Multi-dimensional information adaptive RNA cell-free synthesis system implementation method

Abstract

The application provides a multi-dimensional information adaptive RNA cell-free synthesis system implementation method, comprising: obtaining a preset enzyme conformation adjustment parameter and a microenvironment pH regulation template according to a functional deviation degree, an activity offset amplitude and an ATP regeneration rate fluctuation of the ATP regenerating enzyme, generating an enzyme conformation dynamic regulation configuration, and determining a dynamic energy supply output mode matched with a current synthesis stage; based on real-time feedback after running of the dynamic energy supply output mode, collecting energy supply cycle frequency data, combining an intermediate product accumulation rate and an enzyme and stage adaptation index in the system, processing full-dimensional monitoring data of the energy feedback cycle, and judging an adaptation matching level of the ATP regenerating enzyme.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for realizing a cell-free RNA synthesis system with multi-dimensional information adaptation. Background Technology

[0002] Cell-free RNA synthesis systems are an important technology in the field of biosynthesis. Their core regulatory logic involves monitoring molecular data within the system to determine the RNA synthesis stage, matching the conformation and energy supply mode of ATP regeneration enzymes, and thus achieving dynamic regulation of the system. Conventional regulatory methods typically involve collecting NTP concentration fluctuations and RNA fragment-related data using monitoring devices, integrating temperature and time information to construct a description of the molecular environment. This description is used to determine the RNA polymerization rate and define the synthesis stage. Then, based on preset parameters, the conformation of the ATP regeneration enzyme and the pH of the microenvironment are adjusted to configure the ATP regeneration energy supply pathway.

[0003] However, the core problem with the above-mentioned conventional regulation methods is that they cannot solve the unavoidable micro-perturbations of enzymatic reactions in cell-free RNA synthesis, which leads to the distortion of monitoring data. Consequently, the system lacks a dynamic adaptation mechanism between the RNA synthesis stage and ATP regeneration enzymes and energy supply systems, and it is impossible to establish an ATP regeneration adaptation regulation mechanism that links the entire RNA synthesis stage.

[0004] In laboratory and industrial applications of cell-free RNA synthesis, micro-perturbations such as slight temperature fluctuations, uneven substrate injection, and uneven magnesium ion distribution are objectively present and difficult to completely eliminate. Conventional methods do not consider the impact of free short-chain RNA fragments occupying enzyme binding sites as competitive inhibitors during long-term synthesis. They rely solely on statically preset enzyme conformation adjustment parameters and microenvironment pH regulation to control enzyme activity and energy supply. This approach cannot dynamically match the regulation strategy according to the actual enzyme activity deviation and ATP regeneration rate fluctuations at different synthesis stages. As a result, the degree of deviation of ATP regeneration enzyme function continues to increase, the adaptive regulation mechanism of energy feedback cycle fails, and the compatibility between enzyme and synthesis stage cannot be effectively determined. Consequently, it is impossible to configure an ATP regeneration energy supply pathway that matches the current stage.

[0005] In summary, incorrect stage judgment leads to regulatory mismatch between enzymes and the energy supply system. This regulatory mismatch further prevents the system from achieving dynamic adaptation, ultimately making it impossible for conventional methods to establish an ATP regeneration adaptation and regulatory mechanism that links all stages of RNA synthesis. This directly results in the inability of the cell-free RNA synthesis system's regulatory logic to form a closed loop. In scenarios such as high-density reactions, high-throughput parallel reactions, and large-scale industrial production, this mechanism defect will be continuously amplified, causing problems such as chaotic system regulation and unstable energy supply, thus restricting the industrial application and scenario expansion of cell-free RNA synthesis technology. Summary of the Invention

[0006] This invention provides a method for implementing a cell-free RNA synthesis system with multi-dimensional information adaptation, mainly including: S101 collected data on NTP concentration fluctuations, synthetic RNA transcript length distribution, and free short RNA fragment concentration in a cell-free RNA synthesis system, and performed pseudo-spoofing and perturbation correction. It then fused temperature changes and time point information to construct a description of the molecular environment state and obtain a trend judgment of RNA polymerization rate. S102, based on the trend judgment and corrected NTP concentration data, integrates intermediate product accumulation rate, transcript length distribution and short chain RNA concentration data to obtain substrate matching compatibility data and calculate enzyme-stage adaptation index. Combined with substrate binding efficiency parameters, it determines the transition markers of the entire process stage. S103 Based on the synthesis stage corresponding to the transition marker of the entire process stage, integrate substrate binding efficiency parameters and matching compatibility data, and combine enzyme binding site occupancy data to analyze the ATP regeneration enzyme activity shift amplitude and regeneration rate fluctuation, and determine its functional deviation degree. S104 Based on the degree of functional deviation, the magnitude of activity deviation, and the fluctuation of regeneration rate, obtain preset enzyme conformation adjustment parameters and microenvironment pH control templates, generate enzyme conformation dynamic control configuration, and determine the dynamic energy output mode that matches the current stage. S105 operates based on the aforementioned mode feedback, collects energy supply cycle frequency data, combines intermediate product accumulation rate and enzyme-stage compatibility index, processes full-dimensional monitoring data of energy feedback cycle, and determines the ATP regeneration enzyme compatibility level. If the S106 adaptation matching level is higher than the preset threshold, update the molecular environment state description, integrate the real-time enzyme reaction local pH value and acid-base regulation template, correct the enzyme binding site occupancy deviation, and obtain and execute the ATP regeneration enzyme energy supply pathway configuration. S107 configures the execution effect according to the energy supply path, collects real-time monitoring data on RNA polymerization rate and energy supply cycle frequency, updates molecular environment state and stage transition markers, and establishes an ATP regeneration adaptation regulation mechanism that is linked to the entire stage of RNA synthesis.

[0007] Further, step S101 includes: Data on NTP concentration fluctuations, synthetic RNA transcript length distribution, and free short RNA fragment concentrations within the cell-free RNA synthesis system were collected to obtain the raw monitoring data set. Median filtering and spurious removal are performed on the NTP concentration fluctuation data in the original monitoring dataset, and baseline correction is performed based on the temperature change curve. The mean correction is performed on the free short-chain RNA fragment concentration data based on the concentration difference of multiple sampling areas to obtain the corrected monitoring data. The corrected monitoring data is fused with the temperature change information of the reaction system and the time node information of the synthesis reaction to construct a molecular environment state description. Based on the correspondence between the NTP consumption per unit time and the migration speed of the transcript length interval in the molecular environment state description, the trend of RNA polymerization rate is determined.

[0008] Further, step S102 includes: The rate state category of the current synthesis reaction is determined based on the RNA polymerization rate trend. The rate state category includes rising phase, stable phase and falling phase. Combined with the real-time concentration values ​​of each nucleoside triphosphate in the corrected NTP concentration fluctuation data, the nucleotide consumption distribution characteristics are obtained. The nucleotide consumption distribution characteristics, the rate state category, the system intermediate product accumulation rate, the synthetic RNA transcript length distribution data, and the free short RNA fragment concentration data in the system are fused together, and the weighted sum is calculated according to their respective contributions under different rate state categories to obtain the synthetic state characteristic value. Based on the deviation between the current nucleotide consumption distribution characteristics and the base composition ratio of the target RNA sequence, substrate matching compatibility data is obtained by comparing the numerical values ​​of the synthetic state characteristics with the substrate matching compatibility data. The enzyme-stage fit index is obtained by multiplying the numerical values ​​of the synthetic state characteristics with the substrate matching compatibility data. The ratio of the enzyme-stage fit index to the substrate binding efficiency parameter is calculated, and the transition marker of the entire process stage corresponding to the current time node is determined based on the rate state category and the synthesis reaction time node information.

[0009] Further, step S103 includes: Based on the RNA synthesis stage corresponding to the transition marker of the entire process, obtain substrate binding efficiency parameters, substrate matching compatibility data, and enzyme binding site occupancy data; The substrate binding efficiency parameter and the substrate matching compatibility data are multiplied to obtain the substrate enzyme activity value. The substrate enzyme activity value is combined with the enzyme binding site occupancy data to obtain the measured catalytic activity value of ATP regeneration enzyme. The activity shift amplitude is obtained based on the measured catalytic activity value, and the change in ATP regeneration rate at adjacent sampling times is collected to obtain the ATP regeneration rate fluctuation. The degree of functional deviation of the ATP regeneration enzyme is determined by weighted summation of the activity shift amplitude, the ATP regeneration rate fluctuation, and the site occupancy ratio of free short-chain RNA fragments in the enzyme binding site occupancy data.

