Method and device for predicting epitope information, medium and program product

Abstract

The invention aims to provide a method and equipment for predicting antigen epitope information, a medium and a program product, and the method comprises the following steps: respectively determining antigen surface atomic information and antibody surface atomic information based on to-be-docked antigen information and to-be-docked antibody information; based on the antigen surface atomic information and the antibody surface atomic information, respectively determining corresponding antigen block information and antibody block information; and based on the antigen block information and the antibody block information, determining corresponding antigen epitope information by using a corresponding deep learning network. The method focuses on the surface information in the antigen and antibody binding process, the epitope is predicted by learning the surface information in antigen and antibody binding, and the prediction efficiency and accuracy are improved.

Description

Technical Field

[0001] The present application relates to the technical field of bioinformatics, and in particular to a technology for predicting antigen epitope information. Background Art

[0002] Accurately predicting the binding configuration of antigens and antibodies is very helpful for the development of immunological drugs. Although the structures of antigens and antibodies alone can now be predicted with high precision, it is still a challenge to determine the three-dimensional structure of the antigen-antibody complex. Determining the specific regions on the antigen that can be recognized and bound by the antibody (i.e., antigen epitopes) is of great help in the subsequent prediction of antigen-antibody binding configuration and antibody function. Traditional epitope prediction methods rely on empirical scoring functions and rigid body simulations for prediction. The prediction of empirical scoring functions is limited by the size and quality of known epitope data sets, while rigid body simulations ignore the conformational changes of proteins from the bound state to the bound state, and the prediction accuracy is often limited. Summary of the invention

[0003] One object of the present application is to provide a method and device for predicting antigen epitope information.

[0004] According to one aspect of the present application, a method for predicting antigen epitope information is provided, the method comprising:

[0005] Based on the information of the antigen to be docked and the information of the antibody to be docked, respectively determining the surface atomic information of the antigen and the surface atomic information of the antibody;

[0006] Based on the antigen surface atom information and the antibody surface atom information, respectively determining corresponding antigen block information and antibody block information;

[0007] Based on the antigen block information and the antibody block information, the corresponding antigen epitope information is determined using a corresponding deep learning network.

[0008] According to one aspect of the present application, a computer device for predicting antigen epitope information is provided, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0009] According to one aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of any of the methods described above are implemented.

[0010] According to one aspect of the present application, a computer program product is provided, including a computer program, characterized in that when the computer program is executed by a processor, the steps of any of the methods described above are implemented.

[0011] According to one aspect of the present application, a device for predicting antigen epitope information is provided, the device comprising:

[0012] A module for determining antigen surface atom information and antibody surface atom information respectively based on the antigen information to be docked and the antibody information to be docked;

[0013] A module one or two, for determining corresponding antigen block information and antibody block information respectively based on the antigen surface atom information and the antibody surface atom information;

[0014] A third module is used to determine corresponding antigen epitope information based on the antigen block information and the antibody block information using a corresponding deep learning network.

[0015] Compared with the prior art, the present application determines the antigen surface atom information and the antibody surface atom information respectively based on the antigen information to be docked and the antibody information to be docked; determines the corresponding antigen block information and antibody block information respectively based on the antigen surface atom information and the antibody surface atom information; determines the corresponding antigen epitope information based on the antigen block information and the antibody block information using the corresponding deep learning network. The present application focuses on the surface information in the process of antigen-antibody binding, and predicts the antigen epitope by learning the surface information in the antigen-antibody binding process, thereby improving the prediction efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0017] Figure 1 A flow chart of a method for predicting antigen epitope information according to one embodiment of the present application is shown;

[0018] Figure 2 A flow chart of a method for determining antigen / antibody block information according to an embodiment of the present application is shown;

[0019] Figure 3 A flow chart of a method for predicting antigen epitope information according to one embodiment of the present application is shown;

[0020] Figure 4 A flow chart of a method for predicting antigen epitope information according to one embodiment of the present application is shown;

[0021] Figure 5A structural diagram of a device for predicting antigen epitope information according to an embodiment of the present application is shown;

[0022] Figure 6 An exemplary system is shown that can be used to implement the various embodiments described in this application.

