optimized based on computational materials science methods, the modified particles are synthesized, and the modified particles are coupled with an optimized 4E6 monoclonal antibody to form a detection probe, including: designing a surface modification structure of latex microspheres using computational materials science methods, the structure comprising a dendritic polymer core and linear polymer arms; constructing an atomic model of the surface of the latex microspheres to simulate the effects of different structural parameters on particle performance; calculating the surface charge distribution, hydrophobicity and antibody binding site density of the different structures; optimizing the structural parameters based on the calculation results to maximize the antibody binding site density and signal amplification effect; synthesizing the corresponding dendritic-linear mixed polymer according to the optimization results, and covalently linking it to the surface of the latex microspheres; testing the particle size distribution, Zeta potential and surface morphology of the modified latex microspheres; and coupling the optimized antibody to the surface of the modified latex microspheres to form a detection probe.

[0009] In one possible implementation, an enriched oxidized low-density lipoprotein sample is mixed with a detection probe for reaction under target conditions, and an optical detection device is used to measure the optical signal value of the turbidity change of the solution before and after the reaction, so as to calculate the turbidity change based on the optical signal value, including: mixing the enriched oxidized low-density lipoprotein sample with an optimized antibody-marker probe, incubating at a preset temperature for a predetermined time; measuring the turbidity change of the solution before and after the reaction, and recording the optical signal value at a specific wavelength; calculating the turbidity change based on the optical signal value before and after the reaction, wherein the functional relationship between the optical signal value and the transmittance is an exponential function.

[0010] In one possible implementation, a standard curve for oxidized low-density lipoprotein is established using multiple sets of standard solutions, and the detection limit and quantification limit are determined to calculate the sample concentration, including: preparing a series of oxidized low-density lipoprotein standard solutions of known concentrations, obtaining response values ​​of standards of each concentration using the above-mentioned detection method, and establishing a standard curve between response values ​​and oxidized low-density lipoprotein concentration; fitting the standard curve using a four-parameter logistic regression model to obtain the values ​​of each parameter in the fitting equation; calculating the detection limit and quantification limit based on the standard deviation of the blank sample response value and the slope of the standard curve in the low concentration region; substituting the response value of the sample to be tested into the fitting equation to calculate the corresponding oxidized low-density lipoprotein concentration.

[0011] In one possible implementation, the four-parameter logistic regression model includes: ,in, Indicates turbidity changes, represents the maximum response value, represents the minimum response value, Indicates concentration, represents the inflection point concentration, Represents the slope of the curve.

[0012] In one possible implementation, the detection limit and quantification limit include: , ,in, represents the detection limit, represents the limit of quantification, represents the standard deviation of the blank sample response, Indicates the slope of the standard curve in the low concentration region.

[0013] In the above-provided method for detecting oxidized low-density lipoprotein based on the 4E6 antibody, the embodiment of the present application optimizes the design of the 4E6 monoclonal antibody and latex microspheres by combining multidisciplinary methods such as computational chemistry and materials science, and introduces a magnetic nanoparticle enrichment step, which can improve the antibody recognition ability and signal amplification effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0015] Figure 1 A schematic diagram of a process for detecting oxidized low-density lipoprotein based on the 4E6 antibody provided in an embodiment of the present application; Figure 2 This is a schematic diagram of an oxidized low-density lipoprotein standard curve provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present application.

[0017] Those skilled in the art will understand that the terms "first" and "second" in the embodiments of the present application are only used to distinguish different steps, devices or modules, and do not represent any specific technical meaning or the necessary logical order between them. It should also be understood that in the embodiments of the present application, "multiple" can refer to two or more, and "at least one" can refer to one, two or more. It should also be understood that for any component, data or structure mentioned in the embodiments of the present application, unless explicitly defined or given a contrary suggestion in the context, it can generally be understood as one or more. In addition, the term "and / or" in this application is merely a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this application generally indicates that the associated objects are in an "or" relationship. It should also be understood that the description of each embodiment in this application emphasizes the differences between the embodiments. The same or similar aspects can be referenced to each other. For the sake of brevity, they will not be described one by one.