[0010] Further, step S104 includes: Based on the degree of functional deviation, the magnitude of activity deviation, and the fluctuation of ATP regeneration rate of the ATP regeneration enzyme, the corresponding enzyme conformation adjustment parameters and microenvironment acid-base regulation templates are obtained from the enzyme conformation adjustment parameter library and the microenvironment acid-base regulation template library, respectively. By combining the enzyme conformation adjustment parameters with the microenvironment pH regulation template, a dynamic enzyme conformation regulation configuration is generated. Based on the dynamic regulation configuration of enzyme conformation and the current synthesis stage indicated by the transition marker of the entire process stage, a dynamic energy output mode matching the current synthesis stage is determined.

[0011] Further, step S105 includes: Based on the real-time feedback after the operation of the dynamic energy supply output mode, energy supply cycle frequency data is collected, and the real-time monitoring value of the accumulation rate of intermediate products in the system is obtained to obtain a synchronized energy supply monitoring data set. The energy supply cycle frequency data, the accumulation rate of intermediate products in the system, and the enzyme-stage fit index are processed to obtain full-dimensional monitoring values ​​for the energy feedback cycle. The adaptation matching level of ATP regenerating enzyme is determined based on the deviation between the all-dimensional monitoring values ​​and the standard value of the enzyme and the stage adaptation index. When the absolute value of the deviation between the all-dimensional monitoring values ​​and the standard value is lower than the preset adaptation threshold of the corresponding stage, the adaptation matching level is determined to be higher than the preset adaptation threshold.

[0012] Further, step S106 includes: If the adaptation matching level is higher than the preset adaptation threshold, obtain the current molecular environment state description and real-time collected local pH data of the enzymatic reaction. The updated molecular environment state description is obtained by fusing the local pH data of the enzyme-catalyzed reaction and the microenvironment acid-base regulation template data. Based on the updated molecular environment state description and the difference between the site occupancy ratio of free short-chain RNA fragments in the enzyme binding site occupancy data and the baseline occupancy ratio corresponding to the enzyme and stage adaptation index, a site occupancy correction instruction is generated. Based on the updated molecular environment state description, the site occupancy correction instruction, and the dynamic energy output mode, an ATP regenerating enzyme energy supply path configuration is generated and executed.

[0013] Further, step S107 includes: Based on the execution effect of the ATP regenerating enzyme energy supply pathway configuration, real-time monitoring data of RNA polymerization rate and energy supply cycle frequency are collected to obtain the execution effect feedback data set. Based on the execution effect feedback data set, the trend of RNA polymerization rate is compared with the NTP consumption rate in the molecular environment state description. The molecular environment state description is adjusted according to the comparison results, and the transition marker of the whole process stage is updated according to the matching degree between the energy supply cycle frequency and the enzyme and the stage adaptation index. The ATP regeneration enzyme energy supply pathway configuration, the dynamic energy output mode, and the enzyme conformation dynamic regulation configuration are associated and bound with the updated full-process stage transition markers to establish an ATP regeneration adaptation regulation mechanism that is linked to the entire RNA synthesis process.

[0014] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses a method for implementing a cell-free RNA synthesis system with multi-dimensional information adaptation. The method constructs a precise molecular environment description by real-time acquisition of multi-source data, including NTP concentration fluctuations, RNA transcript length distribution, free short RNA fragment concentration, temperature, and time points, and uses this to determine the trend of RNA polymerization rate. Based on this, it integrates information such as intermediate product accumulation rate, substrate compatibility, enzyme-stage adaptation index, and substrate binding efficiency to determine the transition markers of the entire process, thus clarifying the current RNA synthesis stage. For key stages such as the early extension stage, the analysis frequency is increased, and the activity shift amplitude, regeneration rate fluctuation, and functional deviation of ATP regeneration enzymes are comprehensively evaluated to generate a matching enzyme conformation dynamic regulation configuration and dynamic energy output mode. After operation, based on real-time feedback such as energy supply cycle frequency and adaptation matching level, the method continuously corrects enzyme binding site occupancy deviations, local pH influences, and energy supply path configurations, ultimately establishing a closed-loop mechanism for ATP regeneration adaptation regulation that is deeply linked to the entire RNA synthesis process. The core technical effect of this invention lies in achieving multi-dimensional real-time adaptation and dynamic balance between energy supply and polymerization process in the cell-free RNA synthesis process, effectively inhibiting enzyme inactivation and substrate mismatch caused by the accumulation of short-chain RNA, and significantly improving the yield, synthesis stability and system controllability of long-chain RNA. Attached Figure Description

[0015] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0016] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0017] like Figure 1 This embodiment of a method for implementing a cell-free RNA synthesis system with multi-dimensional information adaptation may specifically include: Step S101: Collect NTP concentration fluctuation data, synthetic RNA transcript length distribution data, and free short RNA fragment concentration data in the RNA cell-free synthesis system through a real-time monitoring device. Perform spurious removal and perturbation correction on the collected raw data, fuse the temperature change information of the reaction system and the time node information of the synthesis reaction, construct a molecular environment state description, and obtain the trend judgment of RNA polymerization rate.

[0018] The NTP concentration fluctuation data in the cell-free RNA synthesis system was collected by a real-time monitoring device. The NTP concentration fluctuation data included the real-time concentration values ​​and changes of four nucleoside triphosphates (ATP, GTP, CTP, and UTP) during the reaction process. Simultaneously, the transcript length distribution data of synthesized RNA was collected, which included the proportion of RNA products in three length ranges: short chain segment, medium chain segment, and long chain segment. The concentration data of free short chain RNA fragments in the system was obtained to obtain the original monitoring data set.

[0019] The real-time monitoring device uses an optical sensor array to continuously detect the cell-free RNA synthesis system. The optical sensor array is configured with specific fluorescent probes for four nucleoside triphosphates: ATP, GTP, CTP, and UTP. The ATP probe uses a fluorescein-antifluorescein system at a concentration of 10 nmol / L; the GTP probe uses a rhodamine B derivative at a concentration of 8 nmol / L; the CTP probe uses a Cy3-labeled probe at a concentration of 10 nmol / L; and the UTP probe uses a FAM-labeled probe at a concentration of 8 nmol / L. Each probe generates a fluorescence signal of a specific wavelength in the presence of its corresponding nucleoside triphosphate. The fluorescence intensity is converted into a concentration value by a photoelectric conversion device. The sampling period is set to a preset time interval, such as 30 seconds / sampling, thereby obtaining the time series of NTP concentration fluctuation data. The conversion formula between fluorescence intensity and NTP concentration is: NTP concentration = (fluorescence intensity - background fluorescence intensity) × 0.005.

[0020] The acquisition of synthetic RNA transcript length distribution data was achieved through an online capillary electrophoresis detection unit. This unit separates RNA molecules based on the difference in migration rate in an electric field. The separation conditions were: electric field strength 15 kV / cm, separation time 20 minutes, and buffer solution 1×TBE buffer at pH 8.3. Short-chain segments corresponded to RNA products with the number of bases below a preset short-chain threshold, medium-chain segments corresponded to RNA products with the number of bases between the short-chain and long-chain thresholds, and long-chain segments corresponded to RNA products with the number of bases above a preset long-chain threshold. The product proportion of each segment was calculated by integrating the peak area.

[0021] Based on the original monitoring data set, a median filtering method is used to set a continuous sampling point window to remove abnormal jump points in the NTP concentration fluctuation data. For concentration drift caused by slight temperature fluctuations, baseline correction is performed based on the temperature change curve. If there is a local concentration deviation caused by uneven distribution of magnesium ions in the free short-chain RNA fragment concentration data, the mean is corrected based on the concentration difference of the three sampling areas of the reaction system to obtain the corrected monitoring data.

[0022] The specific execution process of the median filtering method is as follows: Select continuous sampling points to form a sliding window, which contains an odd number of sampling points, such as 5 sampling points. Sort the concentration values ​​of all sampling points in the window according to their numerical values, and take the value in the middle position after sorting as the correction value of the center point of the window. When the difference between the original concentration value of a sampling point and the concentration values ​​of its adjacent sampling points exceeds the preset jump threshold, such as 5 μmol / L, the sampling point is identified as an abnormal jump point. The median filtering method is used to replace it with the median value in the window, thereby eliminating data distortion caused by equipment noise or instantaneous interference.