[0023] The same or similar reference numerals in the drawings represent the same or similar components. DETAILED DESCRIPTION

[0024] The present application is described in further detail below in conjunction with the accompanying drawings.

[0025] In a typical configuration of the present application, the terminal, the device of the service network and the trusted party all include one or more processors (eg, a central processing unit (CPU)), an input / output interface, a network interface and a memory.

[0026] Memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash memory. Memory is an example of a computer-readable medium.

[0027] Computer readable media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, Phase-Change Memory (PCM), Programmable Random Access Memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of random access memory (RAM), Read-Only Memory (ROM), Electrically-Erasable Programmable Read-Only Memory (EEPROM), Flash memory or other memory technology, Compact Disc Read-Only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0028] The devices referred to in this application include but are not limited to user devices, network devices, or devices formed by integrating user devices and network devices through a network. The user devices include but are not limited to any mobile electronic products that can interact with users (for example, interact with users through a touchpad), such as smart phones, tablet computers, etc. The mobile electronic products can use any operating system, such as Android operating system, iOS operating system, etc. Among them, the network device includes an electronic device that can automatically perform numerical calculations and information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic devices (Programmable Logic Device, PLD), field programmable gate arrays (Field Programmable Gate Array, FPGA), digital signal processors (Digital Signal Processor, DSP), embedded devices, etc. The network devices include but are not limited to computers, network hosts, single network servers, multiple network server sets or multiple servers. The cloud is composed of a large number of computers or network servers based on cloud computing (Cloud Computing), wherein cloud computing is a type of distributed computing, a virtual supercomputer composed of a group of loosely coupled computer sets. The network includes but is not limited to the Internet, a wide area network, a metropolitan area network, a local area network, a VPN network, a wireless self-organizing network (Ad Hoc network), etc. Preferably, the device may also be a program running on the user device, the network device, or a device formed by integrating the user device and the network device, the network device, the touch terminal, or the network device and the touch terminal through a network.

[0029] Of course, those skilled in the art should understand that the above-mentioned devices are only examples, and other existing or future devices that are applicable to the present application should also be included in the scope of protection of the present application and are included here by reference.

[0030] In the description of the present application, “plurality” means two or more, unless otherwise clearly and specifically defined.

[0031] Figure 1A flow chart of a method for predicting antigen epitope information according to an embodiment of the present application is shown, and the method includes step S11, step S12 and step S13. In step S11, device 1 determines antigen surface atom information and antibody surface atom information based on the antigen information to be docked and the antibody information to be docked, respectively; in step S12, device 1 determines corresponding antigen block information and antibody block information based on the antigen surface atom information and the antibody surface atom information, respectively; in step S13, device 1 determines corresponding antigen epitope information based on the antigen block information and the antibody block information using a corresponding deep learning network.

[0032] In step S11, the device 1 determines the antigen surface atom information and the antibody surface atom information respectively based on the antigen information to be docked and the antibody information to be docked. In some embodiments, the device 1 includes but is not limited to user devices and network devices with information processing or computing capabilities, such as tablet computers, computers, and servers. In some embodiments, the antigen information to be docked includes but is not limited to the sequence information and structural information of the antigen not bound to the antibody (for example, the three-dimensional coordinate information of the atoms in the antigen to be docked). The antibody information to be docked includes but is not limited to the sequence information and structural information of the antibody not bound to the antigen (for example, the three-dimensional coordinate information of the atoms in the antibody to be docked). In some embodiments, the device 1 determines the atoms located on the surface of the antigen to be docked and the antibody to be docked respectively, and determines the antigen surface atom information and antibody surface atom information corresponding to these antigen surface atoms and antibody surface atoms. The antigen surface atom information includes but is not limited to the residue features and paired features corresponding to the antigen surface atoms, wherein the residue features include but are not limited to the element type, atomic coordinates, centrality encoding information and / or main chain torsion angle information of the antigen surface atoms, and the paired features include but are not limited to the relative coordinate relationship between the antigen surface atom pairs. The antibody surface atom information also includes but is not limited to residue features and paired features corresponding to the antibody surface atoms. The residue features and paired features are similar to those in the antigen surface atom information, so they are not repeated here and are included here by reference.

