Efficient screening and preparation method of dominant epitope antigen for autoimmune disease diagnosis

Abstract

The invention relates to the technical field of biological medicines, and discloses a high-efficiency screening and preparation method of dominant epitope antigens for autoimmune disease diagnosis, which comprises the following steps: collecting autoimmune disease related antigen sequences to construct an autoimmune antigen data fusion platform; integrating multi-source data in the self-immune antigen data fusion platform, and outputting a candidate epitope sequence set by using a hybrid intelligent prediction model; according to the candidate epitope sequence set, carrying out affinity screening by taking serum or an autoantibody of a patient with the autoimmune disease as a target, inducing epitope exposure by combining phage gene editing, and determining a dominant epitope sequence; preparing a high-purity dominant epitope antigen according to the dominant epitope sequence by adopting a self-assembly guided recombinant expression optimization and green aqueous two-phase affinity precipitation purification process; the reaction of the dominant epitope antigen in autoimmune disease patients and healthy control serum is dynamically monitored, analyzed and detected by adopting an ELISA technology, and the diagnosis efficiency of the dominant epitope antigen is verified; the invention provides a powerful tool for early and accurate diagnosis of autoimmune diseases.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technologies, and particularly to a method for efficiently screening and preparing a dominant epitope antigen for autoimmune disease diagnosis. Background Art

[0002] Autoimmune diseases are diseases in which the body's immune system mistakenly attacks its own tissues and organs, causing a series of pathological changes, such as rheumatoid arthritis, systemic lupus erythematosus, multiple sclerosis, etc. Early and accurate diagnosis of such diseases is crucial for timely intervention and improving the prognosis of patients. Currently, the diagnosis of autoimmune diseases mainly relies on detecting specific antibodies against autoantigens in patients. However, the traditional intact autoantigens have many problems. On the one hand, they contain a large amount of non-critical epitope information, which may lead to low specificity in detection and false positive results. On the other hand, the preparation process of intact antigens is complex, costly, and has poor stability. A dominant epitope antigen refers to an antigen epitope fragment that can be preferentially recognized by the body's immune system and trigger a strong immune response in autoimmune reactions. Screening and preparing high-purity and high-activity dominant epitope antigens are expected to overcome the deficiencies of traditional diagnostic antigens and improve the accuracy and efficiency of autoimmune disease diagnosis. However, in the prior art, the screening methods for dominant epitope antigens often have low throughput and long cycles, and the preparation process is also difficult to meet the requirements of large-scale clinical applications. Summary of the Invention

[0003] The purpose of the present invention is to solve the above problems and design a method for efficiently screening and preparing a dominant epitope antigen for autoimmune disease diagnosis.

[0004] The present invention provides a method for efficiently screening and preparing a dominant epitope antigen for autoimmune disease diagnosis, and the method for efficiently screening and preparing a dominant epitope antigen for autoimmune disease diagnosis includes the following steps:

[0005] S1. Bioinformatics prediction: Collect antigen sequences related to autoimmune diseases to construct an autoimmune antigen data fusion platform, integrate multi-source data in the autoimmune antigen data fusion platform, and use a hybrid intelligent prediction model to output a set of candidate epitope sequences;

[0006] S2. Screening by phage display technology: According to the set of candidate epitope sequences, use the serum or autoantibodies of autoimmune disease patients as targets for affinity screening, and combine phage gene editing to induce epitope exposure to determine the dominant epitope sequences;

[0007] S3. Recombinant expression and purification preparation: According to the dominant epitope sequences, adopt a self-assembly-guided recombinant expression optimization and green aqueous two-phase affinity precipitation purification process to prepare high-purity dominant epitope antigens;

[0008] S4. Validation of dominant epitope antigens: The ELISA technique is used to dynamically monitor and analyze the reactions of dominant epitope antigens in the sera of patients with autoimmune diseases and healthy controls to verify the diagnostic efficacy of dominant epitope antigens.

[0009] Optionally, in the first implementation manner of the present invention, step S1 specifically includes the following processes:

[0010] Collect antigen sequences, single-cell sequencing data, proteomic dynamic maps, and patient-specific antigen sequence information in clinical samples related to autoimmune diseases to obtain multi-source data, and construct an autoimmune antigen data fusion platform based on the multi-source data;

[0011] Clean, deduplicate, and standardize the collected multi-source data, and label the integrated data, where the labeled information at least includes the disease association, tissue specificity, and expression level of the antigen;

[0012] Construct a hybrid intelligent prediction model composed of a convolutional neural network and a molecular simulation model based on the principles of quantum mechanics. Use the labeled multi-source data to train the hybrid intelligent prediction model, and input the integrated antigen sequences into the trained hybrid intelligent prediction model to output a set of candidate epitope sequences.

