Method for predicting affinity of major histocompatibility complex and polypeptide

Abstract

The invention relates to a method for predicting affinity between a main histocompatibility complex and polypeptide, and belongs to the technical field of data prediction. Comprising the following steps: acquiring data of main histocompatibility complex MHCI and polypeptide affinity; preprocessing the data of the main histocompatibility complex MHCI and the polypeptide affinity; embedding the MHCI sequences on the basis of amino acid embedding rules, wherein each MHCI sequence correspondingly generates an MHCI sequence matrix; polypeptide sequences are embedded on the basis of amino acid embedding rules, each polypeptide sequence correspondingly generates a polypeptide sequence matrix, and size unification processing is conducted on the polypeptide sequence matrixes; constructing a pre-trained MHCI and polypeptide affinity prediction model; training a prediction model based on the MHCI sequence matrix and the preprocessed polypeptide sequence matrix; and predicting affinity based on the trained prediction model. The method disclosed by the invention shows better performance in predicting the affinity of MHCI molecules with 10 polymer peptide and 11 polymer peptide, and the prediction is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of data prediction, and in particular to a method for predicting the affinity between a major histocompatibility complex and a polypeptide, which can be used for vaccine design, immunotherapy and disease prevention. Background Art

[0002] Recently, with the development of deep learning technology, many algorithms for predicting the affinity of MHCI binding to peptides have emerged. Most existing computational methods can be divided into two categories: pan-specific and allele-specific. Allele-specific methods have limited generalization to unseen MHCI molecules because they use a separate model for each molecule. In contrast, pan-specific methods integrate information about MHC molecules and peptides into a single model, allowing the binding specificity of all MHC molecules to be learned simultaneously.

[0003] Previous prediction methods mostly used data from the International Immune Epitope Database (IEDB). These data were mainly for binding assays with a peptide length of 9 amino acids, which resulted in the algorithm performing better when dealing with peptides of this specific length. This bias becomes a problem because in actual biological processes, the length of peptides is not always strictly 9 amino acids. Many biological phenomena and therapeutic processes involve peptides that may be longer or shorter, and the existing algorithms are often insufficient in their ability to predict these peptides and cannot meet the needs of practical applications.

[0004] Some algorithms have been developed to more accurately predict the affinity of MHCI and peptides, especially non-9-mer peptides.

[0005] In 2021, Jing Jin et al. proposed an algorithm called DeepAttentionPan in PROTEINS: Structure, Function, and Bioinformatics, which predicts the affinity of MHCI and peptides based on convolutional neural networks and attention mechanisms. The input MHCI and peptide sequences passed through a submodule called DCNN and an attention module, and finally obtained the predicted affinity score. With the help of the attention mechanism, higher and more stable prediction performance was achieved.

[0006] In 2023, Mahmood Kalemati et al. proposed a deep learning algorithm called CapsNet-MHC in Communications Biology. CapsNet-MHC first used Blosum 62 to encode and embed MHCI and peptide sequences, and then extracted features through the CNN-Attention module. Then, CapsuleNet multiplied the weight matrix to achieve the encoding of spatial dependencies and the extraction of high-level features in the input vector. Finally, a more accurate and interpretable MHCI peptide binding prediction was achieved.

[0007] In 2023, Wei Qu et al. proposed a new deep learning-based method, DeepMHCI, in Bioinformatics, which predicts MHC-I peptide binding affinity by incorporating biological knowledge into model design. Through the gating layer and residual combined with interactive convolutional layer, the model shows more accurate motifs at different lengths to understand the MHCI peptide binding mode.

[0008] The above algorithms failed to show good performance in predicting MHCI and non-9 specific peptides, especially peptides with lengths of 10 and 11. Summary of the invention

[0009] The technical problems to be solved by the present invention are:

[0010] In order to avoid the shortcomings of the prior art, the present invention provides a method for predicting the affinity of the major histocompatibility complex and a polypeptide, so as to accurately predict the affinity of MHCI and 9-mer, 10-mer and 11-mer polypeptides, and improve the interpretability and generalization ability of the prediction method.