[0023] Baseline correction corrects for concentration drift caused by minute temperature fluctuations. Every change in the temperature of the reaction system can cause a shift in the response sensitivity of the fluorescent probe. The specific method of baseline correction is to establish a correspondence table between the temperature change curve and the amount of concentration drift. Specifically, within the range of 25-37℃, for every 1℃ increase in temperature, the NTP concentration drift is 0.3μmol / L. The drift compensation amount at the current temperature is obtained by looking up the correspondence table based on the real-time temperature data. The original concentration value is then subtracted from the drift compensation amount to obtain the temperature-corrected concentration value.

[0024] When there is an uneven distribution of magnesium ions in the reaction system, there is a local deviation in the concentration of free short-chain RNA fragments detected in different sampling areas. The mean correction method is to obtain the concentration detection values ​​of multiple sampling areas in the reaction system, calculate the difference between the concentration value of each area and the overall mean, and adjust the original concentration value of each area to the overall mean, thereby eliminating the concentration deviation caused by the uneven spatial distribution.

[0025] Based on the corrected monitoring data, the temperature gradient curve in the temperature change information of the fusion reaction system and the time markers of the start, extension, and termination stages in the synthesis reaction time node information are used to construct a molecular environment state description. The molecular environment state description includes the NTP consumption per unit time, the trend of transcript length distribution, and the degree of accumulation of free short chain fragments. By the correspondence between the NTP consumption per unit time and the migration speed of transcript length intervals, it is determined whether the RNA polymerization rate is increasing, stable, or decreasing, thus obtaining the RNA polymerization rate trend judgment.

[0026] The process of constructing a description of the molecular environment state requires the fusion of corrected monitoring data and time-series information. The NTP consumption per unit time is obtained by dividing the NTP concentration difference between adjacent sampling times by the time interval. The trend of transcript length distribution change is determined by comparing the increase or decrease direction of the proportion of each length segment at adjacent times. The degree of accumulation of free short chain fragments is characterized by the slope of the change in short chain RNA concentration over time. The data of the above three dimensions together constitute a state vector describing the molecular environment of the current reaction system.

[0027] The RNA polymerization rate trend is determined based on the correlation between NTP consumption per unit time and the migration speed of transcript length regions. When NTP consumption continues to increase and the migration speed from medium-chain segments to long-chain segments accelerates, the RNA polymerization rate is judged to be on an upward trend. When NTP consumption remains stable and the change in the proportion of each length segment is within a preset fluctuation range of ±5%, the RNA polymerization rate is judged to be on a stable trend. When NTP consumption gradually decreases and the growth rate of the proportion of long-chain segments slows down, the RNA polymerization rate is judged to be on a downward trend.

[0028] Step S102: Based on the RNA polymerization rate trend judgment and the corrected NTP concentration fluctuation data, the data fusion method is used to process the intermediate product accumulation rate, synthetic RNA transcript length distribution data and free short RNA fragment concentration data in the system. Simultaneously, substrate matching compatibility data is obtained and the enzyme-stage adaptation index is calculated. Combined with the substrate binding efficiency parameter, the transition markers of the entire process stage corresponding to the synthesis reaction time node are determined.

[0029] Based on the trend of RNA polymerization rate, the rate state category of the current synthesis reaction is obtained. The rate state category includes three types: rising phase, stable phase, and falling phase. Combined with the real-time concentration values ​​of four nucleoside triphosphates (ATP, GTP, CTP, and UTP) in the corrected NTP concentration fluctuation data, the nucleotide consumption distribution characteristics are obtained by the ratio of the decrease in the concentration of each nucleoside triphosphate at adjacent sampling times.

[0030] Based on the nucleotide consumption distribution characteristics and the rate state categories, multi-dimensional data are obtained by fusing the intermediate product accumulation rate, synthetic RNA transcript length distribution data, and free short RNA fragment concentration data within the system. The intermediate product accumulation rate is obtained by detecting the slope of the change in concentration of untranscribed RNA intermediates over time. The multi-dimensional data are weighted and summed according to their respective contributions under different rate state categories. The specific weight allocation is as follows: during the rising phase, intermediate product accumulation rate is 0.4, long transcript segment proportion is 0.3, short RNA concentration is 0.2, and nucleotide consumption distribution characteristics are 0.1; during the stationary phase, the weight of each dimension is 0.25; during the falling phase, intermediate product accumulation rate is 0.2, long transcript segment proportion is 0.3, short RNA concentration is 0.4, and nucleotide consumption distribution characteristics are 0.1. The weighted summation formula is: synthetic state characteristic value = Σ(data of each dimension × corresponding weight), thus obtaining the synthetic state characteristic value.

[0031] Based on the deviation between the current nucleotide consumption distribution characteristics and the base composition ratio of the target RNA sequence, substrate matching compatibility data is obtained by comparing the numerical values ​​of the synthetic state characteristics. The preset compatibility threshold is 0.1, the preset quasi-compatibility threshold is 0.3, and the preset quasi-compatibility threshold is greater than the preset compatibility threshold. The enzyme-stage adaptation index is obtained by multiplying the numerical values ​​of the synthetic state characteristics and the substrate matching compatibility data. Based on the enzyme-stage fit index and substrate binding efficiency parameter, which is obtained by detecting the number of NTP incorporation reactions catalyzed by RNA polymerase per unit time, the detection method is an isotope labeling method. Specifically, ³²P-labeled NTP substrate with a specific activity of 3000 Ci / mmol is added to the reaction system, and the number of labeled nucleotides incorporated into the nascent RNA chain within 1 minute is detected. The substrate binding efficiency parameter is calculated as: number of labeled nucleotides incorporated / total moles of RNA polymerase. The ratio of the enzyme-stage fit index to the substrate binding efficiency parameter is then calculated based on the rate state category and... Synthetic reaction time node information is used to determine the full-process stage transition identifier corresponding to the current time node. The full-process stage transition identifier includes five stage identifier types: transition from start to extension, early extension, middle extension, late extension, and transition from extension to termination. The specific determination criteria are as follows: when the rate state category is rising and the ratio is < the first preset threshold of 0.5, it is the transition from start to extension; when the rate state category is rising and 0.5 ≤ ratio < the second preset threshold of 1.0, it is the early extension; when the rate state category is stable and the ratio is ≥ 1.0, it is the middle extension; when the rate state category is stable and the ratio is < 1.0, it is the late extension; and when the rate state category is falling, it is the transition from extension to termination.

[0032] The determination of RNA polymerization rate trend is based on the molecular environment state description constructed in the previous step. The determination of the rate state category is based on the combination characteristics of the change direction of NTP consumption per unit time and the migration speed of transcript length intervals. When NTP consumption shows a continuous increase and the migration from medium chain segments to long chain segments accelerates, it is determined to be the rising period. When NTP consumption remains stable and the proportion of each length segment fluctuates within a preset range, it is determined to be the stable period. When NTP consumption gradually decreases and the growth rate of long chain segments slows down, it is determined to be the falling period.

[0033] The process of obtaining the nucleotide consumption distribution characteristics involves the simultaneous processing of the concentration changes of four nucleoside triphosphates: ATP, GTP, CTP, and UTP. Between two adjacent sampling times, the concentration decrease of each nucleoside triphosphate is calculated separately. The concentration decreases of the four nucleoside triphosphates are then compared pairwise to obtain six sets of ratio data. These six sets of ratio data together constitute the nucleotide consumption distribution characteristics, which reflect the relative consumption rate relationship of different types of nucleotides during synthesis.

[0034] The accumulation rate of intermediate products in the system was detected by a fluorescent labeling method. A fluorescent probe with specific binding ability to untranscribed RNA intermediates was added to the reaction system. The time series data of RNA intermediate concentration was obtained by real-time fluorescence intensity monitoring. The accumulation rate of intermediate products was obtained by calculating the ratio of the concentration difference between adjacent time points to the time interval.

[0035] The multi-dimensional data fusion process employs a weighted summation method. The contribution is determined in relation to the current rate state category. During the rising phase, the contribution of intermediate product accumulation rate is assigned a higher weight, while the contribution of free short RNA fragment concentration is assigned a lower weight. During the stable phase, all three dimensions are weighted equally. During the declining phase, the contribution of free short RNA fragment concentration increases while the contribution of intermediate product accumulation rate decreases. The specific weighted summation process is as follows: multiply the intermediate product accumulation rate value by its corresponding contribution weight, multiply the proportion of long segments in the transcript length distribution data by its corresponding contribution weight, multiply the free short RNA fragment concentration value by its corresponding contribution weight, and add the three products together to obtain the synthesis state characteristic value. This value comprehensively reflects the overall synthesis state of the current reaction system.

