A computational biology-based stat3 hybridoma-derived antibody affinity maturation method

Abstract

The application relates to the fields of biological medicine and antibody engineering technology, and particularly relates to a STAT3 hybridoma-derived antibody affinity maturation method based on computational biology, which comprises the following steps: (1) obtaining an antibody variable region sequence; (2) constructing STAT3 antigen and antibody models and evaluating; (3) performing antigen-antibody molecular docking and optimization, and screening out models; (4) performing docking model analysis, and obtaining potential saturation mutation sites; (5) performing site saturation mutation, and obtaining mutation results; (6) performing mutation site analysis, and determining final mutation sites; (7) amplifying a target fragment containing the mutation sites; (8) constructing a recombinant expression vector, and obtaining a reformed STAT3 recombinant antibody through eukaryotic expression and purification; and (9) performing relative affinity determination.

Description

Technical Field

[0001] This application belongs to the field of biomedicine and antibody engineering technology, and in particular relates to a method for maturation of affinity of STAT3 hybridoma-derived antibodies based on computational biology. Background Technology

[0002] Monoclonal antibodies, due to their high targeting specificity, have become indispensable tools in scientific research, disease diagnosis, and targeted drug therapy. Currently, antibody development primarily stems from mouse hybridoma technology and in vitro antibody library technology. Candidate antibodies are initially obtained through antigen design and antibody screening, and then undergo a series of related biological property tests and functional verifications to ultimately obtain antibodies with application value. In actual development, antibodies obtained through conventional screening methods require improvement in many properties, such as affinity, immunogenicity, and half-life. Among these, antibody affinity maturation is one of the most important areas for improvement.

[0003] Antibody affinity refers to the binding force between an antibody and an antigenic epitope or antigenic determinant. It is a non-covalent interaction, including ionic bonds, hydrogen bonds, and hydrophobic interactions. Sufficient affinity for the antigen at the molecular level is a fundamental requirement for an antibody to exert its therapeutic effect. In antibody-based therapies, high affinity between the biological target and the antibody is also a basic requirement. The development of protein-based therapies largely focuses on improving and optimizing affinity. Many processes in antibody development revolve around affinity optimization, including animal immunization, hybridoma development, directed evolution, and various display methods or selection platforms. However, robust and effective affinity optimization methods in antibody drug development often require multiple mutations of the starting protein to screen all possibilities for producing antibodies with high affinity, which often involves excessive randomness and uncontrollability.

[0004] Antibody affinity maturation refers to a normal immune function state in the body. In humoral immunity, the average affinity of antibodies produced in a secondary immune response is higher than that of the primary immune response; this phenomenon is called antibody affinity maturation. Currently, in vitro antibody affinity maturation methods are mostly proposed based on the understanding of the in vivo antibody affinity maturation patterns. They largely mimic the in vivo antibody affinity maturation process, primarily employing error-prone PCR, DNA shuffling technology, CDR region recombination, strand substitution technology, PD-1 immunotherapy, and PD-1-PD-L1 immunotherapy. However, the resulting antibodies often exhibit low affinity maturation levels, leading to significant individual variability and serious side effects, potentially causing severe adverse reactions or even life-threatening conditions.

[0005] Discovery Studio is a comprehensive computational simulation platform for antibody drug design and optimization. It can rapidly predict current antibody-antigen binding modes at the computational simulation level and quickly identify amino acid mutations that can potentially improve antibody affinity through virtual amino acid mutations, guiding rational antibody design to achieve affinity maturation. Computer-guided antibody affinity maturation methods mainly include three steps: predictive model construction, model docking, and docking complex analysis. Compared to random mutagenesis methods, using machine learning models to simplify in vitro affinity maturation of recombinant antibodies provides better targeting and reduces the challenges associated with randomization. With the continuous advancement and optimization of deep learning and machine learning models, computer-guided antibody affinity modification will become more systematic and streamlined, while also addressing a series of issues encountered in antibody drug development, such as reduced affinity after humanization and low intrinsic affinity of nanobodies.