[0033] In some embodiments, the step S11 includes: the device 1 determines the exposed area corresponding to each atom in the antigen and antibody based on the antigen information to be docked and the antibody information to be docked; and determines the antigen surface atomic information and the antibody surface atomic information based on the exposed area corresponding to each atom in the antigen and antibody. For example, atoms of different element types have different atomic radii. The atomic radius of each atom in the antigen can be determined based on the antigen information to be docked. Furthermore, based on the atomic radius of each atom in the antigen and the three-dimensional coordinate information of each atom, the surface area of ​​each atom in the antigen that is not covered by other atoms is determined. The surface area not covered by other atoms is used as the exposed area corresponding to the atom. Atoms in the antigen whose exposed area is greater than a preset area threshold are determined as antigen surface atoms, and the corresponding antigen surface atomic information is determined. The method for determining the antibody surface atomic information is the same as the method for determining the antigen surface atomic information mentioned above, so it will not be repeated and is included here by reference. The area threshold is pre-set based on actual needs. For example, the area threshold can be set to The exposed area is larger than Atoms of the surface are determined as surface atoms. Here, those skilled in the art should understand that the setting of the above area threshold is only an example, and other existing or future area thresholds that are applicable to the present application should also be included in the protection scope of the present application and are included here by reference.

[0034] In step S12, the device 1 determines the corresponding antigen patch information and antibody patch information based on the antigen surface atom information and the antibody surface atom information. In some embodiments, the device 1 can cluster the antigen surface atom information and the antibody surface atom information respectively. Cluster the adjacent antigen surface atoms to obtain antigen patches, and cluster the adjacent antibody surface atoms to obtain antibody patches. And obtain the corresponding antigen patch information and antibody patch information. The antigen patch information includes the antigen surface atom information corresponding to each antigen surface atom in the antigen patch. The antibody patch information includes the antibody surface atom information corresponding to each antibody surface atom in the antibody patch. In some embodiments, the aforementioned clustering can be performed based on different chains of antigens and antibodies. For example, based on the surface atoms of the antigen chain, the heavy chain and / or light chain of the antibody, the patch information of each chain is clustered separately.

[0035] refer to Figure 2 As shown in the flow chart, in some embodiments, the step S12 includes: step S121, device 1 determines the integration information corresponding to each surface atom of the antigen and the antibody based on the antigen surface atom information and the antibody surface atom information; step S122, device 1 determines the corresponding antigen block information and antibody block information based on the integration information corresponding to each surface atom of the antigen and the antibody.

[0036] In some embodiments, in order to provide more abundant information for epitope prediction in subsequent steps and improve the accuracy of antigen epitope prediction, the atomic information of multiple antigen atoms near each antigen surface atom of the antigen can be integrated, and the atomic information of multiple antibody atoms near each antibody surface atom of the antibody can be integrated to obtain the integrated information corresponding to each surface atom. Then, based on these integrated information, the antigen block information and antibody block information can be obtained by clustering.

[0037] In some embodiments, based on the antigen information and the corresponding antigen surface atom information, multiple antigen atoms closest to the surface atoms of the antigen can be selected to determine the integrated information corresponding to the surface atoms. The integrated information includes, but is not limited to, the antigen surface atom information, the interaction characteristics between the antigen surface atoms and the multiple antigen atoms closest to the antigen, the spatial structure, the chemical properties and other physical and chemical characteristics. The method for determining the integrated information corresponding to each surface atom of the antibody is the same as the method for determining the integrated information corresponding to each surface atom of the aforementioned antigen, so it will not be repeated here, and is included here by reference. In some embodiments, the number of atoms selected that are adjacent to the surface atoms of the antigen / antibody can be set based on actual needs. For example, the number can be set to 16 or other values.

