Efficient drug optimization method based on network proximity method

Abstract

The invention provides an efficient drug optimization method based on a network proximity method, and aims at solving the problem of repeated calculation existing in large-scale drug optimization calculation through a traditional network proximity algorithm, the node distance calculation problem of a single drug is converted into the data query problem by optimizing a calculation strategy, and the optimal selection of the drug is realized. And the calculation time is greatly shortened. Specifically, the method comprises the following steps: firstly, acquiring a protein interaction network containing a disease target network and a drug target, and calculating and storing the distance between every two node pairs in the network; and then, in the drug optimization process, network proximity d values and z values between the drugs and the target network are quickly calculated by querying node distance information in the data matrix, and drug optimization is realized according to sorting of the z values. The method effectively avoids repeated calculation, has the advantages of being high in calculation speed, low in resource consumption and suitable for optimization of drugs for various diseases, and has wide application prospects in the large-scale drug optimization process.

Description

Technical Field

[0001] The invention relates to the fields of bioinformatics and drug design, and in particular to a technical method for improving the efficiency of drug optimization using a network proximity method. Background Art

[0002] Network pharmacology combines bioinformatics, network science and pharmacology to study the interaction network between drug molecules and targets, pathways, genes, proteins and other molecules in the body. The core of this approach lies in the concept of systems biology, that is, the life system is regarded as a complex network with complex interactions between the components. The occurrence of disease involves changes in some or all nodes in this complex network, and drugs act on limited targets in the complex network, thereby affecting the network's effect on the treatment of the disease.

[0003] The drug optimization method based on network proximity is a strategy based on complex network analysis, which is used to screen and optimize potential drug candidate molecules in the drug development process. It predicts the effect of a drug by analyzing the network distance or connectivity between the drug and its potential target. The core idea of ​​this method is that the closer the distance between the drug target and the disease-related protein in the biological network, the greater the possibility of interaction between them.

[0004] However, the calculation strategy used in the traditional network proximity algorithm has the problem of repeated calculation of d values. When it is necessary to calculate the proximity between a large number of molecules and the target protein network, the proteins acted by these molecules may overlap. The network proximity calculation is performed independently for each molecule, which inevitably leads to numerous repeated calculations that reduce the efficiency of drug optimization. Summary of the invention

[0005] The present invention aims to solve the problem of low efficiency of network proximity calculation in large-scale drug optimization in the prior art, and provide an efficient drug optimization method based on network proximity. By converting the distance calculation problem between drug nodes into a data query problem, the computing resource consumption and time are significantly reduced.

[0006] The present invention is achieved through the following technical solutions:

[0007] An efficient drug optimization method based on network proximity method, comprising:

[0008] Obtain protein interaction networks including disease target networks and drug targets;

[0009] Calculate and save the shortest distance between every two node pairs in the protein interaction network;

[0010] According to the drug target information, query the distance from the target to the target network in the stored protein interaction network node distance data, and then calculate the network proximity value between the drug and the target network, wherein the network proximity value includes a d value and / or a z value;

[0011] Drug optimization is performed based on network proximity values.

[0012] Furthermore, protein interaction networks including disease target networks and drug targets were obtained based on public literature and databases.

[0013] Furthermore, the protein interaction network including the disease target network and the drug target is the largest connected component in the original protein interaction network graph.

[0014] Furthermore, the shortest distance is implemented using the shortest.paths function in the igraph package.

[0015] Furthermore, it also includes optimizing drugs based on the perturbation similarity scores of the drugs in other databases.

[0016] Furthermore, the perturbation similarity scores in the other databases are scaled_score values ​​in the camp and lincs databases.

[0017] Furthermore, the drug optimization based on the network proximity value is specifically as follows:

[0018] All drugs are sorted from high to low according to their network proximity values, and the drugs ranked first are selected as the preferred drugs.

[0019] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, an efficient drug optimization method based on network proximity method is implemented.

[0020] A storage medium containing computer executable instructions, which, when executed by a computer processor, implement the efficient drug optimization method based on network proximity method.

[0021] A computer program product comprises a computer program / instruction, which, when executed by a processor, implements the steps of the efficient drug optimization method based on network proximity method.

