Method for directionally mining glutamine transaminase based on structural analysis

Abstract

The invention discloses a method for directionally mining glutamine transaminase based on structural analysis, which comprises the following steps of: 1, retrieving a bioinformatics database, downloading a sequence containing transglutaminase annotation information, and performing duplicate removal processing; step 2, obtaining a protein structure file corresponding to the sequence through an AlphaFoldDB database by using a programming language; 3, performing structural similarity analysis on the protein structure files and known TGase locally in batches by using a programming language through DaliLite software; 4, screening to obtain a potential novel TGase sequence and structure with high similarity with the known TGase; and 5, carrying out activity verification so as to confirm the novel Tgase. According to the bioinformatics database mining method suitable for glutamine transaminase, redundant information in a protein database can be processed in batches, the workload of traditional manual data processing and experimental screening is reduced, rapid and accurate database resource mining for target industrial enzyme is achieved, and universality is achieved.

Description

Technical Field

[0001] The invention relates to a method for directed mining of glutamine transaminase based on structure analysis, and belongs to the technical field of bioinformatics. Background Art

[0002] Transglutaminase (TGase) catalyzes the formation of ε-(γ-glutamyl)lysine isopeptide bonds in proteins through acyl transfer reactions. Acyl donors cross-link with acyl acceptors such as the ε-amino group of lysine residues to achieve intramolecular or intermolecular cross-linking. TGase has great application potential in many fields such as food, medicine, and bioengineering due to its excellent protein cross-linking properties.

[0003] Since TGase was first isolated from Maoyuan Streptomyces in 1989, researchers have been continuously exploring new sources of TGase from various microbial resources. However, the currently known sources of TGase genes are relatively single, mainly concentrated in the genera Streptomyces and Bacillus, which limits the diversity of TGase enzymatic properties and its flexibility in application in different fields. Currently, all commercially available TGases are derived from Streptomyces, and their optimal action temperatures are generally concentrated around 50°C, and they are only highly active within a narrow temperature range (about 45°C-55°C), which makes it difficult to meet diverse market demands and industrial application environments. Therefore, in order to meet market needs, it is necessary to explore new TGases.

[0004] The traditional method of screening and mining new TGases from databases is mainly based on screening from related environmental microorganisms based on sequence information. This strategy lacks effective use of structural information, resulting in a large screening workload, low screening efficiency, and often a certain degree of blindness. In addition, with the development of bioinformatics technology, although the number of TGase-related sequence entries in the database has increased dramatically, most of them are still unreported, and there are a large number of repeated sequences, which further increases the difficulty and complexity of traditional screening strategies.

[0005] In order to overcome these limitations, researchers began to try to classify and screen TGase based on evolutionary information and structural characteristics. For example, the Pfam database was used to classify TGase from microorganisms into five categories. However, most of the current research is still focused on the two major categories of Streptomyces and Bacillus, while the industrial application potential of the remaining three categories of TGase has not been fully explored. The single source of TGase genes not only limits the diversity of TGase enzymatic properties, but also hinders its application and development in a wider range of fields.

[0006] The inventors conducted a big data analysis of the Uniprot protein database. As of September 16, 2024, there were 164,558 entries related to TGase, of which 164,314 were unreported, and there were still 149,981 TGase genes after deduplication. The microbial distribution characteristics of TGase increase the difficulty of screening new TGases using traditional strategies.

[0007] In summary, the traditional strategy for screening new TGase has problems such as low screening efficiency, incomplete evaluation, and long cycle. Therefore, there is an urgent need for a method for targeted mining of glutamine transaminase based on structure analysis to mine new TGase gene resources from massive bioinformatics data, especially those TGase types that have not been fully studied, in order to enrich the enzymatic property library of TGase and meet the market's urgent demand for new, efficient and stable TGase. Summary of the invention

[0008] To solve the above problems, the present invention provides a method for directed mining of glutamine transaminase based on structural analysis, which uses Python programming language combined with protein database and DaliLite software to directed mine glutamine transaminase based on structural analysis, improves the mining efficiency of new TGase, and has universal applicability.