[0011] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0012] A method for predicting the affinity between a major histocompatibility complex and a polypeptide, comprising:

[0013] Obtain data on major histocompatibility complex MHCI and peptide affinity;

[0014] Preprocessing of major histocompatibility complex MHCI and peptide affinity data;

[0015] The MHCI sequence is embedded based on the embedding rules of amino acids, and an MHCI sequence matrix is ​​generated for each MHCI sequence;

[0016] The peptide sequence is embedded based on the embedding rules of amino acids, and a peptide sequence matrix is ​​generated for each peptide sequence, and the size of the peptide sequence matrix is ​​unified;

[0017] Constructing a pre-trained prediction model for MHCI and peptide affinity; the prediction model sequentially comprises a convolutional layer, a bidirectional long short-term memory network layer, a pooling layer, a fully connected layer, and an output layer;

[0018] The prediction model was trained based on the MHCI sequence matrix and the preprocessed peptide sequence matrix;

[0019] Predict the affinity of major histocompatibility complex to peptides based on the trained prediction model.

[0020] A further technical solution of the present invention: the preprocessing of the data of major histocompatibility complex MHCI and polypeptide affinity comprises:

[0021] Major histocompatibility complex MHCI and peptide affinity IC 50 The value is converted to an affinity score in [0,1]: Wherein, nM is nanomolar;

[0022] For each MHCI sequence, 25 random natural peptides with lengths of 8, 9, 10 and 11 were introduced respectively, and the affinity score after introduction was set to 0.1. They were used as artificial negative samples to participate in the training of the prediction model, but not in the verification of the prediction model.

[0023] A further technical solution of the present invention: the embedding rules for amino acids are specifically as follows:

[0024] Different combinations of position 3 0 and position 3 1 are used to represent 20 amino acids;

[0025] Different combinations of 2 0s and 2 1s are used to distinguish the four charge properties of amino acids: the four charge properties include: if it is non-polar, it is represented by [0, 1, 1, 1]; if it is polar and uncharged, it is represented by [0, 1, 0, 1]; if it is polar and positively charged, it is represented by [0, 1, 1, 0]; if it is polar and negatively charged, it is represented by [1, 0, 0, 1];

[0026] For unknown residue X, specify the embedding representation method.

[0027] A further technical solution of the present invention: the size unification processing of the polypeptide sequence matrix comprises:

[0028] If the length of the peptide sequence is less than 11, the matrix is ​​padded with 0 to fill the matrix size to 11×10

[0029] If the length of the peptide sequence is equal to 11, no operation is performed on the matrix;

[0030] If the length of the polypeptide sequence is greater than 11, the first 11 rows of the matrix are truncated to obtain an 11×10 matrix.

[0031] A further technical solution of the present invention: The prediction model of the affinity between MHCI and polypeptide is specifically:

[0032] Convolution layer: includes four parallel convolution kernels of different sizes. All layers use ReLU function as activation function. Before each convolution kernel performs convolution operation, the MHCI and peptide sequence matrices are filled with zeros in two different ways. The MHCI and peptide sequence information obtained by convolution with the same zero filling method are connected. The data obtained by the four convolution kernels are connected and output.

[0033] Bidirectional LSTM network layer: It includes two bidirectional LSTM network blocks, one of which is composed of a single-layer bidirectional LSTM network and the other is composed of a two-layer bidirectional LSTM network. The output results of the convolutional layer are respectively input into the two bidirectional LSTM network blocks, and the results of the two bidirectional LSTM network blocks are connected and output;

[0034] Pooling layer: Perform maximum pooling on the output of the feature extraction layer;

[0035] Fully connected layer: includes four hidden layers of different dimensions. All four hidden layers use ReLU function as activation function, and a Dropout ratio of 0.5 is added between the first layer and the second layer.

[0036] Output layer: The results of the four hidden layers of the fully connected layer are cascaded to output the affinity scores of MHCI and peptides. This layer uses the sigmoid function as the activation function.

[0037] A further technical solution of the present invention is as follows: the training of the prediction model based on the MHCI sequence matrix and the pre-processed polypeptide sequence matrix comprises:

[0038] The MHCI sequence matrix, the preprocessed peptide sequence matrix and the corresponding affinity scores were divided into five groups, each of which was used as a training set, and the remaining four groups were used as validation sets for cross-training.

