Efficient anti-silk fibroin polyclonal antibody and optimized preparation method thereof

Abstract

The invention discloses an efficient anti-silk fibroin polyclonal antibody and an optimized preparation method thereof. The method comprises the following steps: step 1, preparing an antigen; step 2, selecting experimental animals; step 3, animal immunization; step 4, taking blood for testing; 5, antibody collection; step 6, antibody purification; step 7, antibody detection; according to the invention, through extraction, cloning and prokaryotic expression of an RSA gene, a large amount of recombinant plasmids pGEX-4T-2-RSA are effectively prepared, then a recombinant protein GST-RSA is prepared, the recombinant protein GST-RSA is used as an antigen to prepare the anti-silk fibroin polyclonal antibody, so that the obtained polyclonal antibody has high specificity on the recombinant protein GST-RSA, and the anti-silk fibroin polyclonal antibody can be used for preparing the anti-silk fibroin polyclonal antibody. Therefore, the problem of lack of a high-specificity antibody aiming at the anti-silk fibroin in the prior art is solved, and the research effect on the anti-silk fibroin is improved.

Description

Technical Field

[0001] The invention relates to the field of biotechnology, in particular to a highly effective anti-silk fibroin polyclonal antibody and an optimized preparation method thereof. Background Art

[0002] Anti-silk fibroin polyclonal antibody is an antibody specific to silk fibroin. This antibody is produced by a variety of different immune cells (such as B cells), so it is called "polyclonal". Polyclonal antibodies are often used in biological and medical research, especially when it is necessary to identify, quantify or study the functions and interactions of specific proteins (such as silk fibroin). Silk fibroin has excellent biocompatibility, which means that it can coexist harmoniously with cells and tissues in the body without causing rejection or toxicity problems. This property makes silk fibroin an ideal choice for cell culture and tissue engineering. The basic amino acids in the non-crystalline region of silk fibroin also have a certain adsorption effect on cells, which can support the adhesion, proliferation and differentiation of various cells.

[0003] The existing anti-silk fibroin polyclonal antibodies cannot accurately identify silk fibroin. Since there is no antibody with high specificity for silk fibroin, it is impossible to accurately identify, capture or block the B subtype RSA receptor protein. Therefore, a highly efficient anti-silk fibroin polyclonal antibody and an optimized preparation method thereof are proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a highly effective anti-silk fibroin polyclonal antibody and an optimized preparation method thereof, so as to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: an anti-silk fibroin polyclonal antibody, wherein the polyclonal antibody is obtained by biological immunization of the recombinant protein GST-RSA encoded and expressed by the recombinant plasmid pGEX-4T-2-RSA.

[0006] Furthermore, the recombinant protein GST-RSA is prepared from the Escherichia coli strain BL21 with a preservation number of GDMCC60225.

[0007] An optimized preparation method for anti-silk fibroin polyclonal antibodies comprises the following steps: step 1, antigen preparation; step 2, selecting experimental animals; step 3, animal immunization; step 4, sampling blood for testing; step 5, antibody collection; step 6, antibody purification; step 7, antibody detection;

[0008] In the above step 1, silk fibroin is extracted and purified as an antigen;

[0009] Silk fibroin was prepared as an antigen, a vector was constructed by molecular cloning, and induced expression was performed in Escherichia coli to obtain recombinant silk fibroin RSA;

[0010] (1) Molecular cloning construction vector:

[0011] 1) Gene synthesis or cloning: amplifying the amino acid sequence of the silk fibroin gene by PCR, such as SEQ ID NO: 1;

[0012] 2) Vector preparation: pGEX-4T-2 vector was digested to expose its multiple cloning site for insertion of target gene. pGEX-4T-2 vector contains tac promoter and GST tag sequence;

[0013] 3) Gene insertion: The RSA gene was inserted into the multiple cloning site of the pGEX-4T-2 vector through enzyme digestion and ligation reaction to construct the recombinant plasmid pGEX-4T-2-RSA;

[0014] 4) Transformation and screening: transform the recombinant plasmid into competent E. coli cells, and screen out positive clones containing the recombinant plasmid by blue-white screening and other methods;

[0015] 5) The selected positive clones were further verified to ensure that the RSA gene had been successfully inserted into the multiple cloning site of the vector;

[0016] (2) Inducible expression in Escherichia coli;

[0017] (3) Purification of recombinant silk protein RSA;

[0018] (4) Verification of the activity of recombinant silk protein RSA: The biological activity of RSA is verified through functional assays, such as enzyme activity and binding ability, and the function of recombinant silk protein RSA is verified through biological activity experiments to ensure that it meets expectations;

[0019] The purified and activity-verified silk fibroin can be used as an immunogen;

[0020] In the above step 2, an experimental animal suitable for preparing antibodies is selected, such as a rabbit;

[0021] Select two healthy 6-week-old New Zealand white rabbits, weighing about 2 kg, and allow them to adapt to the new living environment. After 3 to 5 days of stabilization, take blood for the first time as a negative reference.

[0022] In the above step three, the antigen in step one is injected into the rabbit to stimulate its immune system to produce antibodies, and different adjuvants, such as Freund's complete adjuvant, are used to help the antigen work better;

[0023] In the above step 4, during the immunization process, blood samples collected from experimental animals are tested regularly to check whether an immune response is successfully generated and to ensure successful immunization by measuring the titer of the antiserum;

[0024] In the above step 5, after the antibody titer detected in step 4 reaches more than 100,000, the rabbit is finally bled, and the serum contains a large amount of anti-silk fibroin polyclonal antibodies;

[0025] In the above step 6, the anti-silk fibroin polyclonal antibody is separated and purified from the serum by chromatography using an affinity column coupled to an antigen;

[0026] In the above step seven, the antibody titer and specificity are determined by enzyme-linked immunosorbent assay (ELISA).

[0027] Further, in step 3, the specific method for separating serum is:

[0028] 1) Place the blood sample in a centrifuge tube or plate at room temperature for 4 hours or at 4°C overnight until a blood clot is formed;

[0029] 2) Use a wooden stick to gently move the blood clot from the side of the centrifuge tube or dish, and then remove the blood clot with the stick;

[0030] 3) Transfer the serum to a 50 ml centrifuge tube and centrifuge at 4000 rpm for 10 minutes at 4°C to precipitate blood cells and debris, then aspirate the serum immediately.

