A protein-centered metabolic engineering target prediction method based on reference-to-high-yield enzyme concentration change trend

Abstract

The present application relates to the technical field of metabolic engineering, and particularly relates to a protein-centered metabolic engineering target prediction method based on reference-to-high-yield enzyme concentration change trend. The present application discloses a protein-centered metabolic engineering target prediction method based on reference-to-high-yield enzyme concentration change trend, which comprises the following steps: obtaining the maximum production rate in overexpression cells and gradiently dividing into N parts; fixing the gradient value, and performing calculation with cell growth as the target; obtaining the change of protein concentration with the maximum product production rate; identifying the enzyme concentration after the increase of the target product production rate, and determining the up-regulation strategy according to the monotonous and continuous increase of the enzyme concentration with the up-regulation of the production rate; and determining the down-regulation strategy according to the monotonous and continuous decrease of the enzyme concentration with the up-regulation of the production rate. The metabolic engineering target prediction method disclosed in the present application improves the accuracy of metabolic engineering strategies.

Description

Technical Field

[0001] This invention relates to the technical field of metabolic engineering, and in particular to a protein-centered method for predicting metabolic engineering targets based on the trend of changes in enzyme concentration from reference to high-yield enzymes. Background Technology

[0002] In the field of biotechnology, metabolic engineering, as a method to optimize cellular metabolic pathways through genetic manipulation to increase the production rate of target products, has been widely applied to the production of important biological compounds such as amino acids, organic acids, and drugs. Traditional metabolic engineering strategies primarily rely on a quantitative understanding of metabolic networks and optimization algorithms, such as FSEOF (FluxSummation and Elimination of Fluxes). These algorithms maximize production by setting a target production rate and optimizing metabolic flux distribution; however, numerous technical and theoretical challenges remain in practical applications.

[0003] With the development of high-throughput technologies such as genomics, transcriptomics, and proteomics, researchers are able to more accurately analyze cellular metabolic networks and use this data for more precise optimization designs. However, despite the increasing availability of technological tools, the practical application of metabolic engineering strategies is still constrained by a variety of factors.

[0004] The technical problems with the metabolic engineering target prediction and design method based on the trend of enzyme concentration changes from reference to high-yield mode using quantitation models include: 1. Limited prediction accuracy The traditional FSEOF algorithm (HS Choi, SY Lee, TY Kim, HM Woo, ApplEnviron Microbiol 2010, 76, 3097.) often relies on simplified metric models when predicting metabolic engineering strategies, failing to fully consider the catalytic efficiency and thermodynamic limitations of enzymes, resulting in significant deviations between the predicted results and actual production conditions.

[0005] 2. Uncertainty regarding the direction of the reaction In metabolic networks, the flux of some reactions may cross zero as production conditions change, meaning the reaction direction changes. Traditional algorithms often fail to accurately determine the reaction direction when dealing with such situations, thus failing to effectively identify effective modification targets.

[0006] To address these issues, researchers have proposed various technical approaches, such as introducing thermodynamic constraints and considering enzyme catalytic efficiency. However, these approaches still have the following drawbacks in practical applications: 1. It does not include enzyme constraints and thermodynamic constraints. Key parameters such as thermodynamic parameters and enzyme catalytic constants are often not included in the constraints of the algorithm, which limits the accuracy and reliability of the model.

[0007] 2. Multi-enzyme synergy issues A single enzyme may catalyze multiple reactions, and these reactions have different pathways and importance in the metabolic network. Traditional algorithms often overlook this inconsistency between enzyme reactions during optimization, resulting in poor performance of optimization strategies in practical applications.

[0008] In summary, current metabolic engineering strategies still face significant challenges in terms of prediction accuracy, reaction direction determinism, and multi-enzyme synergy. To further improve the effectiveness and feasibility of metabolic engineering strategies, it is necessary to develop more accurate and efficient optimization algorithms, while fully considering factors such as enzyme catalytic efficiency and thermodynamic limitations. Summary of the Invention

[0009] To overcome the shortcomings of existing technologies, this invention provides a protein-centered method for predicting metabolic engineering targets based on the trend of enzyme concentration changes from reference to high-yield enzymes. This method constructs a protein-centered algorithm by summing the enzyme concentrations required for all reactions catalyzed by the same enzyme, thereby avoiding the situation of contradiction with enzyme reactions. Furthermore, it introduces enzyme thermal constraints to gradually improve the accuracy of metabolic engineering strategies. At the same time, because enzyme concentration is a non-negative number, it avoids the problem that the strategy cannot be predicted due to the reaction flux crossing zero.

