Antibody de novo design method, device, equipment and storage medium

Abstract

The present application relates to a method, device, equipment and storage medium for de novo antibody design. The method includes: obtaining the structure and epitope residue sequence of the target antigen; generating a library of related antibodies that can potentially bind to the target antigen epitope based on the structure and epitope residue sequence of the target antigen; predicting the affinity of the related antibodies to the target antigen epitope based on the structure and epitope residue sequence of the target antigen, as well as the structure and sequence of the related antibodies in the related antibody library, and screening out candidate antibodies; selecting a corresponding pretreatment method to process the candidate antibodies according to the accuracy of the preset model to obtain experimental antibody molecules and sequences; verifying whether the experimental antibody molecules can bind to the target antigen epitope residues, and screening out experimental antibody molecules that can bind to the target antigen epitope residues as target antibodies. The scheme provided by the present application can achieve antibody design for a specified antigen epitope without animal immunization.

Description

Technical Field

[0001] The present application relates to the fields of artificial intelligence technology and computational structural biology technology, and in particular to methods, devices, equipment and storage media for de novo antibody design. Background Art

[0002] As a type of immunoglobulin, antibodies are composed of variable regions and constant regions. The structure of the variable region determines the specificity of binding. After detecting antigens (such as foreign pathogens), the human immune system can produce antibodies that can specifically bind to antigens, and achieve precision treatment by lysing cells or guiding phagocytes to engulf specific antigens.

[0003] The traditional method of antibody design is to immunize animals and isolate the obtained antibodies. However, due to the insufficient immunogenicity of the antigen and epitope itself, or the instability of the antigen protein itself and the difficulty in expressing it, it will lead to the problem that antibodies cannot be obtained through animal immunization, or antibodies against specific antigen epitopes cannot be obtained. Summary of the invention

[0004] In order to solve or partially solve the problems existing in the related art, the present application provides a method, device, equipment and storage medium for de novo antibody design, which can achieve antibody design for specified antigen epitopes without animal immunization.

[0005] The first aspect of the present application provides a method for de novo antibody design, comprising:

[0006] Obtain the structure and epitope residue sequence of the target antigen;

[0007] According to the structure and epitope residue sequence of the target antigen, generating a library of related antibodies that can potentially bind to the epitope of the target antigen, wherein the library of related antibodies comprises the structure and sequence of related antibodies;

[0008] According to the structure and epitope residue sequence of the target antigen, and the structure and sequence of the related antibodies in the related antibody library, the affinity of the related antibodies to the target antigen epitope is predicted based on a preset model, and candidate antibodies that meet the preset affinity conditions are screened out;

[0009] According to the accuracy of the preset model, a corresponding preprocessing method is selected to process the humanized antibody to obtain an experimental antibody molecule and sequence;

[0010] Verify whether the experimental antibody molecule can bind to the target antigen epitope residue, and screen out the experimental antibody molecule that can bind to the target antigen epitope residue as the target antibody.

[0011] As an optional embodiment, the method predicts the affinity of the relevant antibody to the target antigen epitope based on a preset model according to the structure and epitope residue sequence of the target antigen, and the structure and sequence of the relevant antibody in the relevant antibody library, and screens out candidate antibodies that meet the preset affinity conditions, including:

[0012] According to the structure and epitope residue sequence of the target antigen, and the structure and sequence of the related antibodies in the related antibody library, affinity prediction is performed on the sequence of the related antibody and the epitope residue sequence of the target antigen, and the structure of the related antibody and the structure of the target antigen based on a preset model to obtain the binding probability of the related antibody and the target antigen epitope;

[0013] From the related antibodies whose binding probability is a preset probability, antibodies with a preset proportion are screened out as candidate antibodies.

[0014] As an alternative embodiment:

[0015] The candidate antibodies are subjected to humanization screening to obtain humanized antibodies, and a humanized antibody library is formed.

[0016] As an optional embodiment, the step of performing humanization screening on the candidate antibodies to obtain humanized antibodies and form a humanized antibody library comprises:

[0017] Performing humanization screening on the candidate antibodies, eliminating antibodies composed of non-human parts that are immunogenic, and retaining antibodies composed of human parts that are non-immunogenic;

[0018] The non-immunogenic antibodies composed of human parts are regarded as humanized antibodies, and a humanized antibody library is formed.