[0006] Therefore, finding a computational biology-based method for maturation of STAT3 hybridoma-derived antibody affinity is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0007] To address the shortcomings of existing technologies and practical needs, this invention provides a computational biology-based method for maturing the affinity of STAT3 hybridoma-derived antibodies. This method improves the targeting of high-affinity antibody screening and reduces the challenges caused by randomization, making antibody affinity modification more systematic and simplified. It also solves a series of problems encountered in antibody drug development, such as reduced affinity after humanization of antibodies and low intrinsic affinity of nanobodies.

[0008] The above-mentioned technical objective of this invention is achieved through the following technical solution: a method for maturing the affinity of STAT3 hybridoma-derived antibodies based on computational biology, comprising the following steps:

[0009] (1) Obtaining antibody variable region sequences from hybridoma cells based on STAT3;

[0010] (2) Obtain the STAT3 protein model and perform pre-doping treatments such as dehydration and hydrogenation;

[0011] (3) Establish a STAT3 antibody model based on the template library and evaluate it using the Laplace plot;

[0012] (4) Antigen-antibody molecular docking and flexibility optimization were performed, and three models were selected;

[0013] (5) Perform docking model analysis to obtain amino acid sites for saturation mutation;

[0014] (6) Perform site saturation mutations to obtain the potential site saturation mutation results for each model;

[0015] (7) Analyze the changes in intermolecular forces in the saturation mutation results to determine the final mutation site and type;

[0016] (8) Perform amplification of the target fragment containing the mutation site;

[0017] (9) Construct a heavy chain plasmid vector, connect the target fragment containing the mutation site to the plasmid, and transfect eukaryotic cells for expression and purification to obtain the modified STAT3 recombinant antibody;

[0018] (10) Conduct relative affinity determination and draw affinity valence diagram.

[0019] Preferably, the antibody variable region gene amplification method in step (1) is to extract RNA from hybridoma cells and reverse transcribe it to synthesize cDNA, and then use the cDNA as a template to amplify the antibody variable region gene with Taq DNA enzyme.

[0020] Preferably, the variable region target fragment obtained by amplification in step (1) is the VH1 fragment or the VK1 fragment.

[0021] Preferably, the plasmid vector in step (1) is the heavy chain constant region vector pcDNA3.4-mG2b or the light chain constant region vector pcDNA3.4-mk.

[0022] Preferably, the STAT3 antigen model construction method in step (2) involves using the Uniport website to search for antigen information and obtaining protein structure resolution from the UniProt website. The structure of the STAT3 protein is 6NJS. It was then imported into the software DS, where the proligand and water were removed, and amino acid residues and hydrogen atoms were added, ultimately yielding an antigen model for molecular docking.

[0023] Preferably, the STAT3 antibody model construction method in step (3) is to input the known STAT3 hybridoma antibody sequence into the software DS, and after template search, model construction and CDR region optimization, a STAT3 antibody model with a DOPE score of -24077 is finally constructed with a similarity of 96.1%.

[0024] Preferably, the heavy chain plasmid vector in step (9) is the pcDNA3.4-mG2b vector.

[0025] Preferably, the eukaryotic expression and purification method in step (9) includes: diluting the plasmid carrying the light and heavy chain expression vector of the mutant target fragment and PEI with diluent and adding them to CHOS cell suspension for transfection culture, wherein the mass ratio of the light and heavy chain plasmids is 3:2, and a cell suspension is obtained. After culturing for 18-24 hours, the cells are fed, and after 96-120 hours, the cell supernatant is collected by centrifugation. The supernatant is then filtered through a filter membrane, and the supernatant is passed through a Protein A / G affinity chromatography column 3-4 times. Finally, the cells are eluted with glycine hydrochloric acid solution, and the eluent is collected to obtain the purified modified STAT3 recombinant antibody.

[0026] The beneficial effects of this invention are:

[0027] This invention successfully prepared a STAT3 recombinant antibody with high affinity. Compared with the traditional method of antibody affinity maturation, it has the technical effect of improving antibody affinity, reducing the trouble caused by randomization, and making antibody affinity modification more systematic and simple. At the same time, it solves a series of problems encountered in antibody drug development, such as the decrease in affinity after antibody humanization and the low affinity of nanobodies. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0029] Figure 1 This is a technical roadmap of the present invention;

[0030] Figure 2 This is an agarose gel electrophoresis image of RNA extracted from hybridoma cells using the Trizol method and the gene amplified from the variable region of the STAT3 monoclonal antibody in Example 1 of this invention.