[0010] The technical solution adopted by this application to solve its technical problem is: The primary objective of this application is to provide a protein-centric method for predicting metabolic engineering targets based on the trend of changes in enzyme concentration from reference to high-yield forms, comprising the following steps: The maximum production rate in overexpressing cells was obtained and then divided into N fractions using a gradient method. The gradient value is fixed, and the calculation is performed with cell growth as the objective. The change in protein concentration with maximum product production rate was obtained; The enzyme concentration is identified as it increases with the production rate of the target product. An up-regulation strategy is determined based on the enzyme concentration increasing monotonically and continuously with the increase of the production rate, while a down-regulation strategy is determined based on the enzyme concentration decreasing monotonically and continuously with the increase of the production rate.

[0011] In some implementations, the algorithm for obtaining the maximum production rate in overexpressing cells and gradient-splitting them into N parts includes the following equations: Steady-state material balance constraint equations; Equations for the upper and lower bounds of reaction flux; Thermodynamic driving force equation of the reaction; Thermodynamic minimum driving force constraint equation; Enzyme concentration summation equation; Enzyme catalytic constant equation; Molecular mass equation of enzymes.

[0012] In some implementations, after fixing the gradient value of the target product production rate, the solution is performed with the goal of maximizing cell growth using the following constraints: Minimize the thermodynamic driving force constraint of the reaction; Minimize enzyme concentration constraints; In some embodiments, the change in enzyme concentration with the production rate of the target product is obtained through the following steps: Calculate the maximum specific growth rate of cells under different target product production rates; Obtain the enzyme concentration at the maximum specific growth rate from the optimal solution; Analyze the trend of enzyme concentration variation under the gradient of target product production rate.

[0013] In some embodiments, the prediction method is used to determine whether to strengthen or weaken a target to increase the production rate of amino acid compounds.

[0014] In some implementations, the summation effect of enzyme concentrations when each enzyme catalyzes multiple reactions is also considered in minimizing enzyme concentration constraints.

[0015] In some implementations, the conditions for the thermodynamic driving force constraint of the reaction include imposing thermodynamic constraints on the pathways involved in the reaction to ensure that reactions not in the pathway can escape the thermodynamic constraints.

[0016] The second objective of this application is to provide a metabolic engineering system using the above-described metabolic engineering target prediction method, comprising a data acquisition module, a data processing module, and a strategy output module. The data acquisition module is used to collect metabolic network parameters, the data processing module is used to perform computational analysis, and the strategy output module is used to output enzyme concentration change analysis and corresponding metabolic engineering modification strategies.

[0017] A third objective of this application is to provide a computer-readable storage medium storing computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform the metabolic engineering target prediction method.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. The metabolic engineering target prediction method described in this application significantly improves the prediction accuracy of metabolic engineering strategies by introducing enzyme-thermal constraints and gradient calculation methods, especially in the production of important metabolites such as amino acids. This method constructs an algorithm centered on the total protein amount by summing the enzyme concentrations required for all reactions catalyzed by the same enzyme, avoiding the potential contradictions in same-enzyme reactions that may occur with traditional methods. Furthermore, this method determines the modification target by comparing the monotonicity of enzyme concentrations, avoiding the problem of strategy infeasibility due to reaction flux crossing zero points, and reducing experimental workload and cost.

[0019] 2. The metabolic engineering target prediction method described in this application significantly improves the production efficiency of target metabolites through precise metabolic engineering strategies for prediction and modification, meeting the needs of industrial production. This method boasts high prediction accuracy and low infeasibility, guiding more efficient experimental design and resource allocation while reducing waste. The application of this method not only enhances experimental techniques in the biotechnology field but also provides strong theoretical support and experimental tools for related research, promoting in-depth scientific research. Attached Figure Description

[0020] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0021] Figure 1 A schematic diagram illustrating the identification of key enzymes in metabolic pathways using the metabolic engineering target prediction method of this application; Figure 2 This is a schematic diagram showing the statistical results of indicators for five products—lysine, valine, isoleucine, alanine, and glutamic acid—produced using Corynebacterium glutamicum. Detailed Implementation

[0022] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to embodiments. The content mentioned in the embodiments is not intended to limit the present invention.