[0019] As an optional embodiment, according to the accuracy of the preset model, a corresponding preprocessing method is selected to process the humanized antibody to obtain the experimental antibody molecule and sequence, including:

[0020] If the accuracy of the preset model is high, a first preprocessing method is used to process a first preset number of humanized antibodies to obtain experimental antibody molecules and sequences;

[0021] If the accuracy of the preset model is low, a second preprocessing method is used to process the humanized antibodies in the humanized antibody library, and a second preset number of experimental antibody molecules and sequences are screened from the processed humanized antibodies.

[0022] As an optional embodiment, the first pretreatment method includes recombinant expression; and / or the second pretreatment method includes:

[0023] Dividing the humanized antibody into multiple fragments, connecting the fragments by molecular biological means, and constructing the complete antibody sequence into a vector to form a library for display screening;

[0024] The complete antibody sequence is screened in the library to obtain the processed humanized antibody.

[0025] As an alternative embodiment:

[0026] If the affinity of the candidate antibody does not reach the preset threshold, the candidate antibody is subjected to affinity maturation treatment to obtain a mature antibody whose affinity reaches the preset threshold.

[0027] The second aspect of the present application provides a device for de novo antibody design, comprising:

[0028] An acquisition module, used to obtain the structure and epitope residue sequence of the target antigen;

[0029] An antibody generation module, used to generate a library of related antibodies that can potentially bind to the target antigen epitope according to the structure and epitope residue sequence of the target antigen, wherein the library of related antibodies contains the structure and sequence of related antibodies;

[0030] An affinity prediction module, used to predict the affinity of the relevant antibody to the target antigen epitope based on a preset model according to the structure and epitope residue sequence of the target antigen, as well as the structure and sequence of the relevant antibodies in the relevant antibody library, and screen out candidate antibodies that meet the preset affinity conditions;

[0031] A preprocessing module is used to select a corresponding preprocessing method to process the humanized antibody according to the accuracy of the preset model to obtain an experimental antibody molecule and sequence. The experimental antibody molecule can be used as an experimental object. The experiment is used to verify whether the experimental antibody molecule can bind to the target antigen epitope residues, and screen out the experimental antibody molecule that can bind to the target antigen epitope residues as the target antibody.

[0032] A third aspect of the present application provides an electronic device, including:

[0033] Processor; and

[0034] The memory stores executable codes thereon, and when the executable codes are executed by the processor, the processor is caused to execute the method as described above.

[0035] A fourth aspect of the present application provides a computer-readable storage medium having executable code stored thereon. When the executable code is executed by a processor of an electronic device, the processor is caused to execute the method as described above.

[0036] The technical solution provided by the present application may include the following beneficial effects: the present application embodiment designs a computational process of a high-affinity candidate antibody that can bind to the target antigen epitope specified by the user from scratch, and screens and verifies the computationally designed candidate antibodies through verification experiments, and finally obtains a target antibody that can bind to the target antigen epitope. Therefore, the target antibody computationally designed by the present application embodiment targets the epitope related to the biological function specified by the user, the computational design process is precise and controllable, and the target antibody obtained by design can be used in different fields such as the treatment of different diseases, as a reagent consumable in life sciences, and the development of diagnostic reagents.

[0037] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and other objects, features and advantages of the present application will become more apparent by describing in more detail exemplary embodiments of the present application in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of the present application.

[0039] Figure 1 Schematic diagram of the process of antibody de novo design method shown in the examples of the present application;

[0040] Figure 2 is another schematic diagram of the process of de novo antibody design method shown in the examples of the present application;

[0041] Figure 3 It is a framework diagram of the antibody de novo design method shown in the examples of the present application;

[0042] Figure 4 is a schematic diagram of the structure of an antibody de novo design device shown in an embodiment of the present application;

[0043] Figure 5 It is a schematic diagram of the structure of an electronic device shown in an embodiment of the present application. DETAILED DESCRIPTION

[0044] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0045] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0046] It should be understood that although the terms "first", "second", "third", etc. may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0047] The traditional method of antibody design is to immunize animals and isolate the obtained antibodies. However, due to the insufficient immunogenicity of the antigen and epitope itself, or the instability of the antigen protein itself and the difficulty in expressing it, it will lead to the problem that antibodies cannot be obtained through animal immunization, or antibodies against specific antigen epitopes cannot be obtained.