[0022] The beneficial effects of the present invention are as follows: the method of the present invention converts the calculation process of the d value in the network proximity method into a data query in a data matrix, effectively avoiding the problem of repeated calculation of the d value in the large-scale drug optimization process, and has the advantages of fast calculation speed, can be used for drug optimization for various diseases, and has low resource consumption. The method has broad application prospects in the large-scale drug optimization process. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The present invention is a flowchart of an efficient drug optimization method based on network proximity method;

[0024] Figure 2 It is a data structure of network d value calculation and data matrix. In the figure, C is column, R is row; R1-Ri and C1-Ci are the row name (Row) and column name (Column) of the matrix. Each row name or column name corresponds to a node (gene) name in the total network, that is, all row names or column names correspond to genes in the total network one by one. The value d(j,i) in the matrix corresponds to the shortest distance between two genes in the row and column.

[0025] Figure 3 The drug optimization flow chart of the network proximity method in Example 1;

[0026] Figure 4 This is a time-consuming comparison result diagram of the calculation strategy of the present invention and the traditional calculation strategy in Example 2; DETAILED DESCRIPTION

[0027] The present invention provides an efficient drug optimization method based on network proximity method, which calculates and records the distance between each node pair in the network. When performing large-scale drug optimization, the node distance is directly read from the record to reduce the consumption of computing resources. Specifically, Figure 1 As shown, the following steps are included:

[0028] Step 1: Obtain the protein interaction network including disease target network and drug target;

[0029] In this step, the protein interaction network containing the disease target network and the drug target can be obtained from the public literature and database according to the disease to be screened. As a preferred solution, the igraph package in the R language can also be used to create a graph object and extract the maximum connected component to ensure the integrity of the network and the accuracy of the calculation. The maximum connected component is a concept in graph theory, which refers to the largest subgraph in an undirected graph, in which any two vertices are connected. By obtaining the maximum connected component, it can be ensured that in further network analysis, only the largest and densest part of the graph is concerned, which is convenient for the calculation of the shortest distance between subsequent nodes.

[0030] Step 2: Calculate and save the shortest distance between every two node pairs in the protein interaction network;

[0031] In this step, the shortest distance can be calculated in various ways. In a specific implementation scheme, the shortest distance between every two node pairs in the total network is calculated using the R language third-party package igraph, and the results are saved in a data matrix, such as Figure 2 shown.

[0032] Step 3: According to the drug target information, the distance from the target to the target network is queried in the stored protein interaction network node distance data, and then the network proximity value between the drug and the target network is calculated, and the network proximity value includes a d value and / or a z value;

[0033] Among them, the network proximity d value is calculated according to the following formula:

[0034]

[0035] Where V is the disease-related protein subnetwork, T is the drug target protein subnetwork, ||T|| represents the number of nodes in the drug target protein subnetwork, and d(v,t) is the network distance between each pair of nodes in the subnetworks v∈V and t∈T.

[0036] The relative network proximity z value is calculated based on the average value of d(v,t) and standard deviation Calculated by the following formula:

[0037]

[0038] Step 4: Optimize drugs based on network proximity values.

[0039] Generally, the larger the network proximity value, the closer the distance between the drug target and the disease-related protein in the network, and the greater the therapeutic potential of the drug. Therefore, the drug with a large network proximity value can be selected according to the network proximity value to achieve drug optimization. In a specific embodiment, all candidate drugs are ranked according to the calculated relative network proximity z value. The higher the z value, the closer the distance between the drug target and the disease-related protein in the network, and the greater the therapeutic potential of the drug.

[0040] In another specific embodiment, the optimization process also considers the perturbation similarity scores of the drugs in other databases, such as the scaled_score values ​​in the camp and lincs databases.

[0041] The method of the present invention can quickly screen out high potential drug candidates for specific diseases. The above calculation strategy is implemented using a programming language and can be used multiple times to complete rapid drug optimization for different neurological diseases.

[0042] In order to better illustrate the purpose, technical solutions and advantages of the present invention, the present invention will be further described below in conjunction with specific embodiments.