[0038] In some embodiments, the step S121 includes: device 1 determines multiple atomic information corresponding to each surface atom of the antigen and antibody based on the antigen surface atomic information and the antibody surface atomic information; and combines the multiple atomic information corresponding to each surface atom of the antigen and antibody to determine the integrated information corresponding to each surface atom of the antigen and antibody.

[0039] In some embodiments, the device 1 can use a vector field to calculate the distance between the surface atoms of the antigen and other atoms of the antigen. In some embodiments, the distance calculated using the vector field is based on the surface of the antigen, rather than the Euclidean distance. In some embodiments, based on the distance between the surface atoms of the antigen and other atoms of the antigen, a preset number of the closest antigen atoms are selected for each surface atom of the antigen, and the corresponding atomic information of these antigen atoms is determined. These antigen atoms may be atoms located on the surface of the antigen or atoms located inside the antigen, which are not limited here. The atomic information includes, but is not limited to, the residue features and paired features corresponding to the antigen atom. The method for determining the multiple atomic information corresponding to each surface atom of the antibody is the same as the method for determining the multiple atomic information corresponding to each surface atom of the aforementioned antigen, so it will not be repeated and is included here by reference.

[0040] In some embodiments, the device 1 can integrate information based on multiple atomic information corresponding to each surface atom of the antigen and the corresponding antigen surface atom information using a geometric aggregation network to obtain integrated information corresponding to each surface atom of the antigen. The geometric aggregation network includes a multi-layer perceptron (MLP). The integrated information includes but is not limited to the antigen surface atom information, the interaction characteristics between the antigen surface atoms and the multiple antigen atoms closest to the antigen, the spatial structure, the chemical properties and other physical and chemical characteristics. The method for determining the integrated information corresponding to each surface atom of the antibody is the same as the method for determining the integrated information corresponding to each surface atom of the aforementioned antigen, so it will not be repeated and is included here by reference.

[0041] In some embodiments, step S122 includes: device 1 performs sampling based on the integrated information corresponding to each surface atom of the antigen and antibody, and obtains the integrated information corresponding to the surface atoms of the sampled antigen and antibody; clusters the integrated information corresponding to the surface atoms of the sampled antigen and antibody, and obtains corresponding antigen block information and antibody block information.

[0042] In some embodiments, in order to improve the computational efficiency, the integrated information corresponding to each surface atom of the antigen or antibody may be sampled first. Here, the farthest point sampling method may be used to sample each surface atom of the antigen / antibody to obtain the integrated information corresponding to the sampled surface atom. Those skilled in the art should understand that the above sampling method is only an example, and other existing or future sampling methods that may appear, if applicable to the present application, should also be included in the scope of protection of the present application and are hereby included by reference.

[0043] In some embodiments, the device 1 can cluster the integrated information corresponding to the surface atoms of the antigens that are close in distance in combination with the corresponding clustering algorithm to obtain the corresponding antigen block information. The antigen block information includes the integrated information corresponding to each surface atom in the antigen block. The calculation method of the antibody block information is the same as the calculation method of the aforementioned antigen block information, so it will not be repeated here and is included here by reference. Here, the distance is similar to the interatomic distance calculated using the vector field, which also refers to the distance based on the antigen / antibody surface. The clustering algorithm includes but is not limited to the K-Nearest Neighbors algorithm.

[0044] In some embodiments, in order to facilitate subsequent calculation and processing, the step S122 further includes performing Morton encoding on the antigen block information and the antibody block information, respectively, so as to convert the antigen block information and the antibody block information from high-dimensional space data into a one-dimensional sequence. This allows some machine learning models for one-dimensional sequences to conveniently process antigen / antibody block information, thereby reducing restrictions on the models used in epitope prediction.

