1. A method for predicting a drug-target interaction, comprising:

obtaining a training data set, the training data set comprising: the protein sequence of the target subject and the chemical structure of the drug subject, and the interaction relationship between the target subject and the drug subject;

extracting target features of the protein sequence of the target subject and drug features of the chemical structure of the drug subject;

performing feature selection on the target feature and the drug feature;

performing feature fusion on the selected target features and the selected drug features;

constructing a sequencing model for predicting the strength and weakness sequence of the drug-target interaction based on the fused target characteristics and the drug characteristics;

acquiring a target object to be predicted and a drug object to be predicted;

predicting, based on the ranking model, an affinity of the target object to be predicted and the drug object to be predicted.

2. The prediction method of claim 1, wherein prior to extracting the target feature of the protein sequence of the target subject and the drug feature of the chemical structure of the drug subject, the prediction method further comprises:

optimizing the target object, the drug object and the interaction relationship between the target object and the drug object respectively;

extracting target features of the protein sequence of the target subject and drug features of the chemical structure of the drug subject, comprising:

extracting target characteristics of the protein sequence of the target object after optimization processing and drug characteristics of the chemical structure of the drug object after optimization processing.

3. The prediction method of claim 2, wherein the optimizing the target object, the drug object, and the interaction relationship between the target object and the drug object, respectively, comprises:

deleting the protein sequences of the target objects which contain invalid characters and have the lengths less than the preset number, and performing redundancy removal treatment on the protein sequences of the remaining target objects;

representing the overall characteristics of the drug objects, calculating the similarity between the drug objects, and removing the drug objects with higher similarity based on the similarity between the drug objects;

and carrying out logarithm processing on the interaction relation between the target object and the drug object, and carrying out inverse number processing on the interaction relation between the target object and the drug object after logarithm removal.

4. The method of predicting according to claim 1, wherein the target subject is a kinase protein and the pharmaceutical subject is a kinase inhibitor;

characterizing an interaction relationship between the target object and the drug object with a semi-inhibitory concentration, an inhibition constant, and a dissociation constant between the two when the drug object is capable of acting on the target object;

when the drug object is unable to act on the target object, characterizing the interaction relationship between the two with a preset constant.

5. The prediction method of claim 1, wherein extracting the target feature of the protein sequence of the target subject and the drug feature of the chemical structure of the drug subject comprises:

extracting target characteristics of the protein sequence of the target object based on amino acid composition, pseudo-amino acid composition, physicochemical properties of amino acids, evolution information of the protein sequence and attribute information among residues in the sequence; and

extracting drug characteristics of the chemical structure of the drug object based on the 2D fingerprint, the drug descriptor information.

6. The prediction method of claim 1, wherein feature selection for the target feature and the drug feature comprises:

acquiring Euclidean distance, cosine distance and valley coefficient between the target feature and the medicine feature;

summing the Euclidean distance, the cosine distance and the valley coefficient;

and taking the target feature and the drug feature with the maximum sum value as the selected remaining features.

7. The method of predicting according to claim 1, wherein feature fusing the selected target feature and the drug feature comprises:

and performing feature fusion on the selected target features and the selected drug features based on a similarity network fusion algorithm.

8. A device for predicting drug-target interaction, comprising:

a first obtaining module configured to obtain a training data set, the training data set including: the protein sequence of the target subject and the chemical structure of the drug subject, and the interaction relationship between the target subject and the drug subject;

a feature extraction module for extracting target features of the protein sequence of the target object and drug features of the chemical structure of the drug object;

a feature selection module for performing feature selection on the target feature and the drug feature;

the characteristic fusion module is used for carrying out characteristic fusion on the selected target characteristic and the medicine characteristic;

the model construction module is used for constructing a sequencing model for predicting the strength and weakness sequence of the drug-target interaction based on the fused target characteristics and the drug characteristics;

the second acquisition module is used for acquiring a target object to be predicted and a drug object to be predicted;

a prediction module for predicting an affinity of the target object to be predicted and the drug object to be predicted based on the ranking model.

9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.

10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executed implements the steps of the method according to any of claims 1-7.

Background

Prediction of drug-target interactions is an important step in new drug development and drug redirection. In order to save time and cost, researchers have increasingly applied computer technology to the prediction research of drug-target interaction. The most common are molecular simulation docking and machine learning based methods.