[0031] Further, in step six, the specific steps of preparing the affinity column are as follows:

[0032] (1) Weigh 1 mg of CNBr-Sepherose 4B agarose gel and add it to 2 mmol / L hydrochloric acid. Allow it to swell fully at 4°C overnight to obtain 3 ml of swollen gel.

[0033] (2) Transfer the gel into a purification column and wash the medium three times with about 20 ml of 2 mmol / L hydrochloric acid;

[0034] (3) Wash the medium once with coupling buffer. Because the activated group is easily hydrolyzed at the pH of the coupling buffer, this step is best completed within a few minutes.

[0035] (4) Add 5-10 mg of antigen dissolved in 5 ml of coupling buffer to the gel;

[0036] (5) Gently mix and shake at room temperature for 2-4 hours, or at 4°C overnight. If the binding efficiency needs to be determined, take a small amount of solution after this step and wash the medium once with 20 ml of coupling buffer.

[0037] (6) Add 15 ml of 1% BSA solution and incubate at room temperature for 2 hours or at 4°C overnight;

[0038] (7) The medium is washed with phosphate buffer for more than 3 times, each time with more than 15 ml, and sealed with 20% ethanol. The antigen solid phase is completed, that is, the preparation of the affinity column is completed, which can be used to purify anti-silk fibroin antibodies.

[0039] Furthermore, an intelligent control algorithm is used to adjust the concentration of the inducer in the experiment.

[0040] Output the final classification result. Compared with the prior art, the present invention has the following beneficial effects: the present invention constructs a vector by molecular cloning, and induces expression in Escherichia coli to obtain recombinant silk protein RSA, and then purifies the recombinant silk protein RSA by using the rivano precipitation method, so as to obtain a large amount of purified recombinant silk protein RSA, ensure the purity and stability of the antigen, and adjust the pH and antigen concentration of the culture medium to ensure the optimal expression of the antibody; obtain recombinant protein by gene cloning and expression, and prepare anti-silk protein antibodies with high specificity, which are used to accurately identify, capture or block B subtype RSA receptor protein, and are conducive to in-depth research on RSA receptor protein disease resistance breeding methods; through the extraction, cloning and prokaryotic expression of RSA gene, a large amount of recombinant plasmid pGEX-4T-2-RSA is effectively prepared, and then the recombinant protein GST-RSA is prepared, and the recombinant protein GST-RSA is used as an antigen to prepare anti-silk protein polyclonal antibodies, so that the obtained polyclonal antibodies have high specificity to the recombinant protein GST-RSA, thereby solving the problem of lack of high specificity antibodies against anti-silk protein in the prior art, and improving the research effect of anti-silk protein.

[0041] Intelligent control algorithms can dynamically adjust the temperature, pH, and inducer concentration during experiments or production processes. This adjustment is based on the system's current performance feedback and can continuously optimize parameter settings to achieve better process efficiency and output quality. Precise control of environmental conditions during chemical reactions or biological cultivation can significantly improve yield and stability. Through fuzzy logic, intelligent control algorithms can handle uncertainty and ambiguity, allowing the system to respond more reasonably when faced with complex and changing inputs and environmental conditions. This adaptability is difficult to achieve with traditional hard logic control methods, especially when dealing with fuzzy thresholds and incomplete data. Intelligent control algorithms such as particle swarm optimization can effectively search in the global solution space to avoid falling into local optimal solutions, thereby improving the quality of the solution and the stability of the system. This is especially important for systems that need to operate stably for a long time. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a flow chart of the preparation method of the present invention;

[0043] Figure 2 The present invention is a flow chart of the method for extracting and purifying silk fibroin as an antigen;

[0044] Figure 3 A flow chart of the method for constructing a vector for molecular cloning of the present invention;

[0045] Figure 4 is a flow chart of the inducible expression method of the present invention;

[0046] Figure 5 It is a flow chart of the purification method of the recombinant silk protein RSA of the present invention;

[0047] Figure 6 It is a flow chart of the RSA purity detection method of the present invention;

[0048] Figure 7 The flowchart of the method for producing antibodies of the present invention is as follows;

[0049] Figure 8 is a flow chart of the potency determination method of the present invention;

[0050] Fig. 9 The present invention is a flow chart of the affinity column preparation method;

[0051] Fig.10 The flowchart of the method for purifying anti-silk fibroin polyclonal antibodies of the present invention is as follows;

[0052] Fig.11 This is a flow chart of the intelligent control algorithm of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] See also Figure 1-10 , a technical solution provided by the present invention:

[0055] The invention discloses an anti-silk fibroin polyclonal antibody, which is prepared by biological immunization with a recombinant protein GST-RSA encoded and expressed by a recombinant plasmid pGEX-4T-2-RSA.

[0056] Furthermore, the recombinant protein GST-RSA was prepared from the Escherichia coli strain BL21 with the accession number of GDMCC60225.

[0057] An anti-silk fibroin polyclonal antibody and a preparation method thereof, comprising step 1, antigen preparation; step 2, selecting experimental animals; step 3, animal immunization; step 4, sampling blood for testing; step 5, antibody collection; step 6, antibody purification; step 7, antibody detection;

[0058] In the above step 1, silk fibroin is extracted and purified as an antigen;

[0059] Silk fibroin was prepared as an antigen, a vector was constructed by molecular cloning, and induced expression was performed in Escherichia coli to obtain recombinant silk fibroin RSA;

[0060] (1) Molecular cloning construction vector:

[0061] 1) Gene synthesis or cloning: amplifying the amino acid sequence of the silk fibroin gene by PCR, such as SEQ ID NO: 1;

[0062] A. Design primers based on specific regions of the target DNA sequence:

[0063] The primer length is usually between 18 and 30 bp, the GC content is between 30% and 60%, and the Tm value is between 55 and 65°C;

[0064] GC content is the total ratio of guanine (G) and cytosine (C) in DNA and RNA sequences, usually expressed as a percentage, and the calculation formula is: GC content (%) = (G + C) / (A + T + G + C) × 100, where A represents adenine and T represents thymine;

[0065] The Tm value melting temperature is an important parameter in PCR (polymerase chain reaction). The formula is based on the characteristics of the base pair structure of DNA and RNA. By determining the number of G, C, A and T in the sequence and substituting them into the formula, the Tm value can be estimated. The classic formula for calculating the Tm value is: Tm = 4 (G + C) + 2 (A + T), where G (guanine) and C (cytosine), A (adenine) and T (thymine);

[0066] B. Prepare a PCR reaction mixture, including a DNA template, i.e., a sample containing the RSA gene, primers, DNA polymerase, dNTPs (deoxynucleotide triphosphates) and an appropriate buffer, which constitutes the basis of the PCR reaction;

[0067] C. Perform PCR amplification. The PCR amplification process includes three main steps: denaturation, annealing, and extension. The three steps are cycled in a PCR instrument.