[0023] As used herein, “and / or” includes all combinations of any one or more of the associated listed items. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a,” “an,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. Further understanding is needed; when used in this specification, “comprising” designates the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof.

[0024] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Further understanding is that terms, such as those defined in common dictionaries, are interpreted in accordance with their meaning in the context of the relevant field and are not idealized or overly formal, unless expressly defined herein.

[0025] The exemplary invention described herein may suitably omit any one or more limiting elements, which are not specifically disclosed herein. Therefore, terms such as “comprising,” “including,” “containing,” etc., should be interpreted broadly and non-limitingly. Furthermore, the terminology used herein is for descriptive purposes without limitation, and it is unintentional to use terms that do not include any equivalent characteristics, but only to describe a portion of their characteristics; however, various modifications are possible within the scope of the invention according to the claims. Therefore, while the invention has been specifically disclosed through preferred embodiments and optional features, variations of the invention embodied by the modifications disclosed herein may be noted by those skilled in the art, and such modifications and variations are considered to be within the scope of the invention.

[0026] Terminology Explanation: Metabolic engineering is the science of altering an organism's metabolic pathways through genetic engineering and molecular biology techniques to enhance its ability to produce specific chemical substances, such as drugs, enzymes, and fuels. It typically involves the expression or repression of specific genes in an organism's genome to optimize metabolic flux.

[0027] Enzyme-thermodynamic constraints refer to the design of metabolic engineering strategies that simultaneously consider the catalytic efficiency and thermodynamic limitations of an enzyme. The catalytic efficiency of an enzyme determines the reaction rate, while thermodynamic limitations (such as changes in Gibbs free energy) determine whether the reaction can proceed spontaneously.

[0028] The ET-FSEOF algorithm is a metabolic engineering strategy prediction algorithm based on enzyme-thermal constraints. It optimizes enzyme expression in metabolic pathways by analyzing the monotonic change trend of enzyme concentration as the production rate of the target product increases, thereby improving the production rate of the target product.

[0029] Overexpression refers to increasing the production rate of a target protein or enzyme by increasing the copy number of the target gene in an organism or improving its transcription efficiency.

[0030] Maximum production rate refers to the maximum rate at which an organism can produce a target metabolite under specific conditions. In metabolic engineering strategy design, this rate is usually determined through experiments or model predictions.

[0031] The maximum thermodynamic driving force (MDF) is the greatest thermodynamic force propelling a chemical reaction; it is equal to the negative of the change in Gibbs free energy. In metabolic engineering, MDF is used to assess the spontaneity and directionality of reactions.

[0032] The enzyme catalytic constant (kcat) is the rate constant of an enzyme catalyzing a chemical reaction; it represents the enzyme's catalytic efficiency on its substrate. A larger kcat value indicates a faster enzyme-catalyzed reaction.

[0033] A metric matrix is ​​a matrix that describes the transformation relationships between substances in a metabolic network within an organism. It is commonly used to represent metabolic flux balance equations and is fundamental to metabolic network analysis.

[0034] Flux value refers to the rate or volume of each reaction in a metabolic network. In metabolic engineering strategy design, flux values ​​are adjusted to optimize metabolic pathways, thereby increasing the production rate of target metabolites.

[0035] Downregulation strategy refers to reducing the impact of a gene or enzyme on metabolic pathways by decreasing its expression level. In metabolic engineering, this can be used to optimize metabolic flux distribution and increase the production rate of target products.

[0036] Upregulation strategy refers to enhancing the contribution of a gene or enzyme to a metabolic pathway by increasing its expression level. In metabolic engineering, this is often used to improve the flux or efficiency of metabolic pathways.

[0037] Target prediction refers to the calculation and analysis used to predict key genes or enzymes in metabolic engineering. Accurate target prediction is crucial for improving the efficiency of metabolic engineering.

[0038] The traditional FSEOF algorithm has the following drawbacks: 1. It is impossible to consider the catalytic efficiency and thermodynamic limitations of enzymes. Traditional FSEOF algorithms are mainly based on quantitative models and cannot fully consider the catalytic efficiency and thermodynamic limitations of enzymes. These factors play a crucial role in actual biological metabolism, thus limiting the accuracy of the algorithm's predictions.

[0039] 2. Uncertainty regarding the direction of the reaction Because the traditional FSEOF algorithm cannot effectively handle changes in the direction of reaction flux, it cannot accurately determine the direction of the reaction under certain conditions, thus failing to identify effective metabolic engineering targets.

