A high-throughput enzyme mutant data screening and management system

Abstract

This invention relates to the field of bioinformatics management technology and discloses a high-throughput enzyme mutant data screening and management system, comprising: generating a unique identifier for each physical experimental sample and associating it with the location coordinates recorded in the experimental container; communicating with high-throughput experimental equipment to collect raw experimental data, binding it in real time with the corresponding unique identifier to generate standardized experimental data records; integrating and storing the standardized experimental data records, location coordinates, and experimental process metadata in a central database to establish a complete traceability data chain; performing multi-dimensional analysis on the standardized experimental data records to identify target mutants; generating an analysis report based on the location coordinate information and performance indicators of the target mutants, and outputting it to a designated experimental device or interface, significantly improving the data consistency, process traceability, and intelligent decision-making efficiency of high-throughput enzyme mutant screening experiments.

Description

Technical Field

[0001] This invention relates to the field of bioinformatics management technology, and more specifically, to a high-throughput enzyme mutant data screening and management system. Background Technology

[0002] With the rapid development of synthetic biology and protein engineering, directed evolution and rational design have become core technologies for obtaining high-performance industrial enzymes, therapeutic proteins, and biosynthetic components. In this process, researchers need to construct and test massive mutant libraries and rapidly obtain multi-dimensional performance data on enzyme activity, stability, expression levels, and other aspects through high-throughput screening platforms. However, such experiments typically generate large-scale, heterogeneous, and structurally complex data streams, which currently rely heavily on manual recording, scattered spreadsheets, and general data processing software for management. This leads to problems such as error-prone data acquisition, sample-data disconnect, difficulty in process traceability, and limited analytical dimensions with low efficiency, severely restricting screening throughput, result reliability, and the iterative speed of the "design-build-test-learn" cycle.

[0003] While existing technologies include general-purpose laboratory information management systems and some bioinformatics analysis tools, they often lack deep adaptation and integration for high-throughput enzyme mutant screening scenarios. Specifically: 1. They struggle to achieve automated, high-fidelity correlation and end-to-end traceability between physical experimental samples and their multidimensional experimental data; 2. They fail to embed specialized models for enzymatic characterization analysis and multi-objective intelligent screening algorithms, making it impossible to efficiently and accurately identify candidate mutants with optimal overall performance from complex data; 3. They cannot form a closed-loop command flow from intelligent analysis to experimental verification, hindering the improvement of automation and intelligence across the entire process. Therefore, a novel system that deeply integrates experimental automation, data management, and professional analysis is urgently needed to overcome these bottlenecks and significantly improve the R&D efficiency and success rate of enzyme protein modification. Summary of the Invention

[0004] This application provides a high-throughput enzyme mutant data screening and management system, which solves the core technical problems that may exist in the high-throughput enzyme mutant screening process in the prior art, such as chaotic sample management, scattered data sources, isolated analysis and decision-making, and the inability to close the loop in the process. By establishing an integrated management system for the entire process of sample identification, automatic data collection, intelligent analysis, and instruction generation, it realizes real-time correlation and full traceability of experimental data, intelligent evaluation of multi-dimensional performance and accurate target identification, and automated closed loop from data analysis to experimental verification, thereby significantly improving the accuracy, efficiency, and intelligence level of enzyme protein directed evolution research and development.

[0005] To achieve the above objectives, the present invention provides a high-throughput enzyme mutant data screening and management system, comprising:

[0006] The sample tagging module is used to generate a unique identifier for each physical experiment sample and associate it with the location coordinates recorded in the experimental container; The data association module is used to communicate with high-throughput experimental equipment, collect raw experimental data, bind the raw experimental data with the corresponding unique identifier in real time, and generate standardized experimental data records. The data integration module is used to integrate and store standardized experimental data records, location coordinates, and experimental process metadata in a central database to establish a complete traceability data chain. The analysis and identification module is used to perform multi-dimensional analysis on standardized experimental data records, calculate mutant performance indicators based on preset screening algorithms, and identify target mutants. The results output module is used to generate an analysis report based on the location coordinates and performance indicators of the target mutant, and output it to the specified experimental equipment or interface.

