A method, system and computer readable carrier for dynamic analysis of protein-small molecule interactions

Abstract

The present invention relates to a method, system and computer-readable carrier for dynamically analyzing the interaction of protein-small molecules. Specifically disclosed is that the method comprises the following steps: S1) inputting structural coordinate data and / or trajectory data of protein-small molecules; S2) pre-detecting and storing the data obtained in step S1); S3) processing and analyzing the data stored in step S2); S4) performing 2D visualization according to the data obtained in step S3); S5) performing 3D visualization according to the data obtained in step S3). The method or system of the present invention can support a variety of file types and formats, and can provide dynamic interaction analysis. Based on trajectory files, 3D dynamic interaction visualization results can be obtained. And the unique 2D visualization display provides radar charts and 2D dynamic trajectory interaction distribution to more clearly display the entire change process of all interactions.

Description

Technical Field

[0001] The invention belongs to the field of bioinformatics, and in particular relates to a system for dynamically analyzing the interaction between proteins and small molecules. Background Art

[0002] The Protein Data Bank (PDB) has nearly 100,000 deposited protein structures, of which more than 75% are in complex with small molecule ligands. The binding of a ligand to its host protein requires a specific arrangement of attractive, usually non-covalent contacts between the two molecules. With the rich data available, we can gain a deeper understanding of how ligands interact with their protein targets. Detailed characterization of these interaction patterns is essential for understanding molecular recognition and protein function or developing and optimizing lead compounds. In theory, comparative high-throughput analysis of interaction patterns can greatly improve protein-ligand docking or virtual screening, thereby enhancing computer methods in drug discovery. However, there are few studies in the prior art on how to use existing data for in-depth analysis. Instead, biopharmaceutical researchers conduct independent research on a certain drug or protein, but lack a platform that can provide protein-small molecule interaction analysis. More importantly, the current analysis system requires researchers to understand a lot of programming and analysis capabilities, and cannot directly display intuitive and visual analysis results through the data in the hands of researchers, nor can it achieve the interface operation that allows researchers to more easily obtain the expected analysis results. Although some platforms currently provide visual analysis of proteins and small molecules, they can only output and input static results, such as only inputting pdb files, but cannot output and input dynamic results, such as motion trajectory files. As we all know, dynamic trajectories are more accurate and have higher research value than static data, whether for the interaction between proteins and small molecules or for the stability of proteins. Summary of the invention

[0003] Based on the above problems, the present invention provides a method that combines computer methods with the field of chemistry to quickly calculate the dynamic interaction between proteins and small molecules based on static files and dynamic trajectory-type original protein-small molecule data files provided by users, and to perform a visual display.

[0004] One aspect of the present invention provides a method for dynamically analyzing protein-small molecule interactions, the method comprising the following steps:

[0005] S1) inputting structural coordinate data and / or trajectory data of protein-small molecule;

[0006] S2) pre-detecting and storing the data obtained in step S1);

[0007] S3) Processing and analyzing the data stored in step S2):

[0008] S31) Processing the data stored in step S2) to obtain a pdb format file;

[0009] S32) For the data in the pdb format file obtained in step S31), model building and MD simulation are performed using gromacs to obtain the root mean square deviation (RMSD) and root mean square fluctuation (RMSF) data of the original data, generate an xvg format result file, and then draw the corresponding RMSD and RMSF graphs through a python script, and save the result graph;

[0010] S33) cleaning the data in the pdb format file obtained in step S31), and then performing calculation analysis using physical geometric properties to obtain an interaction type result file of xml type; and saving the corresponding data;

[0011] S4) performing 2D visualization of the data obtained according to step S3):

[0012] S41) drawing and displaying protein-small molecule interaction radar maps, wherein there are 9 interaction radar maps, including hydrophobic interaction radar map, hydrogen bond interaction radar map, water bridge interaction radar map, salt bridge interaction radar map, pi-pi stacking interaction radar map, cation-pi interaction radar map, halogen bond interaction radar map, metal complex interaction radar map and total interaction radar map;

[0013] S42) drawing and displaying a heat map of the interaction distribution of the trajectory data, wherein the abscissa of the heat map represents different frames in the trajectory data, the ordinate represents the amino acid sites in the protein that interact with the small molecule, and each grid represents the number and type of interactions between the corresponding amino acid site in the corresponding frame and the small molecule;

[0014] S43) displaying the RMSD and RMSF graphs obtained in step S32);

[0015] S5) Perform 3D visualization of the data obtained according to step S3):

[0016] Using NGL technology, the XML type result file obtained by the calculation and analysis in step S33) is displayed.

