Computational biology-based STAT3 hybridoma derived antibody affinity maturation method

Abstract

The invention relates to the technical field of biological medicine and antibody engineering, in particular to an STAT3 hybridoma derived antibody affinity maturation method based on computational biology, which comprises the following steps: (1) acquiring an antibody variable region sequence; (2) constructing an STAT3 antigen and antibody model, and evaluating the STAT3 antigen and antibody model; (3) carrying out antigen-antibody molecular docking and optimization, and screening out a model; (4) performing docking model analysis to obtain potential saturated mutation sites; (5) performing site saturation mutation to obtain a mutation result; (6) carrying out mutation site analysis to determine a final mutation site; (7) amplifying a target fragment containing a mutation site; (8) constructing a recombinant expression vector, performing eukaryotic expression, and purifying to obtain a modified STAT3 recombinant antibody; and (9) carrying out relative affinity determination.

Description

Technical Field

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

[0002] Monoclonal antibodies have become an indispensable tool for scientific research, disease diagnosis, and drug targeted therapy due to their high targeting specificity. At present, the development of antibodies mainly originates from mouse hybridoma technology and in vitro antibody library technology. Candidate antibodies are initially obtained through antigen design and antibody screening, and then antibodies with application value are finally obtained through a series of related biological property tests and functional verification. In the actual development process, there are many properties of antibodies obtained by conventional screening methods that need to be improved, such as affinity, immunogenicity, half-life, etc. Among them, antibody affinity maturation is one of the most important improvement directions.

[0003] Antibody affinity refers to the binding force between an antibody and an antigen epitope or antigenic determinant, which is a non-covalent force, including ionic bonds, hydrogen bonds, and hydrophobic forces; having sufficient affinity for antigens at the molecular level is a basic requirement for antibodies to exert their efficacy. In antibody-based therapy, high affinity between biological targets and antibodies is also a basic requirement. The development process of protein-based therapies is mostly focused on improving and optimizing affinity. Many processes in antibody development are centered on affinity optimization, including animal immunization, hybridoma development, directed evolution, and a variety of display methods or selection platforms. However, powerful and effective affinity optimization methods in antibody drug development usually require multiple mutations of the starting protein to screen all possibilities as much as possible to produce high-affinity antibodies, which often have too much randomness and uncontrollability.

[0004] Antibody affinity maturation refers to a state of immune function that exists normally in the body. In humoral immunity, the average affinity of antibodies produced by the secondary response is higher than that of the primary immune response. This phenomenon is called antibody affinity maturation. At present, in vitro antibody affinity maturation is mostly proposed based on the understanding of the laws of in vivo antibody affinity maturation. Most of them simulate the in vivo antibody affinity maturation method, mainly using error-prone PCR method, DNA shuffling technology, CDR region recombination method, chain replacement technology, PD-1 immunotherapy, PD-1-PD-L1 immunotherapy, etc. However, the degree of antibody affinity maturation produced is not high, resulting in large individual differences and serious side effects, which may be serious adverse reactions or even life-threatening.

[0005] Discovery Studio is a computational simulation platform for comprehensive antibody drug design and optimization. It can quickly predict the current antibody-antigen binding mode at the computational simulation level, and perform virtual amino acid mutations to quickly discover the types of amino acid mutations that can potentially improve antibody affinity, guiding the rational design of antibodies to achieve the purpose of affinity maturation. The computer-guided antibody affinity maturation method mainly includes three steps: prediction model construction, model docking, and docking complex analysis. Compared with the random mutagenesis method, simplifying the in vitro affinity maturation of recombinant antibodies through machine learning models has better purposefulness and reduces the troubles caused by randomization. With the continuous deepening and optimization of deep learning and machine models, computer-guided antibody affinity modification will become systematic and concise, and will also solve a series of problems encountered in antibody drug development, such as reduced affinity after antibody humanization modification and low self-affinity of nanoantibodies.