[0048] In response to the above problems, the embodiments of the present application provide a method for de novo antibody design, which can achieve antibody design against specified antigen epitopes without animal immunization.

[0049] The technical solution of the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0050] Figure 1 Schematic diagram of the process of antibody de novo design shown in the examples of the present application.

[0051] See also Figure 1 , the present application embodiment provides a method for de novo antibody design, comprising:

[0052] Step S1: Obtain the structure and epitope residue sequence of the target antigen.

[0053] The target antigen in the embodiments of the present application may be a protein, and the structure of the target antigen may be a structure predicted by experimental means (such as cryo-electron microscopy, X-ray diffraction) or computational methods (such as AlphaFold, etc.).

[0054] The embodiment of the present application does not limit the storage format of the structure of the target antigen, which can be stored in pdb format or cif format. For example, the structure of the antigen is 1ZVM.pdb, and the antigen epitope residue sequence can be the epitope residue sequence on any chain or multiple chains.

[0055] The present invention obtains the sequence of the target antigen to the epitope residue level, providing a basis for subsequently obtaining antibodies against the target antigen epitope.

[0056] Step S2: Based on the structure and epitope residue sequence of the target antigen, a library of related antibodies that can potentially bind to the target antigen epitope is generated, wherein the library of related antibodies contains the structure and sequence of related antibodies.

[0057] In the embodiment of the present application, the number of related antibodies in the related antibody library is in the tens of millions, for example, 1000w level. The related antibodies are related to the target antigen epitope, so a large number of related antibodies can be generated, so that the related antibodies have diversity, and mainly the diversity of the CDR region, so that there are more related antibodies in the related antibody library that can potentially bind to the target antigen epitope. And after generating a large number of related antibody sequences, data support is provided for the next step of narrowing the scope to antibodies that bind to specific epitopes.

[0058] Step S3: According to the structure and epitope residue sequence of the target antigen, as well as the structure and sequence of the relevant antibodies in the relevant antibody library, the affinity of the relevant antibodies to the target antigen epitope is predicted based on the preset model, and candidate antibodies that meet the preset affinity conditions are screened out.

[0059] The candidate antibodies of the embodiment of the present application are antibodies that can bind to the target antigen epitope with a high probability. Step S3 can screen out candidate antibodies in the following manner: based on the structure and epitope residue sequence of the target antigen, and the structure and sequence of the relevant antibodies in the relevant antibody library, the sequence of the relevant antibody is compared with the epitope residue sequence of the target antigen, and the structure of the relevant antibody is compared with the structure of the target antigen based on a preset model to perform affinity prediction, and obtain the binding probability of the relevant antibody with the target antigen epitope; from the relevant antibodies with a binding probability of a preset probability, antibodies with a preset proportion are screened out as candidate antibodies.

[0060] The prediction model in the embodiment of the present application can be an antigen epitope-antibody affinity prediction model. The number of candidate antibodies screened is in the millions, for example, 2 million.

[0061] Step S4: According to the accuracy of the preset model, a corresponding preprocessing method is selected to process the humanized antibody to obtain the experimental antibody molecule and sequence.

[0062] The embodiment of the present application also includes the steps of performing humanization screening on candidate antibodies to obtain humanized antibodies and form a humanized antibody library.

[0063] The embodiment of the present application can verify whether the experimental antibody molecule can bind to the target antigen epitope residue through the following steps: if the accuracy of the preset model is high, a first pretreatment method is used to process a first preset number of humanized antibodies to obtain experimental antibody molecules and sequences; if the accuracy of the preset model is low, a second pretreatment method is used to process the humanized antibodies in the humanized antibody library, and a second preset number of experimental antibody molecules and sequences are screened out from the treated humanized antibodies.