[0045] In step S13, device 1 determines corresponding antigen epitope information based on the antigen block information and the antibody block information using a corresponding deep learning network. In some embodiments, the deep learning network includes an encoder and a decoder. The encoder can encode the input antigen block information and antibody block information into a vector of fixed size. The vector can capture the key features of the atomic composition and structure of the antigen and antibody surfaces and the connection between the antigen block information and the antibody block information (for example, the strong and weak relationship between different blocks). The decoder predicts the antigen epitope information based on the vector. The deep learning network can be pre-trained and deployed on the device 1, or it can be obtained by training the device 1. The antigen epitope information includes but is not limited to antigen epitope sequence information, undocked antigen epitope coordinate information and / or docked antigen epitope coordinate information.

[0046] In some embodiments, step S13 includes: device 1 uses a quasi-geodesic convolutional network to determine the corresponding antigen block integration information and antibody block integration information based on the antigen block information and the antibody block information; and uses a corresponding deep learning network to determine the corresponding antigen epitope information based on the antigen block integration information and the antibody block integration information.

[0047] In some embodiments, in order to improve the accuracy of antigen epitope prediction, antigen block information can also be integrated. For each antigen block information, a quasi-geodesic convolution network is used to integrate it with other antigen block information adjacent to the surface distance to obtain antigen block integration information. The antigen block integration information includes but is not limited to the antigen block information and other antigen block information adjacent to the surface distance. In some embodiments, for each antigen block information, the weight information corresponding to other antigen block information can be determined based on the surface distance of the antigen block information and other antigen block information. Then, in combination with the weight information, the antigen block integration information corresponding to the antigen block information is determined. The weight information is negatively correlated with the surface distance. If the surface distance of the antigen block information and other antigen block information is smaller, the more important the other antigen block information is to the antigen block information, the larger the corresponding weight information. The determination method of the antibody block integration information is the same as the determination method of the aforementioned antigen block integration information, so it will not be repeated here, and it is included here by reference.

[0048] refer to Figure 3 As shown in the flow chart, in some embodiments, the method further includes: step S14, the device 1 trains and obtains the corresponding deep learning network under the encoder-decoder architecture based on the antigen sample information, the antibody sample information and the corresponding antigen-antibody binding information. In some embodiments, the antigen sample information includes but is not limited to the sequence information and structural information of the undocked sample antigen (for example, the three-dimensional coordinate information of atoms in the sample antigen). The antibody sample information includes but is not limited to the sequence information and structural information of the undocked sample antibody (for example, the three-dimensional coordinate information of atoms in the sample antibody). The antigen-antibody binding information includes but is not limited to the sequence information and structural information of the complex after the sample antigen and the sample antibody are docked, and the epitope coordinates, epitope sequence and other epitope information corresponding to the sample antigen.

[0049] In some embodiments, before executing the steps S11-S13, the device 1 first obtains the deep learning network based on the corresponding sample data training. Since only the surface of the antigen and antibody participate in docking, in order to improve the efficiency and accuracy of epitope prediction, the antigen sample information and the antibody sample information can be processed in the same or similar manner as the aforementioned steps S11 and S12 to obtain the corresponding sample antigen block information / sample antigen block integration information and sample antibody block information / sample antibody block integration information, thereby focusing on the surface information of the antigen and antibody in the training of the deep learning network. The sample antigen block information / sample antigen block integration information and the sample antibody block information / sample antibody block integration information are input into the deep learning network to obtain the corresponding epitope prediction results. Based on the corresponding loss function, the difference between the epitope prediction result and the antigen-antibody binding information is calculated, and based on this, the deep learning network parameters are updated by back propagation to finally obtain the corresponding deep learning network.

[0050] refer to Figure 4 As shown in the flowchart, in some embodiments, the method further includes: step S15, the device 1 determines the antigen-antibody complex structure information based on the antigen epitope information. For example, the antigen epitope information includes the antigen epitope coordinate information after docking. The device 1 then combines the coordinate information of other atoms in the antigen information to be docked and the antibody information to be docked to obtain the coordinate information of each atom in the antigen-antibody complex, that is, the antigen-antibody complex structure information.