[0013] Optionally, in the second implementation manner of the present invention, the construction of the hybrid intelligent prediction model composed of a convolutional neural network and a molecular simulation model based on the principles of quantum mechanics includes:

[0014] Convolutional neural network part: Determine the number of neurons in the input layer of the convolutional neural network according to the characteristic dimension of the antigen sequence. The antigen sequence is represented by amino acid coding, each amino acid is represented by a vector of length n, and the sequence length is L, then the number of neurons in the input layer is n×L; 4 convolutional kernels are set in the convolutional layer, the size of the convolutional kernel is 4, the ReLU function is used as the activation function, the max pooling is used in the pooling layer, the size of the pooling window is 3, the number of neurons in the fully connected layer is set to 128, and the number of neurons in the last fully connected layer is 1.

[0015] Molecular simulation model part based on the principles of quantum mechanics: Select the B3LYP functional and the 6-31G(d,p) basis set, and set the energy convergence criterion to 10 -6 Hartree, and set the force convergence criterion to 10 -3 Hartree / Bohr.

[0016] Optionally, in the third implementation manner of the present invention, the input of the integrated antigen sequences into the trained hybrid intelligent prediction model to output a set of candidate epitope sequences includes:

[0017] Encode the integrated antigen sequence, convert it into the format for model input, and normalize the encoded antigen sequence data. Input the normalized antigen sequence data into the trained hybrid intelligent prediction model;

[0018] The antigen sequence data enters the convolutional neural network part. Through the calculations of the convolutional layer, pooling layer, and fully connected layer, a preliminary prediction score is obtained. At the same time, the molecular structure information corresponding to the antigen sequence is input into the molecular simulation model based on the principles of quantum mechanics to calculate the energy and interaction of the antigen epitope binding to the antibody.

[0019] Fuse the prediction score of the convolutional neural network and the calculation result of the molecular simulation model, and use the weighted average method to obtain a comprehensive prediction score;

[0020] Judge whether the comprehensive prediction score is greater than the prediction threshold. If the comprehensive prediction score is greater than the threshold, it is considered that the epitope is a candidate epitope; otherwise, it is considered not to be a candidate epitope, so as to output the candidate epitope sequence set, where the prediction threshold is 0.5.

[0021] Optionally, in the fourth implementation manner of the present invention, step S2 specifically includes the following process:

[0022] According to the candidate epitope sequence set, synthesize the epitope sequence with specific cleavage sites and linkers, clone the synthesized epitope sequence into the phage display vector, use restriction endonucleases to digest the vector and the epitope sequence, and then use DNA ligase to link them to construct a recombinant phage display vector;

[0023] Transform the recombinant phage display vector into host bacteria, and prepare a phage display library through culture and infection;

[0024] Coat the serum of patients with autoimmune diseases or purified autoantibodies on the solid-phase carrier, add the phage display library to the solid-phase carrier coated with the target, and screen after the phage fully binds to the target, where the solid-phase carrier is an enzyme-linked immunosorbent assay (ELISA) plate or magnetic beads;

[0025] Design sgRNA targeting the phage surface protein to guide the Cas9 protein to cleave and modify specific gene loci. For photosensitive gene elements, irradiate the phage culture with 405 nm blue light for 10 - 30 minutes, and for chemical stress, add hydrogen peroxide for an induction time of 30 - 60 minutes;

[0026] Detect the exposure of the phage surface epitope by immunofluorescence method to ensure the induction effect;

[0027] Phage sequencing: Sequencing the phages after multiple rounds of screening and induction treatments to determine the epitope sequences they carry, and analyzing the sequenced epitope sequences to determine the dominant epitope sequences, where the analysis process includes sequence alignment, conservation analysis, and antigenicity prediction.

[0028] Optionally, in the fifth implementation manner of the present invention, the coating conditions during the target coating process are as follows: Dilute the target with a coating buffer to 1 - 10 μg / ml, add 100 - 200 μl to each well, and incubate overnight at 4°C; during the screening process after target coating, wash 3 - 5 times with a washing buffer to remove unbound phages, elute the phages bound to the target with an elution buffer, and after neutralization, use them for the next round of screening, and perform 3 - 5 rounds of screening; where the coating buffer is a carbonate buffer with a pH of 9.6, the washing buffer is PBS containing 0.05% Tween-20, and the elution buffer is 0.1 M glycine-HCl.