[0031] Figure 3 This is a colony PCR agarose gel electrophoresis image of T-clone amplification of the STAT3 monoclonal antibody variable region gene in Example 1 of the present invention.

[0032] Figure 4 This is a functional analysis diagram of the variable region sequence analysis of the IMGT / QUEST website in Embodiment 1 of the present invention;

[0033] Figure 5 This is a gel image of the target fragment of the antibody variable region in Example 1 of the present invention;

[0034] Figure 6 This is an agarose gel electrophoresis image of the colony PCR identification of plasmid construction in Example 1 of this invention;

[0035] Figure 7 This is a schematic diagram of the sequencing results of positive clones in Example 1 of the present invention;

[0036] Figure 8 This is a schematic diagram of the STAT3 antigen structure in Example 2 of the present invention;

[0037] Figure 9 This is a schematic diagram of the STAT3 antibody structure in Example 3 of the present invention;

[0038] Figure 10 This is a Laplace plot evaluation result of the STAT3 antibody structure in Example 3 of the present invention;

[0039] Figure 11 These are schematic diagrams of the three model structures screened after molecular docking in Example 4 of the present invention;

[0040] Figure 12 This is a graph showing the energy variation trend of point mutations in the model under different pH environments in Example 5 of the present invention;

[0041] Figure 13 This is a graph showing the change in the mutagenic force at site 1 of the POSE3 heavy chain in Example 7 of the present invention.

[0042] Figure 14 This is a schematic diagram of the overlapping extension PCR method for the target fragment containing the mutation site in Example 8 of the present invention;

[0043] Figure 15 This is an amplification diagram of the target fragment containing the mutation site in Example 8 of the present invention;

[0044] Figure 16 This is a PCR agarose gel electrophoresis image of a single clone colony in Example 9 of the present invention;

[0045] Figure 17 This is a sequencing analysis diagram of the positive clone containing the mutation site in Example 9 of the present invention;

[0046] Figure 18 This is a gel image of the purified STAT3 antibody after modification in Example 9 of the present invention;

[0047] Figure 19 This is a titer graph of affinity assay for the unmodified STAT3 antibody in Example 10 of the present invention;

[0048] Figure 20 This is a titer chart for the affinity assay of the modified M2-STAT3 antibody in Example 10 of the present invention. Detailed Implementation

[0049] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0050] Experimental methods not specified in the examples are generally performed under standard conditions or according to the conditions provided by the manufacturer.

[0051] This invention addresses the problems of randomness, uncontrollability, individual variability, and serious side effects associated with current antibody affinity maturation methods. It provides a computational biology-based method for maturing STAT3 hybridoma-derived antibodies, which effectively improves antibody affinity, enhances the targeting of high-affinity antibody screening, reduces the challenges of randomization, and simplifies and systematizes antibody affinity modification. Simultaneously, it solves a series of problems encountered in antibody drug development, such as reduced affinity after humanization and low intrinsic affinity of nanobodies. Figure 1 As shown, the specific implementation plan is as follows:

[0052] (1) Obtaining antibody variable region sequences from hybridoma cells based on STAT3;

[0053] (2) Obtain the STAT3 protein model and perform pre-doping treatments such as dehydration and hydrogenation;

[0054] (3) Establish a STAT3 antibody model based on the template library and evaluate it using the Laplace plot;

[0055] (4) Antigen-antibody molecular docking and flexibility optimization were performed, and three models were selected;

[0056] (5) Perform docking model analysis to obtain amino acid sites for saturation mutation;

[0057] (6) Perform site saturation mutations to obtain the potential site saturation mutation results for each model;

[0058] (7) Analyze the changes in intermolecular forces in the saturation mutation results to determine the final mutation site and type;

[0059] (8) Perform amplification of the target fragment containing the mutation site;

[0060] (9) Construct a heavy chain plasmid vector, connect the target fragment containing the mutation site to the plasmid, and transfect eukaryotic cells for expression and purification to obtain the modified STAT3 recombinant antibody;

[0061] (10) Conduct relative affinity determination and draw affinity valence diagram.