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

[0007] In view of the deficiencies of the prior art and actual needs, the present invention provides a method for affinity maturation of STAT3 hybridoma-derived antibodies based on computational biology to improve the purposefulness of screening high-affinity antibodies and reduce the troubles caused by randomization, making antibody affinity modification systematic and simplified, while solving a series of problems encountered in antibody drug development, such as reduced affinity after antibody humanization modification and low self-affinity of nanobodies.

[0008] The above technical objectives of the present invention are achieved through the following technical solutions: A method for affinity maturation of STAT3 hybridoma-derived antibodies based on computational biology, comprising the following steps:

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

[0010] (2) Obtaining the STAT3 protein model and performing pre-docking treatments such as dehydration and hydrogenation;

[0011] (3) Establish a STAT3 antibody model based on the template library and perform a Ramachandran plot evaluation;

[0012] (4) Perform antigen-antibody molecular docking and flexibility optimization to screen out three models;

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

[0014] (6) Perform site saturation mutation to obtain 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) amplifying the target fragment containing the mutation site;

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

[0018] (10) Perform relative affinity determination and draw an affinity potency diagram.

[0019] Preferably, the antibody variable region gene amplification method in step (1) is to extract hybridoma cell RNA and reverse transcribe it into cDNA, and use the cDNA as a template to amplify the antibody variable region gene using TaqDNA enzyme.

[0020] Preferably, the variable region target fragments obtained by amplification in step (1) are VH1 fragments and VK1 fragments.

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

[0022] Preferably, the STAT3 antigen model construction method in step (2) is to use the website Uniport to search for antigen information and obtain the protein structure resolution from the UniProt website. -STAT3 protein structure 6NJS. Then it was imported into the software DS, and after removing the original ligand and water, adding amino acid residues and hydrogen atoms, the antigen model for molecular docking was finally obtained.

[0023] Preferably, the STAT3 antibody model construction method described 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, finally construct a STAT3 antibody model with a DOPE score of -24077 with a similarity of 96.1%.

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

[0025] Preferably, the eukaryotic expression and purification method described in step (9) comprises: diluting the plasmid carrying the light and heavy chain expression vector of the mutant target fragment and PEI with a diluent respectively, and adding them to a CHOS cell suspension for transfection culture, wherein the mass ratio of the plasmid containing the light and heavy chains is 3:2, and a cell suspension can be obtained. Cultivate and feed for 18-24 hours, collect the cell supernatant by centrifugation after 96 hours to 120 hours, filter the supernatant with a filter membrane, and then pass the supernatant through a Protein A / G affinity chromatography column 3-4 times, and finally elute with a glycine hydrochloride solution, collect the eluate, and obtain the purified modified STAT3 recombinant antibody.

[0026] Beneficial effects of the present invention:

[0027] The present invention successfully prepares a STAT3 recombinant antibody with high affinity. Compared with the traditional antibody affinity maturation method, it has the technical effect of improving antibody affinity, can reduce the troubles caused by randomization, and make antibody affinity modification systematic and simple. At the same time, it solves a series of problems encountered in antibody drug development, such as reduced affinity after antibody humanization modification and low self-affinity of nano antibodies. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below.

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

[0030] Figure 2 The agarose gel electrophoresis diagram of hybridoma cell RNA extracted by Trizol method and STAT3 monoclonal antibody variable region amplified gene in Example 1 of the present invention;

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

[0032] Figure 4 This is a functional analysis diagram of variable region sequences analyzed by the IMGT / QUEST website in Example 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 the agarose gel electrophoresis diagram of the colony PCR identification of the plasmid constructed in Example 1 of the present invention;

[0035] Figure 7 This is a schematic diagram of the sequencing results of positive cloned bacteria 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] Fig. 9 This is a schematic diagram of the structure of the STAT3 antibody in Example 3 of the present invention;

[0038] Fig.10 This is a diagram showing the evaluation results of the STAT3 antibody structure Ramachandran plot in Example 3 of the present invention;

[0039] Fig.11 Schematic diagram of three model structures screened after molecular docking in Example 4 of the present invention;