[0064] Step S5: verify whether the experimental antibody molecule can bind to the target antigen epitope residue, and screen out the experimental antibody molecule that can bind to the target antigen epitope residue as the target antibody.

[0065] The embodiments of the present application can verify whether the screened experimental antibody molecules (solution or other forms) can bind to the target antigen epitope residues through wet experiments such as ELISA / BLI / SPR.

[0066] The verification can obtain the affinity of each experimental antibody molecule for the target antigen epitope residues, and can screen out dozens of target antibody sequences.

[0067] The embodiments of the present application can also verify whether the epitope to which the experimental antibody molecule binds to the target antigen is correct through methods such as CryoEM and HDX, thereby finding the target antibody that can bind to the target epitope, which is more helpful in finding out the antibody that contains the target function and obtaining the target antibody sequence that binds to the correct epitope.

[0068] The present application embodiment designs a computational process for designing a high-affinity candidate antibody that can bind to the target antigen epitope specified by the user from scratch, and screens and verifies the computationally designed candidate antibodies through verification experiments, and finally obtains a target antibody that can bind to the target antigen epitope. Therefore, the target antibody computationally designed by the present application embodiment targets the epitope related to the biological function specified by the user, the computational design process is accurate and controllable, and the target antibody obtained by design can be used in different fields such as the treatment of different diseases, as reagent consumables for life sciences, and the development of diagnostic reagents.

[0069] The embodiments of the present application are suitable for the discovery of target antibodies for difficult-to-express, low-immunogenic, and highly homologous antigens and epitopes. Unlike the antibody design method based on physical energy functions, the embodiments of the present application generate fully human antibodies, which can avoid problems such as immunogenicity and developability, and theoretically ensure better drugability. In addition, the AI ​​antibody de novo design process of the embodiments of the present application is used to design antibodies in combination with experimental verification and screening. After the computing platform matures, the cycle is much shorter than traditional antibody discovery (1 to 2 months VS 4 to 6 months). Moreover, the embodiments of the present application do not require animal immunization, which greatly reduces animal deaths and is beneficial to animal protection.

[0070] Figure 2 1 is another schematic flow chart of the method for de novo antibody design shown in the examples of the present application.

[0071] Figure 3 It is a framework diagram of the antibody de novo design method shown in the examples of the present application.

[0072] See also Figure 2 and Figure 3 , the present application embodiment provides a method for de novo antibody design, comprising:

[0073] Step S10: Obtain the structure and epitope residue sequence of the target antigen.

[0074] The target antigen in the embodiments of the present application may be a protein, and the structure of the target antigen may be a structure predicted by experimental means (such as cryo-electron microscopy, X-ray diffraction) or computational methods (such as AlphaFold, etc.).

[0075] The embodiment of the present application does not limit the storage format of the structure of the target antigen, which can be stored in pdb format or cif format. For example, the structure of the antigen is 1ZVM.pdb, and the antigen epitope residue sequence can be the epitope residue sequence on any chain or multiple chains.

[0076] Step S20: generating a library of related antibodies that can potentially bind to the target antigen epitope according to the structure and epitope residue sequence of the target antigen, wherein the related antibody library contains the structure and sequence of the related antibodies.

[0077] The number of related antibodies in the related antibody library in the embodiment of the present application is in the tens of millions, for example, 1000w level. The related antibodies are related to the target antigen epitope, so that a large number of related antibodies can be generated, so that the related antibodies have diversity, and the diversity is mainly in the CDR region, so that there are more related antibodies in the related antibody library that can potentially bind to the target antigen epitope. And after a large number of related antibody sequences are generated, data support is provided for the next step of narrowing the scope to antibodies that bind to specific epitopes. The related antibody sequence output by the embodiment of the present application can be an antibody light and heavy chain sequence (sequence format is [VH; VL], for example:

[0078] [EVQLLESGGGLVQPGGSLRLSCAVSGFTFNSFAMSWVRQAPGKGLEWVSAISGSGGGTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYFCAKDKILWFGEPVFDYWGQG TLVTVS;EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPTFGQGTKVEIK].