[0018] At the same time, it should be understood that for ease of description, the dimensions of the various parts shown in the drawings are not drawn according to the actual proportional relationship. The following description of at least one exemplary embodiment is actually only illustrative and is in no way intended to limit the present application and its application or use. Technologies, methods and equipment known to ordinary technicians in the relevant fields may not be discussed in detail, but where appropriate, the technologies, methods and equipment should be considered part of the specification. It should be noted that similar numbers and letters represent similar items in the following figures, so once an item is defined in one figure, it does not need to be further discussed in subsequent figures.

[0019] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] Figure 1A flow chart illustrating a method for detecting oxidized low-density lipoprotein (O-LDL) based on the 4E6 antibody, as provided in the examples of this application. It should be understood that O-LDL (O-LDL) is an important biomarker, and its levels are closely associated with the development and progression of various cardiovascular diseases. Accurate and sensitive detection of O-LDL is crucial for the early diagnosis and prevention of diseases. However, existing detection methods suffer from insufficient specificity and sensitivity, making them difficult to meet clinical needs.

[0021] The present invention aims to optimize the design of the 4E6 monoclonal antibody and latex microspheres by combining computational chemistry and materials science methods to enhance antibody recognition and signal amplification. Computational chemistry refers to methods that use computers to simulate and predict chemical reactions, molecular structures, and properties. These methods enable precise design and optimization of the antibody and latex microspheres at the molecular level, thereby simultaneously addressing the limited specificity and sensitivity of the 4E6 monoclonal antibody and the limited signal amplification of latex-enhanced turbidimetry in low-concentration samples.

[0022] like Figure 1 As shown, in step S101, a model constructed by the target 4E6 monoclonal antibody sequence is obtained, the interaction of the antigen binding region is analyzed by docking, mutants are designed to predict the impact, the optimal combination is selected to construct a new sequence, and expression and purification are performed to verify the optimization effect by measuring the binding kinetic parameters.

[0023] The "model" here can be a three-dimensional structural model based on the antibody's amino acid sequence, constructed through methods such as homology modeling or de novo design. Next, docking analysis is performed to analyze the interactions between the antigen-binding region. This step involves using molecular docking software, such as AutoDock or GOLD, to simulate the binding process between the antibody and the ox-LDL antigen and analyze the interactions between them, including hydrogen bonds, hydrophobic interactions, and electrostatic interactions. Based on the docking analysis results, mutants are designed and their effects predicted. This step can utilize computer-assisted protein engineering techniques, such as ROSETTA design or FoldX, to perform site-directed mutagenesis on the antibody's amino acid sequence and predict the effects of the mutations on antibody-antigen binding. By comprehensively considering multiple factors, such as binding affinity and structural stability, the optimal combination of mutations is selected to construct a new antibody sequence.

[0024] Subsequently, the optimized antibody sequence is expressed and purified. This involves recombinant DNA technology. The optimized antibody gene is cloned into an appropriate expression vector and transfected into host cells (such as CHO cells or HEK293 cells) for expression. The expressed antibody is purified using methods such as affinity chromatography and ion exchange chromatography. Finally, the optimization effect is verified by measuring binding kinetic parameters. This can be done using techniques such as surface plasmon resonance (SPR) or biolayer interferometry (BLI) to measure the binding affinity and kinetic parameters of the optimized antibody to ox-LDL, such as the binding constant (KD), association rate constant (kON), and dissociation rate constant (kOFF).

[0025] Specifically, the method includes: obtaining the amino acid sequence information of the target 4E6 monoclonal antibody, and constructing a corresponding three-dimensional structural model using homology modeling; performing molecular docking on the antigen-binding region of the antibody and the target antigen to determine the interaction between the two; designing multiple mutants based on the docking results, and using a preset calculation method to predict the effect of the mutations on the antibody-antigen interaction, including: calculating the change in binding free energy, analyzing the stability of the complex through molecular dynamics simulation, and calculating the change in solvent accessible surface area; selecting a target mutation combination based on the calculation results to design a new antibody sequence; expressing and purifying the optimized antibody using a protein expression system; and determining the binding kinetic parameters of the optimized antibody and the target antigen by a surface plasmon resonance method, and comparing them with those before optimization.