[0036] Obtaining substrate matching compatibility data requires prior knowledge of the base composition information of the target RNA sequence. The molar percentages of the four bases adenine, guanine, cytosine, and uracil in the target RNA sequence constitute the target base composition ratio. The relative consumption ratios of each nucleoside triphosphate in the nucleotide consumption distribution characteristics are compared one by one with the target base composition ratio. The absolute values ​​of the differences at corresponding positions are calculated and summed. The summation result is the deviation value. The smaller the deviation value, the higher the degree of matching between substrate supply and transcription requirements.

[0037] The physical significance of the enzyme-stage fit index lies in its quantitative characterization of the degree of fit between the current catalytic state of RNA polymerase and the requirements of the synthesis stage. When the synthetic state characteristic value is high and the substrate compatibility data indicates a good match, the enzyme-stage fit index obtained by multiplying the two values ​​shows a large value, indicating that the RNA polymerase is in a working state highly adapted to the current stage. Furthermore, the detection of the substrate binding efficiency parameter is based on enzyme kinetics principles. By adding isotopically labeled NTP substrates to the reaction system, the number of labeled nucleotides catalyzed and incorporated into the nascent RNA chain by RNA polymerase per unit time is measured. This number is divided by the total number of moles of RNA polymerase in the reaction system to obtain the substrate binding efficiency parameter, which characterizes the catalytic efficiency of a single enzyme molecule. This parameter directly reflects the ability of RNA polymerase to form an effective catalytic complex with the substrate.

[0038] The determination of the transition markers for the entire process stage is based on a combination of three factors: enzyme-stage compatibility index, substrate binding efficiency parameter, and rate state category. When the rate state category is in the rising phase and the ratio of enzyme-stage compatibility index to substrate binding efficiency parameter is lower than a first preset threshold, it is determined as the transition marker from the beginning to the extension phase. When the rate state category is in the rising phase and the ratio is between the first and second preset thresholds, it is determined as the early extension phase. When the rate state category is in the stable phase and the ratio is higher than the second preset threshold, it is determined as the mid-extension phase. When the rate state category is in the stable phase and the ratio begins to decrease but is still higher than a third preset threshold, it is determined as the late extension phase. When the rate state category is in the falling phase, it is determined as the transition marker from the end to the end of the extension phase.

[0039] In the industrial production scenario of cell-free RNA synthesis, the real-time update frequency of the transition markers for the entire process is related to the reaction scale. For high-density reaction systems, a shorter update cycle is used to capture rapid stage changes, while for conventional density reaction systems, a standard update cycle is used. By accurately determining the transition markers, the entire RNA synthesis process can be finely divided into stages.

[0040] Step S103: Based on the RNA synthesis stage corresponding to the transition marker of the entire process stage, the substrate binding efficiency parameter and substrate matching compatibility data are processed by data fusion method, combined with enzyme binding site occupancy data, to analyze the activity shift amplitude of ATP regeneration enzyme and the fluctuation of ATP regeneration rate, and to determine the degree of functional deviation. If the transition marker of the entire process stage indicates that RNA synthesis is in the early stage of extension, the real-time analysis frequency of enzyme activity shift amplitude is simultaneously enhanced.

[0041] Based on the RNA synthesis stage corresponding to the transition marker of the entire process stage, obtain the substrate binding efficiency parameter, substrate matching compatibility data, and enzyme binding site occupancy data corresponding to the current stage. Multiply the substrate binding efficiency parameter and substrate matching compatibility data to obtain the substrate enzyme action value. Simultaneously obtain the enzyme binding site occupancy data, which is obtained by detecting the binding ratio of free short-chain RNA fragments to the active site of ATP regenerator.

[0042] Based on the substrate enzyme activity value and enzyme binding site occupancy data, the measured catalytic activity value of ATP regenerator in the current synthesis stage is obtained. The measured catalytic activity value is obtained by detecting the number of ADP molecules converted into ATP by ATP regenerator per unit time. The ratio of the measured catalytic activity value to the substrate enzyme activity value is calculated to obtain the activity offset amplitude of ATP regenerator. The standard ratio is set to 1.0 based on the activity ratio of wild-type ATP regenerator. The activity offset amplitude = |(measured catalytic activity value / substrate enzyme activity value) - 1.0|. The change in ATP regeneration rate at adjacent sampling times is collected simultaneously to obtain the ATP regeneration rate fluctuation. The ATP regeneration rate fluctuation = |current sampling time regeneration rate - previous sampling time regeneration rate| / previous sampling time regeneration rate. If the transition marker RNA synthesis of the whole process stage is in the early extension stage, it is determined to be a high-frequency sampling cycle, which is 1 / 2 of the basic sampling cycle. The sampling interval of the ATP regenerator activity offset amplitude is shortened to 15 seconds / time.

[0043] Based on the activity shift amplitude of the ATP regenerating enzyme, the fluctuation of the ATP regeneration rate, and the site occupancy ratio of free short-chain RNA fragments in the enzyme binding site occupancy data, the functional deviation degree is calculated according to the following formula: Functional deviation degree = (Activity shift amplitude × 0.4) + (ATP regeneration rate fluctuation × 0.3) + (Site occupancy ratio × 0.3), where the first preset weight = 0.4, the second preset weight = 0.3, and the third preset weight = 0.3. The functional deviation degree of the ATP regenerating enzyme is used to determine the difference between the current working state of the ATP regenerating enzyme and the ideal catalytic state.

[0044] The calculation of substrate-enzyme activity values ​​is based on the product of substrate binding efficiency parameters and substrate compatibility data. The substrate binding efficiency parameters reflect the ability of RNA polymerase to form an effective catalytic complex with NTP substrates, and the substrate compatibility data reflects the degree of fit between the current substrate supply and the transcription requirements of the target RNA sequence. The product of the two values ​​characterizes the overall state of synergistic interaction between the substrate and the enzyme.

[0045] Enzyme binding site occupancy data were obtained using fluorescence resonance energy transfer (FRET) detection. Donor fluorescent groups were labeled on the active site region of ATP regenerase, and acceptor fluorescent groups were labeled on free short-chain RNA fragments. When the free short-chain RNA fragment binds to the active site of ATP regenerase, energy transfer occurs between the donor and acceptor, leading to a change in the fluorescence signal. The relationship between fluorescence signal intensity and binding ratio is as follows: for every 1000 increase in signal intensity, the binding ratio increases by 10%. The binding ratio was calculated using the formula: Binding ratio = (Measured signal intensity - Blank signal intensity) / (Saturated binding signal intensity - Blank signal intensity) × 100%.

[0046] The measured catalytic activity of ATP regenerator was obtained using a coupled enzyme reaction detection method. Luciferase and a luciferin substrate were added to the reaction system. During the ATP regenerator's catalytic conversion of ADP to ATP, the ATP produced was utilized by luciferase and emitted fluorescence. The conversion formula between fluorescence intensity and the number of ATP molecules was: Number of ATP molecules = (Fluorescence intensity - Background fluorescence intensity) × 1.2 × 10⁻⁶ 6 The amount of ATP produced per unit time is calculated by monitoring the rate of increase in fluorescence intensity in real time.

[0047] The activity offset is calculated by comparing the measured catalytic activity with the substrate enzyme activity. When the ratio is higher than the preset upper limit threshold, it indicates that the actual catalytic capacity of the ATP regenerating enzyme exceeds the expected range under the current substrate conditions. When the ratio is lower than the preset lower limit threshold, it indicates that the actual catalytic capacity of the ATP regenerating enzyme fails to reach the expected level under the current substrate conditions. The activity offset is the absolute value of the difference between the ratio and the standard ratio.

[0048] When RNA synthesis is in the early elongation stage, RNA polymerase is in the transition period from the initial elongation state to the stable elongation state. At this time, the micro-perturbations of the enzymatic reaction have a significant impact on the subsequent synthesis process. Therefore, the sampling interval of the activity offset amplitude is shortened to a preset high-frequency sampling period in order to capture the subtle changes in the activity state of ATP regenerator in a timely manner.