[0029] Optionally, in the sixth implementation manner of the present invention, step S3 specifically includes the following process:

[0030] According to the dominant epitope sequence, optimize the gene sequence. At the same time, add restriction enzyme sites and tag sequences at both ends of the gene, select a recombinant expression vector, and clone the optimized dominant epitope gene into the vector to construct a recombinant expression vector, where the recombinant expression vector is a prokaryotic expression vector of the pET series or a eukaryotic expression vector of pPICZα. Clone the optimized dominant epitope gene into the vector to construct a recombinant expression vector;

[0031] Transform the recombinant expression vector into a host cell. Based on the analysis of the three-dimensional structure of the dominant epitope antigen, design an artificial auxiliary scaffold composed of small molecule self-assembly units, fuse the gene encoding the auxiliary scaffold with the dominant epitope gene for expression, and guide the antigen to self-assemble and fold in the host cell according to the natural correct conformation;

[0032] Select a biocompatible polymer and salt to construct an aqueous two-phase system, mix the fermentation broth with the aqueous two-phase system, and oscillate at room temperature for 10 - 20 minutes to partition the antigen into the upper phase, where the aqueous two-phase system is polyethylene glycol and ammonium sulfate;

[0033] Prepare a specific affinity precipitant, mix the aptamer with metal ions in an appropriate buffer, and incubate to form a stable complex;

[0034] Add the affinity precipitant to the aqueous two-phase system containing the antigen, incubate at room temperature for 30 - 60 minutes to bind the antigen to the affinity precipitant to form a precipitate, collect the precipitate by centrifugation or filtration, wash the precipitate 2 - 3 times with a washing buffer to remove impurities, and elute the antigen with an elution buffer to obtain a high-purity dominant epitope antigen.

[0035] Optionally, in the seventh implementation manner of the present invention, the host cell is Escherichia coli or Pichia pastoris. When the host cell is Escherichia coli, the culture conditions and induction expression conditions of the host cell are optimized as follows: the culture temperature is 37°C, the concentration of the inducer IPTG is 0.1 - 1 mM, and the induction time is 3 - 6 hours; when the host cell is Pichia pastoris, the culture conditions and induction expression conditions of the host cell are optimized as follows: the culture temperature is 28 - 30°C, the methanol induction concentration is 0.5 - 1%, and the induction time is 48 - 72 hours.

[0036] Optionally, in the eighth implementation manner of the present invention, step S4 specifically includes the following process:

[0037] Dilute the purified dominant epitope antigen with coating buffer to 1 - 10 μg / ml, add 100 μl to each well of the ELISA plate, and incubate overnight at 4°C;

[0038] Discard the coating solution, wash 3 times with washing buffer, 5 minutes each time. Then add 200 μl of blocking solution to each well and incubate at 37°C for 1 - 2 hours;

[0039] Discard the blocking solution, wash 3 times with washing buffer, 5 minutes each time. Dilute the sera of patients with autoimmune diseases and healthy control sera with diluent at a ratio of 1:100 - 1:1000 (generally 1:100 - 1:1000), add 100 μl to each well, and incubate at 37°C for 1 - 2 hours;

[0040] Discard the sample solution, wash 3 times with washing buffer, 5 minutes each time. Add horseradish peroxidase-labeled secondary antibody, dilute it with diluent to 1:1000 - 1:5000, add 100 μl to each well, and incubate at 37°C for 1 - 2 hours;

[0041] Discard the secondary antibody solution, wash 3 times with washing buffer, 5 minutes each time. Add 100 μl of chromogenic substrate to each well, incubate at room temperature in the dark for 15 - 30 minutes, and then add 50 μl of stop solution to each well to terminate the chromogenic reaction;

[0042] Conduct long-term follow-up on patients with autoimmune diseases, collect serum samples at different stages of the disease, perform ELISA detection, read the absorbance values of each well using an ELISA reader, and verify the diagnostic efficacy of the dominant epitope antigen according to the ELISA detection results.