[0062] Preferably, the antibody variable region gene amplification method in step (1) is to extract RNA from hybridoma cells and reverse transcribe it to synthesize cDNA, and then use the cDNA as a template to amplify the antibody variable region gene with Taq DNA enzyme.

[0063] Preferably, the variable region target fragment obtained by amplification in step (1) is the VH1 fragment or the VK1 fragment.

[0064] Preferably, the plasmid vector in step (1) is the heavy chain constant region vector pcDNA3.4-mG2b or the light chain constant region vector pcDNA3.4-mk.

[0065] Preferably, the STAT3 antigen model construction method in step (2) involves using the Uniport website to search for antigen information and obtaining protein structure resolution from the UniProt website. The structure of the STAT3 protein is 6NJS. It was then imported into the software DS, where the proligand and water were removed, and amino acid residues and hydrogen atoms were added, ultimately yielding an antigen model for molecular docking.

[0066] Preferably, the STAT3 antibody model construction method in step (3) is to input the known STAT3 hybridoma antibody sequence into the software DS, and after template search, model construction and CDR region optimization, a STAT3 antibody model with a DOPE score of -24077 is finally constructed with a similarity of 96.1%.

[0067] Preferably, the heavy chain plasmid vector in step (9) is the pcDNA3.4-mG2b vector.

[0068] Preferably, the eukaryotic expression and purification method in step (9) includes: diluting the plasmid carrying the light and heavy chain expression vector of the mutant target fragment and PEI with diluent and adding them to CHOS cell suspension for transfection culture, wherein the mass ratio of the light and heavy chain plasmids is 3:2. After culturing for 18-24 hours, the cells are fed, and after 96-120 hours, the cell supernatant is collected by centrifugation. The supernatant is then filtered through a filter membrane, and the supernatant is passed through a Protein A / G affinity chromatography column 3-4 times. Finally, the cells are eluted with glycine hydrochloric acid solution, and the eluent is collected to obtain the purified modified STAT3 recombinant antibody.

[0069] Example 1

[0070] This invention provides a computational biology-based method for maturation of STAT3 hybridoma-derived antibodies affinity. Specifically, the method for obtaining the target fragment in the variable region is as follows:

[0071] RNA was extracted from hybridoma cells using the Trizol method, and the integrity of the RNA was examined by agarose gel electrophoresis. The electrophoresis results are shown in the figure below. Figure 2 The results showed clear 28S and 18S bands, indicating good RNA integrity. Subsequently, cDNA was synthesized using RNA as a template via reverse transcription, and the antibody variable region gene was amplified using Taq DNAase with the cDNA as a template. Three upstream and downstream primers for the light / heavy chains were mixed in a specific ratio to amplify the complete light / heavy chain gene sequence. The amplified products were examined for gene fragment length by agarose gel electrophoresis; the electrophoresis results are shown below. Figure 3 The results showed that the VH gene fragment was approximately 350-400 bp in length, and the VL gene fragment was approximately 350 bp in length, consistent with the length of the target fragment.

[0072] Example 2

[0073] This invention provides a computational biology-based method for STAT3 hybridoma-derived antibody affinity maturation. Specifically, the T-load ligation method and sequence analysis of the target fragment in the variable region are as follows:

[0074] The VH and VL genes were ligated into the pGEM-T vector, respectively, and transformed into *E. coli*. Several single clones from each ligation were selected, and colony PCR was performed using universal primers. The colony PCR results of the variable region gene T clone amplification were examined by agarose gel electrophoresis. (See figure below for electrophoresis results.) Figure 4 The results showed that the target fragment was approximately 500 bp in size, and the bright-banded positive clones were sent to Kexin Biotechnology Co., Ltd. for sequencing. Functional analysis of the light and heavy chain sequences was performed using the IMGT / QUEST online analysis website. The results are shown below. Figure 4 Based on the analysis results, one light chain and one heavy chain were finally determined.