[0026] In one implementation scenario, for the structural optimization of the 4E6 monoclonal antibody, computational chemistry methods are first used to optimize the structure of the 4E6 monoclonal antibody to improve its specificity and affinity for oxidized low-density lipoprotein (oxLDL). The specific steps are as follows: Obtain the amino acid sequence of the 4E6 antibody and construct a three-dimensional structural model using homology modeling. Modeling can be performed using online tools such as SWISS-MODEL or local software such as Modeller. Molecular docking software (such as AutoDock Vina) is used to dock the primary antigenic determinant of oxLDL with the antigen-binding region of the 4E6 antibody to analyze their interaction. Based on the docking results, a series of mutants are designed, and computational chemistry methods are used to predict the effects of these mutations on the antibody-antigen interaction. Specifically, the following methods can be used: Use software such as FoldX to calculate the effect of mutations on binding free energy: ΔΔG = ΔG(mutant) - ΔG(wild type), where ΔG is the binding free energy. Select mutations with ΔΔG < 0, indicating mutations that enhance binding.

[0027] Perform molecular dynamics simulations to analyze the effects of mutations on the stability of the antibody-antigen complex. Software such as GROMACS can be used for simulations to calculate parameters such as the root mean square deviation (RMSD) and the number of hydrogen bonds: ; Where N is the number of atoms, is the position of the i-th atom at time t, is the position of the i-th atom in the reference structure.

[0028] The change in solvent accessible surface area (SASA) of the mutation site was calculated to evaluate its effect on antigen binding: ΔSASA = SASA(mutant) - SASA(wild type).

[0029] Based on the calculation results, the optimal mutation combination is selected to design a new 4E6 antibody sequence. Optimization methods such as genetic algorithms can be used to search for the best mutation combination. The optimized 4E6 antibody is expressed using a protein expression system (such as E. coli or CHO cells) and purified. Next, the binding kinetic parameters of the optimized antibody to oxLDL, such as the dissociation constant (KD), association rate constant (kon), and dissociation rate constant (koff), are determined by methods such as surface plasmon resonance (SPR): KD = koff / kon. Finally, the parameters before and after optimization can be compared to verify the optimization effect. In step S102, the dendritic-linear polymer modification structure on the surface of the latex microspheres is designed and optimized based on computational materials science methods, the modified particles are synthesized, and the optimized 4E6 monoclonal antibody is coupled to form a detection probe.

[0030] Based on the optimization of the 4E6 monoclonal antibody, the present invention also involves the optimized design of latex microspheres. Specifically, a dendritic-linear polymer-modified structure on the surface of the latex microspheres was designed and optimized based on computational materials science methods. Computational materials science is a discipline that uses computers to simulate and predict material properties, which can help design material structures with specific properties. In this embodiment, the dendritic-linear polymer-modified structure can improve the dispersibility and stability of the latex microspheres, while providing more binding sites for antibodies.

[0031] After the optimized design is complete, the modified latex microspheres need to be synthesized. This involves emulsion polymerization techniques, such as seeded emulsion polymerization or microemulsion polymerization. During the polymerization process, the designed dendritic-linear polymer is introduced onto the surface of the latex microspheres. After synthesis, the optimized 4E6 monoclonal antibody is conjugated to the modified latex microspheres to form a detection probe. This conjugation process involves chemical cross-linking techniques, such as EDC / NHS conjugation or click chemistry.

[0032] Furthermore, it includes: using computational materials science methods to design a surface modification structure of latex microspheres, the structure including a dendritic polymer core and linear polymer arms; constructing an atomic model of the latex microsphere surface to simulate the effects of different structural parameters on particle performance; calculating the surface charge distribution, hydrophobicity and antibody binding site density of the different structures; optimizing the structural parameters based on the calculation results to maximize the antibody binding site density and signal amplification effect; synthesizing corresponding dendritic-linear mixed polymers according to the optimization results, and covalently linking them to the surface of the latex microspheres; testing the particle size distribution, Zeta potential and surface morphology of the modified latex microspheres; and coupling the optimized antibodies to the surface of the modified latex microspheres to form detection probes.

[0033] In one implementation scenario, to improve the signal amplification effect of latex-enhanced turbidimetry in low-concentration samples, the surface design of the latex microspheres can be optimized. The specific steps are as follows: Based on computational materials science methods, a new type of latex microsphere surface modification structure is constructed. The present application embodiment discloses a "dendritic-linear" mixed polymer structure, including a dendritic polymer core and a linear polymer arm. Molecular dynamics simulation software (such as Materials Studio) is used to construct an atomic model of the latex microsphere surface, and the effect of different structural parameters (such as the number of generations of dendritic polymers, the length of linear polymers, etc.) on particle performance is simulated. Parameters such as surface charge distribution, hydrophobicity, and antibody binding site density of different structures are calculated. The electrostatic potential distribution can be calculated using software such as APBS (Adaptive Poisson-Boltzmann Solver), and the hydrophobicity distribution can be calculated using tools such as PLATINUM.