[0040] Fig.12 This is a trend diagram of the model point mutation energy change under different pH environments in Example 5 of the present invention;

[0041] Fig.13 This is a graph analyzing the change in the effect of mutation at site 1 of the POSE3 heavy chain in Example 7 of the present invention;

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

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

[0044] Fig.16 This is the agarose gel electrophoresis diagram of the monoclonal colony PCR in Example 9 of the present invention;

[0045] Fig.17 This is a sequencing analysis diagram of positive clones containing mutation sites in Example 9 of the present invention;

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

[0047] Fig.19 This is a titer diagram of the affinity determination of the unmodified STAT3 antibody in Example 10 of the present invention;

[0048] Fig. 20 This is a titer diagram for affinity determination of the modified M2-STAT3 antibody in Example 10 of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with 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.

[0050] The experimental methods without specific conditions specified in the examples are usually carried out according to conventional conditions or the conditions specified by the manufacturers.

[0051] The present invention aims to solve the problems of randomness, uncontrollability, individual differences, and serious side effects in the current antibody affinity maturation methods, and provides a STAT3 hybridoma-derived antibody affinity maturation method based on computational biology, which has the technical effect of improving antibody affinity, can effectively improve the purpose of screening high-affinity antibodies, and reduce the troubles caused by randomization, making antibody affinity modification systematic and simple, and at the same time solving a series of problems encountered in antibody drug development, such as reduced affinity after antibody humanization modification and low self-affinity of nano antibodies. Figure 1 As shown, the specific implementation plan is as follows:

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

[0053] (2) Obtaining the STAT3 protein model and performing pre-docking treatments such as dehydration and hydrogenation;

[0054] (3) Establish a STAT3 antibody model based on the template library and perform a Ramachandran plot evaluation;

[0055] (4) Perform antigen-antibody molecular docking and flexibility optimization to screen out three models;

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

[0057] (6) Perform site saturation mutation to obtain 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) amplifying the target fragment containing the mutation site;

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

[0061] (10) Perform relative affinity determination and draw an affinity potency diagram.

[0062] Preferably, the antibody variable region gene amplification method in step (1) is to extract hybridoma cell RNA and reverse transcribe it into cDNA, and use the cDNA as a template to amplify the antibody variable region gene using TaqDNA enzyme.

[0063] Preferably, the variable region target fragments obtained by amplification in step (1) are VH1 fragments and VK1 fragments.

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

[0065] Preferably, the STAT3 antigen model construction method in step (2) is to use the website Uniport to search for antigen information and obtain the protein structure resolution from the UniProt website. -STAT3 protein structure 6NJS. Then it was imported into the software DS, and after removing the original ligand and water, adding amino acid residues and hydrogen atoms, the antigen model for molecular docking was finally obtained.

[0066] Preferably, the STAT3 antibody model construction method described 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, finally construct a STAT3 antibody model with a DOPE score of -24077 with a similarity of 96.1%.

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

[0068] Preferably, the eukaryotic expression and purification method described in step (9) comprises: diluting the plasmid carrying the light and heavy chain expression vector of the mutant target fragment and PEI with a diluent respectively, and adding them to a CHOS cell suspension for transfection culture, wherein the mass ratio of the plasmid containing the light and heavy chains is 3:2. Cultivate and feed for 18-24 hours, collect the cell supernatant by centrifugation after 96 hours to 120 hours, filter the supernatant with a filter membrane, and then pass the supernatant through a Protein A / G affinity chromatography column 3-4 times, and finally elute with a glycine hydrochloride solution, collect the eluate, and obtain the purified modified STAT3 recombinant antibody.