[0039] A further technical solution of the present invention is: during the training process, the Mean Squared Error is introduced as a loss function to update the network parameters until the loss function MSE is minimized. The MSE formula is as follows:

[0040]

[0041] Among them, y i Indicates the true affinity score between MHCI and the peptide, represents the predicted affinity score of the prediction model for the i-th MHCI and peptide, and n is the size of the training set.

[0042] A further technical solution of the present invention is as follows: during the training process, an Adam optimizer is introduced to optimize network parameters, the initial learning rate is 0.001, and the learning rate is adjusted to half of the original rate every 10 rounds during the training process.

[0043] A computer system, characterized in that it comprises: one or more processors, and a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned method.

[0044] A computer-readable storage medium, characterized in that it stores computer-executable instructions, which are used to implement the above method when executed.

[0045] The beneficial effects of the present invention are:

[0046] The method for predicting the affinity between the major histocompatibility complex and a polypeptide provided by the present invention has the following advantages compared with the prior art:

[0047] 1. The present invention uses an equal number of 0s and 1s to embed amino acids, which can make the characteristic information of different sequences more obvious.

[0048] 2. Since the present invention uses four convolution kernels of different sizes, it can capture the information and mutual dependence between different numbers of adjacent amino acids.

[0049] 3. The present invention uses two different zero-filling methods for four different sizes of convolution kernels before the convolution operation, and connects them after the convolution, so as to better capture the first and last amino acids and the overall continuous sequence information.

[0050] The method of the present invention shows better performance in predicting the affinity of MHC class I molecules with 10-mer peptides and 11-mer peptides, and the prediction is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The drawings are only for the purpose of illustrating specific embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0052] Figure 1 Implementation flow chart of the method of the present invention.

[0053] Figure 2 A framework diagram for implementing the method of the present invention.

[0054] Figure 3Detailed diagram of the convolutional layer of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0056] like Figure 1 As shown, the embodiment of the present invention provides a method for predicting the affinity between a major histocompatibility complex and a polypeptide, and the implementation steps are as follows:

[0057] Step 1: Obtain the data of MHCI binding to peptides from the International Immune Epitope Database (IEDB);

[0058] This example takes the data set of MHCI and peptide binding affinity obtained from the International Immune Epitope Database (IEDB) as an example, and there are 185,985 records of MHCI and peptide binding in the data set. The data set contains a total of 153 MHCI molecules, including 7 BoLA, 1 Gogo, 7 H-2, 104 HLA, 19 Mamu, 11 Patr and 4 SLA molecules.

[0059] Step 2: Preprocess the acquired MHCI-peptide binding data

[0060] (2a) The obtained MHCI is combined with the affinity IC of the peptide 50 The value is converted to an affinity score of [0,1] using the following formula:

[0061] 1-log(IC 50 nM) / log(50000)

[0062] Wherein, nM is nanomolar;

[0063] (2b) After converting all affinities to [0,1], 25 random natural peptides with lengths of 8, 9, 10, and 11 were introduced for each MHCI molecule, and the converted affinity score was set to 0.1. They were used as artificial negative samples to participate in the training of the model, but not in the validation of the model;

[0064] Step 3: Data Partitioning

[0065] The 2101285 data items are divided into five groups in order, of which the first group contains 40534 items, the second group contains 40956 items, the third group contains 39186 items, the fourth group contains 40048 items, and the fifth group contains 40561 items.