[0043] In the technical solution provided by the present invention, the screening efficiency of the dominant epitope antigen is greatly improved, and it is possible to accurately locate the epitopes highly related to the disease from a vast number of potential epitopes in a short time. The dominant epitope antigen can be efficiently and stably expressed, with high purity and good activity, meeting the quality requirements for large-scale production of clinical diagnostic reagents, significantly improving the sensitivity and specificity of diagnosis, effectively reducing false positive and false negative results, providing a powerful tool for the early and accurate diagnosis of autoimmune diseases, and is expected to improve the treatment effect and quality of life of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention.

[0045] Figure 1 It is a schematic diagram of the method for efficient screening and preparation of the dominant epitope antigen for autoimmune disease diagnosis provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and above-mentioned drawings of the present invention are used to distinguish similar objects and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" or "having" and any variation thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0047] For ease of understanding, the specific process of the embodiment of the present invention will be described below. Please refer to Figure 1 It is a schematic diagram of the method for efficient screening and preparation of the dominant epitope antigen for autoimmune disease diagnosis provided by the embodiment of the present invention. The method specifically includes the following steps:

[0048] S1. Bioinformatics prediction: Collect antigen sequences related to autoimmune diseases to construct an autoimmune antigen data fusion platform, integrate multi-source data in the autoimmune antigen data fusion platform, and use a hybrid intelligent prediction model to output a candidate epitope sequence set;

[0049] S2. Screening by phage display technology: Use the serum or autoantibodies of autoimmune disease patients as targets for affinity screening according to the candidate epitope sequence set, and combine phage gene editing to induce epitope exposure to determine the dominant epitope sequence;

[0050] S3. Recombinant expression and purification preparation: According to the dominant epitope sequence, an optimized self-assembly-guided recombinant expression and a green aqueous two-phase affinity precipitation purification process are adopted to prepare a high-purity dominant epitope antigen;

[0051] S4. Verification of the dominant epitope antigen: The ELISA technology is used to dynamically monitor and analyze the reaction of the dominant epitope antigen in the sera of patients with autoimmune diseases and healthy controls, and the diagnostic efficacy of the dominant epitope antigen is verified.

[0052] In this embodiment, step S1 specifically includes the following processes:

[0053] Collect the antigen sequences, single-cell sequencing data, proteomic dynamic maps, and patient-specific antigen sequence information in clinical samples related to autoimmune diseases to obtain multi-source data, and construct an autoimmune antigen data fusion platform based on the multi-source data;

[0054] Clean, deduplicate, and standardize the collected multi-source data, and label the integrated data. The information to be labeled includes at least the disease relevance, tissue specificity, and expression level of the antigen;

[0055] Construct a hybrid intelligent prediction model composed of a convolutional neural network and a molecular simulation model based on the principles of quantum mechanics. Use the labeled multi-source data to train the hybrid intelligent prediction model, and input the integrated antigen sequence into the trained hybrid intelligent prediction model to output a candidate epitope sequence set.

[0056] In this embodiment, constructing a hybrid intelligent prediction model composed of a convolutional neural network and a molecular simulation model based on the principles of quantum mechanics includes:

[0057] Convolutional neural network part: Determine the number of neurons in the input layer of the convolutional neural network according to the characteristic dimension of the antigen sequence. The antigen sequence is represented by amino acid coding, each amino acid is represented by a vector of length n, and the sequence length is L, so the number of neurons in the input layer is n×L; 4 convolutional kernels are set in the convolutional layer, the size of the convolutional kernel is 4, the ReLU function is used as the activation function, the max pooling is adopted in the pooling layer, the size of the pooling window is 3, the number of neurons in the fully connected layer is set to 128, and the number of neurons in the last fully connected layer is 1.

[0058] Molecular simulation model part based on the principles of quantum mechanics: Select the B3LYP functional and the 6-31G(d,p) basis set, and set the energy convergence criterion to 10 -6 Hartree, and set the force convergence criterion to 10 -3 Hartree / Bohr.

[0059] In this embodiment, the integrated antigen sequence is input into the trained hybrid intelligent prediction model, and a candidate epitope sequence set is output, including:

[0060] Encode the integrated antigen sequence, convert it into the format for model input, perform normalization processing on the encoded antigen sequence data, and input the normalized antigen sequence data into the trained hybrid intelligent prediction model;

[0061] The antigen sequence data enters the convolutional neural network part. Through the calculations of the convolutional layer, pooling layer, and fully connected layer, a preliminary prediction score is obtained. At the same time, the molecular structure information corresponding to the antigen sequence is input into the molecular simulation model based on the principles of quantum mechanics to calculate the energy and interaction of antigen epitope binding to antibodies.