Molecular simulation docking is an important technique for computer-aided drug design. Under the condition that the three-dimensional structure of the protein on the target is known, molecular simulation docking places the small molecule of the drug compound at the active site of the target molecule, finds the optimal conformation of the interaction between the small molecule compound of the receptor and the target macromolecule by continuously transforming the conformation of the ligand, and predicts the binding mode and the affinity of the small molecule compound of the receptor and the target macromolecule. This approach has substantial disadvantages, and molecular docking is the in silico simulation of the binding between ligand and receptor molecules. The process of ligand binding to the receptor is complex and requires extensive and comprehensive sampling of the available conformational space to obtain a true (or near-true) binding conformation. Then, how to find the best binding site involves the problem of conformational search, in which the simultaneous calculation of multiple conformations results in a very large search space and computation amount, and even the screening of a single target by means of a high-performance computer requires a long time and consumes a high computation cost. In addition, it is important to use molecular modelling docking provided that the three-dimensional structure of the protein is known, whereas some kinases are not known and not readily accessible.

In recent years, there has been increasing interest in machine learning-based methods, which have the obvious advantage of not requiring knowledge of the three-dimensional structure of the protein. At present, a great deal of research work on prediction of drug-target interaction is carried out, and the research work not only achieves better performance results, but also research problems are more and more deeply embodied, which has positive effects on research and development of drugs and research of drug redirection. However, these studies still have some disadvantages to be improved: firstly, most of the current researches on drug-target interaction prediction only extract the characteristic information of a certain angle or a certain layer of the drug or the target, and the information of the drug or the target cannot be accurately and comprehensively described by the information of a single layer. In addition, many current researches only simply splice various types of characteristics of drugs and targets, so that deeper information cannot be mined. These are not conducive to the construction of high performance models. In contrast, the study of the degree of interaction between the drug and the target can more effectively narrow the wide search space for drug candidates for downstream experimental verification, thereby significantly reducing the high cost and long time for developing new drugs.

Disclosure of Invention

The application provides a method, a device, equipment and a storage medium for predicting drug-target interaction, which can predict the interaction between a drug and a target.

In a first aspect, embodiments of the present application provide a method for predicting drug-target interactions, comprising:

obtaining a training data set, the training data set comprising: the protein sequence of the target subject and the chemical structure of the drug subject, and the interaction relationship between the target subject and the drug subject;

extracting target features of the protein sequence of the target subject and drug features of the chemical structure of the drug subject;

performing feature selection on the target feature and the drug feature;

performing feature fusion on the selected target features and the selected drug features;

constructing a sequencing model for predicting the strength and weakness sequence of the drug-target interaction based on the fused target characteristics and the drug characteristics;

acquiring a target object to be predicted and a drug object to be predicted;

predicting, based on the ranking model, an affinity of the target object to be predicted and the drug object to be predicted.

Optionally, prior to extracting the target feature of the protein sequence of the target subject and the drug feature of the chemical structure of the drug subject, the prediction method further comprises:

optimizing the target object, the drug object and the interaction relationship between the target object and the drug object respectively;

extracting target features of the protein sequence of the target subject and drug features of the chemical structure of the drug subject, comprising:

extracting target characteristics of the protein sequence of the target object after optimization processing and drug characteristics of the chemical structure of the drug object after optimization processing.

Optionally, the optimizing the target object, the drug object, and the interaction relationship between the target object and the drug object respectively includes:

deleting the protein sequences of the target objects which contain invalid characters and have the lengths less than the preset number, and performing redundancy removal treatment on the protein sequences of the remaining target objects;

representing the overall characteristics of the drug objects, calculating the similarity between the drug objects, and removing the drug objects with higher similarity based on the similarity between the drug objects;

and carrying out logarithm processing on the interaction relation between the target object and the drug object, and carrying out inverse number processing on the interaction relation between the target object and the drug object after logarithm removal.

Optionally, the target object is a kinase protein, and the drug object is a kinase inhibitor;

characterizing an interaction relationship between the target object and the drug object with a semi-inhibitory concentration, an inhibition constant, and a dissociation constant between the two when the drug object is capable of acting on the target object;

when the drug object is unable to act on the target object, characterizing the interaction relationship between the two with a preset constant.

Optionally, extracting target features of the protein sequence of the target subject and drug features of the chemical structure of the drug subject, comprising:

extracting target characteristics of the protein sequence of the target object based on amino acid composition, pseudo-amino acid composition, physicochemical properties of amino acids, evolution information of the protein sequence and attribute information among residues in the sequence; and

extracting drug characteristics of the chemical structure of the drug object based on the 2D fingerprint, the drug descriptor information.

Optionally, feature selection for the target feature and the drug feature comprises:

acquiring Euclidean distance, cosine distance and valley coefficient between the target feature and the medicine feature;

summing the Euclidean distance, the cosine distance and the valley coefficient;

and taking the target feature and the drug feature with the maximum sum value as the selected remaining features.