[0049] The assessment of functional deviation comprehensively considers data from three dimensions: activity deviation amplitude, ATP regeneration rate fluctuation, and site occupancy ratio. Among them, the activity deviation amplitude reflects the deviation of the ATP regeneration enzyme's catalytic ability, the ATP regeneration rate fluctuation reflects the fluctuation of ATP supply stability, and the site occupancy ratio reflects the degree of competitive inhibition of the enzyme's active site by free short-chain RNA fragments. The three dimensions are multiplied by their respective preset weight values ​​and then summed to obtain a numerical value that comprehensively characterizes the functional deviation of the ATP regeneration enzyme. The larger the value, the greater the gap between the current working state of the ATP regeneration enzyme and the ideal catalytic state.

[0050] Step S104: Based on the degree of functional deviation, activity shift, and ATP regeneration rate fluctuation of the ATP regeneration enzyme, obtain preset enzyme conformation adjustment parameters and microenvironment pH regulation templates, generate enzyme conformation dynamic regulation configuration, and determine the dynamic energy output mode that matches the current synthesis stage.

[0051] Based on the degree of functional deviation, activity shift amplitude, and ATP regeneration rate fluctuation of the ATP regeneration enzyme, enzyme conformation adjustment parameters corresponding to the current functional deviation are retrieved from a pre-established enzyme conformation adjustment parameter library. These parameters include adjustments for enzyme molecule folding angle, active site exposure, and cofactor binding strength. Exemplary data from the enzyme conformation adjustment parameter library are as follows: low deviation (0-0.2) corresponds to a folding angle adjustment of 5°, an active site exposure adjustment of 10%, and a cofactor binding strength adjustment of 1.0; medium deviation (0.2-0.5) corresponds to a folding angle adjustment of 10°, an active site exposure adjustment of 20%, and a cofactor binding strength adjustment of 1.2; and high deviation (>0.5) corresponds to a folding angle adjustment of 15°, an active site exposure adjustment of 30%, and a cofactor binding strength adjustment of 1.5. Simultaneously, microenvironment pH regulation templates corresponding to the current activity shift amplitude are retrieved from a pre-established microenvironment pH regulation template library.

[0052] The microenvironment pH regulation template includes a target pH range and a pH regulation rate. Exemplary data from the microenvironment pH regulation template library are as follows: a low offset of 0-0.1 corresponds to a target pH range of 7.2-7.4 and a regulation rate of 0.05 pH / min; a medium offset of 0.1-0.3 corresponds to a target pH range of 7.0-7.6 and a regulation rate of 0.1 pH / min; and a high offset >0.3 corresponds to a target pH range of 6.8-7.8 and a regulation rate of 0.15 pH / min.

[0053] Based on the enzyme conformation adjustment parameters and the microenvironment pH regulation template, and combined with the amplitude range of the ATP regeneration rate fluctuation, a preset fluctuation threshold of 10% is set. If the ATP regeneration rate fluctuation exceeds the preset fluctuation threshold, the enzyme molecule folding angle adjustment amount and the active site exposure degree adjustment amount are added together to obtain a comprehensive adjustment amount. The comprehensive adjustment amount is combined with the target pH range to form an enzyme conformation dynamic regulation configuration. The enzyme conformation dynamic regulation configuration includes an enzyme conformation adjustment execution sequence and a microenvironment pH synchronous regulation instruction.

[0054] Based on the dynamic regulation configuration of enzyme conformation and the current synthesis stage indicated by the transition markers of the entire process, the various regulatory quantities in the enzyme conformation adjustment execution sequence are matched with the energy demand characteristics of the current synthesis stage. The energy demand characteristics are obtained based on the trend of RNA polymerization rate corresponding to the current synthesis stage. Combined with the acid-base regulation rate in the microenvironment acid-base synchronization regulation command, a dynamic energy supply output mode matching the current synthesis stage is determined. The dynamic energy supply output mode includes ATP output rate levels and energy supply cycle configurations. The ATP output rate levels are divided into three levels: high speed 100 μmol / L·min, medium speed 50 μmol / L·min, and low speed 20 μmol / L·min. The energy supply cycle configurations are as follows: high speed level corresponds to a pulse interval of 10 seconds and a duration of 5 seconds; medium speed level corresponds to a pulse interval of 20 seconds and a duration of 10 seconds; low speed level corresponds to a pulse interval of 30 seconds and a duration of 15 seconds.

[0055] The construction of the enzyme conformation adjustment parameter library is based on the correspondence between different functional deviation ranges and enzyme conformation adjustment requirements. The parameter library is stored in a lookup table structure. Each row in the table corresponds to a functional deviation range, and each column corresponds to an enzyme conformation adjustment parameter. When the current functional deviation value is input, the corresponding row is located by interval matching and all enzyme conformation adjustment parameters in that row are read.

[0056] The enzyme folding angle regulation modulus characterizes the adjustment range of the degree of folding in a specific region of the tertiary structure of ATP regeneration enzyme; the active site exposure regulation modulus characterizes the adjustment range of the area of ​​the enzyme active site exposed to the reaction medium; and the cofactor binding strength regulation modulus characterizes the adjustment range of the tightness of the binding between magnesium ions, zinc ions and enzyme molecules. The above three regulation modulus together determine the catalytic conformation state of ATP regeneration enzyme.

[0057] The microenvironment pH regulation template library also adopts a lookup table structure, using the activity offset range as the search key. Each template contains the upper and lower limits of the target pH range, as well as the rate parameter when adjusting from the current pH to the target range. The pH adjustment rate is expressed as the number of pH units changed per minute.

[0058] When the ATP regeneration rate fluctuates beyond a preset fluctuation threshold, it indicates that the current ATP supply is highly unstable. At this time, the enzyme folding angle adjustment amount and the active site exposure degree adjustment amount are directly added together to obtain a comprehensive adjustment amount. This comprehensive adjustment amount is used to simultaneously adjust the enzyme folding state and the active site exposure state to form a synergistic regulatory effect. The comprehensive adjustment amount, combined with the target pH range, forms a dynamic enzyme conformation regulation configuration. The enzyme conformation adjustment execution sequence in this configuration specifies the execution order of each adjustment amount, and the microenvironment pH synchronous regulation instruction specifies the temporal coordination relationship between pH regulation and enzyme conformation adjustment.

[0059] Energy demand characteristics are determined based on the trend of RNA polymerization rate in the current synthesis stage. When the polymerization rate is increasing, the energy demand characteristics correspond to a high energy consumption state; when the polymerization rate is stable, the energy demand characteristics correspond to a steady-state energy consumption state; and when the polymerization rate is decreasing, the energy demand characteristics correspond to a low energy consumption state.

[0060] The ATP output rate level in the dynamic energy supply output mode is determined based on the matching results of energy demand characteristics and enzyme conformation adjustment execution sequence. The level is divided into three levels: high-speed output, medium-speed output, and low-speed output. The energy supply cycle configuration specifies the pulse interval duration and duration of each pulse for ATP supply. The dynamic energy supply output mode achieves dynamic adaptation between ATP supply and the needs of the RNA synthesis stage.

[0061] Step S105: Based on the real-time feedback after the operation of the dynamic energy supply output mode, collect energy supply cycle frequency data, combine the system intermediate product accumulation rate and enzyme-stage compatibility index, process the full-dimensional monitoring data of energy feedback cycle, and determine the compatibility matching level of ATP regeneration enzyme.

[0062] Based on the real-time feedback after the operation of the dynamic energy output mode, energy supply cycle frequency data is collected. The energy supply cycle frequency data includes the periodic interval of ATP output pulses and the number of energy supply cycles completed within a preset statistical time window of 1 minute. The real-time monitoring value of the accumulation rate of intermediate products in the system is obtained simultaneously. The energy supply cycle frequency data and the accumulation rate of intermediate products are aligned with the same sampling time to obtain a synchronized energy supply monitoring data set.

[0063] Based on the synchronized energy supply monitoring data set, combined with the enzyme and stage adaptation index, the energy supply efficiency value is obtained by dividing the number of energy supply cycles by the enzyme and stage adaptation index. The product conversion synergy value is obtained by multiplying the intermediate product accumulation rate by the enzyme and stage adaptation index. The energy supply efficiency value and the product conversion synergy value are added together to obtain the full-dimensional monitoring value of the energy feedback cycle. The full-dimensional monitoring value comprehensively reflects the degree of synergy between the energy supply state and the enzyme catalytic state.