[0009] In a first aspect, the present invention provides a method for directed mining of glutamine transaminase based on structural analysis, the method comprising:

[0010] Step 1: Search the bioinformatics database, download the sequence containing transglutaminase annotation information and perform duplicate removal;

[0011] Step 2: Use a programming language to obtain a protein structure file corresponding to the sequence through the AlphaFoldDB database;

[0012] Step 3, using a programming language to perform structural similarity analysis on the protein structure file in batches locally with known TGases through DaliLite software;

[0013] Step 4, screening to obtain potential new TGase sequences and structures with high similarity to known TGases;

[0014] Step 5: Activity verification to confirm the new TGase.

[0015] In one embodiment of the present invention, step 1 comprises:

[0016] Select the desired protein to download, save it as an Excel file, and use Excel software to remove duplicates from the sequence information containing transglutaminase annotation information;

[0017] Wherein, the bioinformatics database in step 1 includes NCBI database and UniProt database; the programming language in steps 2 and 3 includes Python programming language.

[0018] In one embodiment of the present invention, step 2 comprises:

[0019] Use Python programming language to extract the UniProtID of the protein from the specified Excel file, obtain the corresponding PDB file through the AlphaFoldDB database through network request, and update the corresponding status of the Excel file;

[0020] In one embodiment of the present invention, the step 2 uses PyCharm to build a project, and creating TGase_spider.py includes:

[0021] Step 21: Preparation;

[0022] Prepare the following tools:

[0023] Network request library: used to send HTTP requests to access data on the network, including web pages and APIs;

[0024] Web page parsing library: used to extract information from the acquired web pages;

[0025] Browser automation tools: used to simulate browser actions in headless mode and crawl specific web page elements;

[0026] Excel processing library: used to read and write Excel files;

[0027] Step 22: Randomize the request header.

[0028] To prevent being detected and blocked by the target website, the user agent is randomly selected; this is achieved by maintaining a list of user agents and randomly selecting one from them for each request;

[0029] Step 23, find the PDB file;

[0030] Create a function find_pdb_path(url,uniprotid) to check whether the PDB file corresponding to the UniProtID given in the Excel table already exists; if not, use the browser simulator to load the corresponding web page and extract the download link of the PDB file; use XPath expressions to find links containing "PDBfile" as the target;

[0031] Step 24, download the PDB file;

[0032] Implement the download function download_pdb_from_excel(excel_dir,base_url,start,end=""), receive the URL of the PDB file, and skip downloading if the file already exists; if the download fails, set up a retry mechanism to increase the success rate by catching exceptions and delaying retries;

[0033] Step 25: Process the Excel file;

[0034] Write the function main(), read the Excel file prepared earlier, then traverse the UniProtID column in it, build a complete link in combination with the URL, and call the previously implemented PDB file search and download functions;

[0035] Step 26: Record the download status;

[0036] For each processed UniProtID, update the corresponding status of the Excel file according to the result; use a new Excel file to record whether the ID is successfully downloaded, mark it as "yes" if successful, and mark it as "no" if failed;

[0037] Step 27, main function;

[0038] In the main function, set the base URL, Excel file path, and the range of rows to be processed, and then call the function that processes the Excel file.

[0039] In one embodiment of the present invention, step 3 comprises:

[0040] Use Python programming language to read a specific range of protein UniProtIDs from the updated Excel file in step 2, and run the DaliLite program for local batch protein structure comparison based on the above IDs, and finally save the results into a text file.

[0041] In one embodiment of the present invention, the step 3 of creating dali.py includes:

[0042] Step 31: Preparation;

[0043] Prepare the updated Excel sheet from step 2, i.e., the Excel sheet in which TGase_spider.py records the download status of each processed UniProtID; make sure that the required Python libraries and DaliLite program have been installed;

[0044] Step 32, read the Excel file;

[0045] By using the openpyxl library, dali.py reads data from Excel;

[0046] Step 33, traverse the data rows;

[0047] dali.py traverses the data in specific columns from the start row to the end row; the first column stores the UniProtID, and the eighth column stores the result flag, such as "yes" indicating that the PDB file has been downloaded; through this process, the script extracts relevant information for each row to facilitate subsequent operations;