[0075] Example 3

[0076] This invention provides a computational biology-based method for maturation of STAT3 hybridoma-derived antibody affinity. Specifically, the amplification method, plasmid construction, and expression mode of the target fragment in the variable region are as follows:

[0077] Using the aforementioned T-clone colonies as templates, two fragments, VH1 and VK1, were amplified using designed specific primers. The gene fragment results are shown in Table 1. Figure 5 Subsequently, using the mouse heavy chain constant region vector pcDNA3.4-mG2b and the mouse light chain constant region vector pcDNA3.4-mk as backbones, the amplification products of the mouse antibody heavy chain and light chain variable regions were recovered from the gel and ligated separately. Several single clones were picked from each ligation, and colony PCR was performed using universal primers. The plasmid construction colony PCR identification was checked by agarose gel electrophoresis. The electrophoresis results are shown in the figure below. Figure 6The results showed that the target fragment was approximately 500 bp in size, and the bright-banded positive clones were sent to Qingke Biotechnology Co., Ltd. for sequencing. The sequencing results are shown below. Figure 7 The results showed that the peak shape was single, clear, and distinct.

[0078] Table 1

[0079] Forward primer Reverse primer VH1 CTGGTCAAGCTGCAGGAGTC TGAGGAGACTGTGAGAGTGGT VK1 GACATTCAGCTGACCCA TTTGATCTCCAGCTTGGT

[0080] Example 4

[0081] This invention provides a computational biology-based method for maturing the affinity of STAT3 hybridoma-derived antibodies. Specifically, the methods for constructing STAT3 antigen models and STAT3 antibody models are as follows:

[0082] Using the Uniport website to search for antigen information, protein structure resolution from the UniProt website was obtained. The structure of the STAT3 protein is 6NJS. After importing it into the software DS, and performing operations such as removing the proligand and water, and adding amino acid residues and hydrogen atoms, an antigen model for molecular docking was finally obtained. The model results are shown below. Figure 8 The theoretical basis of homology modeling is that the conservation of protein tertiary structure far exceeds the conservation of primary sequence. Therefore, one can construct the spatial structure of an unknown protein by using one or more proteins with known structures. A known STAT3 hybridoma antibody sequence was input into the software DS. After template search, model construction, and CDR region optimization, a STAT3 antibody model with a DOPE score of -24077 was finally constructed with 96.1% similarity. The model results are shown below. Figure 9 And perform Laplace chart evaluation; the evaluation results are shown in [link to Laplace chart]. Figure 10 The results showed that the permitted area accounted for more than 90%, and the antibody model was well established.

[0083] Example 5

[0084] This invention provides a computational biology-based method for maturation of STAT3 hybridoma-derived antibodies affinity. Specifically, the antigen-antibody analysis docking and optimization method is as follows:

[0085] Molecular docking is a technique that simulates the interaction between ligands and receptors based on the "lock-and-key principle". Before performing molecular docking in Discovery Studio (DS), we need to perform the following operations: (1) Prepare the receptor: STAT3 antigen model; (2) Prepare the ligand: STAT3 antibody model.

[0086] Docking can be categorized based on whether conformational changes occur: rigid docking (no conformational change), semi-flexible docking (partial conformational change), and flexible docking (complete conformational change). Among these molecular docking methods, geometry-based rigid docking is suitable for antigen-antibody docking, where the protein interface exhibits shape complementarity. Rigid docking restricts the binding region to the CDR region and three surrounding amino acids, with a sampling angle of 6, resulting in 54,000 docking models. Using the mean ZRank score (-23.64) as a cutoff, 1096 models were selected for flexible optimization, ultimately yielding the top 17 families of three models. The model results are shown in [link to results]. Figure 11 .

[0087] Example 6

[0088] This invention provides a computational biology-based method for maturation of STAT3 hybridoma-derived antibodies affinity. Specifically, the docking model analysis is as follows:

[0089] Binding site analysis was performed on the three poses mentioned above, and alanine scanning was performed on the CDR region and binding sites within 3A of the surrounding area. Potential saturation mutation sites for each pose were initially screened, and the results are shown in Table 2.