[0064] Based on the comprehensive monitoring values, the deviation value is obtained by calculating the difference between the comprehensive monitoring values ​​and the standard value of the enzyme-stage adaptation index. A second preset threshold is greater than a first preset threshold, for example, the first preset threshold is 0.2 and the second preset threshold is 0.5. If the absolute value of the deviation value is less than 0.2, the adaptation matching level of the ATP regenerating enzyme is determined to be high adaptation. If the absolute value of the deviation value is between 0.2 and 0.5, the adaptation matching level is determined to be medium adaptation. If the absolute value of the deviation value is greater than 0.5, the adaptation matching level is determined to be low adaptation.

[0065] The energy supply cycle frequency data is acquired by an optical sensor set in the ATP regeneration reaction region. The optical sensor detects the periodic changes in ATP concentration and records the time interval between two adjacent ATP concentration peaks as the periodic interval of the ATP output pulse. The total number of ATP concentration peaks occurring within a preset time window is counted to obtain the number of energy supply cycles completed per unit time.

[0066] The timing alignment process is implemented as follows: obtain the sampling time sequence of energy supply cycle frequency data and the sampling time sequence of intermediate product accumulation rate, perform interpolation on the two time sequences to make them have the same time resolution, extract the corresponding data values ​​at the same time node to form a data pair, thereby forming a synchronized energy supply monitoring data group.

[0067] The principle behind calculating the energy supply efficiency value is to measure the energy output capacity per unit of enzyme activity. The number of energy supply cycles reflects the frequency of ATP supply, and the enzyme-stage fit index reflects the current working state of the enzyme. The energy supply efficiency value obtained by dividing the two represents the energy supply cycle capacity corresponding to each unit of fit in the current enzyme state.

[0068] The principle behind calculating the product conversion synergy value is to measure the amplification effect of enzyme activity state on intermediate product conversion. The intermediate product accumulation rate reflects the progress of RNA synthesis. The product conversion synergy value obtained by multiplying the enzyme and stage fit index characterizes the synergistic relationship between enzyme activity state and product conversion efficiency. The larger the value, the higher the degree of cooperation between enzyme catalysis and product conversion.

[0069] The comprehensive monitoring values ​​are obtained by adding the energy supply efficiency value and the product conversion synergy value. These values ​​comprehensively reflect the working status of ATP regenerating enzyme in the current synthesis stage from two aspects: energy supply and product conversion. The fluctuation of the comprehensive monitoring values ​​can characterize the overall operation of the energy feedback cycle.

[0070] The determination of the fit level is based on the degree of deviation between the full-dimensional monitoring values ​​and the standard value of the enzyme and the stage fit index. The standard value is predetermined according to the ideal operating state of each synthesis stage. The smaller the absolute value of the deviation value, the closer the current working state of the ATP regenerating enzyme is to the ideal state. The high fit state corresponds to the case where the absolute value of the deviation value is lower than the preset first threshold, the medium fit state corresponds to the case where the absolute value of the deviation value is between the preset first threshold and the preset second threshold, and the low fit state corresponds to the case where the absolute value of the deviation value is higher than the preset second threshold. The above three-level judgment realizes the quantitative assessment of the fit level of the ATP regenerating enzyme.

[0071] Step S106: If the adaptation matching level is higher than the preset adaptation threshold, the molecular environment state description is updated by a continuous data fusion method. The local pH value of the enzyme-catalyzed reaction and the microenvironment acid-base regulation template data are fused in real time. The enzyme binding site occupancy deviation caused by the free short-chain RNA fragment is corrected simultaneously, and the ATP regeneration enzyme energy supply path configuration is obtained and executed.

[0072] If the matching level is higher than the preset matching threshold of 0.2, the current molecular environment state description and real-time collected local pH data of the enzymatic reaction are obtained. The local pH data of the enzymatic reaction is obtained by collecting a micro pH sensor array arranged in the ATP regenerating enzyme reaction area. The spatial spacing of the sensor array is 0.5 cm and the pH detection response time is 2 seconds, thus obtaining a set of local pH data reflecting the spatial distribution of acidity and alkalinity in the reaction area.

[0073] Based on the local pH data set and the microenvironment acid-base regulation template data, the local pH data is smoothed by averaging the values ​​at continuous sampling times. The smoothed pH data is then compared with the target acid-base range in the microenvironment acid-base regulation template to obtain a pH deviation value. This pH deviation value is then fused with the NTP consumption rate and transcript length distribution trend in the molecular environment state description to obtain an updated molecular environment state description.

[0074] Based on the updated molecular environment state description, the site occupancy ratio of free short-chain RNA fragments in the enzyme binding site occupancy data is obtained. The site occupancy deviation value is obtained by performing a difference calculation between the site occupancy ratio and the baseline occupancy ratio corresponding to the enzyme-stage adaptation index. The baseline occupancy ratio is determined according to the enzyme-stage adaptation index: an adaptation index of 0.8-1.2 corresponds to a baseline occupancy ratio of 10%, an adaptation index of 0.5-0.8 corresponds to a baseline occupancy ratio of 15%, and an adaptation index <0.5 corresponds to a baseline occupancy ratio of 20%. A site occupancy correction instruction is generated based on the positive or negative direction and deviation magnitude of the site occupancy deviation value. The site occupancy correction instruction includes the adjustment direction and adjustment magnitude.

[0075] Based on the updated molecular environment state description and the site occupancy correction instruction, the NTP consumption rate in the updated molecular environment state description is matched with the ATP output rate level in the dynamic energy output mode. Combined with the adjustment direction and adjustment magnitude in the site occupancy correction instruction, an ATP regenerating enzyme energy supply path configuration is generated. The ATP regenerating enzyme energy supply path configuration includes the ATP supply rate setting value and energy supply cycle adjustment parameters. The ATP regenerating enzyme energy supply path configuration is executed. If the adaptation matching level is lower than the preset adaptation threshold of 0.2, the functional deviation of the ATP regenerating enzyme is re-analyzed, and the enzyme conformation adjustment parameters and microenvironment pH regulation template are iteratively updated. The preset maximum number of iterations is 5. When the number of iterations reaches 5, the iteration is terminated and an abnormal warning is output.

[0076] The determination of the fitness matching level is based on the classification results of high fitness, medium fitness or low fitness obtained from the previous steps. When the fitness matching level is determined to be high fitness, it indicates that the current working state of the ATP regenerating enzyme has a good fit with the requirements of the synthesis stage. At this time, the subsequent molecular environment state description update and energy supply path configuration process are triggered.

[0077] The acquisition of local pH data in the enzyme-catalyzed reaction is achieved through a miniature pH sensor array, which consists of multiple independent pH-sensitive electrodes. These electrodes are evenly arranged at different positions in the ATP regeneration enzyme reaction area according to a preset spatial spacing. Each electrode independently detects the hydrogen ion concentration at its location and outputs the corresponding pH value in real time, thereby forming a set of local pH data reflecting the spatial distribution characteristics of acidity and alkalinity in the reaction area.

[0078] The smoothing of pH data adopts the time-series averaging method. The specific process is as follows: for each spatial location pH sensor, the pH value sequence output by the sensor at multiple consecutive sampling times is obtained, and the arithmetic mean of the value sequence is calculated as the smoothed pH value at that location. The same averaging calculation is performed on all spatial locations to obtain the smoothed local pH data.

[0079] The process of comparing the smoothed pH data with the target pH range in the microenvironment pH control template is as follows: Obtain the upper and lower limits of the target pH range; for the smoothed pH value at each spatial location, determine whether the pH value falls within the target pH range; if the pH value is higher than the upper limit, calculate the difference between the pH value and the upper limit as a positive pH deviation value; if the pH value is lower than the lower limit, calculate the difference between the lower limit and the pH value as a negative pH deviation value; if the pH value falls within the range, the pH deviation value is zero; and summarize the pH deviation values ​​at each spatial location to obtain the overall pH deviation distribution data.