[0079] Since the embodiment of the present application designs a common light chain antibody, it is used in the development of polyclonal antibodies to improve the efficiency of polyclonal antibody assembly, reduce light and heavy chain mismatches, and reduce the difficulty of CMC (chemical composition production and control).

[0080] Step S30: According to the structure and epitope residue sequence of the target antigen, as well as the structure and sequence of the related antibodies in the related antibody library, affinity prediction is performed between the sequence of the related antibody and the epitope residue sequence of the target antigen, as well as between the structure of the related antibody and the structure of the target antigen based on a preset model to obtain the binding probability of the related antibody and the target antigen epitope.

[0081] The present application embodiment uses a preset model to predict the affinity of the sequence of the relevant antibody and the epitope residue sequence of the target antigen, as well as the structure of the target antigen and the structure of the relevant antibody, to obtain the binding probability of the relevant antibody and the target antigen epitope. The prediction model in the present application embodiment can be an antigen epitope-antibody affinity prediction model.

[0082] Step S31: Screening out antibodies with a preset proportion from related antibodies with a preset binding probability as candidate antibodies.

[0083] In the present embodiment, the sequences with the highest binding probability of 20%-30% can be selected as candidate antibodies with high affinity.

[0084] The candidate antibodies outputted in the present embodiment may be in the form of antibody light and heavy chain sequences + binding probability, for example:

[0085] [EVQLLESGGGLVQPGGSLRLSCAVSGFTFNSFAMSWVRQAPGKGLEWVSAISGSGGGTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYFCAKDKILWFGEPVFDYWGQGTL VTVS;EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPTFGQGTKVEIK; 0.6].

[0086] Step S40: Perform humanization screening on candidate antibodies, exclude antibodies composed of non-human parts that are immunogenic, and retain antibodies composed of human parts that are non-immunogenic.

[0087] The antibodies obtained by humanization screening in the embodiments of the present application are substantially non-immunogenic, providing subsequent development feasibility and reducing the cost of antibody optimization.

[0088] Step S41: The non-immunogenic antibodies composed of human parts are used as humanized antibodies, and a humanized antibody library is formed.

[0089] The number of humanized antibodies in the humanized antibody library of the embodiment of the present application is in the millions, which is an order of magnitude smaller than that of related antibody libraries, greatly reducing the cost of subsequent verification experiments.

[0090] Step S42: If the accuracy of the preset model is high, a first preprocessing method is used to process a first preset number of humanized antibodies to obtain experimental antibody molecules and sequences.

[0091] If the candidate antibodies predicted by the prediction model have a high probability of actually binding to the target antibody epitope through subsequent experimental verification, the prediction model has a high accuracy. The first pretreatment method in the embodiment of the present application includes recombinant expression, and the experimental antibody molecules and sequences are obtained by recombinantly expressing humanized antibodies.

[0092] Step S43: If the accuracy of the preset model is low, a second preprocessing method is used to process the humanized antibodies in the humanized antibody library, and a second preset number of experimental antibody molecules and sequences are screened out from the processed humanized antibodies.

[0093] If the prediction model predicts that the candidate antibody has a low probability of actually binding to the target antibody epitope through subsequent experimental verification, it means that the prediction model has low accuracy. The second preprocessing method of the embodiment of the present application includes:

[0094] The humanized antibody is divided into multiple fragments, the fragments are connected by molecular biological means, and the complete antibody sequence is constructed into a vector to form a library for display and screening; the complete antibody sequence is screened in the library to obtain the processed humanized antibody.

[0095] For example, the humanized antibody framework region is gene synthesized, the CDRH3 part is synthesized into an oligo pool, and a full-length antibody library is constructed by molecular biology methods. The antibody library is displayed and screened by display technologies such as phage display / yeast display to obtain processed humanized antibodies, and then a second preset number of experimental antibody molecules and sequences are screened from the processed humanized antibodies.