Optionally, feature fusing the selected target feature and the drug feature, comprising:

and performing feature fusion on the selected target features and the selected drug features based on a similarity network fusion algorithm.

A second aspect of embodiments of the present application provides a device for predicting drug-target interactions, comprising:

a first obtaining module configured to obtain a training data set, the training data set including: the protein sequence of the target subject and the chemical structure of the drug subject, and the interaction relationship between the target subject and the drug subject;

a feature extraction module for extracting target features of the protein sequence of the target object and drug features of the chemical structure of the drug object;

a feature selection module for performing feature selection on the target feature and the drug feature;

the characteristic fusion module is used for carrying out characteristic fusion on the selected target characteristic and the medicine characteristic;

the model construction module is used for constructing a sequencing model for predicting the strength and weakness sequence of the drug-target interaction based on the fused target characteristics and the drug characteristics;

the second acquisition module is used for acquiring a target object to be predicted and a drug object to be predicted;

a prediction module for predicting an affinity of the target object to be predicted and the drug object to be predicted based on the ranking model.

A third aspect of embodiments of the present application provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, performs the steps in the method according to the first aspect of the present application.

A fourth aspect of the embodiments of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the method according to the first aspect of the present application when executed.

By adopting the method for predicting the drug-target interaction provided by the embodiment of the application, the prediction of the drug-target interaction is realized.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments of the present application will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.

FIG. 1 is a flow chart of a method for predicting drug-target interactions provided in an embodiment of the present application;

FIG. 2 is a schematic representation of samples used in the methods for predicting drug-target interactions provided in the examples herein.

Fig. 3 is a schematic diagram illustrating data information of drugs and targets in a digital format in the method for predicting drug-target interaction provided in the embodiment of the present application.

Fig. 4 is a schematic diagram of characteristic information of a sample subjected to network fusion in the method for predicting drug-target interaction provided in the embodiment of the present application.

Fig. 5 is a schematic diagram illustrating format requirements of input files for ranking learning in the method for predicting drug-target interaction provided in the embodiment of the present application.

Fig. 6 is a schematic diagram comparing the output of the data set used in the method for predicting drug-target interaction provided in the example of the present application with the truly ordered data.

Fig. 7 is a schematic structural diagram of a prediction device for drug-target interaction provided in an embodiment of the present application.

Detailed Description

In order to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description.

Referring to fig. 1, a flow chart of a method of predicting drug-target interaction of the present application is shown. As shown in fig. 1, the method comprises the steps of:

s101, a training data set is obtained, wherein the training data set comprises: the protein sequence of the target subject and the chemical structure of the drug subject, as well as the interaction relationship between the target subject and the drug subject.

S102, extracting target characteristics of the protein sequence of the target object and medicine characteristics of the chemical structure of the medicine object.

In some alternative embodiments, the target subject is a kinase protein and the pharmaceutical subject is a kinase inhibitor. Based on the protein sequence (sample) of the kinase protein and the chemical structure (sample) of the kinase inhibitor, feature extraction is performed on the kinase protein and the kinase inhibitor from a plurality of different angles.

In some alternative embodiments, prior to extracting the target feature of the protein sequence of the target subject and the drug feature of the chemical structure of the drug subject, the prediction method further comprises:

optimizing the target object, the drug object and the interaction relationship between the target object and the drug object respectively;

extracting target features of the protein sequence of the target subject and drug features of the chemical structure of the drug subject, comprising:

extracting target characteristics of the protein sequence of the target object after optimization processing and drug characteristics of the chemical structure of the drug object after optimization processing.

Wherein, optimizing the target object, the drug object and the interaction relationship between the target object and the drug object respectively comprises:

deleting the protein sequences of the target objects which contain invalid characters and are less than 50 in length in preset number, and performing redundancy removal treatment on the protein sequences of the remaining target objects by using CD-Hit;

abstract representing the overall characteristics of the drug objects through MACCS fingerprints, calculating the similarity between the drug objects by utilizing a valley coefficient, and removing the drug objects with higher similarity based on the similarity between the drug objects;

and carrying out logarithm processing on the interaction relation between the target object and the drug object, and carrying out inverse number processing on the interaction relation between the target object and the drug object after logarithm removal.

In some alternative embodiments, the drug subject is represented in smiles form, the target subject is represented in sequence form with a protein, and the half inhibitory concentration IC between the target subject and the drug subject is used when the drug subject is capable of acting on the target subject₅₀Inhibition constant K_iAnd dissociation constant K_dAnd characterizing the interaction relationship between the two. When the drug object is unable to act on the target object, the interaction relationship between the two is characterized by a preset constant 10000.