[0017] In some specific technical solutions of the present invention, in step S1), structural coordinate data and trajectory data are input, and the structural coordinate file format that the data upload module can accept is selected from the pdb format or the gro format, and the trajectory file format is selected from at least one of the xtc format, dcd, and gro formats.

[0018] In some specific technical solutions of the present invention, the method for obtaining the pdb format file in step S31) is: for trajectory data, first calculate the number of trajectories of the trajectory data, then sample each trajectory at equal intervals, and synthesize a pdb format file containing 30-100 frames (Frame); for gro format, xtc format, and dcd format files, they are converted into pdb format files through the analysis tool MDAnalysis.

[0019] In some specific technical schemes of the present invention, the type of interaction is selected from hydrophobic interaction, hydrogen bond, water bridge, salt bridge, pi-pi stacking, cation-pi interaction, halogen bond, and metal complex interaction.

[0020] In some specific technical solutions of the present invention, each axis of the radar chart represents an amino acid in a protein having an interaction site with a small molecule, and the scale is the frequency of the corresponding interaction.

[0021] In some specific technical solutions of the present invention, in the total radar chart, each axis represents an amino acid in a protein having an interaction site with a small molecule, and the scale is the sum of the frequencies of various interactions of the amino acid at the site.

[0022] In some specific technical schemes of the present invention, in the heat map, each grid represents the number and type of interactions between the corresponding amino acid site of the corresponding frame and the small molecule, and different colors are used to represent different types of interactions. When the corresponding frame contains multiple interaction sites, different corresponding colors are used to evenly divide the grid.

[0023] Another aspect of the present invention provides a computer-readable carrier for dynamically analyzing protein-small molecule interactions, wherein the computer-readable storage medium is used to store program codes, and the program codes are used to execute the above-mentioned method for dynamically analyzing protein-small molecule interactions.

[0024] In another aspect, the present invention provides a system for dynamically analyzing protein-small molecule interactions, the system comprising a data uploading module, a data pre-detection and storage module, a data processing and analysis module, and a result visualization module;

[0025] The data uploading module is used to input the structural coordinate data and / or trajectory data of protein-small molecules;

[0026] The data pre-detection and storage module is used to pre-detect and store the data obtained by the data upload module;

[0027] The data processing and analysis module is used to process and analyze the data stored in the pre-detection and storage modules; the data processing and analysis module: 1) is used to process the data in the pre-detection and storage modules to obtain a pdb format file; 2) is used to construct a model and perform MD simulation through gromacs to obtain the root mean square deviation (RMSD) and root mean square fluctuation (RMSF) data of the original data, generate an xvg format result file, and then draw the corresponding RMSD and RMSF graphs through a python script, and save the result graph; 3) is used to clean the pdb format file obtained in 1), and then use physical geometric properties to perform calculation analysis to obtain an xml type interaction result file; and save the corresponding data;

[0028] The result visualization module is used to perform 2D visualization and / or 3D visualization on the data of the data processing and analysis module:

[0029] The 2D visualization refers to: 1) drawing and displaying protein-small molecule interaction radar maps, and there are 9 interaction radar maps, including hydrophobic interaction radar map, hydrogen bond interaction radar map, water bridge interaction radar map, salt bridge interaction radar map, pi-pi stacking (pi_stack) interaction radar map, cation-pi interaction radar map, halogen bond interaction radar map, metal complex interaction radar map and total interaction radar map; 2) drawing and displaying a heat map of the interaction distribution of trajectory data, the horizontal axis of the heat map is different frames in the trajectory data, the vertical axis represents the amino acid sites in the protein that interact with the small molecule, and each grid represents the number and type of interaction between the corresponding amino acid site of the corresponding frame and the small molecule; 3) displaying the root mean square deviation (RMSD) and root mean square fluctuation (RMSF) data results obtained by the data processing and analysis module;

[0030] The 3D visualization refers to: using NGL technology to display the XML result file obtained by calculation and analysis of the data processing and analysis module.

[0031] In some specific technical solutions of the present invention, the structural coordinate file format that can be accepted by the data upload module is selected from at least one of the pdb format, the gro format, the xtc format, and the dcd format.