[0090] Table 2

[0091] POSE3 pose123 pose857 H:ASN36>ALA H:ASN66>ALA H:ASN36>ALA H:ASN66>ALA H:ASP116>ALA H:ASN66>ALA H:ASP116>ALA H:GLU55>ALA H:ASP116>ALA H:GLU55>ALA H:LEU1>ALA H:GLU55>ALA H:GLY27>ALA H:LYS3>ALA H:LEU1>ALA H:LEU1>ALA H:TRP38>ALA H:LYS3>ALA H:LYS3>ALA H:TYR114>ALA H:THR109>ALA H:PRO108>ALA H:TYR37>ALA H:TRP38>ALA H:TRP38>ALA L:ASN28>ALA H:TYR114>ALA H:TYR114>ALA L:ASN56>ALA H:TYR37>ALA H:TYR37>ALA L:GLU69>ALA L:ASN56>ALA L:GLU69>ALA L:HIS107>ALA L:GLU69>ALA L:GLY70>ALA L:THR114>ALA L:HIS107>ALA L:THR66>ALA L:THR66>ALA L:LEU116>ALA L:TYR108>ALA L:TYR108>ALA L:THR66>ALA L:TYR36>ALA L:TYR36>ALA L:TYR108>ALA L:TYR38>ALA L:TYR38>ALA L:TYR36>ALA L:TYR38>ALA

[0092] Example 7

[0093] This invention provides a computational biology-based method for maturation of STAT3 hybridoma-derived antibodies affinity. Specifically, the saturation mutation method for model sites under different pH conditions is as follows:

[0094] Three docking postures were subjected to saturation mutagenesis considering pH conditions. The results of the model point mutation energy variation trend under different pH environments are shown in […]. Figure 12 The results showed that mutations with a mutation energy less than -0.5 were considered meaningful. Mutation sites and types for three docking poses were also obtained, and the potential site saturation mutation results for each pose are shown in Table 3.

[0095] Table 3

[0096]

[0097]

[0098]

[0099] Example 8

[0100] This invention provides a computational biology-based method for maturation of STAT3 hybridoma-derived antibodies affinity. Specifically, the mutation site analysis and determination are as follows:

[0101] Incorporating saturation mutagenesis results into docking posture analysis to examine specific changes in intermolecular interactions allows for the selection of potential mutation sites. For example, using POSE3 as an example, single-point mutations of the corresponding amino acids reveal a slight increase in affinity due to pi accumulation and a decrease in affinity due to the different hydrophilicity / hydrophobicity of Tyr with multiple amino acids of the antigen. These mutations are ultimately not considered for subsequent mutation site selection. The interaction force change analysis diagram is shown below. Figure 13 The three docking posture saturation mutation sites obtained were all included in the example to obtain the final four mutation sites and types. The final mutation site and type results are shown in Table 4.

[0102] Table 4

[0103] Mutation sites and types Changes in force CDR Area M1(H:TYR28SER) Increased hydrogen bonds It is (CDR3) M2(H:ASN36PHE) Increased number of PI bonds It is (CDR3) M3(H:ASN66TYR) Pi key increased no M4(H:ASN66TRP) Increased Pi bonds and hydrogen bonds no

[0104] Example 9

[0105] This invention provides a computational biology-based method for maturation of STAT3 hybridoma-derived antibodies, specifically, the method for amplifying the target fragment containing the mutation site is as follows:

[0106] Based on the four identified mutation sites and types, primers were designed using the overlap extension PCR method. A schematic diagram of the protocol is shown below. Figure 14 The primer design for the mutation site is shown in Table 5. The target fragment containing the mutation site was ultimately amplified, and the amplification results are shown in Table 5. Figure 15 .

[0107] Table 5

[0108]

[0109] Example 10

[0110] This invention provides a computational biology-based method for maturation of STAT3 hybridoma-derived antibodies' affinity. Specifically, the method for constructing and expressing plasmids containing target fragments with mutation sites is as follows:

[0111] The target fragment containing the mutation site and the vector pcDNA3.4-mG2b containing the mouse heavy chain constant region were ligated and transformed into competent cells. Several single clones were selected from each ligation and colony PCR was performed using universal primers. The colony PCR identification of the single clones was checked by agarose gel electrophoresis. The electrophoresis results are shown below. Figure 16 The results showed that the target fragment was approximately 500 bp in size, and the bright-banded positive clones were sent to Qingke Biotechnology Co., Ltd. for sequencing. The sequencing analysis results are shown in the figure below. Figure 17The results showed a single, clear, and well-defined peak shape. CHO-S cells were co-transfected with the correctly sequenced heavy chain plasmid and the unmutated light chain plasmid at a certain ratio. After 96-120 hours, the cell culture supernatant was collected for protein HP affinity chromatography purification. Agarose gel electrophoresis was then used to examine the eukaryotic expression and purification of the modified STAT3 antibody. The electrophoresis results are shown below. Figure 18 .