[0069] Example 1

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

[0071] The hybridoma cell RNA was extracted by Trizol method and the integrity of RNA was checked by agarose gel electrophoresis. The electrophoresis results are shown in Figure 2 The results showed clear 28S and 18S bands, indicating that the RNA integrity was good. Then, RNA was used as a template for reverse transcription to synthesize cDNA, and cDNA was used as a template to amplify the antibody variable region gene using TaqDNA enzyme. The three light / heavy chain upstream and downstream primers were mixed in a certain ratio to amplify the full set of light / heavy chain genes. The amplified product was checked by agarose gel electrophoresis to check the length of the amplified gene fragment. The electrophoresis results are shown in Figure 3 ,The results showed that the length of the VH gene fragment was approximately 350-400bp, and the length of the VL gene fragment was approximately 350bp, ,which was consistent with the target fragment length.

[0072] Example 2

[0073] The present invention provides a method for affinity maturation of STAT3 hybridoma-derived antibodies based on computational biology. Specifically, the T-linking method and sequence analysis of the variable region target fragment are as follows:

[0074] The VH gene and VL gene were connected to the pGEM-T vector respectively and transformed into E. coli. Several single clones were selected from each connection, and colony PCR was performed using universal primers. The colony PCR of variable region gene T clone amplification was checked by agarose gel electrophoresis. The electrophoresis results are shown in the figure Figure 4 The results showed that the target fragment size was about 500 bp, and the bright positive clones were sent to Qingke Biotechnology Company for sequencing. The light and heavy chain sequences were functionally analyzed using the IMGT / QUEST online analysis website. The analysis results are shown in Figure 4 , and based on the analysis results, finally determine one light chain and one heavy chain.

[0075] Example 3

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

[0077] Using the above T clone colony as a template, the designed specific primers were used to amplify the two fragments of VH1 and VK1. The gene fragment results are shown in Table 1. Figure 5 Then, the amplified products of the heavy chain and light chain variable regions of the above mouse antibodies were recovered by gel recovery and connected separately using the vector pcDNA3.4-mG2b containing the mouse heavy chain constant region and the vector pcDNA3.4-mk containing the mouse light chain constant region as the backbone. Several single clones were selected from each connection, and colony PCR was performed with universal primers. The plasmid construction colony PCR identification was checked by agarose gel electrophoresis. The electrophoresis results are shown in the figure. Figure 6The results showed that the target fragment size was about 500 bp, and the bright positive clones were sent to Qingke Biotechnology Company for sequencing. The sequencing results are shown in Figure 7 The results show that the peak shape is single, clear and unambiguous.

[0078] Table 1

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

[0080] Example 4

[0081] The present invention provides a method for affinity maturation of STAT3 hybridoma-derived antibodies based on computational biology. Specifically, the methods for constructing a STAT3 antigen model and a STAT3 antibody model are as follows:

[0082] The antigen information was searched using the Uniport website, and the protein structure resolution from the UniProt website was obtained. -STAT3 protein structure 6NJS. Then it was imported into the software DS. After removing the original ligand and water, adding amino acid residues and hydrogen atoms, the antigen model for molecular docking was finally obtained. The model results are shown in Figure 8 The theoretical basis of homology modeling is that the conservation of protein tertiary structure far exceeds the conservation of primary sequence. Therefore, people can construct the spatial structure of unknown structural proteins by using one or more proteins with known structures. The 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 a similarity of 96.1%. The model results are shown in Fig. 9 , and conduct Laplace diagram evaluation. The evaluation results are shown in Fig.10 ,The results showed that the allowed area accounted for more than 90%, and the antibody model was well established.

[0083] Example 5

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

[0085] Molecular docking is a technical method that simulates the interaction between ligand and receptor based on the "lock-key principle" of ligand-receptor interaction. 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] During docking, according to whether the conformation changes, it can be divided into rigid docking without conformational changes, semi-flexible docking with partial conformational changes, and flexible docking with complete conformational changes. Among these molecular docking methods, rigid docking based on geometric shapes is suitable for antigen-antibody docking, and the interface region of the protein shows shape complementarity. During rigid docking, the binding region is restricted to the CDR region and the three surrounding amino acids, and the sampling angle is 6, resulting in 54,000 docking models. Using the mean ZRank score (-23.64) as the cutoff value, 1,096 models were screened out for flexibility optimization, and finally three models of the top 17 families were obtained. The model results are shown in Fig.11 .