[0048] Step 34, determine and run DaliLite;

[0049] Write a function run_dali(start, end) to run DaliLite software; mark the result of each line: if the mark is "yes", it means that a structural comparison is required; at this time, dali.py uses the OS library to build a command to run DaliLite for structural comparison and submit it to the system for execution;

[0050] Step 35: Process the execution result;

[0051] After executing DaliLite, dali.py will try to read the result file associated with the current UniProtID; if the file content is successfully read, it will write the content to a text file named result.txt for subsequent viewing and analysis;

[0052] Step 36, main function;

[0053] Define the Excel file row range that needs to be processed and call the function that runs the DaliLite software.

[0054] In one embodiment of the present invention, step 4 determines whether it is a potential new Tgase by comparing the Z value score of the result.txt structure analysis result file generated in step 3, thereby screening out potential new TGase sequences and structures with high similarity to known TGases; step 5 obtains the potential new TGase sequence obtained by screening, constructs and synthesizes the plasmid, expresses and purifies it for enzyme activity verification, and confirms whether it is a new Tgase.

[0055] In a second aspect, the present invention provides a device for directional mining of glutamine transaminase based on structural analysis, the device comprising:

[0056] Download module, used to search the bioinformatics database, download sequences containing transglutaminase annotation information and perform duplicate removal;

[0057] An acquisition module, used to acquire a protein structure file corresponding to the sequence through the AlphaFoldDB database using a programming language;

[0058] A structural similarity analysis module, for performing structural similarity analysis on the protein structure file in batches locally with known TGases using a programming language through DaliLite software;

[0059] A screening module is used to screen for potential new TGase sequences and structures with high similarity to known TGases;

[0060] Activity verification module, used for activity verification to confirm new TGase.

[0061] In a third aspect, the present invention provides a computer device comprising 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, the steps of the method for directed mining of glutamine transaminase based on structural analysis are implemented.

[0062] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for directed mining of glutamine transaminase based on structural analysis.

[0063] The method for directional mining of glutamine transaminase based on structural analysis provided by the present invention has the following beneficial effects:

[0064] Compared with traditional strategies for screening TGase, the method for directed mining of glutamine transaminase based on structural analysis provided by the present invention realizes efficient directed mining of new TGase in protein databases through a new bioinformatics method focusing on structural information, and has the advantages of high screening efficiency, comprehensive evaluation, and short cycle.

[0065] Among them, the open source of AlphaFold2 code and the establishment of AlphaFoldDB database mark the arrival of an era of explosive growth in reliable protein structure information, which is a strong guarantee for the development of the present invention. At the same time, the present invention uses the open source programming tool Python to make the directional mining of new TGase universal.

[0066] In summary, the present invention constructs a bioinformatics database mining method suitable for glutamine transaminase based on computer structure analysis tools and computer programming languages, which can batch process redundant information in protein databases, reduce the workload of traditional manual data processing and experimental screening, and realize rapid and accurate database resource mining for target industrial enzymes, which has universal applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings required to be used in the embodiments or the prior art description are briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without creative work.

[0068] Figure 1 A flow chart of a method for directed mining of glutamine transaminase based on structural analysis provided by the present invention.

[0069] Figure 2 This is a partial schematic diagram of the DaliLite software structure analysis results provided by the present invention.

[0070] Figure 3 This is the protein sequence evolution tree constructed by CcTG provided by the present invention.

[0071] Figure 4 This is a schematic diagram of the results of the enzyme activity verification of CcTG provided by the present invention.

[0072] Figure 5 This is a schematic diagram of the results of investigating the cross-linking ability of CcTG provided by the present invention. DETAILED DESCRIPTION

[0073] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] like Figure 1 As shown, the present invention provides a method for directed mining of glutamine transaminase based on structural analysis. In some embodiments, the method comprises:

[0075] Step 1, search the bioinformatics databases such as NCBI database and UniProt database, download the sequences containing transglutaminase annotation information and perform duplicate removal;

[0076] Step 2, using Python programming language to obtain the protein structure file corresponding to the sequence through the AlphaFoldDB database;

[0077] Step 3, using Python programming language through DaliLite software to perform structural similarity analysis on the protein structure file in batches locally with known TGases;

[0078] Step 4, screening to obtain potential new TGase sequences and structures with high similarity to known TGases;

[0079] Step 5: Activity verification to confirm the new TGase.