[0040] 3. Limited prediction accuracy Due to the aforementioned shortcomings, the traditional FSEOF algorithm has limited prediction accuracy and cannot accurately guide the design and optimization of metabolic engineering strategies.

[0041] 4. Feasibility of the strategy Because the thermodynamic constraints and catalytic efficiency of enzymes are not fully considered, the modification strategies proposed by the traditional FSEOF algorithm may have feasibility issues in practical applications, leading to experimental failures or poor results.

[0042] To overcome these shortcomings, this application provides a protein-centric method for predicting metabolic engineering targets based on the trend of changes in enzyme concentration from reference to high-yield forms, comprising the following steps: Step 1: Obtain the maximum production rate in overexpressing cells and divide them into N parts using a gradient method; Step 2: Calculate the maximum specific growth rate of cells under different target product production rates; Step 3: Obtain the enzyme concentration change at the maximum specific growth rate from the optimal solution; Step 4: Identify the enzyme concentration as the production rate of the target product increases. Determine the upregulation strategy for the enzyme based on the monotonically and continuously increasing enzyme concentration with increasing production rate, and determine the downregulation strategy for the enzyme based on the monotonically and continuously decreasing enzyme concentration with increasing production rate.

[0043] In some implementations, step 1, the algorithm for obtaining the maximum production rate in overexpressing cells and gradient-splitting them into N parts, includes the following equations: Steady-state material balance constraint equations; Equations for the upper and lower bounds of reaction flux; Thermodynamic driving force equation of the reaction; Thermodynamic minimum driving force constraint equation; Enzyme concentration summation equation; Enzyme catalytic constant equation; Molecular mass equation of enzymes.

[0044] Specifically, the gradient is divided into 10 parts, and the relevant formulas are as follows:

[0045] Where S represents the metric matrix of the model, v represents the flux value of each reaction in the metabolic network, formulas (1) and (2) describe the steady-state material balance constraints, LB and UB are the upper bound and lower bound of the reaction flux, respectively; formula (3) represents the determination of the reactions in the set. The thermodynamic driving force is equal to the negative value of the Gibbs free energy; It is the coefficient of metabolite i in reaction j. R is the standard Gibbs free energy of reaction j, R is the gas constant (8.3145 J / mol / K), and T is the default temperature (298 K). It is the logarithmic concentration of metabolite i in reaction j. Formulas (4) and (5) represent applying thermodynamic constraints to the pathways involved in the reaction, where B represents The minimum thermodynamic driving force among all reactions within the set, where K is a constant, is used to ensure that reactions not on the path can escape thermodynamic constraints. Since thermodynamic constraints should only apply to reactions participating in the path, integer variables (binary variables) are needed to distinguish between participating and non-participating reactions. If a reaction does not participate, then... =0, and = 0. Formula (6) This represents the sum of enzyme concentrations across multiple reactions catalyzed by a single enzyme. This represents the enzyme cost allocated to reaction j; where j belongs to the set of reactions catalyzed by enzyme p; formula (7) where The enzyme catalytic constant of reaction j is represented; Equation (7) defines that the maximum flux of reaction j is determined by the enzyme catalytic constant of reaction j and the enzyme concentration allocated to reaction j; in Equation (8) (g / mmol) is the molecular weight of enzyme p, and Epool represents the mass fraction of the enzyme in stem cells considered in the calculation (g protein / g cell dry weight).

[0046] Continue to divide the maximum production rate gradient into 10 parts; The formula involved is formula (9):

[0047] Specifically, in step 1, by segmenting the maximum production rate gradient, the metabolic response of cells at different production rate levels can be analyzed in more detail, thereby more accurately predicting the effect of metabolic engineering strategies; by comparing the changes in enzyme concentration under different production rate gradients, the key enzymes that have the greatest impact on the production rate of the target product can be identified, providing important information for subsequent strategy optimization.

[0048] Gradient partitioning involves dividing the solved maximum production rate into 10 equal parts, each representing a production rate gradient. It is crucial to ensure appropriate spacing between gradients to adequately reflect the sensitivity and adaptability of cell metabolism to changes in production rate. For example, a maximum production rate of 25 mmol / g DW / h can be partitioned into 10 parts: [text{gradient list}=[0,2.78,5.56,8.33,11.11,13.89,16.67,19.44,22.22,25].