[0080] The update process of the molecular environmental state description integrates pH deviation distribution data with the original molecular environmental state description, which includes two dimensions: NTP consumption rate and transcript length distribution trend. During integration, the average deviation value of the pH deviation distribution data is added as a third dimension to the state description. Simultaneously, the expected value of the NTP consumption rate is adjusted according to the direction of the pH deviation. A positive pH deviation indicates an alkaline reaction region, thus lowering the expected value of the NTP consumption rate; a negative pH deviation indicates an acidic reaction region, thus raising the expected value of the NTP consumption rate. This yields the updated molecular environmental state description. Furthermore, the site occupancy deviation value is calculated based on the baseline occupancy ratio determined by the enzyme-stage adaptation index. This baseline occupancy ratio characterizes the expected occupancy of the enzyme active site by the short-chain RNA fragment in the ideal state corresponding to the current enzyme-stage adaptation index. The site occupancy deviation value is obtained by subtracting the baseline occupancy ratio from the actual detected site occupancy ratio. A positive deviation value indicates that the actual occupancy exceeds expectations, while a negative deviation value indicates that the actual occupancy is lower than expected.

[0081] The generation of the site occupancy correction command is based on the positive or negative direction and the absolute value of the site occupancy deviation value. When the site occupancy deviation value is positive and the absolute value exceeds the preset deviation threshold, the adjustment direction is set to the enhanced clearing direction, and the adjustment level is determined according to the ratio of the absolute value of the deviation value to the preset deviation threshold. The larger the ratio, the higher the adjustment level. When the site occupancy deviation value is negative or the absolute value does not exceed the preset deviation threshold, the adjustment direction is set to maintain the status quo, and the adjustment level is set to zero.

[0082] The process of generating the ATP regenerating enzyme energy supply pathway configuration involves matching the NTP consumption rate in the updated molecular environment state description with the ATP output rate level in the dynamic energy supply output mode. The matching rule is as follows: when the NTP consumption rate is in the high consumption range, a high-speed output level is selected; when the NTP consumption rate is in the medium consumption range, a medium-speed output level is selected; and when the NTP consumption rate is in the low consumption range, a low-speed output level is selected. The selected level is then fine-tuned based on the adjustment direction and adjustment magnitude in the site occupancy correction instruction. If the adjustment direction is to enhance the clearance direction and the adjustment magnitude is high, the frequency of the energy supply cycle is increased based on the selected level to coordinate with the site clearance operation. Finally, an energy supply pathway configuration containing the ATP supply rate setting value and energy supply cycle adjustment parameters is generated and executed.

[0083] The execution of the ATP regenerating enzyme energy supply pathway configuration is achieved through an ATP supply regulation device in the reaction system. The ATP supply regulation device adjusts the injection rate of ATP precursor substances according to the ATP supply rate setting value in the configuration, and sets the time interval of ATP supply pulses according to the energy supply cycle adjustment parameters. By executing this energy supply pathway configuration, the dynamic adaptation of ATP supply to the needs of the current RNA synthesis stage is achieved.

[0084] Step S107: Based on the execution effect of the ATP regeneration enzyme energy supply pathway configuration, collect real-time monitoring data of RNA polymerization rate and energy supply cycle frequency in the system, update the molecular environment state description and the transition markers of the entire process stage, and establish an ATP regeneration adaptation and regulation mechanism that is linked to the entire stage of RNA synthesis.

[0085] Based on the execution effect of the ATP regenerating enzyme energy supply pathway configuration, real-time monitoring data of RNA polymerization rate and energy supply cycle frequency are collected within the system. The real-time monitoring data of RNA polymerization rate is obtained by detecting the number of bases incorporated into the newly generated RNA chain per unit time, and the real-time monitoring data of energy supply cycle frequency is obtained by detecting the number of ATP supply pulses occurring per unit time. The RNA polymerization rate and energy supply cycle frequency are synchronized in time to obtain the execution effect feedback data set.

[0086] Based on the execution effect feedback data set, the trend of RNA polymerization rate is compared with the NTP consumption rate in the molecular environment state description. If the trend of RNA polymerization rate is consistent with the trend of NTP consumption rate, the current molecular environment state description is maintained unchanged. If there is a deviation between the trend of RNA polymerization rate and the trend of NTP consumption rate, the expected value of NTP consumption rate and the trend of transcript length distribution in the molecular environment state description are adjusted according to the direction of the deviation. Simultaneously, the transition marker of the entire process stage is updated according to the matching degree between the energy supply cycle frequency and the enzyme and stage adaptation index.

[0087] Based on the updated molecular environment state description and the transition markers of the entire process stage, the ATP regeneration enzyme energy supply path configuration, the dynamic energy output mode, and the enzyme conformation dynamic regulation configuration are associated and bound with the updated transition markers of the entire process stage, forming a complete regulatory link from stage identification to enzyme conformation adjustment and then to energy supply path execution, and establishing an ATP regeneration adaptation regulation mechanism that is linked to the entire stage of RNA synthesis.

[0088] Real-time monitoring of RNA polymerization rate is achieved by adding fluorescently labeled nucleotides to the reaction system. When RNA polymerase incorporates fluorescently labeled nucleotides into the nascent RNA chain, it releases a fluorescent signal. The number of bases incorporated is calculated by detecting the cumulative intensity of the fluorescent signal per unit time, thereby obtaining the real-time value of RNA polymerization rate.

[0089] The detection of energy supply cycle frequency is based on the periodic fluctuation characteristics of ATP concentration. When the ATP supply regulation device executes the energy supply path configuration, it injects ATP precursor substances in a pulse manner. After each pulse injection, the ATP concentration rises instantaneously and then gradually decreases. The energy supply cycle frequency is obtained by counting the number of times the ATP concentration peak occurs per unit time.

[0090] The specific implementation method of time synchronization alignment is to obtain the sampling timestamp sequence of RNA polymerization rate monitoring data and the sampling timestamp sequence of energy supply cycle frequency monitoring data, perform linear interpolation on the two timestamp sequences to make them have the same time resolution, and extract the corresponding data values ​​at the aligned time nodes to form the execution effect feedback data group.

[0091] The update of the molecular environment state description is performed based on the consistency judgment result of the change trend of RNA polymerization rate and the change trend of NTP consumption rate. The change trend is judged by comparing the positive and negative directions of the difference between adjacent time values. When the RNA polymerization rate increases but the expected value of NTP consumption rate is not adjusted accordingly, it indicates that there is a deviation between the molecular environment state description and the actual state. At this time, the expected value of NTP consumption rate is adjusted upward or downward according to the direction and magnitude of the deviation, and the expected direction of the transcript length distribution change trend is adjusted simultaneously.

[0092] The update of the transition markers for the entire process stage is based on the matching degree between the energy supply cycle frequency and the enzyme-stage fit index. When the energy supply cycle frequency is consistently higher than the standard frequency range corresponding to the current stage, it indicates that the synthesis reaction has entered the next stage. At this time, the transition marker for the entire process stage is updated from the current stage to the next stage. When the energy supply cycle frequency is consistently lower than the standard frequency range, it indicates that the synthesis reaction is lagging behind. At this time, the current stage marker is kept unchanged.

[0093] The ATP regeneration adaptation regulation mechanism is established by associating and binding the energy supply path configuration, dynamic energy output mode, enzyme conformation dynamic regulation configuration, and transition markers of the entire process stage. The specific association and binding method is as follows: each time the transition marker of the entire process stage is updated, the enzyme conformation dynamic regulation configuration is automatically re-searched and the dynamic energy output mode is re-matched. Then, the ATP regeneration enzyme energy supply path configuration is re-executed according to the updated configuration and mode. This forms a complete regulatory link from stage identification, enzyme conformation adjustment, energy supply mode matching to path configuration execution, realizing the linkage adaptation of the entire stage of ATP regeneration and RNA synthesis.

[0094] The basic conditions of the cell-free RNA synthesis reaction system used in this application are supplemented as follows: the buffer is 50 mmol / L Tris-HCl at pH 7.5, the initial magnesium ion concentration is 10 mmol / L, the ATP regenerating enzyme concentration is 20 nmol / L, and the RNA polymerase concentration is 15 nmol / L; all fluorescently labeled probes and reagents meet the standards of industrial-grade biological reagents, the specific activity of isotope-labeled NTPs is 3000 Ci / mmol, and the total volume of the reaction system can be adjusted according to the application scenario, such as 100 μL-1 mL in laboratory scenarios and 10-100 L in industrial production scenarios.