[0007] Furthermore, a communication connection is established with high-throughput experimental equipment to collect raw experimental data. This raw experimental data is then bound in real-time with corresponding unique identifiers to generate standardized experimental data records, specifically including: Establish a data connection with at least one of the following through a preset device communication interface protocol: microplate reader, high-throughput sequencer, and automated liquid handling workstation; The experimental equipment can receive structured or unstructured data files containing raw measurement values ​​in real time. By parsing the sample container identifier or experimental batch information carried in the data file, the original measurement value is automatically matched and bound with the unique identifier. The bound data is formatted, standardized in units, and outlier removed to generate standardized experimental data records with complete structure and uniform fields, and is stamped with timestamps and experimental batch labels.

[0008] Furthermore, by parsing the sample container identifier or experimental batch information carried in the data file, specifically including: Identify the filename or identifier field in the file header of the data file that conforms to a preset naming rule; The identifier field is compared with the experimental sample board number or experimental batch number pre-registered in the system; After successful comparison, the measurement value sequence in the data file is mapped to the corresponding sample unique identifier code according to the sample layout diagram corresponding to the sample plate number or experimental batch number in a preset order.

[0009] Furthermore, the bound data undergoes format standardization, unit standardization, and outlier removal processing, specifically including: The format standardization process maps and reorganizes data records from different experimental devices and with different data structures according to the preset central database data model, converting them into a unified format with the same field order and data type. Unit standardization processing identifies the measurement units in the raw data and automatically converts all numerical data into the standard measurement units specified by the system based on the preset unit conversion rule library. Outlier removal involves applying threshold judgment algorithms based on statistical distribution or reasonableness rules based on experimental logic to verify the transformed data, automatically identifying and marking or removing outlier data points that exceed a preset reasonable range, and generating standardized experimental data records with controllable quality.

[0010] Furthermore, standardized experimental data records, location coordinates, and experimental process metadata are integrated and stored in a central database to establish a complete traceability data chain, specifically including: Using the unique identifier as the primary key, a set of related data tables is established in the central database, including a sample basic information table, an experimental data record table, a physical location change table, and an experimental process log table. The standardized experimental data records are synchronously written into the corresponding data tables through the database transaction mechanism, and foreign key relationships between tables are maintained. Based on the relationship between the primary key and foreign key, a data link can be constructed that allows for the reverse tracing of all historical experimental data, location trajectories, and complete experimental contexts from the final performance data of any target mutant.

[0011] Furthermore, a set of related data tables is established in the central database, including a sample basic information table, an experimental data record table, a physical location change table, and an experimental process log table, specifically including: The sample basic information table shall include at least the following fields: unique identifier, original gene sequence, mutation site information, vector information, and creation time; The physical location change table shall include at least the following fields: unique identifier, container type, location coordinates, operation time, and change type; The experimental process log table shall contain at least the following fields: experimental batch number, associated unique identifier set, experimental type, instrument and equipment number, core reaction parameters, operator and environmental temperature and humidity records.

[0012] Furthermore, it is used for multi-dimensional analysis of standardized experimental data records, calculating mutant performance indicators based on a preset screening algorithm, and identifying target mutants, specifically including: Call the preset multi-dimensional analysis model to perform data analysis and output the results; Based on the output of the multi-dimensional analysis model, a weighted fusion algorithm or a machine learning classification algorithm is used to calculate the comprehensive performance index score of each mutant. The comprehensive performance index scores are sorted or thresholded according to preset screening rules, and the target mutant set that meets the preset screening conditions is automatically identified and marked. The filtering algorithm can dynamically adjust the weight coefficients of the multi-dimensional analysis model or the threshold parameters of the filtering rules based on user configuration or historical data feedback.