[0051] Figure 5The structure diagram of a device for predicting antigen epitope information according to an embodiment of the present application is shown, and the device 1 includes a first module 11, a second module 12 and a third module 13. The first module 11 determines the antigen surface atom information and the antibody surface atom information based on the antigen information to be docked and the antibody information to be docked; the first module 12 determines the corresponding antigen block information and the antibody block information based on the antigen surface atom information and the antibody surface atom information; the first module 13 determines the corresponding antigen epitope information based on the antigen block information and the antibody block information using the corresponding deep learning network. Here, the Figure 5 The specific implementations corresponding to the one-one module 11, the one-two module 12 and the one-three module 13 shown are respectively the same as or similar to the specific embodiments of the aforementioned step S11, step S12 and step S13, so they are not repeated here and are included here by reference.

[0052] In some embodiments, the one-two module 12 includes a one-two-one unit 121 and a one-two-two unit 122. The one-two-one unit 121 determines the integrated information corresponding to each surface atom of the antigen and the antibody based on the antigen surface atom information and the antibody surface atom information; the one-two-two unit 122 determines the corresponding antigen block information and antibody block information based on the integrated information corresponding to each surface atom of the antigen and the antibody. Here, the specific implementation methods of the one-two-one unit 121 and the one-two-two unit 122 are respectively the same or similar to the specific embodiments of the aforementioned step S121 and step S122, so they are not repeated here and are included here by reference.

[0053] In some embodiments, the device 1 further includes a four-module 14 (not shown). The four-module 14 is based on the antigen sample information, the antibody sample information and the corresponding antigen-antibody binding information, and trains and obtains the corresponding deep learning network under the encoder-decoder architecture. Here, the specific implementation method corresponding to the four-module 14 is the same or similar to the specific implementation method of the aforementioned step S14, so it will not be repeated here, and is included here by reference.

[0054] In some embodiments, the device 1 further comprises a one-five module 15 (not shown). The one-five module 15 determines the antigen-antibody complex structure information based on the antigen epitope information. Here, the specific implementation of the one-five module 15 is the same or similar to the specific implementation of the aforementioned step S15, so it will not be repeated here and is included here by reference.

[0055] Figure 6 An exemplary system is shown that can be used to implement the various embodiments described in this application.

[0056] like Figure 6In some embodiments shown, the system 300 can be used as any of the devices in the various described embodiments. In some embodiments, the system 300 may include one or more computer-readable media (e.g., system memory or NVM / storage device 320) with instructions and one or more processors (e.g., (one or more) processors 305) coupled to the one or more computer-readable media and configured to execute instructions to implement modules to perform the actions described in this application.

[0057] For one embodiment, system control module 310 may include any suitable interface controller to provide any suitable interface to at least one of processor(s) 305 and / or any suitable device or component in communication with system control module 310 .

[0058] The system control module 310 may include a memory controller module 330 to provide an interface to the system memory 315. The memory controller module 330 may be a hardware module, a software module, and / or a firmware module.

[0059] The system memory 315 may be used, for example, to load and store data and / or instructions for the system 300. For one embodiment, the system memory 315 may include any suitable volatile memory, such as a suitable DRAM. In some embodiments, the system memory 315 may include double data rate type four synchronous dynamic random access memory (DDR4 SDRAM).

[0060] For one embodiment, system control module 310 may include one or more input / output (I / O) controllers to provide interfaces to NVM / storage device 320 and communication interface(s) 325 .

[0061] For example, NVM / storage device 320 may be used to store data and / or instructions. NVM / storage device 320 may include any suitable non-volatile memory (e.g., flash memory) and / or may include any suitable non-volatile storage device(s) (e.g., one or more hard disk drives (HDDs), one or more compact disk (CD) drives, and / or one or more digital versatile disk (DVD) drives).