[0043] Embodiment 1:

[0044] This embodiment is based on the interaction network data containing 351,444 protein interactions (PPI) pairs obtained in the reported document 1 (DOI: 10.1038 / s43587-021-00138-z). Read the protein interaction information in the interaction network, create a graph object from the protein interaction data frame using the graph_from_data_frame function of the igraph package, and obtain the maximum connected component in the graph object using the getgcc function. The maximum connected component is a concept in graph theory, which refers to the largest subgraph in an undirected graph, in which any two vertices are connected. That is, the maximum connected component is the connected subgraph containing the most vertices. By obtaining the maximum connected component, it can be ensured that in further network analysis, only the largest, densest, and possibly most important part of the graph is concerned, and for those smaller, isolated connected components, they may contain less information, or are not important enough in a specific analysis context, so they will be ignored in many cases, and it can also be ensured that there are no isolated nodes in the processed network, which is convenient for the calculation of the shortest distance between subsequent nodes.

[0045] The calculation method of the network proximity between drug target proteins and disease-related proteins is defined as follows: V is defined as the disease-related protein subnetwork, T is defined as the drug target protein subnetwork, and d(v,t) is the network distance between two nodes in the subnetwork v∈V and t∈T, then the following formula is obtained:

[0046]

[0047] The absolute network proximity d value between the drug target protein and the disease-related node can be calculated by formula ①. The relative network proximity z value can be calculated by formula ② based on the mean and standard deviation of d(v,t).

[0048] The shortest.paths function is used to calculate the shortest distance between two nodes in the largest connected component of the protein interaction network. The shortest distance between two nodes and the total network of the largest connected component are saved in the data file, and the matrix structure of the shortest distance between two nodes is saved as follows: Figure 2 As shown, when calculating the network proximity between drug targets and disease-related proteins, the Figure 3 The shortest distance between related nodes is directly read from the data file, and the absolute network proximity and relative network proximity are calculated according to formulas ① and ②. Finally, the drug optimization purpose is completed in ascending order of z value.

[0049] In the total network constructed in Example 1, 50, 100, 200, and 300 protein nodes were randomly selected as the test disease protein set. Four small molecule drugs (DB00002, DB00004, DB00005, and DB00013) whose target proteins were included in the total network were selected from the DrugBank database for testing. The network proximity d value and z value between the four small molecule drugs and the four test disease protein sets were calculated using the method of this embodiment, and the time consumption was recorded to evaluate the computational efficiency of the traditional calculation strategy.

[0050] The calculation results are shown in Table 1. As the number of disease proteins increases, the calculation complexity increases, but the time consumption of the calculation strategy of the present invention only increases slightly.

[0051] Table 1 Calculation results of calculation strategy of Example 1

[0052]

[0053] Comparative Example 1:

[0054] The traditional calculation strategy for network proximity in reference 2 (https: / / doi.org / 10.1073 / pnas.2025581118) is to calculate the absolute network proximity and relative network proximity of each drug target to the disease-related protein one by one according to equations ① and ②. The traditional calculation strategy in reference 2 is implemented using a programming language: (1) Read the node information of the drug target in the total network. (2) Read the node information of the disease-related protein in the total network. (3) According to equations ① and ②, calculate the network proximity between the drug target and the disease-related protein one by one. (4) Combine the calculated results and sort them in ascending order of relative network proximity z value to complete the drug optimization for the disease.

[0055] In the total network constructed in Example 1, 50, 100, 200, and 300 protein nodes were randomly selected as the test disease protein set, and four small molecule drugs (DB00002, DB00004, DB00005, and DB00013) whose target proteins were included in the total network were selected from the DrugBank database for testing. The network proximity d value and z value between the four small molecule drugs and the four test disease protein sets were calculated using the traditional calculation strategy in Document 2, and the time consumption was recorded to evaluate the computational efficiency of the traditional calculation strategy.

[0056] The calculation results are shown in Table 2. As the number of disease proteins increases, the calculation complexity increases and the corresponding time consumption also increases.

[0057] Table 2 Calculation results of traditional calculation strategy

[0058]

[0059] The calculation strategy of the present invention and the traditional calculation strategy calculate the same network proximity d value, but the time consumption of the two strategies can be dozens of times different. Figure 4 As shown, the calculation time of the strategy of the present invention is 4 to 5 seconds, while the time consumption of the traditional calculation strategy increases rapidly with the increase in the number of disease proteins.

[0060] Embodiment 2:

[0061] The present invention is applied in drug optimization for depression. The protein expression information of depression patients in multiple brain regions is obtained from six data sets (GSE102556, GSE185855, GSE45468, GSE45642, GSE53987, GSE87610) in the GEO database. The up- and down-regulation information of each protein is analyzed by the protein expression difference (logFC) between depression patients and normal controls, and 359 important proteins related to depression are obtained for drug optimization.