[0066] Step 4: Embedding MHCI and peptide sequences

[0067] The amino acids are embedded using a combination of ten 0s and 1s, as follows:

[0068] Different combinations of position 3 0 and position 3 1 are used to represent 20 amino acids;

[0069] Different combinations of 2 0s and 2 1s are used to distinguish the four charge properties of amino acids: if it is non-polar, it is represented by [0,1,1,1]; if it is polar and uncharged, it is represented by [0,1,0,1]; if it is polar and positively charged, it is represented by [0,1,1,0]; if it is polar and negatively charged, it is represented by [1,0,0,1];

[0070] In particular, for unknown residue X, an embedding representation method is specified;

[0071] The final amino acid embedding rule is as follows:

[0072] A:[1,1,1,0,0,0,0,0,1,1],R:[1,1,0,1,0,0,0,1,1,0],N:[1,1,0,0,1,0, 0,1,0,1],D:[1,1,0,0,0,1,1,0,0,1],C:[1,0,1,1,0,0,0,1,0,1],Q:[1,0 ,1,0,1,0,0,1,0,1],E:[1,0,1,0,0,1,1,0,0,1],G:[1,0,0,1,1,0,0,0,1, 1],H:[1,0,0,1,0,1,0,1,1,0],I:[1,0,0,0,1,1,0,0,1,1],L:[0,1,1,1,0 ,0,0,0,1,1],K:[0,1,1,0,1,0,0,1,1,0],M:[0,1,1,0,0,1,0,0,1,1],F:[ 0,1,0,1,1,0,0,0,1,1],P:[0,1,0,1,0,1,0,0,1,1],S:[0,1,0,0,1,1,0,1 ,0,1],T:[0,0,1,1,1,0,0,1,0,1],W:[0,0,1,1,0,1,0,0,1,1],Y:[0,0,1, 0,1,1,0,0,1,1],V:[0,0,0,1,1,1,0,0,1,1],X:[1,1,1,1,0,0,0,0,0,0].

[0073] The MHCI sequence is embedded according to the embedding rules for amino acids, and a 34×10 matrix is ​​generated for each MHCI sequence;

[0074] The peptide sequence is embedded according to the embedding rules of amino acids. Each peptide sequence generates an L×10 matrix, where L is the length of the peptide. The shape of the generated peptide matrix is ​​unified. For peptides with a length of less than 11, the corresponding matrix is ​​filled to 11×10 by filling with 0. For peptides with a length greater than 11, the matrix composed of the first 11 amino acids is used as the corresponding peptide embedding matrix. For peptides with a length equal to 11, no operation is performed on the peptide embedding matrix.

[0075] Step 5: Construct a prediction model for the affinity between MHCI and peptides, such as Figure 2 As shown in the figure, it includes convolutional layer, bidirectional long short-term memory network layer, pooling layer, fully connected layer, and output layer in sequence; the details are as follows:

[0076] (5a) For four different convolution kernels, the two zero-filling methods corresponding to each convolution kernel are as follows:

[0077]

[0078] (5b) Convolutional layers with 128 kernels and kernel sizes of 1×10, 3×10, 5×10, and 7×10 are established. All layers use the ReLU function as the activation function.

[0079] (5c) The MHCI and peptide matrices embedded in step 3 are filled with 0 according to the method of step 5a. After filling, they are passed through the convolution layer and connected. Finally, four data with a dimension of 128 and a shape of 45×1 can be obtained. The data output by these four convolutions are connected in the order of the convolution kernel size from small to large, and finally the data with a dimension of 128 and a shape of 360×1 is obtained; Figure 3 As shown;

[0080] (5d) Establish a feature extraction part with an input dimension of 128, consisting of two bidirectional long short-term memory network blocks and a pooling layer. The first block consists of a single-layer bidirectional long short-term memory network, and the second block consists of two layers of bidirectional long short-term memory network. Each layer contains 128 hidden units. The results obtained by the two bidirectional long short-term memory network blocks are connected to obtain data with an output dimension of 512 and a shape of 360×1. Finally, the input data is passed through a pooling layer with a kernel size of 360×1;

[0081] (5e) Establish a combined score output layer consisting of a cascade of 512-dimensional input, 1024-dimensional first hidden layer, 768-dimensional second hidden layer, 640-dimensional third hidden layer, 340-dimensional fourth hidden layer, and 1-dimensional output, and use the ReLU function as the activation function for all layers except the last layer, and the activation function for the last layer is sigmoid; and a Dropout ratio of 0.5 is added between the first and second layers;

[0082] Step 6: Train a model to predict MHCI peptide affinity

[0083] (6a) According to the grouping in step (3), each group is used as a validation set, and the remaining four groups are used as training sets. The MHCI and peptide embedding matrix obtained in step (4) and the true score converted by step (2a) are input into the model, and finally five models and validation results with complete data sets are obtained;

[0084] (6b) Mean Squared Error is introduced as the loss function to update the network parameters during the model training process until the loss function MSE is reduced to the minimum, thus completing the model training; the MSE formula is as follows:

[0085]

[0086] Among them, y i Indicates the true affinity score between MHCI and the peptide, represents the predicted affinity score of the model for the i-th MHCI and peptide, and n is the size of the training set.