[0062] NVM / storage device 320 may include storage resources that are physically part of the device on which system 300 is installed, or it may be accessible to the device without being part of the device. For example, NVM / storage device 320 may be accessed over a network via communication interface(s) 325.

[0063] Communication interface(s) 325 may provide an interface for system 300 to communicate over one or more networks and / or with any other suitable devices. System 300 may wirelessly communicate with one or more components of a wireless network in accordance with any of one or more wireless network standards and / or protocols.

[0064] For one embodiment, at least one of the processor(s) 305 may be packaged together with the logic of one or more controllers of the system control module 310 (e.g., the memory controller module 330). For one embodiment, at least one of the processor(s) 305 may be packaged together with the logic of one or more controllers of the system control module 310 to form a system-in-package (SiP). For one embodiment, at least one of the processor(s) 305 may be integrated on the same die with the logic of one or more controllers of the system control module 310. For one embodiment, at least one of the processor(s) 305 may be integrated on the same die with the logic of one or more controllers of the system control module 310 to form a system on chip (SoC).

[0065] In various embodiments, the system 300 may be, but is not limited to: a server, a workstation, a desktop computing device, or a mobile computing device (e.g., a laptop computing device, a handheld computing device, a tablet computer, a netbook, etc.). In various embodiments, the system 300 may have more or fewer components and / or a different architecture. For example, in some embodiments, the system 300 includes one or more cameras, a keyboard, a liquid crystal display (LCD) screen (including a touch screen display), a non-volatile memory port, multiple antennas, a graphics chip, an application specific integrated circuit (ASIC), and a speaker.

[0066] In addition to the methods and devices described in the above embodiments, the present application also provides a computer-readable storage medium, which stores computer code. When the computer code is executed, the method described in any of the preceding items is executed.

[0067] The present application also provides a computer program product. When the computer program product is executed by a computer device, the method described in any of the preceding items is executed.

[0068] The present application also provides a computer device, the computer device comprising:

[0069] one or more processors;

[0070] a memory for storing one or more computer programs;

[0071] When the one or more computer programs are executed by the one or more processors, the one or more processors are caused to implement the method as described in any of the preceding items.

[0072] It should be noted that the present application can be implemented in software and / or a combination of software and hardware, for example, can be implemented using an application specific integrated circuit (ASIC), a general purpose computer or any other similar hardware device. In one embodiment, the software program of the present application can be executed by a processor to implement the steps or functions described above. Similarly, the software program of the present application (including relevant data structures) can be stored in a computer-readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and similar devices. In addition, some steps or functions of the present application can be implemented using hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0073] In addition, a part of the present application may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present application through the operation of the computer. Those skilled in the art should understand that the existence of computer program instructions in computer-readable media includes but is not limited to source files, executable files, installation package files, etc., and accordingly, the way in which computer program instructions are executed by a computer includes but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to the computer.

[0074] Communication media include media by which communication signals containing, for example, computer readable instructions, data structures, program modules, or other data are transmitted from one system to another. Communication media may include guided transmission media such as cables and wires (e.g., fiber optic, coaxial, etc.) and wireless (unguided transmission) media that can propagate energy waves, such as acoustic, electromagnetic, RF, microwave, and infrared. Computer readable instructions, data structures, program modules, or other data may be embodied as a modulated data signal in, for example, a wireless medium such as a carrier wave or similar mechanism such as embodied as part of spread spectrum technology. The term "modulated data signal" refers to a signal whose one or more characteristics are changed or set in such a manner as to encode information in the signal. Modulation may be analog, digital, or a hybrid modulation technique.

[0075] By way of example and not limitation, computer-readable storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. For example, computer-readable storage media include, but are not limited to, volatile memory, such as random access memory (RAM, DRAM, SRAM); and non-volatile memory, such as flash memory, various read-only memories (ROM, PROM, EPROM, EEPROM), magnetic and ferromagnetic / ferroelectric memories (MRAM, FeRAM); and magnetic and optical storage devices (hard disks, magnetic tapes, CDs, DVDs); or other media now known or later developed that can store computer-readable information / data for use with a computer system.