In particular, drug objects and targetsInteraction relationship between objects by Affi₁、Affi₂、Affi₃、Affi₄The four values are expressed as follows:

wherein, IC₅₀、K_i、K_dThe smaller the value, the stronger the interaction force of the drug with the target, so Affi is used₁、Affi₂、Affi₃、Affi₄The relationship between the two can be more intuitively expressed. FIG. 2 is a sample used in the example of the present invention, in which the interaction of 3 drugs and 4 proteins is involved.

In some alternative embodiments, the drug characteristics are expressed in general descriptors and the target characteristics are characterized based on DT algorithms. Fig. 3 illustrates part of the information representing the drug smile, protein sequence in numerical form.

In some optional embodiments, there are multiple feature extraction methods, and feature extraction can be performed based on different angles. Extracting target features of the protein sequence of the target subject and drug features of the chemical structure of the drug subject, comprising:

extracting target characteristics of the protein sequence of the target object based on amino acid composition, pseudo-amino acid composition, physicochemical properties of amino acids, evolution information of the protein sequence and attribute information among residues in the sequence; and

extracting drug characteristics of the chemical structure of the drug object based on the 2D fingerprint, the drug descriptor information.

Moreover, information of each angle can be mined by different feature extraction algorithms, such as a DT algorithm, an ACC-PSSM algorithm and the like which are methods for extracting target features based on evolution information, but the information of each angle is different.

Specifically, the feature information extraction method for the plurality of angles is as follows:

1. the 2D fingerprint information and 200 drug descriptors of the drug can be calculated by a chemical information tool kit (RDkit).

2. The information about the various angular characteristics of proteins can be extracted from the currently integrated kits by researchers, such as pse-in-one, ifeacuture, ileam, etc. These kits all describe the information contained in the protein sequence in numerical form.

S103, performing feature selection on the target feature and the drug feature, wherein the feature selection comprises the following steps:

acquiring Euclidean distance, cosine distance and valley coefficient between the target feature and the medicine feature;

summing the Euclidean distance, the cosine distance and the valley coefficient;

and taking the target feature and the drug feature with the maximum sum value as the selected remaining features.

Extracting features based on multiple angles results in higher-dimension features, and the inevitable cross property exists between information contained in the features, namely feature redundancy. And carrying out primary screening on the features by using three distance formulas including Euclidean distance, cosine distance and valley coefficient, and removing redundant features. The basis of the selection is max (ED (X, Y) + Cos (X, YY + TC (X, Y)), wherein ED (X, Y) is Euclidean distance, Cos (X, Y) is cosine distance, TC (X, Y) is valley coefficient, X, Y are two eigenvectors, and the calculation method of the three distance formulas is as follows₁，y₁)，Y(x₂，y₂) For example, as

S104, performing feature fusion on the selected target features and the selected drug features, wherein the feature fusion comprises the following steps:

and performing feature fusion on the selected target features and the selected drug features based on a similarity network fusion algorithm.

In some optional embodiments, a similarity network fusion algorithm is adopted to replace the simple feature splicing or feature mapping processing strategy in most of the current researches. Similarity network fusion is a classic algorithm in multi-view learning, and the algorithm has more potential to mine intrinsic association information between features. The converged network can capture shared and complementary information of different features. Fig. 4 shows the process of network convergence and the feature information of the sample used in the present embodiment after similarity network convergence. The edges connecting the nodes represent different feature types. The similarity network fusion algorithm comprises the following steps:

s41, constructing a similarity network between each sample and other samples, wherein the similarity network can be represented by a similarity matrix W, elements W (i, j) in the matrix are the similarity between the samples, and the calculation method of W (i, j) is as follows:

wherein ED (n)_i，n_j) Is a sample n_i，n_jThe Euclidean distance of [ mu ] is a set range of [0.3, 0.8 ]]Hyperparameter of between, N_i，N_jAre each n_i，n_jOf the neighbor(s).

S42, in order to calculate the fusion network with various types of characteristics, the similarity matrix obtained in the S41 needs to be standardized, and the calculation mode is as follows:

s43, constructing a similarity network between each sample and a neighbor sample of the sample, wherein the neighbor sample is calculated by a K neighbor algorithm. A kernel matrix is calculated based on the network, and the method comprises the following steps:

s44, integrating the networks into a single similarity network by using a nonlinear combination method, wherein the process is as follows:

wherein the content of the first and second substances,p⁽¹⁾,p⁽²⁾is a normalized matrix of the two types of features, and t is the number of iterations.