[0049] In some implementations, after fixing the gradient value in step 2, the following constraints are used to solve the problem with cell growth as the objective: Reaction thermodynamic driving force constraint; Minimize enzyme concentration constraints; Cell growth objective function.

[0050] Specifically, the algorithm solves for the reaction thermodynamic driving force under constraints, and the constraints are as follows:

[0051] The constraint of the algorithm used to solve for the minimum total enzyme concentration under constraints is related to formula (10):

[0052] Among them, the process of minimizing enzyme concentration constraints also considers the summation effect of enzyme concentration when each enzyme catalyzes multiple reactions; the conditions for reaction thermodynamic driving force constraints include applying thermodynamic constraints on the pathways involved in the reaction to ensure that reactions not in the pathway can escape thermodynamic constraints.

[0053] Specifically, thermodynamic driving force is the force that propels a chemical reaction, determining whether it can proceed spontaneously. In metabolic networks, some reactions may be thermodynamically limited, meaning insufficient thermodynamic driving force leads to a reduced reaction rate or even cessation. The introduction of MDF constraints ensures that thermodynamic factors are fully considered during optimization, thereby avoiding the design of thermodynamically infeasible metabolic pathways. Enzyme synthesis and maintenance consume cellular resources, including energy and nutrients. In metabolic engineering design, excessively increasing enzyme concentration not only increases cellular burden but may also disrupt the normal functioning of other metabolic pathways. Therefore, incorporating a minimum enzyme concentration constraint during optimization can minimize enzyme usage while ensuring the production rate of the target product, thereby improving the overall metabolic efficiency of the cell. Cell growth is one of the core objectives of cellular metabolic activities and an important indicator of the success of metabolic engineering strategies. Optimizing cell growth can ensure that the basic physiological functions of cells are not impaired during the optimization process, while increasing the production rate of the target product.

[0054] In some implementations, in step 3, obtaining the change in enzyme concentration with the maximum production rate of the target product is achieved through the following steps: Calculate the maximum specific growth rate of cells under different target product production rates; Obtain the enzyme concentration at the maximum specific growth rate from the optimal solution; Analyze the trend of enzyme concentration variation under the gradient of target product production rate.

[0055] Specifically, according to metabolic engineering theory, enzyme concentration often exhibits a monotonic relationship with the production rate of the target product. That is, as the production rate increases, the concentration of some key enzymes may continuously increase (these enzymes may be limiting factors for increasing the production rate), while the concentration of other enzymes may remain constant or decrease. By identifying this monotonicity, we can determine which enzymes play a crucial role in increasing the production rate. Therefore, through step 3, by analyzing the changes in protein concentration under different production rate gradients, we can identify enzymes that significantly affect the production rate. This allows us to further determine which enzymes need to be enhanced (i.e., have their expression increased) and which need to be weakened (i.e., have their expression reduced), thus providing precise targets for subsequent metabolic engineering modifications.

[0056] Specifically, in cellular metabolism, enzyme concentration is often closely related to the production rate of the product. When the concentration of a certain enzyme increases continuously with the increase of the production rate, it indicates that the enzyme may be a bottleneck in increasing the production rate, and therefore it is necessary to enhance the enzyme (such as increasing its expression level or improving its catalytic efficiency) to further increase the production rate. Conversely, if the concentration of a certain enzyme decreases continuously with the increase of the production rate, it may mean that the flux of the reaction catalyzed by the enzyme is reduced during the process of increasing the production rate. In this case, it is advisable to weaken the enzyme to save resources or avoid unnecessary energy consumption. In step 4, by analyzing the monotonicity of the relationship between enzyme concentration and production rate, it is possible to identify which enzymes have a significant impact on the production rate and select those enzymes that change significantly with the increase or decrease of the production rate as targets for metabolic engineering. For enzymes that need to be enhanced, methods such as overexpression, gene knockout (removal of inhibitory regulatory elements), or introduction of highly efficient mutants can be adopted. For enzymes that need to be weakened, methods such as reducing their expression level, introducing negative regulatory elements, or knocking out key genes can be used.