[0076] Here, according to an embodiment of the present application, a device is included, which includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein, when the computer program instructions are executed by the processor, the device is triggered to run the methods and / or technical solutions based on the aforementioned multiple embodiments of the present application.

[0077] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or basic features of the present application. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present application is limited by the attached claims rather than the above description, so it is intended to include all changes that fall within the meaning and scope of the equivalent elements of the claims in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

Claims

1. A method for predicting antigen epitope information, wherein: The method comprises: Based on the information of the antigen to be docked and the information of the antibody to be docked, respectively determining the surface atomic information of the antigen and the surface atomic information of the antibody; Based on the antigen surface atom information and the antibody surface atom information, respectively determining corresponding antigen block information and antibody block information; Based on the antigen block information and the antibody block information, the corresponding antigen epitope information is determined using a corresponding deep learning network.

2. The method according to claim 1, wherein: The step of determining the antigen surface atom information and the antibody surface atom information based on the antigen information to be docked and the antibody information to be docked comprises: Based on the information of the antigen to be docked and the information of the antibody to be docked, the exposed area corresponding to each atom in the antigen and the antibody is determined respectively; Based on the exposed area corresponding to each atom in the antigen and antibody, the surface atomic information of the antigen and the surface atomic information of the antibody are determined respectively.

3. The method according to claim 1 or 2, wherein: The determining of corresponding antigen block information and antibody block information based on the antigen surface atom information and the antibody surface atom information respectively comprises: Based on the antigen surface atom information and the antibody surface atom information, respectively determining the integration information corresponding to each surface atom of the antigen and the antibody; Based on the integrated information corresponding to each surface atom of the antigen and antibody, the corresponding antigen block information and antibody block information are determined respectively.

4. The method according to claim 3, wherein: The step of determining the integrated information corresponding to each surface atom of the antigen and the antibody based on the surface atom information of the antigen and the surface atom information of the antibody comprises: Based on the antigen surface atomic information and the antibody surface atomic information, respectively determine multiple atomic information corresponding to each surface atom of the antigen and the antibody; The integrated information corresponding to each surface atom of the antigen and antibody is determined by combining multiple atomic information corresponding to each surface atom of the antigen and antibody.

5. The method according to claim 3 or 4, wherein: The step of determining corresponding antigen block information and antibody block information based on the integrated information corresponding to each surface atom of the antigen and the antibody comprises: Based on the integration information corresponding to each surface atom of the antigen and antibody, sampling is performed respectively to obtain the integration information corresponding to the surface atoms of the sampled antigen and antibody; The integrated information corresponding to the surface atoms of the sampled antigens and antibodies is clustered to obtain corresponding antigen block information and antibody block information.

6. The method according to claim 5, wherein: The step of determining corresponding antigen block information and antibody block information based on the integrated information corresponding to each surface atom of the antigen and antibody also includes: The antigen block information and the antibody block information are Morton encoded respectively.

7. The method according to any one of claims 1 to 6, wherein: The determining of corresponding antigen epitope information based on the antigen block information and the antibody block information by using a corresponding deep learning network includes: Based on the antigen block information and the antibody block information, using a quasi-geodesic convolutional network, respectively determining corresponding antigen block integration information and antibody block integration information; Based on the antigen block integration information and the antibody block integration information, the corresponding antigen epitope information is determined using a corresponding deep learning network.

8. The method according to any one of claims 1 to 7, wherein: The method further comprises: Based on the antigen sample information, antibody sample information and the corresponding antigen-antibody binding information, the corresponding deep learning network is trained under the encoder-decoder architecture.

9. The method according to any one of claims 1 to 8, wherein: The method further comprises: Based on the antigen epitope information, the antigen-antibody complex structure information is determined.

10. A computer device for predicting antigen epitope information, comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 9.

11. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method as claimed in any one of claims 1 to 9 are implemented.

12. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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