[0080] In some embodiments, step 1 specifically includes:

[0081] Databases such as NCBI and Uniprot are core resources for bioinformatics research, which store and provide a large amount of biological data. In this example, transglutaminase is entered into the Uniprot database (https: / / www.uniprot.org / ) to search for transglutaminase family proteins, and the desired protein can be screened through the Advanced function.

[0082] Select the desired protein to download, save it as an Excel file, and use Excel software to remove duplicates from the sequence information containing the "transglutaminase" annotation information.

[0083] In some embodiments, step 2 specifically includes:

[0084] Use Python programming language to extract the UniProtID (number) of the protein from the specified Excel file, and obtain the corresponding PDB (protein structure data) file through the AlphaFoldDB database (https: / / alphafold.ebi.ac.uk / ) through the network request. Please ensure that the corresponding Python environment has been configured before implementation. The present embodiment uses PyCharm to build the project of the embodiment. The following is the logical idea of ​​creating TGase_spider.py:

[0085] Step 21: Preparation;

[0086] First, you need to prepare some basic tools:

[0087] Network request library: used to send HTTP requests to access data on the network, especially web pages and APIs.

[0088] Web page parsing library: used to extract information from the obtained web pages.

[0089] Browser automation tool: used to simulate browser actions in headless mode and crawl specific web page elements.

[0090] Excel processing library: used to read and write Excel files.

[0091] Step 22: Randomize the request header (optional).

[0092] In order to prevent being detected and blocked by the target website, the user agent can be randomly selected. This can be achieved by maintaining a list of user agents and randomly selecting one from them each time a request is made.

[0093] Step 23, find the PDB file;

[0094] Create a function find_pdb_path(url,uniprotid) to check if the PDB file corresponding to the given UniProtID in the Excel table already exists. If not, use the browser simulator to load the corresponding web page and extract the download link of the PDB file. Use XPath expression to find the link containing "PDBfile" as the target.

[0095] Step 24, download the PDB file;

[0096] Implement the download function download_pdb_from_excel(excel_dir,base_url,start,end=""), receive the URL of the PDB file, and skip downloading if the file already exists; if the download fails, you can set a retry mechanism to increase the success rate by catching exceptions and delaying retries.

[0097] Step 25: Process the Excel file;

[0098] Write the function main() to read the Excel file prepared earlier, then traverse the UniProtID column in it, build a complete link in combination with the URL, and call the previously implemented PDB file search and download functions.

[0099] Step 26: Record the download status;

[0100] For each processed UniProtID, update the corresponding status of the Excel file according to the results. A new Excel file can be used to record whether the ID is successfully downloaded, with success marked as "yes" and failure marked as "no" to facilitate step 3 analysis and processing.

[0101] Step 27, main function;

[0102] In the main function, set the base URL, Excel file path, and the range of rows to be processed, and then call the function that processes the Excel file.

[0103] In some embodiments, step 3 specifically includes:

[0104] Use Python programming language to read a specific range of protein UniProtIDs from the updated Excel file in step 2, run the DaliLite program for local batch protein structure comparison based on these IDs, and finally save the results into a text file.

[0105] The following is the logical idea for creating dali.py:

[0106] Step 31: Preparation;

[0107] Prepare the updated Excel table in step 2, i.e., the Excel table in which TGase_spider.py records the download status of each processed UniProtID. In addition, ensure that the required libraries for Python (such as openpyxl) and DaliLite program (http: / / ekhidna2.biocenter.helsinki.fi / dali / ) are installed. DaliLite.v5 needs to be installed in the LinuxOS system. The embodiment of the present invention uses the Windows system to use VMware to run dali.py using a Linux virtual machine. Please visit its official website for detailed installation requirements and installation steps of DaliLite software.

[0108] Step 32, read the Excel file;

[0109] By using the openpyxl library, dali.py reads data from Excel.