[0068] Step 1: pre-denaturation at 98°C for 1 minute;

[0069] Step 2: Perform 30 cycles, each cycle is as follows: in the denaturation step, treat at 98°C for 10 seconds to separate the double-stranded DNA into single-stranded DNA by high temperature; in the annealing step: treat at 50°C for 30 seconds, the primer binds to the single-stranded target DNA; in the extension step, treat at 72°C for 30 seconds, and the DNA polymerase synthesizes a new DNA chain under the guidance of the primer;

[0070] Step 3: Treatment at 72°C for 5 minutes for extension, mixing and purification of PCR products: Mix samples at equal concentrations according to the concentration of PCR products, and purify PCR products by electrophoresis using 1×TAE 2% agarose gel. Detect the quality and size of PCR products by electrophoresis and other methods to ensure that they are consistent with the target DNA sequence. The product purification kit uses the GeneJET gel recovery kit from ThermoScientific.

[0071] By repeating the above steps, PCR can exponentially amplify the target DNA sequence. The amount of DNA doubles with each cycle, thus obtaining a large amount of amplified products in a short period of time.

[0072] 2) Vector preparation: pGEX-4T-2 vector was digested to expose its multiple cloning site for insertion of target gene. pGEX-4T-2 vector contains tac promoter and GST tag sequence;

[0073] 3) Gene insertion: The RSA gene was inserted into the multiple cloning site of the pGEX-4T-2 vector through enzyme digestion and ligation reaction to construct the recombinant plasmid pGEX-4T-2-RSA;

[0074] A. Enzyme digestion reaction steps include:

[0075] The RSA gene and the expression vector are mixed with the corresponding restriction endonuclease, reaction buffer and an appropriate amount of water to form an enzyme digestion reaction system; the reaction system is placed at 37°C for 1.5 hours of enzyme digestion reaction; after the reaction is completed, the enzyme digestion reaction is terminated by adding an enzyme inactivator;

[0076] B. Gel electrophoresis analysis: After the enzyme digestion is completed, gel electrophoresis is usually used to check the digestion effect, confirm whether the RSA gene and vector have been correctly cut, and determine the size of the cut fragments;

[0077] C. Ligation reaction: The RSA gene and pGEX-4T-2 vector are successfully digested, and DNA ligase is used to connect the digested fragments to form a recombinant plasmid. Ligase can catalyze the formation of phosphodiester bonds between DNA fragments and insert the RSA gene into the multiple cloning site of the vector;

[0078] The specific connection reaction is as follows:

[0079] The RSA gene after enzyme digestion, the vector, the ligase and the ligation buffer are mixed together to form a ligation reaction system;

[0080] Place the ligation reaction system at 16-25°C for 1-2 hours to obtain a ligation product;

[0081] 4) Transformation and screening: The recombinant plasmid is transformed into competent E. coli cells, and positive clones containing the recombinant plasmid are screened by blue-white screening and other methods. The specific method is as follows:

[0082] The screening method based on α-complementation phenomenon and color change uses a special vector in the blue-white screening. The vector carries the gene sequence encoding the N-terminal α peptide segment of β-galactosidase. When the vector is not inserted with exogenous DNA, the α peptide segment can be normally expressed and complement the ω fragment in the host cell to form active β-galactosidase. The enzyme can hydrolyze the colorless compound X-gal in the culture medium to turn it blue, thereby forming blue colonies on the culture medium. However, when exogenous DNA is inserted into the multiple cloning site of the vector, it usually destroys the coding sequence of the α peptide segment, resulting in the inability to form active β-galactosidase. Therefore, on the culture medium containing X-gal, the recombinant plasmid-transformed cells that have successfully inserted the RSA gene will form white colonies. By observing and selecting white colonies, positive clones containing the recombinant plasmid are screened out.

[0083] 5) The selected positive clones were further verified to ensure that the RSA gene had been successfully inserted into the multiple cloning site of the vector;

[0084] (2) Induced expression in E. coli:

[0085] 1) Expanded culture: Pick positive clones for expanded culture, place the inoculated test tubes or culture bottles in a shaker or constant temperature incubator, and expand the culture under appropriate temperature and conditions. During the culture process, regularly observe the growth of the bacteria. The growth state can be evaluated by measuring the OD value of the bacterial solution, that is, the optical density value, or observing the morphology and number of the bacteria under a microscope. When the bacteria grow to a sufficient number, stop the culture and harvest the bacteria. The bacteria can be separated from the culture medium by centrifugation or other methods to obtain pure bacterial precipitation, so as to obtain sufficient bacteria for inducing expression;

[0086] 2) Induced expression: Use an inducer, such as IPTG, to induce the E. coli strain BL21 to initiate transcription and translation of the silk protein gene to produce recombinant silk protein RSA;

[0087] (3) Purification of recombinant silk protein RSA

[0088] 1) Cell lysis: Collect the E. coli cells after induced expression, and lyse the cells by ultrasonication or enzymatic hydrolysis to release the recombinant silk protein RSA;

[0089] 2) Crude extraction and purification: Remove bacterial fragments and macromolecular impurities by centrifugation, filtration, etc., and then use the characteristics of the GST tag to purify the recombinant silk protein RSA by the rivano precipitation method. The following steps are used:

[0090] Add an appropriate amount of rivanol solution to the recombinant silk fibroin RSA sample. After adding rivanol, centrifuge or let stand to form a precipitate, separate the supernatant from the precipitate, and the supernatant should contain relatively pure recombinant silk fibroin RSA.