[0096] Since the candidate antibodies contain a large number of negative antibodies, the presence of negative antibodies will make the subsequent verification experiment cost too high, so they need to be removed. After the second pretreatment method is used, antibodies that may bind to the target antigen are found from the humanized library to narrow the scope of subsequent experimental verification.

[0097] Step S50: verify whether the experimental antibody molecule can bind to the target antigen epitope residue, and screen out the experimental antibody molecule that can bind to the target antigen epitope residue as the target antibody.

[0098] In the present application, wet experiments such as ELISA / SPR / BLI can be used to verify whether the experimental antibody molecule can block the binding of the known antibody / ligand to the target antigen protein to verify whether the binding epitope meets the design prediction. The complex structure of the computationally designed antibody and antigen can also be obtained by cryo-electron microscopy to verify whether the binding epitope meets the design prediction.

[0099] Cell function experiments related to antigen function can also be used to detect whether the antibody has the expected biological function, and disease animal models related to antigen function can also be used to detect whether the antibody can treat the corresponding disease in the animal model.

[0100] The embodiments of the present application can also verify whether the epitope to which the experimental antibody molecule binds to the target antigen is correct through methods such as CryoEM and HDX, thereby finding the target antibody that can bind to the target epitope, and is more helpful in finding out the antibody that contains the target function, and can obtain dozens of target antibody sequences that bind to the correct epitope.

[0101] Step S60: If the affinity of the candidate antibody does not reach the preset threshold, affinity maturation is performed on the candidate antibody to obtain a mature antibody whose affinity reaches the preset threshold.

[0102] Perform affinity maturation on candidate antibodies to simulate the evolutionary process of affinity maturation of antibodies in mice, allowing for faster and more accurate affinity maturation of known candidate antibodies.

[0103] In the embodiment of the present application, affinity is characterized by binding probability, and the preset threshold may be the binding probability.

[0104] As an optional embodiment, after obtaining the mature antibody, the embodiment of the present application may further include:

[0105] The candidate antibodies are screened for humanization to exclude antibodies composed of non-human parts that are immunogenic, and retain antibodies composed of human parts that are non-immunogenic.

[0106] Antibodies composed of human parts and having no immunogenicity are referred to as humanized antibodies, and a humanized antibody library is formed.

[0107] If the accuracy of the preset model is high, the first pretreatment method is used to process the first preset number of humanized antibodies to obtain experimental antibody molecules and sequences; if the accuracy of the preset model is low, the second pretreatment method is used to process the humanized antibodies in the humanized antibody library, and the second preset number of experimental antibody molecules and sequences are screened from the processed humanized antibodies. It is verified whether the experimental antibody molecules can bind to the target antigen epitope residues, and the experimental antibody molecules that can bind to the target antigen epitope residues are screened as target antibodies.

[0108] Through the above method, target antibodies with higher affinity are screened.

[0109] Corresponding to the aforementioned application function implementation method embodiment, the present application also provides an antibody de novo design device, an electronic device and corresponding embodiments.

[0110] Figure 4 Schematic diagram of the structure of the antibody de novo design device shown in the embodiment of the present application.

[0111] See also Figure 4 , the present application embodiment provides a device for designing antibodies from scratch, comprising:

[0112] The acquisition module 40 is used to obtain the structure and epitope residue sequence of the target antigen.

[0113] The antibody generation module 41 is used to generate a library of related antibodies that can potentially bind to the target antigen epitope based on the structure and epitope residue sequence of the target antigen, and the related antibody library contains the structure and sequence of the related antibodies. The antibody generation module 41 of the embodiment of the present application is configured with a sequence- and structure-based antibody pre-training and antibody generation model.

[0114] The affinity prediction module 42 is used to predict the affinity of the relevant antibody to the target antigen epitope based on the structure and epitope residue sequence of the target antigen, as well as the structure and sequence of the relevant antibodies in the relevant antibody library, and screen out candidate antibodies that meet the preset affinity conditions. The affinity prediction module 42 of the embodiment of the present application is configured with an antigen epitope-antibody affinity prediction model.