[0110] Step 33, traverse the data rows;

[0111] dali.py traverses the data in specific columns from the start row to the end row. The first column stores the UniProtID, and the eighth column stores the result flag (such as "yes" means the PDB file has been downloaded). Through this process, the script can extract relevant information for each row to facilitate subsequent operations.

[0112] Step 34, determine and run DaliLite;

[0113] Write a function run_dali(start, end) to run the DaliLite software. For each line of result mark: If the mark is "yes", it means that a structural comparison is required. At this time, dali.py uses the OS library to build a command to run DaliLite for structural comparison and submit it to the system for execution. The command to run DaliLite refers to its official website description.

[0114] Step 35: Process the execution result;

[0115] After executing DaliLite, dali.py will try to read the result file associated with the current UniProtID. If the file content is successfully read, it will be written to a text file named result.txt for subsequent viewing and analysis.

[0116] Step 36, main function;

[0117] Define the Excel file row range that needs to be processed and call the function that runs the DaliLite software.

[0118] In some embodiments, step 4 specifically includes:

[0119] Query the result.txt structural analysis result file generated in step 3 to screen out potential new TGase sequences and structures with high similarity to known TGases.

[0120] In some embodiments, step 5 specifically includes:

[0121] The potential new TGase sequence obtained by screening was obtained, and the plasmid was constructed and synthesized. The expression and purification were performed to verify the enzyme activity to confirm whether it was a new TGase.

[0122] Below, the method of the present invention is verified by a specific example, and the specific process is as follows:

[0123] 1. Visit the Uniprot website, enter "transglutaminase" and limit the length to no more than 500, select the data of the unverified TrEMBL database for download, and as of September 16, 2024, there are 93,388 results in total; save the query results as an Excel file, and use Excel software to deduplicate the obtained sequence information. In this embodiment, the three columns of Proteinnames, Organism and length in the table are selected for deduplication. After deduplication, 84,424 results remain, which are saved as non_redundant_data.xlsx.

[0124] 2. Use TGase_spider.py to obtain the corresponding protein structure file through the AlphaFoldDB database, and save the updated Excel table as new_excel.xlsx.

[0125] 3. Select the known TGase crystal structures 1iu4.pdb, 4p8i.pdb, 4pa5.pdb, 5m6q.pdb and non-TGase functional protein structures 3esw.pdb, 3isr.pdb as the basis for comparison and store them in the known_pdb folder for use by DaliLite software. Call dali.py in the VMware virtual machine to use DaliLite software to perform structural similarity analysis on the obtained structure files in batches with known TGases locally to obtain the structural analysis results result.txt.

[0126] 4. Figure 2 The figure shows part of the structural analysis results of this example. The Z value of the protein with UniprotID A0A3N1GEB2 and the 1iu4.pdb structure is 20.1, reflecting that it has a high similarity with 1iu4.pdb and is a potential new TGase.

[0127] 5. The plasmid of the protein with Uniprot ID A0A3N1GEB2 was constructed, and the expression and purification were verified. The strain of A0A3N1GEB2 recorded in Uniprot was Couchioplanes caeruleus, so it was recorded as CcTG. Figure 3 The figure shows an evolutionary tree constructed using the neighbor-joining method for CcTG and the TGase sequences of microorganisms from the reviewed marker in uniprot, with human TGase as the root. The evolutionary tree shows that CcTG is closely related to the TGase sequence of Streptomyces mobaraensis, which is the source of commercial microbial TGase, reflecting its potential for glutamine aminotransferase activity.

[0128] 1) Enzyme activity verification;

[0129] The reaction system was placed at different temperatures (30°C, 40°C, 50°C, 60°C, 70°C, 80°C) and pH = 6. The enzyme activity of the purified CcTG enzyme solution at different temperatures was determined by colorimetric method using CBZ-Gln-Gly as substrate, and the relative enzyme activity was plotted. Figure 4 As shown, it was measured that CcTG achieved a maximum enzyme activity of 1.14U / mg at 60°C. In the range of 30°C-60°C, the enzyme activity gradually increased with the increase of temperature. When the temperature increased to 70°C, the enzyme activity decreased rapidly.