[0091] The supernatant is subjected to further purification steps, such as exchange chromatography, to remove residual rivanol and other trace impurities;

[0092] 3) Purity detection: The purity of the purified recombinant silk protein RSA was detected by SDS-PAGE method. The specific operation steps are as follows:

[0093] A. Mix the recombinant silk protein RSA sample to be tested with SDS and a reducing agent, and heat to denature the protein and dissociate it into subunits;

[0094] B. Add the treated samples to the loading wells of SDS-PAGE gel and add standard proteins as reference;

[0095] C Under the action of the electric field, the protein sample migrates and separates in the gel;

[0096] D. After electrophoresis, protein bands are visualized by staining, such as Coomassie Brilliant Blue staining.

[0097] E. The purity of the recombinant silk protein RSA is determined based on the number and distribution of protein bands in the electrophoresis pattern. If there is only a single, clear band, it means that the purity is high; if there are multiple bands, it may contain impurities or proteins of different molecular weights;

[0098] (4) Verification of the activity of recombinant silk protein RSA: The biological activity of RSA is verified through functional assays, such as enzyme activity and binding ability, and the function of recombinant silk protein RSA is verified through biological activity experiments to ensure that it meets expectations;

[0099] The purified and activity-verified silk fibroin can be used as an immunogen;

[0100] In the above step 2, an experimental animal suitable for preparing antibodies is selected, such as a rabbit;

[0101] Select two healthy 6-week-old New Zealand white rabbits, weighing about 2 kg, and allow them to adapt to the new living environment. After 3 to 5 days of stabilization, take blood for the first time as a negative reference.

[0102] The specific steps for pre-blood collection are as follows:

[0103] (1) Fix the animal and draw about 10 ml of blood from the ear vein with a syringe;

[0104] (2) The collected blood was inactivated at 37°C for 30 min and then placed at 4°C overnight to allow it to coagulate and release serum;

[0105] (3) Centrifuge the coagulated blood at 10,000 r / min for 10 min. Collect the supernatant, which is the negative reference serum.

[0106] In the above step three, the antigen in step one is injected into the rabbit to stimulate its immune system to produce antibodies, and different adjuvants, such as Freund's complete adjuvant, are used to help the antigen work better;

[0107] The specific steps are as follows:

[0108] (1) Mix 1 ml of Freund's complete adjuvant and 1 ml of the prepared antigen thoroughly until the mixture turns milky white.

[0109] (2) Select four sites on the back for subcutaneous injection, 250 μl per site, and insert the needle at a 45-degree angle 1–2 cm below the skin;

[0110] (3) 10 to 14 days after the initial immunization, a small amount of venous blood was drawn and serum was separated;

[0111] (4) Booster immunization begins 2 to 8 weeks after the initial immunization, and blood samples are drawn 7 to 14 days later to separate serum;

[0112] (5) The interval between booster immunizations is 1 to 2 weeks, and the boosters are repeated several times. Blood samples from animals must be collected and serum separated before and after the initial immunization and after each booster immunization.

[0113] Specific method for separating serum:

[0114] 1) Place the blood sample in a centrifuge tube or plate at room temperature for 4 hours or at 4°C overnight until a blood clot is formed;

[0115] 2) Use a wooden stick to gently move the blood clot from the side of the centrifuge tube or dish, and then remove the blood clot with the stick;

[0116] 3) Transfer the serum to a 50 ml centrifuge tube and centrifuge at 4000 rpm for 10 minutes at 4°C to precipitate blood cells and debris, then aspirate the serum immediately;

[0117] In the above step 4, during the immunization process, blood samples of experimental animals are collected regularly for testing to check whether an immune response is successfully produced and to ensure successful immunization by measuring the titer of the antiserum. The specific steps are:

[0118] (1) Add 100 μl of 1 μg / ml antigen to each well of a 96-well plate and incubate at 4°C overnight or at 37°C for 2 hours.

[0119] (2) Pour off the antigen solution, add 200 μl of blocking solution to each well, and incubate at 4°C overnight or at 37°C for 2 hours;

[0120] (3) Pour off the blocking solution, tap on absorbent paper to dry any remaining liquid, wash the plate three times with detergent, and tap off any remaining liquid each time. If the plate needs to be stored for a period of time, dry it at 37°C and seal it in a sealable bag and store it at -20°C.

[0121] (4) Take the coated 96-well plate, add 100ul of negative reference serum to the first well, 100ul of positive reference serum to the second well, and 1:500 of the test antiserum to the third well. Dilute each subsequent well in multiples based on this, and incubate at 37°C for 1 hour;

[0122] (5) Pour off the liquid, wash the plate three times with washing solution, pat dry, add 100 μl of HRP-labeled mouse anti-rabbit IgG to each well, dilute 1:2000, and incubate at 37°C for 45 min;

[0123] (6) Pour off the liquid, wash the plate three times with washing solution, pat dry, add 100 μl of TMB substrate (Sigma single-component TMB) to each well, incubate at 37°C for 5-20 minutes, and determine the color development time based on the color depth;

[0124] (7) Add 50 μl of stop solution (2N H2SO4) to each well and read the absorbance at 450 nm on a microplate reader;

[0125] In the above step 5, after the antibody titer detected in step 4 reaches more than 100,000, the rabbit is finally bled, and the serum contains a large amount of anti-silk fibroin polyclonal antibodies;

[0126] In the above step 6, the anti-silk fibroin polyclonal antibody is separated and purified from the serum by chromatography using an affinity column coupled to an antigen, and the specific steps are as follows:

[0127] The specific steps for preparing the affinity column are as follows:

[0128] (1) Weigh 1 mg of CNBr-Sepherose 4B agarose gel and add it to 2 mmol / L hydrochloric acid. Allow it to swell fully at 4°C overnight to obtain 3 ml of swollen gel.

[0129] (2) Transfer the gel into a purification column and wash the medium three times with about 20 ml of 2 mmol / L hydrochloric acid;

[0130] (3) Wash the medium once with coupling buffer. Because the activated group is easily hydrolyzed at the pH of the coupling buffer, this step is best completed within a few minutes.