[0087] Example 6

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

[0089] Binding site analysis was performed on the above three poses, and alanine scanning was performed on the CDR region and binding site within the surrounding 3A. The potential saturation mutation sites of each pose can be obtained through preliminary screening. The preliminary screening 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] The present invention provides a method for affinity maturation of STAT3 hybridoma-derived antibodies based on computational biology. Specifically, the saturation mutation method of the model site under different pH environments is as follows:

[0094] The three docking postures were subjected to saturation mutation under the condition of considering pH. The trend of the energy change of the model point mutation under different pH environments is shown in Fig.12 The results show that when the mutation energy is less than -0.5, it is considered a meaningful mutation. At the same time, the mutation sites and types of the three docking poses are obtained, and the potential site saturation mutation results of each pose are shown in Table 3.

[0095] Table 3

[0096]

[0097]

[0098]

[0099] Example 8

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

[0101] The saturation mutation results were incorporated into the docking posture to analyze the specific changes in the intermolecular interaction force, and potential mutation sites could be further selected. For example, taking POSE3 as an example, the corresponding single-point mutations of the amino acids were performed. Analysis showed that there was a slight increase in affinity for pi stacking and a weakening of affinity caused by the different hydrophilicity between Tyr and multiple amino acids in the antigen. In the end, the subsequent mutation sites were not considered. The analysis of the force change is shown in the figure. Fig.13 The three obtained docking pose saturation mutation sites were 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 site and type Force changes CDR region M1(H:TYR28SER) Increased hydrogen bonding Yes (CDR3) M2(H:ASN36PHE) More PI keys Yes (CDR3) M3(H:ASN66TYR) More Pi bonds no M4(H:ASN66TRP) More Pi bonds and hydrogen bonds no

[0104] Example 9

[0105] The present invention provides a method for affinity maturation of STAT3 hybridoma-derived antibodies based on computational biology. Specifically, the method for amplifying a target fragment containing a mutation site is as follows:

[0106] According to the four mutation sites and types determined above, primers were designed according to the overlap extension PCR method. The schematic diagram of the scheme is shown in Fig.14 The primer design of the mutation site is shown in Table 5. Finally, the target fragment containing the mutation site can be amplified. The amplification status is shown in Fig.15 .

[0107] Table 5

[0108]

[0109] Example 10

[0110] The present invention provides a method for affinity maturation of STAT3 hybridoma-derived antibodies based on computational biology. Specifically, the plasmid construction and expression method of the target fragment containing the mutation site are as follows:

[0111] The target fragment containing the mutation site and the vector pcDNA3.4-mG2b containing the mouse heavy chain constant region were connected and transformed into competent cells. Several single clones were selected from each connection, and colony PCR was performed using universal primers. The colony PCR identification of the single clone was checked by agarose gel electrophoresis. The electrophoresis results are shown in Fig.16 The results showed that the target fragment size was about 500 bp, and the bright positive clones were sent to Qingke Biotechnology Company for sequencing. The sequencing analysis is shown in Fig.17The results showed that the peak graph was single, clear and unambiguous. The correctly sequenced heavy chain plasmid and the unmutated light chain plasmid were co-transfected into CHO-S cells in a certain ratio. After 96h-120h, the cultured cell supernatant was collected for protein HP affinity chromatography purification, and the eukaryotic expression and purification of the modified STAT3 antibody were checked by agarose gel electrophoresis. The electrophoresis results are shown in Fig.18 .