[0032] In some specific technical schemes of the present invention, in the data processing and analysis module, for trajectory data, the method for obtaining the pdb format file is: first calculate the number of trajectories in the trajectory data, then sample each trajectory at equal intervals, and synthesize a pdb format file containing 30-100 frames (Frame); for gro format, xtc format, and dcd format files, they are converted into pdb format files through the analysis tool python's MDAnalysis.

[0033] In some specific technical schemes of the present invention, the type of interaction is selected from hydrophobic interaction, hydrogen bond, water bridge, salt bridge, pi-pi stacking, cation-pi interaction, halogen bond, and metal complex interaction.

[0034] In some specific technical solutions of the present invention, each axis of the radar chart represents an amino acid in a protein having an interaction site with a small molecule, and the scale is the frequency of the corresponding interaction.

[0035] In some specific technical solutions of the present invention, in the total radar chart, each axis represents an amino acid in a protein having an interaction site with a small molecule, and the scale is the sum of the frequencies of various interactions of the amino acid at the site.

[0036] In some specific technical solutions of the present invention, in the heat map, different colors are used to display different types of interactions. When there are multiple interactions, the colors corresponding to the multiple interactions are evenly divided into grids in the heat map, and several colors in each grid represent several interactions.

[0037] The present invention discloses a system for dynamically analyzing the interaction between protein and small molecules. The whole system is divided into a data upload and storage module, a server analysis and processing data module, a result visualization module, a system tutorial module, and a case display module. Mainly based on the original data uploaded by the user (including the coordinate type and trajectory type files of the protein-small molecule), the server aligns and preprocesses it, and then calculates and analyzes the interaction between the protein and small molecules according to the geometric properties, and analyzes the stability of the protein itself using the formula variance formula (RMSD) and the root mean square fluctuation formula (RMSF), and finally returns the result to visualize the result. The whole system is stable and fast, and the user can quickly obtain the visualized dynamic interaction results after uploading the file, which greatly facilitates the research in various fields such as bioinformatics research, protein design, drug screening, and small molecule chemical synthesis.

[0038] Beneficial Effects

[0039] 1. The method or system of the present invention can support multiple file types and formats, not just a single pdb file format. Users can also upload other types of file formats such as *gro, *xtc, *dcd, etc. The method or system of the present invention can automatically convert them into pdb files for further analysis.

[0040] 2. The method or system of the present invention can provide dynamic interaction analysis. Since users can upload trajectory data, by uploading data containing dynamic trajectories, 3D dynamic interaction visualization results can be obtained.

[0041] 3. Unique 2D visualization representation: the method or system of the present invention provides radar charts and 2D dynamic trajectory interaction distribution to more clearly display the entire change process of all interactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 In order to dynamically analyze the interaction system structure of proteins and small molecules, the whole system is divided into data upload and storage module, server analysis and processing data module, result visualization module, system tutorial module, and case display module. The server aligns and preprocesses the original data uploaded by the user (including protein-small molecule coordinate and trajectory files), and then calculates and analyzes the interaction between proteins and small molecules based on geometric properties. At the same time, the formula is used to analyze the stability of the protein itself, and finally the results are returned and visualized.

[0043] Figure 2 2D interaction visualization results. The analyzed 2D dynamic interaction results include radar interaction diagram, 2D dynamic trajectory interaction distribution, and RMSD and RMSF result diagrams.

[0044] Figure 3 3D interaction visualization result diagram. 3D visualization result page after analysis. The center part shows the protein-small molecule structure and the analyzed interactions, and the right area is the corresponding function area. From top to bottom, they are interaction type screening, small molecule screening, independent display of interactions, 3D display mode, dynamic interaction display function, and focus function.

[0045] Figure 4 It is a 2D radar analysis chart. The results after analysis are divided into eight interaction type radar charts and one comprehensive radar chart. We take the second radar chart hydrophobic interaction as an example. It can be seen from the figure that we have a total of 6 protein-small molecule hydrophobic interactions, namely A222, V223, V135, V354, F327, and F328. The A in A222 represents the corresponding amino acid, and 222 is the position of the amino acid in the data file. The other meanings are the same, and it can be clearly seen that the interactions with higher frequencies are A222, V135, and F328, which are more likely to maintain stable interactions with movement.