[0131] (4) Add 5-10 mg of antigen dissolved in 5 ml of coupling buffer to the gel;

[0132] (5) Gently mix and shake at room temperature for 2-4 hours, or at 4°C overnight. If the binding efficiency needs to be determined, take a small amount of solution after this step and wash the medium once with 20 ml of coupling buffer.

[0133] (6) Add 15 ml of 1% BSA solution and incubate at room temperature for 2 hours or at 4°C overnight;

[0134] (7) washing the medium with phosphate buffer for more than 3 times, each time with more than 15 ml, and sealing with 20% ethanol. The antigen solid phase is completed, that is, the preparation of the affinity column is completed, which can be used to purify anti-silk fibroin antibodies;

[0135] The specific steps of affinity column chromatography are as follows:

[0136] (1) Antiserum was diluted with an equal volume of PBS, centrifuged at 5000-10000 rpm for 15 min, and the supernatant was collected;

[0137] (2) Wash the antigen affinity column with 10 volumes of PBS to equilibrate the column;

[0138] (3) Add 10 ml of diluted antiserum to the equilibrated column;

[0139] (4) Gently mix and shake at room temperature for 2-4 hours, or at 4°C overnight;

[0140] (5) Wash the antigen column with 10 volumes of PBS to remove the foreign proteins bound to the column;

[0141] (6) Wash the column with 2 volumes of antibody elution buffer to obtain specific antibodies;

[0142] (7) Equilibrate the column with 10 volumes of PBS;

[0143] (8) Seal the column with 20% alcohol and store at 4°C;

[0144] In the above step 7, the antibody titer and specificity are determined by enzyme-linked immunosorbent assay (ELISA), and the specific steps are as follows:

[0145] (1) Take 5 μg / ml GST-RSA recombinant protein solution as the coating antigen and coat the ELISA plate at 80-100 μl / well; use PBS with a pH of 7.4 as the buffer solution to coat the ELISA plate;

[0146] (2) Add antibodies to the ELISA plate, 20 to 100 μl per well, and incubate at 25 to 40°C for 2 h. Discard the solution and wash the ELISA plate three times with a 0.02 mol / L PBS solution to remove the antibodies that are not bound to the antigen and obtain the primary antibody. Add the enzyme-labeled secondary antibody to the ELISA plate to allow it to bind to the primary antibody. This enzyme-labeled secondary antibody retains both its immune activity and enzyme activity. Wash the ELISA plate with a 0.02 mol / L PBS solution to remove the antibodies that are not bound to the antigen. Antibodies, 100 μl of goat anti-rabbit-HRP was added to each well, and after incubation at 25-40°C for 1 hour, 100 μl of TMB colorimetric solution was added to react and color for 10-25 minutes, and then the plate was read using an ELISA plate reader with a wavelength of 450 nm. The ELISA test result was 0.842, which was higher than the positive standard of 0.6, and it was determined that the sample contained silk fibroin, indicating that the prepared anti-silk fibroin polyclonal antibody was specific. The absorbance value of each well was measured using an enzyme marker, and a standard curve was drawn according to the concentration of the standard and the absorbance value, so as to calculate the titer of the antibody in the sample to be tested;

[0147] Determine antibody specificity:

[0148] Using the principle of competitive inhibition test, observe the competition between the test antigen and the reference antigen at the enzyme or antibody binding site, make competitive inhibition curves with different antigens and similar antigens, calculate their respective qualified rates, and find their respective concentrations at IC50. By comparing the binding abilities of different antigens with enzymes or antibodies, the cross-reaction rate can be calculated, thereby evaluating the degree of cross-reaction between them.

[0149] The cross-reaction rate was calculated as follows: C% = ED50 (cAg) / ED50 (iAg) × 100%, where ED50 represents the half effective dose, and cAg and iAg refer to specific antigen and nonspecific antigen, respectively;

[0150] If the IC50 concentration of the antigen used is pg / tube, and the IC50 concentration of some similar antigens is almost infinite, it means that the cross-reaction rate of this antiserum with other antigenic substances is approximately 0, that is, the specificity of the serum is better.

[0151] like Fig.11 In the present invention, an intelligent control algorithm is used to adjust the concentration of the inducer in the experiment. The specific process is as follows:

[0152] Step 1: Use particle swarm optimization to tune the immune process;

[0153] Initialization and variable definition;

[0154] For each particle i, a random initial position x is generated i and initial velocity v i :

[0155] x i =x min +rand(x max -x min )

[0156] v i =v min +rand(v max -v min )

[0157] Among them, x i Indicates the random generation of the initial position, x min and x max are the minimum and maximum values ​​of the position, v i Indicates the random generation of initial velocity, v max and v min are the maximum and minimum values ​​of the speed respectively, and rand is a function that generates uniformly distributed random numbers in the interval [0,1];

[0158] Variable definition: x i (t) represents the position of particle i, which represents a set of possible solutions for immunization parameters, including adjuvant type, dose, and time interval; v i (t) represents the velocity of particle i, which indicates the moving speed and direction of the particle in the parameter space; p best,i represents the optimal position experienced by particle i; g best represents the global optimal position among all particles;

[0159] Step 2: Ensure maximum antibody production and optimal specificity through particle swarm optimization;

[0160] Fitness evaluation formula:

[0161] f(x i )=w1·Antibody Production(x i )+w2·Specificity(x i )

[0162] Among them, w1 and w2 are weight coefficients, f(x i ) is the i-th particle at position x iThe fitness function value, AntibodyProduction represents the antibody production function, and Specificity represents the antibody specificity function;

[0163] Update speed:

[0164] v i (t+1)=w·v i (t)+c1·r1·(p best,i -x i (t))+c2·r2·(g best -x i (t))

[0165] Among them, v i (t+1) is the velocity vector of particle i at time t+1; v i (t) is the velocity vector of particle i at time t; w is the inertia weight, which controls the influence of the velocity at the previous moment on the current velocity; c1 is the individual learning factor, which represents the weight of the particle moving to its historical best position; r1 is a random number used to increase the randomness of the search; p best,i is the best position that particle i has ever experienced in history; x i (t) is the position vector of particle i at time t; c2 is the social learning factor, which represents the weight of the particle moving to the global optimal position; r2 is another random number used to increase the randomness of the search; g best is the best position experienced by the entire particle group in history;

[0166] Speed ​​Limit:

[0167] In order to ensure that the speed does not exceed the maximum allowable value while keeping the speed direction unchanged, a speed limit is introduced. The specific formula is as follows:

[0168] v i (t+1)=sign(v i (t+1))·min(v i (t+1)|,v max )

[0169] Among them, v i (t+1) is the velocity of the ith particle in the t+1th iteration; sign(v i (t+1)) returns v i The sign of (t+1) ensures that the direction of the velocity remains unchanged; min(v i (t+1)|,v max ) means limiting the absolute value of the speed to the maximum speed v max Inside;

[0170] Update location:

[0171] x i (t+1)=x i (t)+v i (t+1)

[0172] Among them, x i (t+1) is to update the position of each particle; x i (t) is the position vector of particle i at time t; v i (t+1) is the velocity vector of particle i at time t+1;

[0173] Step 3: Set fuzzy control rules and membership functions:

[0174] Design fuzzy control rules and membership functions based on experimental data and expert knowledge;

[0175] The temperature range is set to a certain range, and the membership function can be trapezoidal. The specific function is as follows:

[0176]

[0177] Wherein, L(tem), M(tem), and H(tem) represent low temperature membership function, medium temperature membership function, and high temperature membership function respectively; tem represents temperature, max represents the maximum value, and min represents the minimum value;

[0178] The membership function of pH value is as follows:

[0179]

[0180] Wherein, L(p), M(p), and H(p) represent the low pH value membership function, the middle pH value membership function, and the high pH value membership function, respectively; p represents the pH value, max represents the maximum value, and min represents the minimum value;

[0181] The fuzzy rules are as follows:

[0182] Rule 1: If the temperature is high and the pH is low, the inducer concentration decreases;

[0183] Rule 2: If the temperature is high and the pH is high, the inducer concentration remains constant;

[0184] Rule 3: If the temperature is low and the pH is low, the inducer concentration increases;

[0185] Rule 4: If the temperature is low and the pH is high, the inducer concentration decreases;

[0186] Rule 5: If the temperature is moderate and the pH is moderate, the inducer concentration remains;

[0187] Rule 6: If the temperature is moderate and the pH is high, the inducer concentration increases;

[0188] Rule 7: If the temperature is moderate and the pH is low, the inducer concentration decreases;

[0189] Step 4: Fuzzy logic dynamically adjusts PSO parameters;

[0190] Combining fuzzy logic with PSO, using fuzzy logic to dynamically adjust PSO parameters:

[0191] w new =FuzzyAdjust(w)

[0192] c 1,new =FuzzyAdjust(c1)

[0193] c 2,new =FuzzyAdjust(c2)

[0194] Among them, w represents the inertia weight, w new represents the adjusted new inertia weight, c 1,new represents the adjusted new individual learning factor, c1 represents the individual learning factor, c 2,new represents the new social learning factor after adjustment, c2 represents the social learning factor, and FuzzyAdjust is a function of the dynamic adjustment parameters of fuzzy sets and fuzzy rules;

[0195] Step 5: Determine the specific adjustment value;

[0196] Use defuzzification technology to convert the output of fuzzy logic into specific operation commands, and use the centroid method to calculate the specific adjustment value;

[0197]

[0198] Among them, A represents an element in the fuzzy set; A * represents the center of gravity of the fuzzy set, μ A (A) represents the membership function of element A, represents the sum of the membership of all elements A, represents the total membership of the fuzzy set, It represents the sum of the products of all elements A and their membership, and it represents the contribution of each element A's membership to the element;

[0199] According to the calculated A * , adjust the inducer concentration setting in the experiment; if the original concentration setting is x, the new concentration setting should be x+A * .

[0200] Using deep learning, especially convolutional neural network (CNN), for image recognition technology to automatically identify and analyze ELISA test results:

[0201] The input layer receives the original input image, which is the reaction plate image after the ELISA test, and extracts features in the convolution layer. The formula is as follows:

[0202] F ij =∑ m ∑ n I(i+m)(j+n)K mn

[0203] Among them, F ij is the value of position (i, j) on the feature map; I is the input image; K is the weight matrix of the convolution kernel; m, n are the row and column positions of the entire convolution kernel element; I(i+m)(j+n) is the element at position (i+m, j+n) in the input image; K mn The element at position (m,n) in the convolution kernel;

[0204] Apply the nonlinear activation function ReLU to increase the nonlinearity of the model and help the network learn complex features. The activation function is as follows;

[0205] g(x)=max(0,x)

[0206] Among them, the parameter x represents the input value, and the function g(x) is the ReLU corrected linear unit activation function;

[0207] The pooling layer reduces the spatial size of the feature map, reduces the number of parameters and computational complexity, and makes feature detection more robust;

[0208] P ij =max a∈[i,i+p],b∈[j,j+q] F ab

[0209] Where F is the input matrix, parameters i and j are the current processing position index of matrix F, parameters p and q are the height and width of the pooling window; the window starts at position (i, j) and covers to position (i+p, j+q), P ij is the output value, which is the maximum value in the pooling window, F ab Represents the value of the input feature map at position (a, b), where a and b are the row and column element indices of the input matrix;

[0210] After the feature map is converted into a one-dimensional vector, classification or regression analysis is performed through a fully connected layer:

[0211] y=σ(Wx+g)

[0212] Among them, y is the output vector; W is the weight matrix; x is the input vector from the previous layer; g is the bias vector; σ is the activation function;

[0213] Output the final classification result. Numerical example:

[0214] Position initialization: the parameter ranges are [0,100] and [0,50] respectively;

[0215] x1=0+rand(100-0)

[0216] x2=0+rand(50-0)

[0217] Speed ​​initialization:

[0218] v1=-50+rand(100-(-50))

[0219] v2=-25+rand(50-(-25))

[0220] Initial parameters: inertia weight w = 0.9; individual learning factor c1 = 1.5; social learning factor c2 = 1.5;

[0221] According to the fuzzy values ​​of specific temperature and pH value, the membership function is used to obtain: the membership degree of high temperature and the membership degree of low pH;

[0222] H(tem)=0.8

[0223] L(p)=0.6

[0224] According to the rule: if the temperature is high and the pH value is low, the inducer concentration decreases; the fuzzy logic output is -10 for decrease, 0 for hold, and 10 for increase.