[0130] 2) Investigation of cross-linking ability;

[0131] The cross-linking ability of CcTG on soy protein isolate (SPI) was investigated. Figure 5 As shown, lane M expresses protein marker, and lanes 1-4 represent samples collected at 0, 0.5h, 1h, and 2h of the cross-linking reaction with the substrate. When the reaction is carried out for 0.5h, a protein polymer band can be observed from the top of the lane, reflecting that CcTG has the ability to cross-link SPI.

[0132] In summary, CcTG was confirmed as a new type of TGase with application potential.

[0133] In addition, the present invention provides a device for directional mining of glutamine transaminase based on structural analysis. In some embodiments, the device comprises:

[0134] Download module, used to search the bioinformatics database, download sequences containing transglutaminase annotation information and perform duplicate removal;

[0135] An acquisition module, used to acquire a protein structure file corresponding to the sequence through the AlphaFoldDB database using a programming language;

[0136] A structural similarity analysis module, for performing structural similarity analysis on the protein structure file in batches locally with known TGases using a programming language through DaliLite software;

[0137] A screening module is used to screen for potential new TGase sequences and structures that are highly similar to known TGases;

[0138] Activity verification module, used for activity verification to confirm new TGase.

[0139] The specific limitations and beneficial effects of the above-mentioned device for directional mining of glutamine transaminase based on structural analysis can be found in the above-mentioned method for directional mining of glutamine transaminase based on structural analysis, which will not be repeated here. The above-mentioned modules can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0140] In addition, the present invention also provides a computer device, which may include a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the processor executes the steps of a method for directional mining of glutamine transaminase based on structural analysis as in any of the above embodiments.

[0141] The working process, working details and technical effects of the computer device provided in this embodiment can be found in the above embodiment of a method for directional mining of glutamine transaminase based on structural analysis, which will not be described in detail here.

[0142] In addition, the present invention also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of a method for directional mining of glutamine transaminase based on structural analysis as in any of the above embodiments are implemented. The computer-readable storage medium refers to a carrier for storing data, which may include but is not limited to a floppy disk, an optical disk, a hard disk, a flash memory, a USB flash drive and / or a memory stick, etc., and the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0143] The working process, working details and technical effects of the computer-readable storage medium provided in this embodiment can be found in the above embodiment of a method for directed mining of glutamine transaminase based on structural analysis, which will not be described in detail here.

[0144] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0145] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent replacements for some of the technical features therein. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for directed mining of glutamine transaminase based on structural analysis, characterized in that: The method comprises: Step 1: Search the bioinformatics database, download the sequence containing transglutaminase annotation information and perform duplicate removal; Step 2: Use a programming language to obtain a protein structure file corresponding to the sequence through the AlphaFoldDB database; Step 3, using a programming language to perform structural similarity analysis on the protein structure file in batches locally with known TGases through DaliLite software; Step 4, screening to obtain potential new TGase sequences and structures with high similarity to known TGases; Step 5: Activity verification to confirm the new TGase.

2. The method for directed mining of transglutaminase based on structural analysis according to claim 1, characterized in that: The step 1 comprises: Select the desired protein to download, save it as an Excel file, and use Excel software to remove duplicates from the sequence information containing transglutaminase annotation information; Wherein, the bioinformatics database in step 1 includes NCBI database and UniProt database; the programming language in steps 2 and 3 includes Python programming language.

3. The method for directed mining of transglutaminase based on structural analysis according to claim 2, characterized in that: The step 2 comprises: Use Python programming language to extract the UniProtID of the protein from the specified Excel file, obtain the corresponding PDB file through the AlphaFoldDB database through a network request, and update the corresponding status of the Excel file.