[0013] Furthermore, the data analysis is performed by invoking a pre-defined multi-dimensional analysis model, specifically including: During the data analysis process, at least one of the following dimensional models is invoked for parallel or sequential analysis processing: The activity dimension analysis model is used to perform nonlinear fitting of the Michaelis equation on enzyme activity kinetic data, calculate and output the maximum reaction rate and Michaelis constant; The stability dimension analysis model is used to fit equations to temperature gradient or time series deactivation mechanical data, calculate and output half-deactivation temperature or half-life parameters; The expression level dimension analysis model is used for statistical analysis of batch protein expression data, and calculates and outputs the mean yield and coefficient of variation. After the data analysis is completed, the analysis results are standardized, and the standardized results are used as the output.

[0014] Furthermore, a weighted fusion algorithm or machine learning classification algorithm is used to calculate the comprehensive performance index score for each mutant, specifically including: A weighted fusion algorithm is adopted: the numerical feature parameters output by the dimensional model are normalized, and the comprehensive performance index score of each mutant is calculated by linear weighted sum formula according to the preset or dynamically optimized weight coefficient vector. A machine learning classification algorithm is adopted: using the multi-dimensional feature parameters of mutants with historically accumulated and labeled performance levels as the training sample set, a performance classification classifier is trained; the multi-dimensional feature parameters of the mutants to be screened are input into the trained classifier, and the predicted level or category confidence score output by the classifier is mapped to the corresponding comprehensive performance index score. The optimization of the weight coefficient vector or the training update of the classifier is performed automatically by the system periodically or automatically based on newly added historical data feedback.

[0015] Furthermore, it is used to generate an analysis report based on the location coordinates and performance indicators of the target mutant, specifically including: Based on the location coordinates and performance indicators of the target mutant, relevant data is extracted from the central database and automatically populated using a preset report template; The populated data is transformed into visual analysis charts, which include at least a visual heatmap based on the sample board location and a multi-dimensional performance comparison chart; The analysis report, which includes structured data and visualization charts, is pushed to the laboratory information management system or printing equipment in a standard file format, and the interactive report is displayed in the system's interactive interface.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: by constructing an integrated management system for the entire process from physical samples to experimental data, the accuracy, traceability and efficiency of high-throughput enzyme mutant screening are significantly improved, and the problems of data silos and decision delays in existing technologies are effectively solved. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A schematic diagram of a high-throughput enzyme mutant data screening and management system according to an embodiment of the present invention is shown. Detailed Implementation

[0018] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0019] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0020] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0021] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0022] The following is a description of preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0023] like Figure 1 As shown, an embodiment of the present invention discloses a high-throughput enzyme mutant data screening and management system, comprising: S110: Sample marking module, used to generate a unique identifier for each physical experiment sample and associate it with the location coordinates recorded in the experimental container; In this embodiment, the generation of unique identifiers begins by creating and assigning a unique digital or QR code identifier to each physical experimental sample (such as a specific enzyme mutant solution in a 96-well plate) through the sample marking module. At the same time, the identifier is systematically associated with and recorded with the precise physical location coordinates (e.g., plate number, row number, column number) of the sample in the experimental container (such as a deep-well plate or microplate), thereby establishing a one-to-one mapping relationship between physical entities and digital identities.

[0024] The beneficial effect of the above technical solution is that by generating a unique identifier for the experimental sample, the accuracy of the sample tracking and traceability data chain in the subsequent screening process is ensured.

[0025] S120: Data association module, used to communicate with high-throughput experimental equipment, collect raw experimental data, bind the raw experimental data with the corresponding unique identifier in real time, and generate standardized experimental data records. In some embodiments of the present invention, a communication connection is established with a high-throughput experimental device to collect raw experimental data. The raw experimental data is then bound in real-time with a corresponding unique identifier to generate standardized experimental data records. Specifically, this includes: Establish a data connection with at least one of the following through a preset device communication interface protocol: microplate reader, high-throughput sequencer, and automated liquid handling workstation; The experimental equipment can receive structured or unstructured data files containing raw measurement values ​​in real time. By parsing the sample container identifier or experimental batch information carried in the data file, the original measurement value is automatically matched and bound with the unique identifier. The bound data is formatted, standardized in units, and outlier removed to generate standardized experimental data records with complete structure and uniform fields, and is stamped with timestamps and experimental batch labels.