[0034] Based on the calculation results, the structural parameters are optimized to maximize the antibody binding site density and signal amplification effect. Optimization algorithms such as the response surface methodology (RSM) can be used to find the best parameter combination. According to the optimization results, the corresponding dendritic-linear mixed polymer is synthesized and covalently attached to the surface of the latex microspheres. The connection can be performed using methods such as EDC / NHS coupling or click chemistry. The physicochemical properties of the modified latex microspheres are tested, such as particle size distribution (using dynamic light scattering), Zeta potential, and surface morphology (using atomic force microscopy). The optimized 4E6 antibody is coupled to the surface of the modified latex microspheres to form a detection probe. The coupling can be performed using the glutaraldehyde method or the EDC / NHS method.

[0035] In step S103, the enriched oxidized low-density lipoprotein sample is mixed with the detection probe for reaction under target conditions. An optical detection device is used to measure the optical signal indicating the change in turbidity of the solution before and after the reaction. The turbidity change is calculated based on the optical signal. After the enrichment step is completed, the enriched ox-LDL sample is mixed with the optimized detection probe (latex microsphere-4E6 antibody complex) described above for reaction under target conditions. The "target conditions" here can include specific pH values, temperature, ionic strength, etc., which can be determined through experimental optimization. During the reaction, ox-LDL binds to the detection probe, causing a change in the turbidity of the solution. An optical detection device, such as a spectrophotometer or turbidimeter, measures the optical signal indicating the change in turbidity of the solution before and after the reaction. The optical signal can be transmittance, scattering intensity, or absorbance. The turbidity change is calculated based on the measured optical signal.

[0036] Specifically, the method includes: mixing an enriched oxidized low-density lipoprotein sample with an optimized antibody-marker probe, and incubating the mixture at a preset temperature for a predetermined time; measuring the turbidity change of the solution before and after the reaction, and recording the optical signal value at a specific wavelength; and calculating the turbidity change based on the optical signal values ​​before and after the reaction, wherein the functional relationship between the optical signal value and the transmittance is an exponential function.

[0037] In one implementation scenario, the enriched oxLDL sample was mixed with the optimized 4E6 antibody-latex microsphere probe and incubated at 37°C for 30 minutes. The turbidity of the solution before and after the reaction was measured using a UV-visible spectrophotometer, and the absorbance was recorded at a wavelength of 500 nm. The turbidity change ΔT was calculated as: ΔT = T(after reaction) - T(before reaction) where T is the transmittance, which is related to the absorbance A as follows: T = 10 -A .

[0038] In step S104, a standard curve for oxidized low-density lipoprotein (OX-LDL) is established using multiple sets of standard solutions, and the detection limit and quantification limit are determined to calculate the sample concentration. To achieve quantitative detection, a standard curve must be established. This can be achieved by preparing a series of ox-LDL standard solutions of known concentrations, measuring their turbidity changes according to the above steps, and plotting a concentration-turbidity change curve. By analyzing the standard curve, the detection limit and quantification limit of the detection method can be determined. The detection limit is generally defined as the lowest concentration that can be reliably detected, while the quantification limit is the lowest concentration that can be accurately quantified. These two parameters are crucial for evaluating the sensitivity and accuracy of the method. Finally, based on the established standard curve and the measured sample turbidity change, the ox-LDL concentration in the test sample can be calculated. This step involves data fitting and interpolation calculations, which can be implemented using specialized data analysis software or a custom program.

[0039] Specifically, the method includes: preparing a series of oxidized low-density lipoprotein standard solutions of known concentrations, obtaining the response values ​​of the standards at each concentration using the above-mentioned detection method, and establishing a standard curve between the response value and the oxidized low-density lipoprotein concentration; fitting the standard curve using a four-parameter logistic regression model to obtain the values ​​of each parameter in the fitting equation; calculating the detection limit and the quantification limit based on the standard deviation of the blank sample response value and the slope of the standard curve in the low-concentration region; substituting the response value of the sample to be tested into the fitting equation to calculate the corresponding oxidized low-density lipoprotein concentration.