[0115] The preprocessing module 43 is used to select a corresponding preprocessing method to process the humanized antibody according to the accuracy of the preset model to obtain an experimental antibody molecule and sequence. The experimental antibody molecule can be used as an experimental object. The experiment is used to verify whether the experimental antibody molecule can bind to the target antigen epitope residues, and screen out the experimental antibody molecule that can bind to the target antigen epitope residues as the target antibody.

[0116] The affinity prediction module 42 of the embodiment of the present application includes a prediction module and a screening module. The prediction module is used to predict the affinity of the sequence of the relevant antibody with the epitope residue sequence of the target antigen, and the structure of the relevant antibody with the structure of the target antigen based on a preset model according to the structure and epitope residue sequence of the target antigen, as well as the structure of the relevant antibody with the structure of the target antigen, to obtain the binding probability of the relevant antibody with the target antigen epitope; the screening module is used to screen out antibodies with a preset proportion as candidate antibodies from the relevant antibodies with a binding probability of a preset probability.

[0117] The present application embodiment designs a computational process for designing a high-affinity candidate antibody that can bind to the target antigen epitope specified by the user from scratch, and screens and verifies the computationally designed candidate antibodies through verification experiments, and finally obtains a target antibody that can bind to the target antigen epitope. Therefore, the target antibody computationally designed by the present application embodiment targets the epitope related to the biological function specified by the user, the computational design process is accurate and controllable, and the target antibody obtained by design can be used in different fields such as the treatment of different diseases, as reagent consumables for life sciences, and the development of diagnostic reagents.

[0118] The antibody de novo design device of the embodiment of the present application further includes a humanization screening module, which is used to perform humanization screening on candidate antibodies to obtain humanized antibodies and form a humanized antibody library. The humanization screening module of the embodiment of the present application is configured with an antibody humanization model.

[0119] The antibody de novo design device of the embodiment of the present application also includes an affinity maturation module, which is used to perform affinity maturation on the candidate antibodies to obtain mature antibodies whose affinity reaches a preset threshold. The affinity maturation module of the embodiment of the present application is configured with an antibody affinity maturation model based on evolution.

[0120] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated again here.

[0121] Figure 5 It is a schematic diagram of the structure of an electronic device shown in an embodiment of the present application.

[0122] See also Figure 5 , the electronic device 500 includes a memory 510 and a processor 520 .

[0123] The processor 520 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0124] The memory 510 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. Among them, ROM can store static data or instructions required by the processor 520 or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that does not lose the stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as a permanent storage device. In some other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as a dynamic random access memory. The system memory may store some or all instructions and data required by the processor at runtime. In addition, the memory 510 may include any combination of computer-readable storage media, including various types of semiconductor storage chips (such as DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, the memory 510 may include a readable and / or writable removable storage device, such as a laser disc (CD), a read-only digital versatile disc (such as a DVD-ROM, a double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (such as an SD card, a mini SD card, a Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0125] The memory 510 stores executable codes, and when the executable codes are processed by the processor 1020 , the processor 520 can execute part or all of the methods described above.

[0126] In addition, the method according to the present application may also be implemented as a computer program or a computer program product, which includes computer program code instructions for executing some or all of the steps in the above method of the present application.

[0127] Alternatively, the present application can also be implemented as a computer-readable storage medium (or non-transitory machine-readable storage medium or machine-readable storage medium) on which executable code (or computer program or computer instruction code) is stored. When the executable code (or computer program or computer instruction code) is executed by a processor of an electronic device (or server, etc.), the processor executes part or all of the steps of the above-mentioned method according to the present application.

[0128] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for de novo antibody design, characterized in that: include: Obtain the structure and epitope residue sequence of the target antigen; According to the structure and epitope residue sequence of the target antigen, a library of related antibodies that can potentially bind to the epitope of the target antigen is generated, wherein the library of related antibodies contains the structure and sequence of related antibodies; wherein the related antibodies are co-light chain antibodies; According to the structure and epitope residue sequence of the target antigen, and the structure and sequence of the related antibodies in the related antibody library, the affinity of the related antibodies to the target antigen epitope is predicted based on a preset model, and candidate antibodies that meet the preset affinity conditions are screened out; Performing humanization screening on the candidate antibodies to obtain humanized antibodies and form a humanized antibody library; According to the accuracy of the preset model, a corresponding preprocessing method is selected to process the humanized antibody to obtain an experimental antibody molecule and sequence; Verify whether the experimental antibody molecule can bind to the target antigen epitope residue, and screen out the experimental antibody molecule that can bind to the target antigen epitope residue as the target antibody.