[0026] In this embodiment, parsing the sample container identifier or experimental batch information carried in the data file specifically includes: Identify the filename or identifier field in the file header of the data file that conforms to a preset naming rule; The identifier field is compared with the experimental sample board number or experimental batch number pre-registered in the system; After successful comparison, the measurement value sequence in the data file is mapped to the corresponding sample unique identifier code according to the sample layout diagram corresponding to the sample plate number or experimental batch number in a preset order.

[0027] In this embodiment, the bound data undergoes format unification, unit standardization, and outlier removal processing, specifically including: The format standardization process maps and reorganizes data records from different experimental devices and with different data structures according to the preset central database data model, converting them into a unified format with the same field order and data type. Unit standardization processing identifies the measurement units in the raw data and automatically converts all numerical data into the standard measurement units specified by the system based on the preset unit conversion rule library. Outlier removal involves applying threshold judgment algorithms based on statistical distribution or reasonableness rules based on experimental logic to verify the transformed data, automatically identifying and marking or removing outlier data points that exceed a preset reasonable range, and generating standardized experimental data records with controllable quality.

[0028] In this embodiment, the automatic matching and binding process first identifies the identifier that conforms to the naming convention in the data file name, file header or specific metadata field inside the file through a predefined text parsing algorithm. This identifier uniquely corresponds to the experimental sample plate number or experimental batch number pre-registered in the system. Subsequently, the system calls the digital sample layout diagram associated with the identifier and establishes a one-to-one mapping relationship between the original measurement value sequence in the data file and the unique identifier of each well position in the sample layout diagram according to the physical scanning order or logical arrangement rules when the device outputs data.

[0029] The beneficial effects of the above technical solution are: by constructing an integrated management system covering the entire process from physical samples to experimental data, the accuracy, traceability and screening efficiency of high-throughput enzyme mutant screening are significantly improved.

[0030] S130: Data integration module, used to integrate and store standardized experimental data records, location coordinates and experimental process metadata in a central database to establish a complete traceability data chain; In some embodiments of the present invention, standardized experimental data records, location coordinates, and experimental process metadata are integrated and stored in a central database to establish a complete traceability data chain, specifically including: Using the unique identifier as the primary key, a set of related data tables is established in the central database, including a sample basic information table, an experimental data record table, a physical location change table, and an experimental process log table. The standardized experimental data records are synchronously written into the corresponding data tables through the database transaction mechanism, and foreign key relationships between tables are maintained. Based on the relationship between the primary key and foreign key, a data link can be constructed that allows for the reverse tracing of all historical experimental data, location trajectories, and complete experimental contexts from the final performance data of any target mutant.

[0031] In this embodiment, a set of related data tables is established in the central database, including a sample basic information table, an experimental data record table, a physical location change table, and an experimental process log table. Specifically, it includes: The sample basic information table shall include at least the following fields: unique identifier, original gene sequence, mutation site information, vector information, and creation time; The physical location change table shall include at least the following fields: unique identifier, container type, location coordinates, operation time, and change type; The experimental process log table shall contain at least the following fields: experimental batch number, associated unique identifier set, experimental type, instrument and equipment number, core reaction parameters, operator and environmental temperature and humidity records.

[0032] In this embodiment, a data link is constructed that allows for the reverse tracing of all historical experimental data, location trajectories, and complete experimental contexts from the final performance data of any target mutant. This is achieved by using the unique identifier of the target mutant as the starting point for the query, and by executing recursive or iterative database association query operations. This automatically and completely retrieves and connects all historical performance values ​​in the experimental data record table, all location movement trajectories in the physical location change table, and the corresponding experimental operation contexts in the experimental process log table. Finally, the retrieval results are integrated and encapsulated into a visualized or structured data link containing time-series information, spatial information, and experimental condition information, realizing visualized backtracking and auditing of the entire process from result to source.