[0057] Therefore, the metabolic engineering target prediction method presented in this application combines different reactions catalyzed by the same enzyme, avoiding the contradictions in metabolic engineering modification strategies. Furthermore, by adding enzyme thermal constraints, this method significantly improves the feasibility of metabolic engineering strategies. Considering that the modification strategy in metabolic engineering is an enzyme, a strategy algorithm targeting enzyme concentration is developed to avoid the problem of strategy infeasibility caused by changes in reaction flux direction, thereby reducing the workload and cost of experiments. This method is used to determine the target to be strengthened or weakened in order to improve the production rate of amino acid compounds.

[0058] A second objective of this application is to provide a metabolic engineering system using the method described above, comprising a data acquisition module, a data processing module, and a strategy output module. The data acquisition module is used to collect metabolic network parameters, the data processing module is used to perform computational analysis, and the strategy output module is used to output enzyme concentration change analysis and corresponding metabolic engineering modification strategies.

[0059] A third objective of this application is to provide a computer-readable storage medium storing computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform the metabolic engineering target prediction method.

[0060] The following embodiments and application examples further illustrate the content and uses of the metabolic engineering target prediction method described in this application; Example 1: As Figure 1 As shown, a protein-centric method for predicting metabolic engineering targets based on the trend of enzyme concentration changes from reference to high-yield forms includes the following steps: Step 1: Solve for the maximum production rate of the target product in the overexpressing cells and divide the cells into 10 parts by gradient. Step 2: Fix the gradient value, and solve the problem with cell growth as the objective under the constraints of MDF (reaction thermodynamic driving force) and minimizing enzyme concentration. Obtain the enzyme concentration at the maximum specific growth rate from the optimal solution. Step 3: Analyze the trend of enzyme concentration under the gradient of the target product production rate; Step 4: Identify the enzyme concentration as the production rate of the target product increases. Determine the upregulation strategy for the enzyme based on the monotonically and continuously increasing enzyme concentration with increasing production rate, and determine the downregulation strategy for the enzyme based on the monotonically and continuously decreasing enzyme concentration with increasing production rate.

[0061] In step 1, the maximum production rate of overexpressing cells is calculated and the cells are divided into 10 gradient segments. The relevant formula and algorithm are as follows:

[0062] Subsequently, the theoretical maximum productivity was calculated, and an ET-FSEOF analysis was performed. The objective function was set to maximize cell productivity while progressively increasing (forcing) the target productivity (this is our actual goal) from the initial flux value to a value close to the theoretical maximum productivity (the maximum productivity gradient is divided by 10 increments). The mathematical formula is as follows:

[0063] The next step is to determine the optimal growth rate of the cells and solve for the optimal thermodynamic minimum driving force in the wild type, which is determined by solving the following set of formulas:

[0064] This involves determining the optimal thermodynamic driving force and calculating the minimum total enzyme concentration required to achieve maximum growth in the wild type. This is determined by solving the following set of formulas:

[0065] Under the constraints of the above conditions, the maximum specific growth rate of cells under 10 production rate gradients was obtained; Step 3: Determine how enzyme concentration changes with the production rate gradient at the maximum specific cell growth rate. Step 4: Identify the enzyme concentration as the production rate of the target product increases. Determine the upregulation strategy for the enzyme based on the monotonically and continuously increasing enzyme concentration with increasing production rate, and determine the downregulation strategy for the enzyme based on the monotonically and continuously decreasing enzyme concentration with increasing production rate.

[0066] Therefore, after determining the minimum total enzyme concentration through the above equations, the optimal thermodynamic minimum driving force and the minimum total enzyme concentration are finally determined, and then the flux distribution of each increment is solved; reactions with continuously increasing or decreasing absolute flux values ​​are identified as targets for enhancement or attenuation.

[0067] Application example: Taking the production of lysine by Corynebacterium glutamicum as an example, such as... Figure 2 As shown in Table 1, the strategies for lysine production predicted by the metabolic engineering target prediction method (ET-FSEOF algorithm) have been experimentally verified. Table 1. Correctly predicted modification targets for lysine production by the ET-FSEOF algorithm.

[0068] The metabolic engineering target prediction method of this invention improves accuracy by an average of 35.48% compared to FSEOF, and increases minimum precision by 63.51%. Specifically, hom, thrA, and thrB may be involved in the biosynthesis of threonine (Thr); ilvB and ilvN may be involved in the synthesis of branched-chain amino acids (such as valine, leucine, and isoleucine); asd is involved in the synthesis of aspartic acid (Asp); lysC and lysA are involved in the synthesis of lysine (Lys); and dapA is involved in the synthesis of diaminopimelic acid (DAP), which is one of the precursors for lysine synthesis.