[0040] In one implementation scenario, a standard curve is established by preparing a series of oxLDL standards of known concentrations (0-100 ng / mL), performing the assay according to the above steps, and obtaining a curve showing the relationship between turbidity change ΔT and oxLDL concentration C. The standard curve is fitted using a four-parameter logistic regression model, wherein the four-parameter logistic regression model includes: ,in, Indicates turbidity changes, represents the maximum response value, represents the minimum response value, Indicates concentration, represents the inflection point concentration, Represents the slope of the curve.

[0041] The limits of detection and quantification were then calculated, including: , ,in, represents the detection limit, represents the limit of quantification, represents the standard deviation of the blank sample response, Indicates the slope of the standard curve in the low concentration region.

[0042] Finally, the unknown samples were quantitatively analyzed by substituting the measured ΔT value into the standard curve equation to calculate the corresponding oxLDL concentration.

[0043] In summary, the examples of this application combined multidisciplinary methods such as computational chemistry and materials science to optimize the design of the 4E6 monoclonal antibody and latex microspheres, and introduced a magnetic nanoparticle enrichment step, successfully improving the specificity and sensitivity of ox-LDL detection.

[0044] Figure 2A schematic diagram of an oxidized low-density lipoprotein (O-LDL) standard curve provided in an embodiment of the present application is provided. This figure demonstrates the relationship between ΔT and oxLDL (O-LDL) concentration. Based on a four-parameter logistic regression equation, the horizontal axis represents O-LDL concentration (in ng / mL), and the vertical axis represents the corresponding ΔT value (dimensionless), which is the signal intensity reflected by the change in absorbance after the detection reaction. The figure clearly shows that as O-LDL concentration increases, the ΔT value gradually increases, exhibiting a typical S-shaped curve. This S-shaped curve is similar to the reaction curves used in common enzyme-linked immunosorbent assays (ELISAs) and other similar biological detection methods. In the low-concentration region of the curve (for example, oxLDL concentrations below 1 ng / mL), the ΔT value changes relatively slowly, indicating limited signal amplification within this concentration range, indicating low sensitivity of the detection system. In the medium-concentration range (approximately 5 to 25 ng / mL), the curve slope is steeper, and the ΔT value increases rapidly. This region is typically the sensitive range of the detection method, meaning that changes in O-LDL concentration can significantly affect the detection signal, resulting in high detection accuracy.

[0045] Specifically, in one example, the methods described in the preceding examples yielded an optimized 4E6 antibody sequence containing three amino acid mutations: Y32F, S53T, and N76K. This antibody was expressed and purified using a CHO cell expression system. SPR analysis revealed that the dissociation constant (KD) of the optimized antibody for oxLDL was reduced from 10 nM to 1 nM, indicating a significant improvement in affinity.

[0046] A hybrid structure consisting of a third-generation dendritic polylysine (G3-PLL) core and polyethylene glycol (PEG, 5000 Da) linear arms was constructed using latex microsphere modification. Molecular dynamics simulations demonstrated that this structure provided optimal antibody binding site density and signal amplification. This structure was then coupled to the surface of 100 nm diameter carboxyl-modified latex microspheres using the EDC / NHS method.

[0047] Sample pretreatment: 100 μL of serum sample was added to 1 mL of PBS buffer containing 50 μg / mL Fe₃O₄ magnetic nanoparticles modified with E06 antibody. The mixture was incubated at room temperature for 30 minutes. The magnetic nanoparticles, enriched with oxLDL, were separated using a magnetic separation rack and washed three times with PBS. The oxLDL was eluted by adding 500 μL of glycine-HCl buffer (pH 2.5) and immediately neutralized to pH 7.4 with 1 M Tris-HCl (pH 8.0).

[0048] Detection reaction: Take 100 μL of enriched sample and add 900 μL of reaction buffer containing optimized 4E6 antibody-latex microsphere probe (0.1 mg / mL). Incubate at 37°C for 30 minutes. Measure absorbance before and after the reaction at 500 nm using a UV-visible spectrophotometer.