[0033] The beneficial effects of the above technical solution are: by establishing an associated database with the unique identifier of the sample as the core and designing a dedicated data link traceability mechanism, it realizes the digital tracking and one-stop query of the complete life cycle of high-throughput enzyme mutants from gene sequence to final performance, improves the ability to backtrack and analyze abnormal results, and provides a data foundation for optimizing experimental design and accelerating the discovery of candidate mutants.

[0034] S140: Analysis and identification module, used to perform multi-dimensional analysis on standardized experimental data records, calculate mutant performance indicators based on preset screening algorithms, and identify target mutants; In some embodiments of the present invention, the method for performing multi-dimensional analysis on standardized experimental data records, calculating mutant performance indicators based on a preset screening algorithm, and identifying target mutants specifically includes: Call the preset multi-dimensional analysis model to perform data analysis and output the results; Based on the output of the multi-dimensional analysis model, a weighted fusion algorithm or a machine learning classification algorithm is used to calculate the comprehensive performance index score of each mutant. The comprehensive performance index scores are sorted or thresholded according to preset screening rules, and the target mutant set that meets the preset screening conditions is automatically identified and marked. The filtering algorithm can dynamically adjust the weight coefficients of the multi-dimensional analysis model or the threshold parameters of the filtering rules based on user configuration or historical data feedback.

[0035] In this embodiment, a preset multi-dimensional analysis model is invoked for data analysis, specifically including: During the data analysis process, at least one of the following dimensional models is invoked for parallel or sequential analysis processing: The activity dimension analysis model is used to perform nonlinear fitting of the Michaelis equation on enzyme activity kinetic data, calculate and output the maximum reaction rate and Michaelis constant; The stability dimension analysis model is used to fit equations to temperature gradient or time series deactivation mechanical data, calculate and output half-deactivation temperature or half-life parameters; The expression level dimension analysis model is used for statistical analysis of batch protein expression data, and calculates and outputs the mean yield and coefficient of variation. After the data analysis is completed, the analysis results are standardized, and the standardized results are used as the output.

[0036] In this embodiment, a weighted fusion algorithm or machine learning classification algorithm is used to calculate the comprehensive performance index score of each mutant, specifically including: A weighted fusion algorithm is adopted: the numerical feature parameters output by the dimensional model are normalized, and the comprehensive performance index score of each mutant is calculated by linear weighted sum formula according to the preset or dynamically optimized weight coefficient vector. A machine learning classification algorithm is adopted: using the multi-dimensional feature parameters of mutants with historically accumulated and labeled performance levels as the training sample set, a performance classification classifier is trained; the multi-dimensional feature parameters of the mutants to be screened are input into the trained classifier, and the predicted level or category confidence score output by the classifier is mapped to the corresponding comprehensive performance index score. The optimization of the weight coefficient vector or the training update of the classifier is performed automatically by the system periodically or automatically based on newly added historical data feedback.

[0037] The beneficial effects of the above technical solution are: by calling a professional enzymatic analysis model to quantify and analyze multidimensional data, and by combining dynamically optimizable weighted fusion or machine learning algorithms, the evaluation and ranking of the comprehensive performance of massive mutants can be continuously optimized, thereby enabling the rapid and accurate identification of the target mutant with the best comprehensive performance from complex data, and significantly improving the screening hit rate.

[0038] S150: Result output module, used to generate an analysis report based on the location coordinates and performance indicators of the target mutant, and output it to the specified experimental equipment or interface.

[0039] In this embodiment, an analysis report is generated based on the location coordinates and performance indicators of the target mutant, specifically including: Based on the location coordinates and performance indicators of the target mutant, relevant data is extracted from the central database and automatically populated using a preset report template; The populated data is transformed into visual analysis charts, which include at least a visual heatmap based on the sample board location and a multi-dimensional performance comparison chart; The analysis report, which includes structured data and visualization charts, is pushed to the laboratory information management system or printing equipment in a standard file format, and the interactive report is displayed in the system's interactive interface.