[0062] Fuse the prediction score of the convolutional neural network and the calculation result of the molecular simulation model, and use the weighted average method to obtain a comprehensive prediction score;

[0063] Judge whether the comprehensive prediction score is greater than the prediction threshold. If the comprehensive prediction score is greater than the threshold, it is considered that this epitope is a candidate epitope; otherwise, it is considered not to be a candidate epitope, so as to output the candidate epitope sequence set, where the prediction threshold is 0.5.

[0064] In this embodiment, step S2 specifically includes the following processes:

[0065] According to the candidate epitope sequence set, synthesize the epitope sequence with specific cleavage sites and linkers, clone the synthesized epitope sequence into the phage display vector, use restriction endonucleases to digest the vector and the epitope sequence, and then use DNA ligase to link them to construct a recombinant phage display vector;

[0066] Transform the recombinant phage display vector into host bacteria, and prepare a phage display library through cultivation and infection;

[0067] Coat the serum of patients with autoimmune diseases or purified autoantibodies on the solid-phase carrier, add the phage display library to the solid-phase carrier coated with the target, and screen after the phages are fully bound to the target, where the solid-phase carrier is an enzyme-linked immunosorbent assay (ELISA) plate or magnetic beads;

[0068] Design sgRNA targeting the phage surface protein to guide the Cas9 protein to cleave and modify specific gene sites. For photosensitive gene elements, irradiate the phage culture with 405 nm blue light for 10 - 30 minutes. For chemical stress, add hydrogen peroxide for 30 - 60 minutes for induction;

[0069] Detect the exposure of the phage surface epitope by immunofluorescence method to ensure the induction effect;

[0070] Phage sequencing: Sequencing the phages after multiple rounds of screening and induction treatments to determine the epitope sequences they carry, and analyzing the sequenced epitope sequences to determine the dominant epitope sequences, where the analysis and processing include sequence alignment, conservation analysis, and antigenicity prediction.

[0071] In this embodiment, the coating conditions during the target coating process are as follows: Dilute the target with a coating buffer to 1 - 10 μg / ml, add 100 - 200 μl per well, and incubate overnight at 4°C; during the screening process after target coating, wash 3 - 5 times with a washing buffer to remove unbound phages, elute the phages bound to the target with an elution buffer, and after neutralization, use them for the next round of screening, and perform 3 - 5 rounds of screening; where the coating buffer is a carbonate buffer with a pH of 9.6, the washing buffer is PBS containing 0.05% Tween - 20, and the elution buffer is 0.1M glycine - HCl.

[0072] In this embodiment, step S3 specifically includes the following process:

[0073] According to the dominant epitope sequence, optimize the gene sequence. At the same time, add restriction enzyme sites and tag sequences at both ends of the gene, select a recombinant expression vector, and clone the optimized dominant epitope gene into the vector to construct a recombinant expression vector, where the recombinant expression vector is a pET series prokaryotic expression vector or a pPICZα eukaryotic expression vector. Clone the optimized dominant epitope gene into the vector to construct a recombinant expression vector;

[0074] Transform the recombinant expression vector into a host cell. Based on the analysis of the three - dimensional structure of the dominant epitope antigen, design an artificial auxiliary scaffold composed of small - molecule self - assembling units, and fuse - express the gene encoding the auxiliary scaffold with the dominant epitope gene to guide the antigen to self - assemble and fold in the host cell according to the natural correct conformation;

[0075] Select a polymer and salts with biocompatibility to construct an aqueous two - phase system, mix the fermentation broth with the aqueous two - phase system, and oscillate at room temperature for 10 - 20 minutes to make the antigen partition into the upper phase, where the aqueous two - phase system is polyethylene glycol and ammonium sulfate;

[0076] Prepare a specific affinity precipitant by mixing the aptamer with metal ions in an appropriate buffer and incubating to form a stable complex;

[0077] Add the affinity precipitant to the aqueous two - phase system containing the antigen, incubate at room temperature for 30 - 60 minutes to make the antigen bind to the affinity precipitant to form a precipitate, collect the precipitate by centrifugation or filtration, wash the precipitate 2 - 3 times with the washing buffer to remove impurities, and elute the antigen with the elution buffer to obtain a high - purity dominant epitope antigen.