2. The method according to claim 1, characterized in that The method predicts the affinity of the relevant antibodies to the target antigen epitope based on a preset model according to the structure and epitope residue sequence of the target antigen, and the structure and sequence of the relevant antibodies in the relevant antibody library, and screens out candidate antibodies that meet the preset affinity conditions, including: According to the structure and epitope residue sequence of the target antigen, and the structure and sequence of the related antibodies in the related antibody library, affinity prediction is performed on the sequence of the related antibody and the epitope residue sequence of the target antigen, and the structure of the related antibody and the structure of the target antigen based on a preset model to obtain the binding probability of the related antibody and the target antigen epitope; From the related antibodies whose binding probability is a preset probability, antibodies with a preset proportion are screened out as candidate antibodies.

3. The method according to claim 1, characterized in that The step of performing humanization screening on the candidate antibodies to obtain humanized antibodies and forming a humanized antibody library comprises: Performing humanization screening on the candidate antibodies, eliminating antibodies composed of non-human parts that are immunogenic, and retaining antibodies composed of human parts that are non-immunogenic; The non-immunogenic antibodies composed of human parts are regarded as humanized antibodies, and a humanized antibody library is formed.

4. The method according to claim 2, characterized in that: According to the accuracy of the preset model, a corresponding preprocessing method is selected to process the humanized antibody to obtain an experimental antibody molecule and sequence, including: If the accuracy of the preset model is high, a first preprocessing method is used to process a first preset number of humanized antibodies to obtain experimental antibody molecules and sequences; If the accuracy of the preset model is low, a second preprocessing method is used to process the humanized antibodies in the humanized antibody library, and a second preset number of experimental antibody molecules and sequences are screened from the processed humanized antibodies.

5. The method according to claim 4, characterized in that The first pretreatment method includes recombinant expression; And / or, the second pretreatment method comprises: Dividing the humanized antibody into multiple fragments, connecting the fragments by molecular biological means, and constructing the complete antibody sequence into a vector to form a library for display screening; The complete antibody sequence is screened in the library to obtain the processed humanized antibody.

6. The method according to claim 1, characterized in that Also includes: If the affinity of the candidate antibody does not reach the preset threshold, the candidate antibody is subjected to affinity maturation treatment to obtain a mature antibody whose affinity reaches the preset threshold.

7. A device for de novo antibody design, characterized in that: include: An acquisition module, used to obtain the structure and epitope residue sequence of the target antigen; An antibody generation module, used to generate a related antibody library that can potentially bind to the target antigen epitope according to the structure and epitope residue sequence of the target antigen, wherein the related antibody library contains the structure and sequence of related antibodies; wherein the related antibodies are co-light chain antibodies; An affinity prediction module, used to predict the affinity of the relevant antibody to the target antigen epitope based on a preset model according to the structure and epitope residue sequence of the target antigen, as well as the structure and sequence of the relevant antibodies in the relevant antibody library, and screen out candidate antibodies that meet the preset affinity conditions; A humanization screening module, used to perform humanization screening on the candidate antibodies to obtain humanized antibodies and form a humanized antibody library; A preprocessing module is used to select a corresponding preprocessing method to process the humanized antibody according to the accuracy of the preset model to obtain an experimental antibody molecule and sequence. The experimental antibody molecule can be used as an experimental object. The experiment is used to verify whether the experimental antibody molecule can bind to the target antigen epitope residues, and screen out the experimental antibody molecule that can bind to the target antigen epitope residues as the target antibody.

8. An electronic device, characterized in that: include: processor; as well as A memory having executable codes stored thereon, which, when executed by the processor, causes the processor to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having executable codes stored thereon, which, when executed by a processor of an electronic device, causes the processor to execute the method according to any one of claims 1 to 6.

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