[0040] The beneficial effects of the above technical solution are: by integrating the intelligent screening results with the physical location, historical data and multi-dimensional performance indicators of the original samples, a comprehensive report integrating intuitive visualization charts and structured data is automatically generated. This not only provides researchers with a one-stop decision-making view from macro distribution to micro details, but also realizes the automated flow of analysis results to downstream experimental systems or collaborative platforms, thereby significantly improving the efficiency of data interpretation and the execution capability of R&D decisions.

[0041] In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0042] Although the invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the embodiments disclosed in this invention can be combined with each other in any way. The fact that not all of these combinations are described in this specification is merely for the sake of brevity and resource conservation.

[0043] It will be understood by those skilled in the art that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A high-throughput enzyme mutant data screening and management system, characterized in that, include: The sample tagging module is used to generate a unique identifier for each physical experiment sample and associate it with the location coordinates recorded in the experimental container; The data association module is used to communicate with high-throughput experimental equipment, collect raw experimental data, bind the raw experimental data with the corresponding unique identifier in real time, and generate standardized experimental data records. The data integration module is used to integrate and store standardized experimental data records, location coordinates, and experimental process metadata in a central database to establish a complete traceability data chain. The analysis and identification module is used to perform multi-dimensional analysis on standardized experimental data records, calculate mutant performance indicators based on preset screening algorithms, and identify target mutants. The results output module is used to generate an analysis report based on the location coordinates and performance indicators of the target mutant, and output it to the specified experimental equipment or interface.

2. The high-throughput enzyme mutant data screening and management system according to claim 1, characterized in that, It communicates with high-throughput experimental equipment to collect raw experimental data, binds the raw experimental data with corresponding unique identifiers in real time, and generates standardized experimental data records, specifically including: Establish a data connection with at least one of the following through a preset device communication interface protocol: microplate reader, high-throughput sequencer, and automated liquid handling workstation; The experimental equipment can receive structured or unstructured data files containing raw measurement values ​​in real time. By parsing the sample container identifier or experimental batch information carried in the data file, the original measurement value is automatically matched and bound with the unique identifier. The bound data is formatted, standardized in units, and outlier removed to generate standardized experimental data records with complete structure and uniform fields, and is stamped with timestamps and experimental batch labels.

3. The high-throughput enzyme mutant data screening and management system according to claim 2, characterized in that, By parsing the sample container identifier or experimental batch information carried in the data file, specifically including: Identify the filename or identifier field in the file header of the data file that conforms to a preset naming rule; The identifier field is compared with the experimental sample board number or experimental batch number pre-registered in the system; After successful comparison, the measurement value sequence in the data file is mapped to the corresponding sample unique identifier code according to the sample layout diagram corresponding to the sample plate number or experimental batch number in a preset order.

4. The high-throughput enzyme mutant data screening and management system according to claim 2, characterized in that, The bound data undergoes format standardization, unit standardization, and outlier removal, specifically including: The format standardization process maps and reorganizes data records from different experimental devices and with different data structures according to the preset central database data model, converting them into a unified format with the same field order and data type. Unit standardization processing identifies the measurement units in the raw data and automatically converts all numerical data into the standard measurement units specified by the system based on the preset unit conversion rule library. Outlier removal involves applying threshold judgment algorithms based on statistical distribution or reasonableness rules based on experimental logic to verify the transformed data, automatically identifying and marking or removing outlier data points that exceed a preset reasonable range, and generating standardized experimental data records with controllable quality.

5. The high-throughput enzyme mutant data screening and management system according to claim 1, characterized in that, Standardized experimental data records, location coordinates, and experimental process metadata are integrated and stored in a central database to establish a complete traceability data chain, specifically including: Using the unique identifier as the primary key, a set of related data tables is established in the central database, including a sample basic information table, an experimental data record table, a physical location change table, and an experimental process log table. The standardized experimental data records are synchronously written into the corresponding data tables through the database transaction mechanism, and foreign key relationships between tables are maintained. Based on the relationship between the primary key and foreign key, a data link can be constructed that allows for the reverse tracing of all historical experimental data, location trajectories, and complete experimental contexts from the final performance data of any target mutant.