4. The method for directed mining of transglutaminase based on structural analysis according to claim 3, characterized in that: Step 2 uses PyCharm to build the project and create TGase_spider.py including: Step 21: Preparation; Prepare the following tools: Network request library: used to send HTTP requests to access data on the network, including web pages and APIs; Web page parsing library: used to extract information from the acquired web pages; Browser automation tools: used to simulate browser actions in headless mode and crawl specific web page elements; Excel processing library: used to read and write Excel files; Step 22: Randomize the request header. To prevent being detected and blocked by the target website, the user agent is randomly selected; this is achieved by maintaining a list of user agents and randomly selecting one from them for each request; Step 23, find the PDB file; Create a function find_pdb_path(url,uniprotid) to check whether the PDB file corresponding to the UniProtID given in the Excel table already exists; if not, use the browser simulator to load the corresponding web page and extract the download link of the PDB file; use XPath expressions to find links containing "PDBfile" as the target; Step 24, download the PDB file; Implement the download function download_pdb_from_excel(excel_dir,base_url,start,end=""), receive the URL of the PDB file, and skip downloading if the file already exists; if the download fails, set up a retry mechanism to increase the success rate by catching exceptions and delaying retries; Step 25: Process the Excel file; Write the function main(), read the Excel file prepared earlier, then traverse the UniProtID column in it, build a complete link in combination with the URL, and call the previously implemented PDB file search and download functions; Step 26: Record the download status; For each processed UniProtID, update the corresponding status of the Excel file according to the result; use a new Excel file to record whether the ID is successfully downloaded, mark it as "yes" if successful, and mark it as "no" if failed; Step 27, main function; In the main function, set the base URL, Excel file path, and the range of rows to be processed, and then call the function that processes the Excel file.

5. The method for directed mining of transglutaminase based on structural analysis according to claim 4, characterized in that: The step 3 comprises: Use Python programming language to read a specific range of protein UniProtIDs from the updated Excel file in step 2, and run the DaliLite program for local batch protein structure comparison based on the above IDs, and finally save the results into a text file.

6. The method for directed mining of transglutaminase based on structural analysis according to claim 5, characterized in that: The step 3 to create dali.py includes: Step 31: Preparation; Prepare the updated Excel sheet from step 2, i.e., the Excel sheet in which TGase_spider.py records the download status of each processed UniProtID; make sure that the required Python libraries and DaliLite program have been installed; Step 32, read the Excel file; By using the openpyxl library, dali.py reads data from Excel; Step 33, traverse the data rows; dali.py traverses the data in specific columns from the start row to the end row; the first column stores the UniProtID, and the eighth column stores the result flag, such as "yes" indicating that the PDB file has been downloaded; through this process, the script extracts relevant information for each row to facilitate subsequent operations; Step 34, determine and run DaliLite; Write a function run_dali(start,end) to run DaliLite software; mark the result of each line: if the mark is "yes", it means that a structural comparison is required; at this time, dali.py uses the OS library to build a command to run DaliLite for structural comparison and submit it to the system for execution; Step 35: Process the execution result; After executing DaliLite, dali.py will try to read the result file associated with the current UniProtID; if the file content is successfully read, it will write the content to a text file named result.txt for subsequent viewing and analysis; Step 36, main function; Define the Excel file row range that needs to be processed and call the function that runs the DaliLite software.

7. The method for directed mining of transglutaminase based on structural analysis according to claim 6, characterized in that: The step 4 determines whether it is a potential new Tgase by comparing the Z value score of the result.txt structure analysis result file generated in step 3, thereby screening out potential new TGase sequences and structures with high similarity to known TGases; the step 5 obtains the potential new TGase sequence obtained by screening, constructs and synthesizes the plasmid, expresses and purifies it for enzyme activity verification, and confirms whether it is a new TGase.

8. A device for directional mining of glutamine transaminase based on structural analysis, characterized in that: The device comprises: Download module, used to search the bioinformatics database, download sequences containing transglutaminase annotation information and perform duplicate removal; An acquisition module, used to acquire a protein structure file corresponding to the sequence through the AlphaFoldDB database using a programming language; A structural similarity analysis module, for performing structural similarity analysis on the protein structure file in batches locally with known TGases using a programming language through DaliLite software; A screening module is used to screen for potential new TGase sequences and structures that are highly similar to known TGases; Activity verification module, used for activity verification to confirm new TGase.

9. A computer 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, the steps of a method for directed mining of transglutaminase based on structural analysis as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for directed mining of transglutaminase based on structural analysis as described in any one of claims 1 to 7 are implemented.

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