Claims

1. A method for implementing a cell-free RNA synthesis system with multi-dimensional information adaptation, characterized in that, The method includes: S101 collected data on NTP concentration fluctuations, synthetic RNA transcript length distribution, and free short RNA fragment concentration in a cell-free RNA synthesis system, and performed pseudo-spoofing and perturbation correction. It then fused temperature changes and time point information to construct a description of the molecular environment state and obtain a trend judgment of RNA polymerization rate. S102, based on the trend judgment and corrected NTP concentration data, integrates intermediate product accumulation rate, transcript length distribution and short chain RNA concentration data to obtain substrate matching compatibility data and calculate enzyme-stage adaptation index. Combined with substrate binding efficiency parameters, it determines the transition markers of the entire process stage. S103 Based on the synthesis stage corresponding to the transition marker of the entire process stage, integrate substrate binding efficiency parameters and matching compatibility data, and combine enzyme binding site occupancy data to analyze the ATP regeneration enzyme activity shift amplitude and regeneration rate fluctuation, and determine its functional deviation degree. S104 Based on the degree of functional deviation, the magnitude of activity deviation, and the fluctuation of regeneration rate, obtain preset enzyme conformation adjustment parameters and microenvironment pH control templates, generate enzyme conformation dynamic control configuration, and determine the dynamic energy output mode that matches the current stage. S105 operates based on the aforementioned mode feedback, collects energy supply cycle frequency data, combines intermediate product accumulation rate and enzyme-stage compatibility index, processes full-dimensional monitoring data of energy feedback cycle, and determines the ATP regeneration enzyme compatibility level. If the S106 adaptation matching level is higher than the preset threshold, update the molecular environment state description, integrate the real-time enzyme reaction local pH value and acid-base regulation template, correct the enzyme binding site occupancy deviation, and obtain and execute the ATP regeneration enzyme energy supply pathway configuration. S107 configures the execution effect according to the energy supply path, collects real-time monitoring data on RNA polymerization rate and energy supply cycle frequency, updates molecular environment state and stage transition markers, and establishes an ATP regeneration adaptation regulation mechanism that is linked to the entire stage of RNA synthesis.

2. The method for implementing a multi-dimensional information-adapted RNA cell-free synthesis system according to claim 1, characterized in that, Step S101 includes: Data on NTP concentration fluctuations, synthetic RNA transcript length distribution, and free short RNA fragment concentrations within the cell-free RNA synthesis system were collected to obtain the raw monitoring data set. Median filtering and spurious removal are performed on the NTP concentration fluctuation data in the original monitoring dataset, and baseline correction is performed based on the temperature change curve. The mean correction is performed on the free short-chain RNA fragment concentration data based on the concentration difference of multiple sampling areas to obtain the corrected monitoring data. The corrected monitoring data is fused with the temperature change information of the reaction system and the time node information of the synthesis reaction to construct a molecular environment state description. Based on the correspondence between the NTP consumption per unit time and the migration speed of the transcript length interval in the molecular environment state description, the trend of RNA polymerization rate is determined.

3. The method for implementing a multi-dimensional information-adapted RNA cell-free synthesis system according to claim 1, characterized in that, Step S102 includes: The rate state category of the current synthesis reaction is determined based on the RNA polymerization rate trend. The rate state category includes rising phase, stable phase and falling phase. Combined with the real-time concentration values ​​of each nucleoside triphosphate in the corrected NTP concentration fluctuation data, the nucleotide consumption distribution characteristics are obtained. The nucleotide consumption distribution characteristics, the rate state category, the system intermediate product accumulation rate, the synthetic RNA transcript length distribution data, and the free short RNA fragment concentration data in the system are fused together, and the weighted sum is calculated according to their respective contributions under different rate state categories to obtain the synthetic state characteristic value. Based on the deviation between the current nucleotide consumption distribution characteristics and the base composition ratio of the target RNA sequence, substrate matching compatibility data is obtained by comparing the numerical values ​​of the synthetic state characteristics with the substrate matching compatibility data. The enzyme-stage fit index is obtained by multiplying the numerical values ​​of the synthetic state characteristics with the substrate matching compatibility data. The ratio of the enzyme-stage fit index to the substrate binding efficiency parameter is calculated, and the transition marker of the entire process stage corresponding to the current time node is determined based on the rate state category and the synthesis reaction time node information.

4. The method for implementing a multi-dimensional information-adapted RNA cell-free synthesis system according to claim 1, characterized in that, Step S103 includes: Based on the RNA synthesis stage corresponding to the transition marker of the entire process, obtain substrate binding efficiency parameters, substrate matching compatibility data, and enzyme binding site occupancy data; The substrate binding efficiency parameter and the substrate matching compatibility data are multiplied to obtain the substrate enzyme activity value. The substrate enzyme activity value is combined with the enzyme binding site occupancy data to obtain the measured catalytic activity value of ATP regeneration enzyme. The activity shift amplitude is obtained based on the measured catalytic activity value, and the change in ATP regeneration rate at adjacent sampling times is collected to obtain the ATP regeneration rate fluctuation. The degree of functional deviation of the ATP regeneration enzyme is determined by weighted summation of the activity shift amplitude, the ATP regeneration rate fluctuation, and the site occupancy ratio of free short-chain RNA fragments in the enzyme binding site occupancy data.

5. The method for implementing a multi-dimensional information-adapted RNA cell-free synthesis system according to claim 1, characterized in that, Step S104 includes: Based on the degree of functional deviation, the magnitude of activity deviation, and the fluctuation of ATP regeneration rate of the ATP regeneration enzyme, the corresponding enzyme conformation adjustment parameters and microenvironment acid-base regulation templates are obtained from the enzyme conformation adjustment parameter library and the microenvironment acid-base regulation template library, respectively. By combining the enzyme conformation adjustment parameters with the microenvironment pH regulation template, a dynamic enzyme conformation regulation configuration is generated. Based on the dynamic regulation configuration of enzyme conformation and the current synthesis stage indicated by the transition marker of the entire process stage, a dynamic energy output mode matching the current synthesis stage is determined.

6. The method for implementing a multi-dimensional information-adapted RNA cell-free synthesis system according to claim 1, characterized in that, Step S105 includes: Based on the real-time feedback after the operation of the dynamic energy supply output mode, energy supply cycle frequency data is collected, and the real-time monitoring value of the accumulation rate of intermediate products in the system is obtained to obtain a synchronized energy supply monitoring data set. The energy supply cycle frequency data, the accumulation rate of intermediate products in the system, and the enzyme-stage fit index are processed to obtain full-dimensional monitoring values ​​for the energy feedback cycle. The adaptation matching level of ATP regenerating enzyme is determined based on the deviation between the all-dimensional monitoring values ​​and the standard value of the enzyme and the stage adaptation index. When the absolute value of the deviation between the all-dimensional monitoring values ​​and the standard value is lower than the preset adaptation threshold of the corresponding stage, the adaptation matching level is determined to be higher than the preset adaptation threshold.

7. The method for implementing a multi-dimensional information-adapted RNA cell-free synthesis system according to claim 1, characterized in that, Step S106 includes: If the adaptation matching level is higher than the preset adaptation threshold, obtain the current molecular environment state description and real-time collected local pH data of the enzymatic reaction. The updated molecular environment state description is obtained by fusing the local pH data of the enzyme-catalyzed reaction and the microenvironment acid-base regulation template data. Based on the updated molecular environment state description and the difference between the site occupancy ratio of free short-chain RNA fragments in the enzyme binding site occupancy data and the baseline occupancy ratio corresponding to the enzyme and stage adaptation index, a site occupancy correction instruction is generated. Based on the updated molecular environment state description, the site occupancy correction instruction, and the dynamic energy output mode, an ATP regenerating enzyme energy supply path configuration is generated and executed.

8. The method for implementing a multi-dimensional information-adapted RNA cell-free synthesis system according to claim 1, characterized in that, Step S107 includes: Based on the execution effect of the ATP regenerating enzyme energy supply pathway configuration, real-time monitoring data of RNA polymerization rate and energy supply cycle frequency are collected to obtain the execution effect feedback data set. Based on the execution effect feedback data set, the trend of RNA polymerization rate is compared with the NTP consumption rate in the molecular environment state description. The molecular environment state description is adjusted according to the comparison results, and the transition marker of the whole process stage is updated according to the matching degree between the energy supply cycle frequency and the enzyme and the stage adaptation index. The ATP regeneration enzyme energy supply pathway configuration, the dynamic energy output mode, and the enzyme conformation dynamic regulation configuration are associated and bound with the updated full-process stage transition markers to establish an ATP regeneration adaptation regulation mechanism that is linked to the entire RNA synthesis process.

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