[0069] The above embodiments are preferred implementations of the present invention. In addition, the present invention can be implemented in other ways. Any obvious substitutions without departing from the concept of the present invention are within the protection scope of the present invention.

Claims

1. A protein-centric method for predicting metabolic engineering targets based on the trend of enzyme concentration changes from reference to high-yield forms, characterized in that, Includes the following steps: The maximum production rate in overexpressing cells was obtained and then divided into N fractions using a gradient method. The gradient value is fixed, and the calculation is performed with cell growth as the objective. The change in protein concentration with maximum product production rate was obtained; The enzyme concentration is identified as it increases with the production rate of the target product. An up-regulation strategy is determined based on the enzyme concentration increasing monotonically and continuously with the increase of the production rate; a down-regulation strategy is determined based on the enzyme concentration decreasing monotonically and continuously with the increase of the production rate. The algorithm for obtaining the maximum production rate in overexpressing cells and dividing them into N parts by gradient includes the following equations: Steady-state material balance constraint equations; Equations for the upper and lower bounds of reaction flux; Thermodynamic driving force equation of the reaction; Thermodynamic minimum driving force constraint equation; Enzyme concentration summation equation; Enzyme catalytic constant equation; Molecular mass equation of enzymes; The formula algorithm involved in the equation is as follows: ; Where s represents the metric matrix of the model, v represents the flux value of each reaction in the metabolic network, formulas (1) and (2) describe the steady-state material balance constraints, LU and BU are the upper bound and lower bound of the reaction flux, respectively; formula (3) represents the determination of the reactions in the set. The thermal driving force is equal to the negative value of the Gibbs free energy; It is the coefficient of metabolite i in reaction j. R is the standard Gibbs free energy of reaction j, R is the gas constant (8.3145 J / mol / K), and T is the default temperature (298K). It is the logarithmic concentration of metabolite i in reaction j; Formulas (4) and (5) represent applying thermodynamic constraints to the pathways involved in the reaction, where B represents The minimum thermodynamic driving force among all reactions within the set, where K is a constant; Equation (6) (mmol / gDW) represents the sum of enzyme concentrations for multiple reactions catalyzed by a single enzyme. (mmol / gDW) represents the enzyme cost allocated to reaction j; where j belongs to the set of reactions catalyzed by enzyme p; formula (7) where (1 / h) represents the enzyme catalytic constant of reaction j; Equation (7) defines that the maximum flux of reaction j is determined by the enzyme catalytic constant of reaction j and the enzyme concentration allocated to reaction j; in Equation (8) (g / mmol) is the molecular weight of enzyme p, and Epool represents the mass fraction of the enzyme in stem cells considered in the calculation (g protein / g cell dry weight).

2. The metabolic engineering target prediction method according to claim 1, characterized in that, After fixing the gradient value, the solution is obtained with cell growth as the objective, under the following constraints: Reaction thermodynamic driving force constraint; Minimize enzyme concentration constraints; Cell growth objective function.

3. The method for predicting metabolic engineering targets according to claim 1, characterized in that, The change in protein concentration with maximum product production rate is obtained through the following steps: Calculate the maximum specific growth rate of cells under different production rate gradients; Analyze the trend of enzyme concentration variation under different production rate gradients.

4. The metabolic engineering target prediction method according to claim 1, characterized in that, The prediction method is used to determine whether to strengthen or weaken a target to increase the production rate of amino acid compounds.

5. The metabolic engineering target prediction method according to claim 4, characterized in that, The process of minimizing enzyme concentration constraints also takes into account the summative effect of enzyme concentrations when each enzyme catalyzes multiple reactions.

6. The metabolic engineering target prediction method according to claim 4, characterized in that, The conditions for thermodynamic driving force constraints on reactions include imposing thermodynamic constraints on the pathways involved in the reaction, ensuring that reactions outside the pathway can escape thermodynamic constraints.

7. A metabolic engineering system using the metabolic engineering target prediction method according to any one of claims 1 to 6, characterized in that, It includes a data acquisition module, a data processing module, and a strategy output module. The data acquisition module is used to collect metabolic network parameters, the data processing module is used to perform computational analysis, and the strategy output module is used to output enzyme concentration change analysis and corresponding metabolic engineering modification strategies.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform the metabolic engineering target prediction method according to any one of claims 1-6.

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