[0049] Data analysis: A standard curve was established using 0, 1, 5, 10, 25, 50, 75, and 100 ng / mL oxLDL standards. Four-parameter logistic regression fitting yielded the following results: Figure 2 The equation shown is: ΔT = 0.02 + (0.98 - 0.02) / (1+ (C / 15.3) 1.2 ), the calculated limit of detection (LOD) was 0.5 ng / mL, and the limit of quantification (LOQ) was 1.5 ng / mL. The linear range was 1.5-100 ng / mL. The measured ΔT value of the sample was substituted into the standard curve equation to calculate the oxLDL concentration. For example, a sample with a measured ΔT of 0.45 was substituted into the equation to obtain C = 22.7 ng / mL. Taking into account the dilution factor (5-fold) during sample preparation, the final serum oxLDL concentration was 22.7 × 5 = 113.5 ng / mL. Validation results showed that the intra-day RSD of precision was <5%, the inter-day RSD of precision was <8%, the spike recovery was between 90-110%, and the correlation coefficient r with the clinical gold standard method (ELISA) was >0.95, indicating that this method has good accuracy and precision.

[0050] Specific to Figure 2 In the four-parameter logistic regression equation shown in Figure 1, the flattened portions of the curve correspond to the two extremes at low and high concentrations, which is related to the characteristics of the equation. When the ΔT value approaches the lower limit (0.02), the detection signal is weak, and the oxLDL concentration at this time is low, even close to the detection limit. As the oxLDL concentration increases, the ΔT value gradually increases, and the curve shows a large upward trend until the ΔT value approaches the upper limit (0.98). This upper limit indicates that at high oxLDL concentrations, the detection system signal tends to saturate. Even if the oxLDL concentration continues to increase, the change in ΔT tends to stabilize and does not increase significantly. This phenomenon is usually due to saturation of the antibody-antigen binding site or limited signal amplification of the probe.

[0051] This curve provides an effective quantitative basis for the oxLDL detection method. When measuring the oxLDL concentration in a sample, the experimenter can measure the ΔT value and substitute it into the standard curve equation to infer the oxLDL concentration in the sample. The shape of this curve indicates that the method is suitable for detecting a mid-range of oxLDL concentrations, covering a linear range of 1.5 ng / mL to 100 ng / mL. Furthermore, the method has a limit of detection (LOD) of 0.5 ng / mL and a limit of quantification (LOQ) of 1.5 ng / mL, demonstrating that the system has a reasonable ability to detect low concentrations of oxLDL.

[0052] Furthermore, an embodiment of the present application also provides an oxidized low-density lipoprotein detection device based on 4E6 monoclonal antibodies and latex-enhanced turbidimetric technology, comprising: a processor, a memory, and a system bus; the processor and the memory are connected via the system bus; the memory is used to store one or more programs, and the one or more programs include instructions, which, when executed by the processor, enable the processor to execute any of the above methods.

[0053] Furthermore, an embodiment of the present application also provides a computer program product, which, when running on a terminal device, enables the terminal device to execute any of the above methods.

[0054] Through the description of the above embodiments, it can be seen that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network communication device such as a media gateway, etc.) to execute the methods described in each embodiment of the present application or certain parts of the embodiments.

[0055] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the methods.

[0056] It should also be noted that, in the embodiments of the present application, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device comprising the elements.

[0057] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined in the embodiments of the present application may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown in the embodiments of the present application, but rather will conform to the widest scope consistent with the principles and novel features disclosed in the embodiments of the present application.

Claims

1. A method for detecting oxidized low-density lipoprotein based on 4E6 antibody, characterized in that: include: Obtain a model of the target 4E6 monoclonal antibody sequence, perform docking analysis on the interactions between the antigen-binding regions, design mutants to predict their effects, select optimal combinations to construct new sequences, express and purify them, and verify the optimization effect by measuring binding kinetic parameters; Based on computational materials science methods, the dendritic-linear polymer modification structure on the surface of latex microspheres was designed and optimized, the modified particles were synthesized, and then coupled with the optimized 4E6 monoclonal antibody to form a detection probe; The enriched oxidized low-density lipoprotein sample is mixed with the detection probe for reaction under target conditions, and the optical signal value of the turbidity change of the solution before and after the reaction is measured using an optical detection device to calculate the turbidity change amount based on the optical signal value; A standard curve of oxidized low-density lipoprotein was established using multiple sets of standard solutions, and the limits of detection and quantification were determined to calculate the sample concentrations.