[0087] (6c) The Adam optimizer (Adaptive Moment Estimation) is introduced to optimize the network parameters during the model training process. The initial learning rate is 0.001, and the learning rate is adjusted to half of the original value every 10 rounds during the training process.

[0088] The following is a detailed description of the effects of this application in conjunction with simulation:

[0089] 1. Simulation conditions:

[0090] The computer hardware CPU of the simulation experiment is Intel Core(TM) i7, and the computer hardware memory is 32G;

[0091] Computer software: Python 3.8 and Pycharm integrated development software on WINDOWS11 system.

[0092] 2. Simulation content:

[0093] Simulation 1: According to the length of the peptide classification, AUROC (AUC) and Pearson correlation coefficient (PCC) are used as indicators to evaluate the prediction ability of the model. The evaluation indicators of the present invention and the three existing methods DeepMHCI, Deep AttentionPan, and CapsNet-MHC are compared in the MHCI and peptide binding data set obtained from IEDB. The results are shown in Table 1:

[0094] Table 1 Evaluation of the present invention and the three existing methods under different polypeptide lengths

[0095]

[0096] As can be seen from Table 1, the performance of the present invention in predicting the affinity of MHCI with 10-mer peptides and 11-mer peptides is significantly higher than the other three methods. When predicting 9-mer peptides, the present invention and DepMHCI are significantly better than DeepAttentionPan and CapsNet-MHC. When predicting 8-mer peptides and >=12-mer peptides, good performance can be maintained. The simulation results show that the present invention maintains a high accuracy when predicting the affinity of polypeptides of different lengths with MHCI.

[0097] Simulation 2: In order to demonstrate the generalization ability and universal applicability of the present invention, the present invention was applied to predict the affinity of MHCII and the peptide, and a similar five-fold division verification was performed. Similarly, AUROC and Pearson correlation coefficient PCC were used as indicators to evaluate the prediction ability of the model. The present invention was compared with DeepAttentionPan and CapsNet-MHC for evaluation indicators, and the results are shown in Table 2:

[0098] Table 2 Evaluation of the present invention and the two existing methods in predicting the affinity between MHCII and peptides

[0099]

[0100] It can be seen from Table 2 that the performance of the present invention in predicting the affinity between MHCII and polypeptide is significantly higher than that of the other two methods. The simulation results show that the model structure of the present invention has strong generalization ability and universal applicability.

[0101] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should be included in the protection scope of the present invention.

Claims

1. A method for predicting the affinity of a major histocompatibility complex to a polypeptide, characterized in that: include: Obtain data on major histocompatibility complex MHCI and peptide affinity; Preprocessing of major histocompatibility complex MHCI and peptide affinity data; The MHCI sequence is embedded based on the embedding rules of amino acids, and an MHCI sequence matrix is ​​generated for each MHCI sequence; The peptide sequence is embedded based on the embedding rules of amino acids, and a peptide sequence matrix is ​​generated for each peptide sequence, and the size of the peptide sequence matrix is ​​unified; Constructing a pre-trained prediction model for MHCI and peptide affinity; the prediction model sequentially comprises a convolutional layer, a bidirectional long short-term memory network layer, a pooling layer, a fully connected layer, and an output layer; The prediction model was trained based on the MHCI sequence matrix and the preprocessed peptide sequence matrix; Predict the affinity of major histocompatibility complex to peptides based on the trained prediction model.