[0078] In this example, the host cell is Escherichia coli or Pichia pastoris. When the host cell is Escherichia coli, the optimized culture conditions and induction expression conditions of the host cell are as follows: the culture temperature is 37 °C, the concentration of the inducer IPTG is 0.1 - 1 mM, and the induction time is 3 - 6 hours; when the host cell is Pichia pastoris, the optimized culture conditions and induction expression conditions of the host cell are as follows: the culture temperature is 28 - 30 °C, the methanol induction concentration is 0.5 - 1%, and the induction time is 48 - 72 hours.

[0079] In this example, step S4 specifically includes the following process:

[0080] Dilute the purified dominant epitope antigen with coating buffer to 1 - 10 μg / ml, add 100 μl to each well of the ELISA plate, and incubate overnight at 4 °C;

[0081] Discard the coating solution, wash 3 times with washing buffer, 5 minutes each time. Then add 200 μl of blocking solution to each well and incubate at 37 °C for 1 - 2 hours;

[0082] Discard the blocking solution, wash 3 times with washing buffer, 5 minutes each time. Dilute the sera of patients with autoimmune diseases and healthy control sera with diluent at a ratio of 1:100 - 1:1000 (generally 1:100 - 1:1000), add 100 μl to each well, and incubate at 37 °C for 1 - 2 hours;

[0083] Discard the sample solution, wash 3 times with washing buffer, 5 minutes each time. Add the horseradish peroxidase-labeled secondary antibody, dilute it with diluent to 1:1000 - 1:5000, add 100 μl to each well, and incubate at 37 °C for 1 - 2 hours;

[0084] Discard the secondary antibody solution, wash 3 times with washing buffer, 5 minutes each time. Add 100 μl of chromogenic substrate to each well, incubate at room temperature in the dark for 15 - 30 minutes, and then add 50 μl of stop solution to each well to terminate the chromogenic reaction;

[0085] Conduct long-term follow-up on patients with autoimmune diseases, collect serum samples at different stages of the disease, perform ELISA detection, read the absorbance values of each well using an ELISA reader, and verify the diagnostic efficacy of the dominant epitope antigen according to the ELISA detection results.

[0086] Through the implementation of the above solution, the present invention greatly improves the accuracy of the screening starting point of potential epitope antigens, reduces the blindness of subsequent experiments. The intelligent microfluidic phage display screening innovation significantly improves the screening throughput and diversity, discovers more hidden dominant epitopes, provides rich materials for the development of diagnostic reagents, enhances diagnostic specificity, and the construction of an efficient and green recombinant expression and purification system not only reduces production costs but also improves product quality.

[0087] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for highly efficient screening and preparation of dominant epitope antigens for autoimmune disease diagnosis, characterized in that, The method for efficient screening and preparation of dominant epitope antigens for autoimmune disease diagnosis includes the following steps: S1. Bioinformatics prediction: Collect antigen sequences related to autoimmune diseases to construct an autoimmune antigen data fusion platform, integrate multi-source data in the autoimmune antigen data fusion platform, and use a hybrid intelligent prediction model to output a set of candidate epitope sequences; S2. Screening by phage display technology: According to the set of candidate epitope sequences, use the serum of patients with autoimmune diseases or autoantibodies as targets for affinity screening, and combine phage gene editing to induce epitope exposure to determine the dominant epitope sequences; S3. Recombinant expression and purification preparation: According to the dominant epitope sequences, adopt a self-assembly-guided recombinant expression optimization and green aqueous two-phase affinity precipitation purification process to prepare high-purity dominant epitope antigens; S4. Verification of dominant epitope antigens: Use ELISA technology to dynamically monitor and analyze the reaction of dominant epitope antigens in the sera of patients with autoimmune diseases and healthy controls to verify the diagnostic efficacy of dominant epitope antigens.

2. The high-efficiency screening and preparation method of the dominant epitope antigen for autoimmune disease diagnosis according to claim 1, characterized in that Step S1 specifically includes the following processes: Collect antigen sequences, single-cell sequencing data, proteomic dynamic maps, and patient-specific antigen sequence information in clinical samples related to autoimmune diseases to obtain multi-source data, and construct an autoimmune antigen data fusion platform based on the multi-source data; Clean, deduplicate, and standardize the collected multi-source data, and label the integrated data. The labeled information includes at least the disease relevance, tissue specificity, and expression level of the antigen; Construct a hybrid intelligent prediction model composed of a convolutional neural network and a molecular simulation model based on the principles of quantum mechanics. Use the labeled multi-source data to train the hybrid intelligent prediction model, and input the integrated antigen sequences into the trained hybrid intelligent prediction model to output a set of candidate epitope sequences.