And S105, constructing a sequencing model for predicting the drug-target interaction strength sequence based on the fused target characteristics and the drug characteristics.

And (3) exploring the strength of the drug-target interaction by adopting sequencing learning. Rank learning was originally applied in the field of information retrieval, and its principle is consistent with the process of querying information on the world wide web, i.e., a user enters a query, a search engine outputs documents related to the query, and the documents are arranged in descending order. A variety of rank learning algorithms have been developed. And such algorithms can be classified into three types according to the number of document objects: single document class, document pair class, list class. Unlike conventional classification and regression algorithms, such algorithms have unique formatting requirements for the input file. The format requirement for the input file is learned for the ordering as shown in fig. 5.

The target which can be acted by the new drug and the new function of the target are inquired, and the method is realized by dividing sample data into different types of test sets and training sets.

And testing the constructed sequencing model by using the test set, and evaluating the performance of the model by comparing the predicted sequencing sequence with the real sequence.

The performance of the trained model is tested with a test set, in this embodiment, the whole query 1 is used as the test set, and fig. 6 is a data comparison graph of the result output based on the data set used in the embodiment and the real ranking. The ranking learning focuses on relative relevance, so the present invention focuses on ranking cases, not on predicted relevance values.

In general, there may be multiple queries to be performed, and there is no limitation to 1, and the number of corresponding drug-protein pairs under each query is also greater than 1. The above examples show fewer samples for ease of reading. In practical cases, considering that the number of corresponding samples is large under each query, and it is more meaningful to explore the drug-target pairs with strong correlation, only the ranking of the top 1/3 samples of the predicted result and the real result should be compared in practice.

S106, obtaining a target object to be predicted and a drug object to be predicted;

s107, based on the ranking model, the affinity of the target object to be predicted and the drug object to be predicted is predicted.

In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:

1. the invention provides an effective drug-target interaction prediction model, which is used for extracting the characteristics of drugs and targets based on a plurality of angles, wherein each angle can represent part of information of the drugs and targets. The information may have complementarity, so that the information of the drug and the target can be more accurately and completely expressed in a digital form, and the construction of a high-performance drug-target interaction model is facilitated.

2. The invention regards the prediction of drug-target interaction as a sequencing task and utilizes sequencing learning to explore the relative strength of the drug-target interaction. The ranking can more effectively narrow the broad search space for drug candidates for downstream experimental validation, compared to the binary task, thereby significantly reducing the high cost and long time of developing new drugs.

3. Rather than simply stitching or feature mapping the drug and target features, the present invention further mines the intrinsic associations between features through multi-perspective learning that can integrate data from multiple perspectives and explore the complex correlations between data from different perspectives. Training the model based on such information may optimize model performance.

4. The discovery divides the test set and the training set in different forms, and the operation is favorable for exploring the multi-target property and the multi-functionality of the target of the new medicine.

Based on the same inventive concept, an embodiment of the present application provides a device for predicting drug-target interaction. Referring to fig. 7, fig. 7 is a schematic diagram of a prediction device for drug-target interaction provided by an embodiment of the present application. As shown in fig. 7, the apparatus includes:

a first obtaining module configured to obtain a training data set, the training data set including: the protein sequence of the target subject and the chemical structure of the drug subject, and the interaction relationship between the target subject and the drug subject;

a feature extraction module for extracting target features of the protein sequence of the target object and drug features of the chemical structure of the drug object;

a feature selection module for performing feature selection on the target feature and the drug feature;

the characteristic fusion module is used for carrying out characteristic fusion on the selected target characteristic and the medicine characteristic;

the model construction module is used for constructing a sequencing model for predicting the strength and weakness sequence of the drug-target interaction based on the fused target characteristics and the drug characteristics;

the second acquisition module is used for acquiring a target object to be predicted and a drug object to be predicted;

a prediction module for predicting an affinity of the target object to be predicted and the drug object to be predicted based on the ranking model.

Based on the same inventive concept, another embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the method according to any of the above-mentioned embodiments of the present application.

Based on the same inventive concept, another embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the electronic device implements the steps of the method according to any of the above embodiments of the present application.

For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.

The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.

As will be appreciated by one of skill in the art, embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present application have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the true scope of the embodiments of the application.

Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.

The plant resistance protein identification method, device, equipment and storage medium provided by the application are described in detail above, and the principle and the implementation mode of the application are explained by applying specific examples, and the description of the above examples is only used for helping to understand the method and the core idea of the application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.