[0046] Figure 5It is a 2D dynamic trajectory interaction distribution diagram, which shows the dynamic trajectory interaction distribution diagram. The horizontal axis is the corresponding trajectory sequence, and the vertical axis represents the position of small molecule-protein-protein in the original data. It can be clearly seen in the entire motion trajectory, which interactions can exist stably and which are relatively unstable.

[0047] Figure 6 This is the RMSD diagram, which shows the RMSD value (Root Mean Square Deviation) of the protein in the entire motion trajectory, describing the degree of change in the protein structure. It can be seen that the protein is relatively stable between 25 and 200.

[0048] Figure 7 This is the RMSF graph, which shows the RMSF value (Root Mean Square Fluctuation) of the protein in the entire motion trajectory, which represents the degree of freedom of movement of each atom in the molecule. It can be seen that in the range of 0-50 and 250-300, the corresponding structure is relatively flexible, and the other parts are relatively stable.

[0049] Figure 8 This is the RMSD diagram, which shows the RMSD value (Root Mean Square Deviation) of the protein-small molecule in the entire motion trajectory. It can be seen that it is relatively stable throughout the entire motion trajectory. DETAILED DESCRIPTION

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and understandable, the specific implementation modes of the present invention are described in detail below, but it should not be understood as limiting the applicable scope of the present invention.

[0051] Combine the following Figure 1 The method of the present invention is described in detail. In one aspect, the present invention provides a method for dynamically analyzing protein-small molecule interactions, the method comprising the following steps:

[0052] Step S1) inputting structural coordinate data and / or trajectory data of protein-small molecule;

[0053] In some specific embodiments, in step S1), structure coordinate data and trajectory data are input, and the structure coordinate file format is selected from at least one of pdb format, gro format, xtc format, and dcd format.

[0054] In some specific embodiments, step S1) is completed through a data upload module.

[0055] In some specific embodiments, the data uploaded by the user is obtained in the data upload module, and the data upload module includes a user selecting a data file unit and clicking an upload unit.

[0056] In some specific embodiments, the upload data module function is implemented through HTML, CSS3 and Javascript, where HTML and CSS3 are responsible for page design, and the logic function is completed through Javascript to detect whether the format and size of the user's long-transferred file meet the requirements.

[0057] Step S2) pre-detecting and storing the data obtained in step S1);

[0058] In some specific embodiments, after the user selects the file to be uploaded in step S2), the system will detect whether its file format meets the corresponding requirements. If it meets the format requirements, it will go to the next module for analysis, otherwise it cannot go to the next step and gives a corresponding error prompt. After the user data is detected to be ok, it is first saved in the database, its unique ID is recorded, and its file path, upload time and other information are saved for analysis and calculation results.

[0059] In some specific embodiments, the database used in step S2) is a MySQL database.

[0060] In some specific embodiments, step S2) is completed by a pre-detection and storage module.

[0061] Step S3) processes and analyzes the data stored in step S2):

[0062] S31) Processing the data stored in step S2) to obtain a pdb format file;

[0063] S32) For the data in the pdb format file obtained in step S31), model building and MD simulation are performed using gromacs to obtain the root mean square deviation (RMSD) and root mean square fluctuation (RMSF) data of the original data, generate an xvg format result file, and then draw the corresponding RMSD and RMSF graphs through a python script, and save the result graph;

[0064] S33) Cleaning the data in the pdb format file obtained in step S31), and then performing calculation analysis using physical geometric properties to obtain an interaction type result file of XML type; and saving the corresponding data.

[0065] In some specific embodiments, the method for obtaining the pdb format file in step S31) is, for trajectory data, first calculating the number of trajectories of the trajectory data, then sampling each trajectory at equal intervals, and synthesizing a pdb format file containing 50 frames (Frame); for gro format, tc format, and dcd format files, they are converted into pdb format files through the MDAnalysis method of the analysis tool python.

[0066] In some specific embodiments, in step S31), the number of uploaded data is first determined, and then the file format is further determined. For the structural coordinate data, it is directly converted into a unified pdb format. For the data containing structural coordinate data and trajectory data, the number of trajectories contained in the trajectory data is first calculated, and then the samples are equally spaced, and finally synthesized into a pdb format file containing 50 frames to be calculated and analyzed.

[0067] In some specific embodiments, in step S33), the data file is cleaned, the water molecules and the synthesized model serial numbers are removed, and then the starting and ending positions of each trajectory are found, and the irrelevant first and last items in each trajectory are removed. Then, the physical geometric properties are used for calculation and analysis to obtain an interaction type result file of type xml, and its path is saved in the database row where the corresponding input file is located.