[0112] Embodiment 11

[0113] The present invention provides a method for affinity maturation of STAT3 hybridoma-derived antibodies based on computational biology. 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 by indirect ELISA method, and then the OD 450 nm is the ordinate, and the logarithm of the antibody concentration is the abscissa. The two curves were fitted using the software Origin 8.5 to calculate the antibody concentration (mol / L) corresponding to ODmax / 2. The affinity constant Ka of the antibody was then calculated according to Ka=(n-1) / 2*(n[Ab]1-[Ab]2). The titer of the affinity determination of the unmodified STAT3 antibody is shown in the figure. Fig.19 , STAT3 modified antibody affinity determination titer chart see Fig. 20 The results showed that the binding constant of the unmodified STAT3 antibody could reach 3.45*10 8 , and the antibody binding constant after STAT3 modification can be as high as 9.11*10 8 The affinity of the antibody was increased by about three times compared with 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 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 may have various changes and modifications. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A computational biology-based method for affinity maturation of STAT3 hybridoma-derived antibodies, comprising the following steps: (1) Obtaining antibody variable region sequences based on STAT3 hybridoma cells; (2) Obtaining the STAT3 protein model and performing pre-docking processing of dehydration and hydrogenation; (3) Establish a STAT3 antibody model based on the template library and perform a Ramachandran plot evaluation; (4) Perform antigen-antibody molecular docking and flexibility optimization to screen out three models; (5) performing docking model analysis to obtain amino acid sites for saturation mutation; (6) Perform site saturation mutation to obtain 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 site and type; (8) amplifying the target fragment containing the mutation site; (9) constructing a heavy chain plasmid vector, connecting the target fragment containing the mutation site to the plasmid, and transfecting eukaryotic cells for expression and purification to obtain the modified STAT3 recombinant antibody; (10) Perform relative affinity determination and draw an affinity potency diagram.

2. The method for affinity maturation of STAT3 hybridoma-derived antibodies based on computational biology according to claim 1, characterized in that: In step (1), the antibody variable region gene is amplified by extracting hybridoma cell RNA and reversely transcribing it into cDNA, and using the cDNA as a template to amplify the antibody variable region gene using TaqDNA enzyme.

3. The method for affinity maturation of STAT3 hybridoma-derived antibodies based on computational biology according to claim 1, characterized in that: The variable region target fragments amplified in step (1) are VH1 fragment and VK1 fragment.

4. The method for affinity maturation of STAT3 hybridoma-derived antibodies based on computational biology according to claim 1, characterized in that: The plasmid vectors of step (1) are heavy chain constant region vector pcDNA3.4-mG2b and light chain constant region vector pcDNA3.4-mk.

5. The method for affinity maturation of STAT3 hybridoma-derived antibodies based on computational biology according to claim 1, characterized in that: The STAT3 antigen model in step (2) was constructed by searching for antigen information using the Uniport website and obtaining protein structure information with a resolution of -STAT3 protein structure 6NJS; then it was imported into the software DS, and after removing the original ligand and water, supplementing amino acid residues and hydrogen atoms, the antigen model for molecular docking was finally obtained.

6. The method for affinity maturation of STAT3 hybridoma-derived antibodies based on computational biology according to claim 1, characterized in that: The STAT3 antibody model of step (3) was constructed by inputting 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 was finally constructed with a similarity of 96.1%.

7. The method for affinity maturation of STAT3 hybridoma-derived antibodies based on computational biology according to claim 1, characterized in that: The heavy chain plasmid vector described in step (9) is a pcDNA3.4-mG2b vector.

8. The method for affinity maturation of STAT3 hybridoma-derived antibodies based on computational biology according to claim 1, characterized in that: The eukaryotic expression and purification method in step (9) comprises: diluting the plasmid carrying the light and heavy chain expression vector of the mutant target fragment and PEI with a diluent respectively, and adding them to a CHOS cell suspension for transfection culture, wherein the mass ratio of the plasmid containing the light and heavy chains is 3:2, and a cell suspension can be obtained; feeding after culturing for 18-24 hours, collecting the cell supernatant by centrifugation after 96 hours to 120 hours, filtering the supernatant with a filter membrane, and then passing the supernatant through a Protein A / G affinity chromatography column 3-4 times, and finally eluting with a glycine hydrochloride solution, collecting the eluate, and obtaining a purified modified STAT3 recombinant antibody.

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