2. The method for detecting oxidized low-density lipoprotein according to claim 1, wherein in, Obtain a model of the target 4E6 monoclonal antibody sequence, analyze the interaction of the antigen-binding region through docking, design mutants to predict their effects, select optimal combinations to construct new sequences, express and purify them, and verify the optimization effect by measuring binding kinetic parameters, including: Obtain the amino acid sequence information of the target 4E6 monoclonal antibody and construct the corresponding three-dimensional structural model using homology modeling; performing molecular docking of the antigen-binding region of the antibody with the target antigen to determine the interaction between the two; Based on the docking results, multiple mutants were designed and the effects of the mutations on the antibody-antigen interaction were predicted using pre-defined computational methods, including calculation of binding free energy changes, molecular dynamics simulation analysis of complex stability, and calculation of solvent accessible surface area changes. Based on the calculation results, target mutation combinations are selected to design new antibody sequences; Express and purify the optimized antibodies using a protein expression system; The binding kinetic parameters of the optimized antibody to the target antigen were determined by surface plasmon resonance and compared with those before optimization.

3. The method for detecting oxidized low-density lipoprotein according to claim 1, wherein in, Based on computational materials science methods, the dendritic-linear polymer modification structure on the surface of latex microspheres was designed and optimized. The modified particles were synthesized and coupled with the optimized 4E6 monoclonal antibody to form a detection probe, including: A computational materials science method is used to design a surface modification structure of latex microspheres, wherein the structure includes a dendritic polymer core and linear polymer arms; Constructing an atomic model of the latex microsphere surface to simulate the effects of different structural parameters on particle performance; calculating the surface charge distribution, hydrophobicity and antibody binding site density of the different structures; Based on the calculation results, optimizing the structural parameters to maximize the antibody binding site density and signal amplification effect; According to the optimized results, the corresponding dendritic-linear mixed polymers were synthesized and covalently linked to the surface of latex microspheres; The particle size distribution, Zeta potential and surface morphology of the modified latex microspheres were tested; The optimized antibody is coupled to the surface of the modified latex microspheres to form a detection probe.

4. The method for detecting oxidized low-density lipoprotein according to claim 1, wherein in, The enriched oxidized low-density lipoprotein sample is mixed with the detection probe for reaction under target conditions, and the optical signal value of the turbidity change of the solution before and after the reaction is measured using an optical detection device to calculate the turbidity change amount based on the optical signal value, including: The enriched oxidized low-density lipoprotein sample is mixed with the optimized antibody-label probe and incubated at a preset temperature for a predetermined time; Measure the change in solution turbidity before and after the reaction, and record the optical signal value at a specific wavelength; The turbidity change is calculated based on the optical signal values ​​before and after the reaction, wherein the functional relationship between the optical signal value and the transmittance is an exponential function.

5. The method for detecting oxidized low-density lipoprotein according to claim 1, wherein in, A standard curve for oxidized low-density lipoprotein (OxLDL) was established using multiple sets of standard solutions, and the limits of detection and quantification were determined to calculate sample concentrations, including: A series of oxidized low-density lipoprotein standard solutions of known concentrations were prepared, and the response values ​​of the standards at each concentration were obtained using the above-mentioned detection method, and a standard curve between the response values ​​and the oxidized low-density lipoprotein concentrations was established; The standard curve was fitted using a four-parameter logistic regression model to obtain the values ​​of the parameters in the fitting equation; Calculate the detection limit and quantification limit based on the standard deviation of the blank sample response value and the slope of the standard curve in the low concentration region; The response value of the sample to be tested is substituted into the fitting equation to calculate the corresponding oxidized low-density lipoprotein concentration.

6. The method for detecting oxidized low-density lipoprotein according to claim 5, wherein: in, The four-parameter logistic regression model includes: ， in, Indicates turbidity changes, represents the maximum response value, represents the minimum response value, Indicates concentration, represents the inflection point concentration, Represents the slope of the curve.

7. The method for detecting oxidized low-density lipoprotein according to claim 5, wherein: in, The detection limit and quantification limit include: ， ， in, represents the detection limit, represents the limit of quantification, represents the standard deviation of the blank sample response, Indicates the slope of the standard curve in the low concentration region.

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