[0062] A large amount of information on drugs and the protein targets of each drug was collected from the drugBank database. A total of 6,244 small molecule drugs and their respective target information were collected.

[0063] Ensure that the above-mentioned depression-related proteins and each drug target protein are all included in the maximum connected component total network constructed in Example 1.

[0064] Based on the above-mentioned small molecule target information and depression-related protein information, the distance between the drug target and the depression protein is obtained in the data matrix saved in Example 1, and the absolute network proximity d value and relative proximity z value between the drug and depression are calculated according to formulas ① and ②.

[0065] All drugs were sorted in ascending order according to the calculated z-values, and the preferred drugs were selected according to z≤-2 and the perturbation similarity score scaled_score≤0.6 of each drug in the camp and lincs databases.

[0066] At the same time, the same network proximity calculation was performed using the traditional calculation strategy in Comparative Example 1, which took more than 240 hours in total, while the calculation strategy of the present invention took only 1.72 hours. Some drug optimization results are shown in Table 3, which include three depression treatment drugs that have been marketed, namely DB06700, DB08918, and DB00234. The specific information of the three drugs is shown in Table 4.

[0067] Table 3 Results of partial drug selection

[0068]

[0069] Table 4 Three marketed antidepressants in the drug selection results

[0070]

[0071] An electronic device provided by an embodiment of the present invention includes one or more processors, which are used to implement an efficient drug optimization method based on network proximity method in the above embodiment.

[0072] The electronic device of the present invention can be applied to any device with data processing capability, and the device with data processing capability can be a device or apparatus such as a computer.

[0073] The device embodiment can be implemented by software, hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of any device with data processing capability in which it is located to read the corresponding computer program instructions in the non-volatile memory into the memory for execution. From the hardware level, in addition to the processor, memory, network interface, and non-volatile memory, any device with data processing capability in which the device in the embodiment is located can also include other hardware according to the actual function of the device with data processing capability, which will not be described in detail.

[0074] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0075] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. Ordinary technicians in this field can understand and implement it without paying creative work.

[0076] An embodiment of the present invention further provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, an efficient drug optimization method based on a network proximity method in the above embodiment is implemented.

[0077] The computer-readable storage medium may be an internal storage unit of any device with data processing capability described in any of the aforementioned embodiments, such as a hard disk or a memory. The computer-readable storage medium may also be any device with data processing capability, such as a plug-in hard disk, a smart media card (SMC), an SD card, a flash card, etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of any device with data processing capability and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capability, and may also be used to temporarily store data that has been output or is to be output.

Claims

1. An efficient drug optimization method based on network proximity method, characterized in that: include: Obtain protein interaction networks including disease target networks and drug targets; Calculate and save the shortest distance between every two node pairs in the protein interaction network; According to the drug target information, query the distance from the target to the target network in the stored protein interaction network node distance data, and then calculate the network proximity value between the drug and the target network, wherein the network proximity value includes a d value and / or a z value; Drug optimization is performed based on network proximity values.

2. The method according to claim 1, characterized in that Protein interaction networks including disease target networks and drug targets were obtained based on public literature and databases.

3. The method according to claim 1, characterized in that The protein interaction network including the disease target network and the drug target is the largest connected component in the original protein interaction network graph.

4. The method according to claim 1, characterized in that The shortest distance is implemented using the shortest.paths function in the igraph package.

5. The method according to claim 1, characterized in that It also includes drug optimization based on the perturbation similarity scores of the drugs in other databases.

6. The method according to claim 5, characterized in that The perturbation similarity scores in the other databases are scaled_score values ​​in the camp and lincs databases.

7. The method according to claim 1, characterized in that The method of performing drug optimization based on the network proximity value specifically includes: sorting all drugs from high to low according to the network proximity value, and selecting the drug with the highest ranking as the preferred drug.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, an efficient drug optimization method based on network proximity method as described in any one of claims 1 to 7 is implemented.

9. A storage medium comprising computer executable instructions, which, when executed by a computer processor, implement an efficient drug optimization method based on network proximity method as described in any one of claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of an efficient drug optimization method based on network proximity method as described in any one of claims 1 to 7 are implemented.

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