[0225] Dynamically adjust PSO parameters w according to fuzzy logic new =0.85, c 1,new =1.55, c 2,new =1.02;

[0226] The specific adjustment value was determined using the centroid method. The centroid was calculated to be 1. If the original concentration setting was 1, the new concentration setting should be 2. The final identification experiment type was serum separation.

[0227] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An anti-silk fibroin polyclonal antibody, characterized in that: The polyclonal antibody is prepared by biological immunization of the recombinant protein GST-RSA encoded and expressed by the recombinant plasmid pGEX-4T-2-RSA.

2. The anti-silk fibroin polyclonal antibody according to claim 1, characterized in that: The recombinant protein GST-RSA is prepared from the Escherichia coli strain BL21 with a preservation number of GDMCC60225.

3. A method for preparing anti-silk fibroin polyclonal antibodies, comprising step 1, antigen preparation; step 2, selecting experimental animals; step 3, animal immunization; step 4, sampling blood for testing; step 5, antibody collection; step 6, antibody purification; step 7, antibody detection; In the above step 1, silk fibroin is extracted and purified as an antigen; Silk fibroin was prepared as an antigen, a vector was constructed by molecular cloning, and induced expression was performed in Escherichia coli to obtain recombinant silk fibroin RSA; (1) Molecular cloning construction vector: 1) Gene synthesis or cloning: amplifying the amino acid sequence of the silk fibroin gene by PCR, such as SEQ ID NO: 1; 2) Vector preparation: pGEX-4T-2 vector was digested to expose its multiple cloning site for insertion of target gene. pGEX-4T-2 vector contains tac promoter and GST tag sequence; 3) Gene insertion: The RSA gene was inserted into the multiple cloning site of the pGEX-4T-2 vector through enzyme digestion and ligation reaction to construct the recombinant plasmid pGEX-4T-2-RSA; 4) Transformation and screening: transform the recombinant plasmid into competent E. coli cells, and screen out positive clones containing the recombinant plasmid by blue-white screening and other methods; 5) The selected positive clones were further verified to ensure that the RSA gene had been successfully inserted into the multiple cloning site of the vector; (2) Inducible expression in Escherichia coli; (3) Purification of recombinant silk protein RSA; (4) Verification of the activity of recombinant silk protein RSA: The biological activity of RSA was verified through functional assays, including enzyme activity and binding ability, and the function of recombinant silk protein RSA was verified through biological activity experiments to ensure that it met expectations; The purified and activity-verified silk fibroin can be used as an immunogen; In the above step 2, a suitable experimental animal for preparing antibodies is selected, and the experimental animal is a rabbit; Select two healthy 6-week-old New Zealand white rabbits, weighing about 2 kg, and allow them to adapt to the new living environment. After 3 to 5 days of stabilization, take blood for the first time as a negative reference. In the above step 3, the antigen in step 1 is injected into the rabbit to stimulate its immune system to produce antibodies, and different adjuvants are used. The present invention uses Freund's complete adjuvant to help the antigen to work better; In the above step 4, during the immunization process, blood samples collected from experimental animals are tested regularly to check whether an immune response is successfully generated and to ensure successful immunization by measuring the titer of the antiserum; In the above step 5, after the antibody titer detected in step 4 reaches more than 100,000, the rabbit is finally bled, and the serum contains a large amount of anti-silk fibroin polyclonal antibodies; In the above step 6, the anti-silk fibroin polyclonal antibody is separated and purified from the serum by chromatography using an affinity column coupled to an antigen; In the above step seven, the antibody titer and specificity are determined by enzyme-linked immunosorbent assay (ELISA).

4. The method for preparing an anti-silk fibroin polyclonal antibody according to claim 3, characterized in that: In the step 3, the specific method of separating serum is also included as follows: 1) Place the blood sample in a centrifuge tube or plate at room temperature for 4 hours or at 4°C overnight until a blood clot is formed; 2) Use a wooden stick to gently move the blood clot from the side of the centrifuge tube or dish, and then remove the blood clot with the stick; 3) Transfer the serum to a 50 ml centrifuge tube and centrifuge at 4000 rpm for 10 minutes at 4°C to precipitate blood cells and debris, then aspirate the serum immediately.

5. The method for preparing an anti-silk fibroin polyclonal antibody according to claim 3, characterized in that: In step 6, the specific steps of preparing the affinity column are as follows: (1) Weigh 1 mg of CNBr-Sepherose 4B agarose gel and add it to 2 mmol / L hydrochloric acid. Allow it to swell fully at 4°C overnight to obtain 3 ml of swollen gel. (2) Transfer the gel into a purification column and wash the medium three times with about 20 ml of 2 mmol / L hydrochloric acid; (3) Wash the medium once with coupling buffer. Because the activated group is easily hydrolyzed at the pH of the coupling buffer, this step is best completed within a few minutes. (4) Add 5-10 mg of antigen dissolved in 5 ml of coupling buffer to the gel; (5) Gently mix and shake at room temperature for 2-4 hours, or at 4°C overnight. If the binding efficiency needs to be determined, take a small amount of solution after this step and wash the medium once with 20 ml of coupling buffer. (6) Add 15 ml of 1% BSA solution and incubate at room temperature for 2 hours or at 4°C overnight; (7) The medium is washed with phosphate buffer for more than 3 times, each time with more than 15 ml, and sealed with 20% ethanol. The antigen solid phase is completed, that is, the preparation of the affinity column is completed, which is used to purify the anti-silk fibroin antibody.