6. The high-throughput enzyme mutant data screening and management system according to claim 5, characterized in that, A set of related data tables is established in the central database, including a sample basic information table, an experimental data record table, a physical location change table, and an experimental process log table. Specifically, it includes: The sample basic information table shall include at least the following fields: unique identifier, original gene sequence, mutation site information, vector information, and creation time; The physical location change table shall include at least the following fields: unique identifier, container type, location coordinates, operation time, and change type; The experimental process log table shall contain at least the following fields: experimental batch number, associated unique identifier set, experimental type, instrument and equipment number, core reaction parameters, operator and environmental temperature and humidity records.

7. The high-throughput enzyme mutant data screening and management system according to claim 1, characterized in that, This is used for multi-dimensional analysis of standardized experimental data records, calculating mutant performance indicators based on a pre-set screening algorithm, and identifying target mutants, specifically including: Call the preset multi-dimensional analysis model to perform data analysis and output the results; Based on the output of the multi-dimensional analysis model, a weighted fusion algorithm or a machine learning classification algorithm is used to calculate the comprehensive performance index score of each mutant. The comprehensive performance index scores are sorted or thresholded according to preset screening rules, and the target mutant set that meets the preset screening conditions is automatically identified and marked. The filtering algorithm can dynamically adjust the weight coefficients of the multi-dimensional analysis model or the threshold parameters of the filtering rules based on user configuration or historical data feedback.

8. The high-throughput enzyme mutant data screening and management system according to claim 7, characterized in that, Data analysis is performed by calling a pre-defined multi-dimensional analysis model, specifically including: During the data analysis process, at least one of the following dimensional models is invoked for parallel or sequential analysis processing: The activity dimension analysis model is used to perform nonlinear fitting of the Michaelis equation on enzyme activity kinetic data, calculate and output the maximum reaction rate and Michaelis constant; The stability dimension analysis model is used to fit equations to temperature gradient or time series deactivation mechanical data, calculate and output half-deactivation temperature or half-life parameters; The expression level dimension analysis model is used for statistical analysis of batch protein expression data, and calculates and outputs the mean yield and coefficient of variation. After the data analysis is completed, the analysis results are standardized, and the standardized results are used as the output.

9. A high-throughput enzyme mutant data screening and management system according to claim 7, characterized in that, A weighted fusion algorithm or machine learning classification algorithm is used to calculate the comprehensive performance index score for each mutant, specifically including: A weighted fusion algorithm is adopted: the numerical feature parameters output by the dimensional model are normalized, and the comprehensive performance index score of each mutant is calculated by linear weighted sum formula according to the preset or dynamically optimized weight coefficient vector. A machine learning classification algorithm is adopted: using the multi-dimensional feature parameters of mutants with historically accumulated and labeled performance levels as the training sample set, a performance classification classifier is trained; the multi-dimensional feature parameters of the mutants to be screened are input into the trained classifier, and the predicted level or category confidence score output by the classifier is mapped to the corresponding comprehensive performance index score. The optimization of the weight coefficient vector or the training update of the classifier is performed automatically by the system periodically or automatically based on newly added historical data feedback.

10. A high-throughput enzyme mutant data screening and management system according to claim 1, characterized in that, Based on the location coordinates and performance indicators of the target mutant, an analysis report is generated, which includes: Based on the location coordinates and performance indicators of the target mutant, relevant data is extracted from the central database and automatically populated using a preset report template; The populated data is transformed into visual analysis charts, which include at least a visual heatmap based on the sample board location and a multi-dimensional performance comparison chart; The analysis report, which includes structured data and visualization charts, is pushed to the laboratory information management system or printing equipment in a standard file format, and the interactive report is displayed in the system's interactive interface.

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