[0068] In some embodiments of the present invention, the type of interaction is selected from hydrophobic interaction, hydrogen bond, water bridge, salt bridge, pi-pi stacking, cation-pi interaction, halogen bond, and metal complex interaction.

[0069] S4) performing 2D visualization of the data obtained according to step S3):

[0070] S41) drawing and displaying protein-small molecule interaction radar maps, wherein there are 9 interaction radar maps, including hydrophobic interaction radar map, hydrogen bond interaction radar map, water bridge interaction radar map, salt bridge interaction radar map, pi-pi stacking interaction radar map, cation-pi interaction radar map, halogen bond interaction radar map, metal complex interaction radar map and total interaction radar map;

[0071] S42) drawing and displaying a heat map of the interaction distribution of the trajectory data, wherein the abscissa of the heat map represents different frames in the trajectory data, the ordinate represents the amino acid sites in the protein that interact with the small molecule, and each grid represents the number and type of interactions between the corresponding amino acid site in the corresponding frame and the small molecule.

[0072] S43) displays the RMSD and RMSF graphs obtained in step S32).

[0073] In some specific embodiments of the present invention, each axis of the radar chart represents an amino acid in a protein having an interaction site with a small molecule, and the scale is the frequency of the corresponding interaction.

[0074] In some specific embodiments of the present invention, in the total radar chart, each axis represents an amino acid in a protein that has an interaction site with a small molecule, and the scale is the sum of the frequencies of various interactions of the amino acid at the site.

[0075] In some specific embodiments of the present invention, each radar chart expresses a specific interaction and the frequency of occurrence of the interaction. The specific interaction is all interactions, hydrophobic interactions, hydrogen bonds, water bridges, salt bridges, pi-pi stacking, cation-pi interactions, halogen bonds, and metal complex interactions. All interactions are the weighted sum of the effects of hydrophobic interactions, hydrogen bonds, water bridges, salt bridges, pi-pi stacking, cation-pi interactions, halogen bonds, and metal complex interactions.

[0076] In some specific embodiments of the present invention, in the heat map, each grid represents the number and type of interactions between the corresponding amino acid site of the corresponding frame and the small molecule, and different colors are used to represent different types of interactions. When the corresponding frame contains multiple interaction sites, different corresponding colors are used to evenly divide the grid.

[0077] In some specific embodiments of the present invention, a method for drawing a 2D dynamic trajectory interaction distribution heat map is to record each interaction and the trajectory sequence where the interaction is located. When drawing, the horizontal axis is the trajectory sequence and the vertical axis is the specific interaction.

[0078] In some specific embodiments of the present invention, RMSD and RMSF graphs are used to characterize the stability of the protein itself, and are calculated by the mean square deviation formula (RMSD) and the root mean square fluctuation formula (RMSF) in combination with the changes in the atomic coordinates in the trajectory file data. The RMSD calculation method is to first calculate the average position of the atomic coordinates (x, y, z) moving n times, and then calculate the square difference between the n coordinates and the average coordinate, and then sum all the square differences and take the average as the RMSD value. The RMSF calculation method is to calculate the average value of the square difference change of each atomic position over time within a time T. RMSF represents the degree of freedom of movement of each atom in the molecule.

[0079] In some specific embodiments of the present invention, since an interaction has multiple interaction types at the same time, its number of interactions is calculated and evenly distributed to the corresponding grids. Different interaction types have corresponding colors, and finally the drawing of the entire heat map is completed.

[0080] S5) Perform 3D visualization of the data obtained according to step S3):

[0081] Using NGL technology, the XML result file obtained by calculation and analysis in step S33) is displayed.

[0082] In some specific embodiments of the present invention, the 3D visualization display initially displays the first xml file and displays all interaction results by default; and has an information filtering function; the filtering function includes selecting the type of interaction to be displayed, selecting a specific small molecule, selecting a background color, selecting whether to display only interactions, display mode, trajectory playback, and focusing.

[0083] Among them, (1) Select the interaction type to be displayed: there are nine interaction type options in total, and users can observe the part they are interested in by checking the specific interaction type.

[0084] (2) Select specific small molecules: Users can click the drop-down menu to select a specific small molecule or all small molecules and observe the interactions related to the small molecule.

[0085] (3) Select background color: The background colors are black, white and gray. Users can choose different background colors to make drawing more convenient.