6. The method for preparing an anti-silk fibroin polyclonal antibody according to claim 3, characterized in that: In step 1, the concentration of the inducer in the experiment is adjusted using an intelligent control algorithm, and the specific process is as follows: Step 1: Use particle swarm optimization to tune the immune process; Initialization and variable definition; For each particle i, a random initial position x is generated i and initial velocity v i : x i =x min +rand(x max -x min ) v i =v min +rand(v max -v min ) Among them, x i Indicates the random generation of the initial position, x min and x max are the minimum and maximum values ​​of the position, v i Indicates the random generation of initial velocity, v max and v min are the maximum and minimum values ​​of the speed respectively, and rand is a function that generates uniformly distributed random numbers in the interval [0,1]; Variable definition: x i (t) represents the position of particle i, which represents a set of possible solutions for immunization parameters, including adjuvant type, dose, and time interval; v i (t) represents the velocity of particle i, which indicates the moving speed and direction of the particle in the parameter space; p best,i represents the optimal position experienced by particle i; g best represents the global optimal position among all particles; Step 2: Ensure maximum antibody production and optimal specificity through particle swarm optimization; Fitness evaluation formula: f(x i )=w1·Antibody Production(x i )+w2·Specificity(x i ) Among them, w1 and w2 are weight coefficients, f(x i ) is the i-th particle at position x i The fitness function value, AntibodyProduction represents the antibody production function, and Specificity represents the antibody specificity function; Update speed: v i (t+1)=w·v i (t)+c1·r1·(p best,i -x i (t))+c2·r2·(g best -x i (t)) Among them, v i (t+1) is the velocity vector of particle i at time t+1; v i (t) is the velocity vector of particle i at time t; w is the inertia weight, which controls the influence of the velocity at the previous moment on the current velocity; c1 is the individual learning factor, which represents the weight of the particle moving to its historical best position; r1 is a random number used to increase the randomness of the search; p best,i is the best position that particle i has ever experienced in history; x i (t) is the position vector of particle i at time t; c2 is the social learning factor, which represents the weight of the particle moving to the global optimal position; r2 is another random number used to increase the randomness of the search; g best is the best position experienced by the entire particle group in history; Speed ​​Limit: In order to ensure that the speed does not exceed the maximum allowable value while keeping the speed direction unchanged, a speed limit is introduced. The specific formula is as follows: v i (t+1)=sign(v i (t+1))·min(v i (t+1)|,v max ) Among them, v i (t+1) is the velocity of the ith particle in the t+1th iteration; sign(v i (t+1)) returns v i The sign of (t+1) ensures that the direction of the velocity remains unchanged; min(v i (t+1)|,v max ) means limiting the absolute value of the speed to the maximum speed v max Inside; Update location: x i (t+1)=x i (t)+v i (t+1) Among them, x i (t+1) is to update the position of each particle; x i (t) is the position vector of particle i at time t; v i (t+1) is the velocity vector of particle i at time t+1; Step 3: Set fuzzy control rules and membership functions: Design fuzzy control rules and membership functions based on experimental data and expert knowledge; The temperature range is set to a certain range, and the membership function is trapezoidal. The specific function is as follows: Wherein, L(tem), M(tem), and H(tem) represent low temperature membership function, medium temperature membership function, and high temperature membership function respectively; tem represents temperature, max represents the maximum value, and min represents the minimum value; The membership function of pH value is as follows: Wherein, L(p), M(p), and H(p) represent the low pH value membership function, the middle pH value membership function, and the high pH value membership function, respectively; p represents the pH value, max represents the maximum value, and min represents the minimum value; The fuzzy rules are as follows: Rule 1: If the temperature is high and the pH is low, the inducer concentration decreases; Rule 2: If the temperature is high and the pH is high, the inducer concentration remains constant; Rule 3: If the temperature is low and the pH is low, the inducer concentration increases; Rule 4: If the temperature is low and the pH is high, the inducer concentration decreases; Rule 5: If the temperature is moderate and the pH is moderate, the inducer concentration remains; Rule 6: If the temperature is moderate and the pH is high, the inducer concentration increases; Rule 7: If the temperature is moderate and the pH is low, the inducer concentration decreases; Step 4: Fuzzy logic dynamically adjusts PSO parameters; Combining fuzzy logic with PSO, using fuzzy logic to dynamically adjust PSO parameters: In new =FuzzyAdjust(in) c 1,new =FuzzyAdjust(c1) c 2,new =FuzzyAdjust(c2) Among them, w represents the inertia weight, w new represents the new inertia weight after adjustment, c 1,new represents the adjusted new individual learning factor, c1 represents the individual learning factor, c 2,new represents the new social learning factor after adjustment, c2 represents the social learning factor, and FuzzyAdjust is a function of the dynamic adjustment parameters of fuzzy sets and fuzzy rules; Step 5: Determine the specific adjustment value; Use defuzzification technology to convert the output of fuzzy logic into specific operation commands, and use the centroid method to calculate the specific adjustment value; Among them, A represents an element in the fuzzy set; A * represents the center of gravity of the fuzzy set, μ A (A) represents the membership function of element A, represents the sum of the membership of all elements A, represents the total membership of the fuzzy set, It represents the sum of the products of all elements A and their membership, and it represents the contribution of each element A's membership to the element; According to the calculated A * , adjust the inducer concentration setting in the experiment; if the original concentration setting is x, the new concentration setting should be x+A * . Using deep learning, especially convolutional neural network (CNN), for image recognition technology to automatically identify and analyze ELISA test results: The input layer receives the original input image, which is the reaction plate image after the ELISA test, and extracts features in the convolution layer. The formula is as follows: F ij =∑ m ∑ n I(i+m)(j+n)K mn Among them, F ij is the value of position (i, j) on the feature map; I is the input image; K is the weight matrix of the convolution kernel; m, n are the row and column positions of the entire convolution kernel element; I(i+m)(j+n) is the element at position (i+m, j+n) in the input image; K mn The element at position (m,n) in the convolution kernel; Apply the nonlinear activation function ReLU to increase the nonlinearity of the model and help the network learn complex features. The activation function is as follows; g(x)=max(0,x) Among them, the parameter x represents the input value, and the function g(x) is the ReLU corrected linear unit activation function; The pooling layer reduces the spatial size of the feature map, reduces the number of parameters and computational complexity, and makes feature detection more robust; P ij =max a∈[i,i+p],b∈[j,j+q] F ab Where F is the input matrix, parameters i and j are the current processing position index of matrix F, parameters p and q are the height and width of the pooling window; the window starts at position (i, j) and covers to position (i+p, j+q), P ij is the output value, which is the maximum value in the pooling window, F ab Represents the value of the input feature map at position (a, b), where a and b are the row and column element indices of the input matrix; After the feature map is converted into a one-dimensional vector, classification or regression analysis is performed through a fully connected layer: y=σ(Wx+g) Among them, y is the output vector; W is the weight matrix; x is the input vector from the previous layer; g is the bias vector; σ is the activation function; Output the final classification results.

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