3. The high-efficiency screening and preparation method of the dominant epitope antigen for autoimmune disease diagnosis according to claim 2, characterized in that, The construction of the hybrid intelligent prediction model composed of a convolutional neural network and a molecular simulation model based on the principles of quantum mechanics includes: Convolutional neural network part: Determine the number of neurons in the input layer of the convolutional neural network according to the feature dimension of the antigen sequence. The antigen sequence is represented by amino acid encoding, each amino acid is represented by a vector of length n, and the sequence length is L, so the number of neurons in the input layer is n×L; There are 4 convolutional kernels in the convolutional layer, the size of the convolutional kernel is 4, the ReLU function is used as the activation function, the max pooling is adopted in the pooling layer, the size of the pooling window is 3, the number of neurons in the fully connected layer is set to 128, and the number of neurons in the last fully connected layer is 1. Part of the molecular simulation model based on the principles of quantum mechanics: The B3LYP functional and 6-31G(d,p) basis set are selected, and the energy convergence criterion is set to 10 -6 Hartree, and the force convergence criterion is set to 10 -3 Hartree / Bohr.

4. The high-efficiency screening and preparation method of the dominant epitope antigen for autoimmune disease diagnosis according to claim 2, characterized in that, The input of the integrated antigen sequences into the trained hybrid intelligent prediction model to output a set of candidate epitope sequences includes: Encode the integrated antigen sequences, convert them into the format required for model input, perform normalization processing on the encoded antigen sequence data, and input the normalized antigen sequence data into the trained hybrid intelligent prediction model; The antigen sequence data enters the convolutional neural network part, and after calculations in the convolutional layer, pooling layer, and fully connected layer, a preliminary prediction score is obtained. At the same time, the molecular structure information corresponding to the antigen sequence is input into the molecular simulation model based on the principles of quantum mechanics to calculate the energy and interaction of antigen epitope binding to antibodies. Fuse the prediction scores of the convolutional neural network and the calculation results of the molecular simulation model, and use the weighted average method to obtain a comprehensive prediction score; Determine whether the comprehensive prediction score is greater than the prediction threshold. If the comprehensive prediction score is greater than the threshold, then consider this epitope as a candidate epitope; otherwise, consider it not a candidate epitope, so as to output the candidate epitope sequence set, where the prediction threshold is 0.

5.

5. The high-efficiency screening and preparation method of the dominant epitope antigen for autoimmune disease diagnosis according to claim 1, characterized in that Step S2 specifically includes the following process: According to the candidate epitope sequence set, synthesize epitope sequences with specific cleavage sites and linkers, clone the synthesized epitope sequences into a phage display vector, use restriction endonucleases to digest the vector and the epitope sequences, and then use DNA ligase to link them together to construct a recombinant phage display vector; Transform the recombinant phage display vector into host bacteria, and prepare a phage display library through culturing and infection; Coat the serum of patients with autoimmune diseases or purified autoantibodies on a solid-phase carrier, add the phage display library to the solid-phase carrier coated with the target, and perform screening after the phages are fully bound to the target, where the solid-phase carrier is an ELISA plate or magnetic beads; Design sgRNA targeting the phage surface protein to guide the Cas9 protein to cleave and modify specific gene loci. For photosensitive gene elements, irradiate the phage culture with 405 nm blue light for 10 - 30 minutes. For chemical stress, add hydrogen peroxide for an induction time of 30 - 60 minutes; Detect the exposure of the phage surface epitope by immunofluorescence method to ensure the induction effect; Phage sequencing: Sequence the phages after multiple rounds of screening and induction treatment to determine the epitope sequences they carry, and analyze the sequenced epitope sequences to determine the dominant epitope sequences, where the analysis process includes sequence alignment, conservation analysis, and antigenicity prediction.

6. The high-efficiency screening and preparation method of the dominant epitope antigen for autoimmune disease diagnosis according to claim 5, wherein The coating conditions during the target coating process are as follows: Dilute the target with coating buffer to 1 - 10 μg / ml, add 100 - 200 μl per well, and incubate overnight at 4°C; During the screening process after target coating, wash 3 - 5 times with washing buffer to remove unbound phages, elute the phages bound to the target with elution buffer, and neutralize them for the next round of screening. Perform 3 - 5 rounds of screening; where the coating buffer is carbonate buffer with pH 9.6, the washing buffer is PBS containing 0.05% Tween - 20, and the elution buffer is 0.1 M glycine - HCl.