[0112] Example 11

[0113] This invention provides a computational biology-based method for maturing the affinity of STAT3 hybridoma-derived antibodies. Specifically, the method for determining the relative affinity of antibodies is as follows:

[0114] The relative affinity of the modified STAT3 recombinant antibody was analyzed using an indirect ELISA method, and then measured by OD. 450 Using nm as the ordinate and the logarithm of the antibody concentration as the abscissa, two curves were fitted using Origin 8.5 software to calculate the antibody concentration (mol / L) corresponding to ODmax / 2. Then, the antibody affinity constant Ka was calculated using Ka = (n-1) / 2 * (n[Ab]1 - [Ab]2). The titer chart for affinity assays of unmodified STAT3 antibodies is shown below. Figure 19 The titer of antibody affinity assay after STAT3 modification is shown in the figure. Figure 20 The results showed that the binding constant of the unmodified STAT3 antibody reached 3.45*10. 8 The antibody binding constant after STAT3 modification can reach as high as 9.11*10. 8 The antibody affinity is about three times higher than that of the unmodified antibody, indicating that the antibody affinity maturation method of the present invention has the technical effect of improving antibody affinity.

[0115] Finally, it should be emphasized that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A computational biology-based method for maturing the affinity of STAT3 hybridoma-derived antibodies, characterized in that, Includes the following steps: (1) RNA was extracted from STAT3-based hybridoma cells and cDNA was synthesized by reverse transcription. Using cDNA as a template, the antibody variable region gene was amplified using Taq DNA enzyme to obtain VH1 and VK1 fragments. (2) The STAT3 protein structure 6NJS with a protein structure resolution of 2.7 Å was obtained by using the UniProt website, and imported into Discovery Studio software. After removing the original ligand and water, and adding amino acid residues and hydrogen atoms, a STAT3 antigen model for molecular docking was obtained. (3) The known STAT3 hybridoma antibody sequence was input into Discovery Studio software. After template search, model construction and CDR region optimization, a STAT3 antibody model with a DOPE score of -24077 was constructed with a similarity of 96.1%, and Laplacian plot evaluation was performed. (4) Molecular docking and flexibility optimization were performed on the STAT3 antigen model and the STAT3 antibody model to screen out three models, namely POSE3, POSE123 and POSE857; (5) Binding site analysis was performed on the three models, and alanine scans were performed on the CDR region containing the surrounding 3 Å to obtain the potential saturation mutation sites for each model; (6) Perform saturation mutations on the potential saturation mutation sites under pH conditions to obtain the potential site saturation mutation results for each model; (7) Analyze the changes in intermolecular forces in the saturation mutation results to determine the final mutation sites and types. The final mutation sites include H:TYR28SER, H:ASN36PHE, H:ASN66TYR and H:ASN66TRP. (8) Design primers based on the final mutation site and amplify the target fragment containing the mutation site using the overlap extension PCR method; (9) The target fragment containing the mutation site was ligated into the heavy chain plasmid vector pcDNA3.4-mG2b and co-transfected with the light chain constant region vector pcDNA3.4-mk into CHO-S cells for expression. The modified STAT3 recombinant antibody was obtained by Protein A / G affinity chromatography. (10) The relative affinity of the modified STAT3 recombinant antibody was determined by indirect ELISA and an affinity titer diagram was plotted.

2. The method as described in claim 1, characterized in that, In step (9), the light chain constant region vector is pcDNA3.4-mk.

3. The method as described in claim 1, characterized in that, In step (9), the specific methods for expression and purification include: The plasmids carrying the light and heavy chain expression vectors containing the target fragment with the mutation site and the PEI transfection reagent were diluted separately and added to CHO-S cell suspension for transfection culture, wherein the mass ratio of the light and heavy chain plasmids was 3:

2. After culturing for 18-24 hours, add nutrients and continue culturing for 96-120 hours. Then, centrifuge and collect the cell supernatant. After filtering the supernatant, perform 3-4 Protein A / G affinity chromatography cycles and elute with glycine hydrochloric acid solution. Collect the eluent to obtain the purified modified STAT3 recombinant antibody.

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