[0086] (4) Choose whether to display only interactions: When the uploaded protein structure is complex, the structure may block the interaction, making it difficult to observe. In this case, you can check this option to display only the interaction and remove the structure display.

[0087] (5) Display mode: You can select stereo mode to observe 3D structures.

[0088] (6) Trajectory playback: By clicking on the trajectory playback, you can dynamically observe the changes of all interactions in the entire trajectory. Of course, you can also select a certain interaction of interest to observe and check its stability during motion. At the same time, we can manually observe the dynamic trajectory by sliding the horizontal axis on the right, or directly locate a specific trajectory sequence for analysis.

[0089] (7) Focus: By clicking the button, you can quickly focus on the center of the structure to prevent the target from being lost due to continuous rotation during observation.

[0090] An example of visualization results generated by uploading structure coordinate files and trajectory files for analysis is shown below:

[0091] 1) 2D radar image Figure 4 As shown, there are eight interaction type radar charts and one comprehensive radar chart. Taking the second radar chart hydrophobic interaction as an example, it can be seen that there are 6 protein-small molecule hydrophobic interactions, namely A222, V223, V135, V354, F327, and F328. The A in A222 represents the corresponding amino acid, and 222 is the position of the amino acid in the data file. The other meanings are the same, and it can be clearly seen that the interactions with higher frequencies are A222, V135, and F328, which are more likely to maintain stable interactions with movement.

[0092] 2) 2D dynamic trajectory interaction distribution diagram Figure 5 shown. Figure 5 The dynamic trajectory interaction distribution diagram is displayed, with the horizontal axis representing the corresponding trajectory sequence and the vertical axis representing the position of small molecule-protein-protein in the original data. It can be clearly seen which interactions can exist stably and which are relatively unstable in the entire motion trajectory.

[0093] 3) RMSD and RMSF graphs Figure 6-8 As shown. Figure 6 This is the RMSD graph, which shows the RMSD value (Root Mean Square Deviation) of the protein in the entire motion trajectory, describing the degree of change in the protein structure. It can be seen that the protein is relatively stable between 25 and 200. Figure 7This is the RMSF graph, which shows the RMSF value (Root Mean Square Fluctuation) of the protein in the entire motion trajectory, which indicates the degree of freedom of movement of each atom in the molecule. It can be seen that in the range of 0-50 and 250-300, the corresponding structure is relatively flexible, and the other parts are relatively stable. Figure 8 This is the RMSD diagram, which shows the RMSD value (Root Mean Square Deviation) of the protein-small molecule in the entire motion trajectory. It can be seen that it is relatively stable throughout the entire motion trajectory.

Claims

1. A method for dynamically analyzing protein-small molecule interactions, characterized in that: The method comprises the following steps: S1) inputting structural coordinate data and / or trajectory data of protein-small molecule; S2) pre-detecting and storing the data obtained in step S1); S3) Processing and analyzing the data stored in step S2): S31) Processing the data stored in step S2) to obtain a pdb format file; S32) For the data in the pdb format file obtained in step S31), model building and MD simulation are performed using gromacs to obtain the root mean square deviation and root mean square fluctuation data of the original data, generate an xvg format result file, and then draw the corresponding root mean square deviation and root mean square graph through a python script, and save the result graph; S33) cleaning the data in the pdb format file obtained in step S31), and then performing calculation analysis using physical geometric properties to obtain an interaction type result file of xml type; and saving the corresponding data; S4) performing 2D visualization of the data obtained according to step S3): S41) drawing and displaying protein-small molecule interaction radar maps, wherein the interaction radar maps include 9 radar maps, including hydrophobic interaction radar map, hydrogen bond interaction radar map, water bridge interaction radar map, salt bridge interaction radar map, pi-pi stacking interaction radar map, cation-pi interaction radar map, halogen bond interaction radar map, metal complex interaction radar map and total interaction radar map; S42) drawing and displaying a heat map of the interaction distribution of the trajectory data, wherein the abscissa of the heat map represents different frames in the trajectory data, the ordinate represents the amino acid sites in the protein that interact with the small molecule, and each grid represents the number and type of interactions between the corresponding amino acid site in the corresponding frame and the small molecule; S43) displaying the RMS deviation and RMS graph obtained in step S32); S5) performing 3D visualization of the data obtained according to step S3): Using NGL technology, the XML type result file obtained by calculation and analysis in step S33) is displayed.