2. A method for predicting the affinity between a major histocompatibility complex and a polypeptide according to claim 1, characterized in that: The pre-processing of the data of major histocompatibility complex MHCI and peptide affinity includes: Major histocompatibility complex MHCI and peptide affinity IC 50 The value is converted to an affinity score in [0,1]: Wherein, nM is nanomolar; For each MHCI sequence, 25 random natural peptides with lengths of 8, 9, 10 and 11 were introduced respectively, and the affinity score after introduction was set to 0.

1. They were used as artificial negative samples to participate in the training of the prediction model, but not in the verification of the prediction model.

3. A method for predicting the affinity between a major histocompatibility complex and a polypeptide according to claim 1, characterized in that: The embedding rules for amino acids are specifically as follows: Different combinations of position 3 0 and position 3 1 are used to represent 20 amino acids; Different combinations of 2 0s and 2 1s are used to distinguish the four charge properties of amino acids: the four charge properties include: if it is non-polar, it is represented by [0, 1, 1, 1]; if it is polar and uncharged, it is represented by [0, 1, 0, 1]; if it is polar and positively charged, it is represented by [0, 1, 1, 0]; if it is polar and negatively charged, it is represented by [1, 0, 0, 1]; For unknown residue X, specify the embedding representation method.

4. A method for predicting the affinity between a major histocompatibility complex and a polypeptide according to claim 1, characterized in that: The size unification process of the polypeptide sequence matrix comprises: If the length of the peptide sequence is less than 11, the matrix is ​​padded with 0 to fill the matrix size to 11×10 If the length of the peptide sequence is equal to 11, no operation is performed on the matrix; If the length of the polypeptide sequence is greater than 11, the first 11 rows of the matrix are truncated to obtain an 11×10 matrix.

5. A method for predicting the affinity between a major histocompatibility complex and a polypeptide according to claim 1, characterized in that: The prediction model of the MHCI and polypeptide affinity is specifically: Convolution layer: includes four parallel convolution kernels of different sizes. All layers use ReLU function as activation function. Before each convolution kernel performs convolution operation, the MHCI and peptide sequence matrices are filled with zeros in two different ways. The MHCI and peptide sequence information obtained by convolution with the same zero filling method are connected. The data obtained by the four convolution kernels are connected and output. Bidirectional LSTM network layer: It includes two bidirectional LSTM network blocks, one of which is composed of a single-layer bidirectional LSTM network and the other is composed of a two-layer bidirectional LSTM network. The output results of the convolutional layer are respectively input into the two bidirectional LSTM network blocks, and the results of the two bidirectional LSTM network blocks are connected and output; Pooling layer: Perform maximum pooling on the output of the feature extraction layer; Fully connected layer: includes four hidden layers of different dimensions. All four hidden layers use ReLU function as activation function, and a Dropout ratio of 0.5 is added between the first layer and the second layer. Output layer: The results of the four hidden layers of the fully connected layer are cascaded to output the affinity scores of MHCI and peptides. This layer uses the sigmoid function as the activation function.

6. A method for predicting the affinity between a major histocompatibility complex and a polypeptide according to claim 1, characterized in that: The method of training the prediction model based on the MHC I sequence matrix and the pre-processed polypeptide sequence matrix comprises: The MHCI sequence matrix, the preprocessed peptide sequence matrix and the corresponding affinity scores were divided into five groups, each of which was used as a training set, and the remaining four groups were used as validation sets for cross-training.

7. A method for predicting the affinity between a major histocompatibility complex and a polypeptide according to claim 6, characterized in that: During the training process, Mean Squared Error is introduced as the loss function to update the network parameters until the loss function MSE is minimized. The MSE formula is as follows: Among them, y i Indicates the true affinity score between MHCI and the peptide, represents the predicted affinity score of the prediction model for the i-th MHCI and peptide, and n is the size of the training set.

8. A method for predicting the affinity between a major histocompatibility complex and a polypeptide according to claim 6, characterized in that: During the training process, the Adam optimizer was introduced to optimize the network parameters. The initial learning rate was 0.001, and the learning rate was adjusted to half of the original value every 10 rounds during the training process.

9. A computer system, characterized in that include: One or more processors, and a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the method of claim 1.

10. A computer-readable storage medium, characterized in that Computer executable instructions are stored, and when the instructions are executed, they are used to implement the method of claim 1.

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