7. The high-efficient screening and preparation method of the dominant epitope antigen for autoimmune disease diagnosis according to claim 1, characterized in that Step S3 specifically includes the following process: Optimize the gene sequence according to the dominant epitope sequence. At the same time, add restriction endonuclease sites and tag sequences at both ends of the gene, select a recombinant expression vector, and clone the optimized dominant epitope gene into the vector to construct a recombinant expression vector, where the recombinant expression vector is a pET series prokaryotic expression vector or a pPICZα eukaryotic expression vector. Clone the optimized dominant epitope gene into the vector to construct a recombinant expression vector; Transform the recombinant expression vector into a host cell. Based on the analysis of the three-dimensional structure of the dominant epitope antigen, design an artificial auxiliary scaffold composed of small molecule self-assembly units, fuse the gene encoding the auxiliary scaffold with the dominant epitope gene for expression, and guide the antigen to self-assemble and fold in the host cell according to the natural and correct conformation. Select biocompatible polymers and salts to construct an aqueous two-phase system. Mix the fermentation broth with the aqueous two-phase system and oscillate at room temperature for 10 - 20 minutes to partition the antigen into the upper phase. The aqueous two-phase system is polyethylene glycol and ammonium sulfate. Prepare a specific affinity precipitant by mixing the aptamer with metal ions in an appropriate buffer and incubating to form a stable complex. Add the affinity precipitant to the aqueous two-phase system containing the antigen, incubate at room temperature for 30 - 60 minutes to allow the antigen to bind to the affinity precipitant to form a precipitate. Collect the precipitate by centrifugation or filtration, wash the precipitate 2 - 3 times with the washing buffer to remove impurities, and elute the antigen with the elution buffer to obtain the dominant epitope antigen with high purity.

8. The high-efficiency screening and preparation method of the dominant epitope antigen for autoimmune disease diagnosis according to claim 7, characterized in that, The host cell is Escherichia coli or Pichia pastoris. When the host cell is Escherichia coli, optimize the culture conditions and induction expression conditions as follows: the culture temperature is 37 °C, the concentration of the inducer IPTG is 0.1 - 1 mM, and the induction time is 3 - 6 hours. When the host cell is Pichia pastoris, optimize the culture conditions and induction expression conditions as follows: the culture temperature is 28 - 30 °C, the methanol induction concentration is 0.5 - 1%, and the induction time is 48 - 72 hours.

9. The high-efficiency screening and preparation method of the dominant epitope antigen for autoimmune disease diagnosis according to claim 1, characterized in that, Step S4 specifically includes the following process: Dilute the purified dominant epitope antigen to 1 - 10 μg / ml with the coating buffer, add 100 μl to each well of the enzyme-linked immunosorbent assay (ELISA) plate, and incubate overnight at 4 °C. Discard the coating solution, wash 3 times with the washing buffer, 5 minutes each time. Then add 200 μl of the blocking solution to each well and incubate at 37 °C for 1 - 2 hours. Discard the blocking solution, wash 3 times with the washing buffer, 5 minutes each time. Dilute the sera of patients with autoimmune diseases and healthy control sera with the diluent at a ratio of 1:100 - 1:1000 (generally 1:100 - 1:1000), add 100 μl to each well, and incubate at 37 °C for 1 - 2 hours. Discard the sample solution, wash 3 times with the washing buffer, 5 minutes each time. Add the horseradish peroxidase-labeled secondary antibody, dilute it with the diluent to 1:1000 - 1:5000, add 100 μl to each well, and incubate at 37 °C for 1 - 2 hours. Discard the secondary antibody solution, wash 3 times with the washing buffer, 5 minutes each time. Add 100 μl of the chromogenic substrate to each well, incubate at room temperature in the dark for 15 - 30 minutes, and then add 50 μl of the termination solution to each well to terminate the chromogenic reaction. Conduct long-term follow-up on patients with autoimmune diseases, collect serum samples at different stages of the disease, perform ELISA detection, use an enzyme-linked immunosorbent assay reader to read the absorbance values of each well, and verify the diagnostic efficacy of the dominant epitope antigen according to the ELISA detection results.

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