2. The method according to claim 1, characterized in that In step S1), inputting structure coordinate data and trajectory data, the acceptable structure coordinate file format is selected from pdb format or gro format, and the trajectory file format is selected from at least one of xtc format, dcd, and gro format; The structural coordinate data and / or trajectory data of the protein-small molecule are input through a data upload module.

3. The method according to claim 1, characterized in that In step S31), the method for obtaining the pdb format file is as follows: for trajectory data, the number of trajectories of the trajectory data is first calculated, and then each trajectory is sampled at equal intervals to synthesize a pdb format file containing 30-100 frames; for gro format, xtc format, and dcd format files, they are converted into pdb format files through the analysis tool MDAnalysis.

4. The method according to claim 1, characterized in that: The type of interaction is selected from hydrophobic interaction, hydrogen bond, water bridge, salt bridge, pi-pi stacking, cation-pi interaction, halogen bond, metal complex interaction.

5. The method according to claim 1, characterized in that Each axis of the radar chart represents an amino acid in a protein that has an interaction site with a small molecule, and the scale is the frequency of the corresponding interaction.

6. The method according to claim 5, characterized in that Each axis in the overall radar chart represents an amino acid in a protein that has an interaction site with a small molecule, and the scale is the sum of the frequencies of various interactions occurring with the amino acid at that site.

7. The method according to claim 1, characterized in that In the heat map, each grid represents the number and type of interactions between the corresponding amino acid site and the small molecule in the corresponding frame. Different colors are used to represent different types of interactions. When the corresponding frame contains multiple interaction sites, different corresponding colors are used to evenly divide the grid.

8. A computer-readable medium for dynamically analyzing protein-small molecule interactions, wherein the computer-readable storage medium is used to store program codes, characterized in that: The program code is used to execute the method for dynamically analyzing protein-small molecule interactions according to any one of claims 1 to 6.

9. A system for dynamic analysis of protein-small molecule interactions, characterized in that: The system includes a data uploading module, a data pre-detection and storage module, a data processing and analysis module, and a result visualization module; The data uploading module is used to input structural coordinate data and / or trajectory data of protein-small molecules; The data pre-detection and storage module is used to pre-detect and store the data obtained by the data upload module; The data processing and analysis module is used to process and analyze the data pre-detected and stored in the storage module; the data processing and analysis module: 1) is used to process the data in the pre-detection and storage modules to obtain a pdb format file; 2) Used to build models and perform MD simulations through gromacs, obtain the root mean square deviation and root mean square fluctuation data of the original data, generate xvg format result files, and then use python scripts to draw the corresponding root mean square deviation and root mean square graphs, and save the result graphs; 3) Used to clean the pdb format files obtained in 1), and then use physical geometric properties for calculation and analysis to obtain xml type interaction result files; and save the corresponding data; The result visualization module is used to perform 2D visualization and / or 3D visualization on the data of the data processing and analysis module: The 2D visualization refers to: 1) drawing and displaying protein-small molecule interaction radar maps, there are 9 interaction radar maps, including hydrophobic interaction radar map, hydrogen bond interaction radar map, water bridge interaction radar map, salt bridge interaction radar map, pi-pi stacking interaction radar map, cation-pi interaction radar map, halogen bond interaction radar map, metal complex interaction radar map and total interaction radar map; 2) drawing and displaying a heat map of the interaction distribution of trajectory data, the horizontal axis of the heat map is different frames in the trajectory data, the vertical axis represents the amino acid sites in the protein that interact with the small molecule, and each grid represents the number and type of interaction between the corresponding amino acid site of the corresponding frame and the small molecule; 3) displaying the root mean square deviation and root mean square fluctuation data results obtained by the data processing and analysis module; The 3D visualization refers to: using NGL technology to display the XML result file obtained by calculation and analysis of the data processing and analysis module.

10. The system according to claim 9, characterized in that The structural coordinate file format that the data upload module can accept is selected from at least one of the pdb format, the gro format, the xtc format, and the dcd format.

11. The system according to claim 9, characterized in that In the data processing and analysis module, for trajectory data, the method to obtain the pdb format file is: first calculate the number of trajectories in the trajectory data, then sample each trajectory at equal intervals, and synthesize a pdb format file containing 30-100 frames; for gro format, xtc format, and dcd format files, they are converted into pdb format files through the analysis tool python's MDAnalysis.

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