Metabolic engineering target spot prediction method taking protein as center and based on concentration change trend from reference type to high-yield type enzyme

Abstract

The invention relates to the technical field of metabolic engineering, in particular to a protein-centered metabolic engineering target prediction method based on a concentration change trend from a reference type to a high-yield type enzyme. The invention discloses a protein-centered metabolic engineering target prediction method based on a concentration change trend from a reference type to a high-yield type enzyme, which comprises the following steps: obtaining a maximum production rate in overexpression cells, and carrying out gradient segmentation into N parts; a gradient value is fixed, and calculation is carried out by taking cell growth as a target; the change condition of the protein concentration along with the maximum product production rate is obtained; identifying the enzyme concentration after the production rate of the target product is increased, and determining an up-regulation strategy on the basis that the enzyme concentration is monotonically and continuously increased along with the up-regulation of the production rate; a down-regulation strategy is determined on the basis that the enzyme concentration is monotonically and continuously decreased along with up-regulation of the production rate. According to the metabolic engineering target spot prediction method, the accuracy of a metabolic engineering strategy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of metabolic engineering, and in particular to a protein-centered metabolic engineering target prediction method based on the concentration variation trend of a reference type to a high-yield type enzyme. Background Art

[0002] In the field of biotechnology, metabolic engineering, as a method of optimizing cellular metabolic pathways through genetic manipulation to increase the production rate of target products, has been widely used in the production of important biological compounds such as amino acids, organic acids, and drugs. Traditional metabolic engineering strategy design mainly relies on quantitative understanding of metabolic networks and optimization algorithms, such as FSEOF (FluxSummation and Elimination of Fluxes). This type of algorithm sets a target production rate and optimizes the distribution of metabolic fluxes to maximize production. However, there are still many technical and theoretical challenges in practical applications.

[0003] With the development of high-throughput technologies such as genomics, transcriptomics, and proteomics, researchers can more accurately analyze cellular metabolic networks and use these data for more precise optimization designs. However, despite the increasing number of technical means, the actual application of metabolic engineering strategies is still restricted by many factors.

[0004] The technical problems of the metabolic engineering target prediction design method based on the concentration change trend of reference to high-yield enzymes in the metrology model are as follows: 1. Limited forecast accuracy The traditional FSEOF algorithm (HS Choi, SY Lee, TY Kim, HM Woo, ApplEnviron Microbiol 2010, 76, 3097.) is often based on a simplified metrology model when predicting metabolic engineering strategies. It fails to fully consider the catalytic efficiency and thermodynamic limitations of enzymes, resulting in a large deviation between the predicted results and the actual production situation.

[0005] 2. Uncertainty in reaction direction In a metabolic network, the flux of some reactions may cross the zero point as production conditions change, that is, the reaction direction changes. When dealing with such situations, traditional algorithms are often unable to accurately determine the reaction direction, and thus are unable to effectively identify effective transformation targets.

[0006] In order to solve the above problems, researchers have proposed a variety of technical routes, such as introducing thermodynamic constraints, considering the catalytic efficiency of enzymes, etc. However, these technical routes still have the following defects in practical applications: 1. Enzyme constraints and thermodynamic constraints are not included Key parameters such as thermodynamic parameters and catalytic constants of enzymes are often not included in the constraints of the algorithm, which limits the accuracy and reliability of the model.

[0007] 2. Multi-enzyme synergy problem The same enzyme may catalyze multiple reactions, and the pathways and importance of these reactions in the metabolic network are different. Traditional algorithms often ignore the contradictions between the same enzyme reactions during optimization, resulting in poor results of optimization strategies in practical applications.

[0008] In summary, the current metabolic engineering strategy design still faces great challenges in terms of prediction accuracy, reaction direction certainty, and multi-enzyme synergy. In order to further improve the effectiveness and feasibility of metabolic engineering strategies, it is necessary to develop more accurate and efficient optimization algorithms and fully consider factors such as the catalytic efficiency and thermodynamic limitations of enzymes. Summary of the invention

[0009] In order to overcome the shortcomings of the prior art, the present invention provides a protein-centric metabolic engineering target prediction method based on the trend of enzyme concentration changes from reference type to high-yield type. The metabolic engineering target prediction method constructs a protein-centric algorithm by adding the enzyme concentrations required for all reactions catalyzed by the same enzyme to perform algorithm calculations, thereby avoiding the situation of inconsistency with the enzyme reaction, and introduces enzyme thermal constraints to gradually improve the accuracy of the metabolic engineering strategy. At the same time, because the enzyme concentration is a non-negative number, the problem of the strategy being unpredictable due to the reaction flux crossing the zero point is avoided.

[0010] The technical solution adopted by this application to solve its technical problem is: The first purpose of the present application is to provide a protein-centered metabolic engineering target prediction method based on the concentration change trend of reference type to high-yield type enzyme, comprising the following steps: Obtain the maximum production rate in the overexpressing cells and divide the gradient into N parts; Fixed gradient value, calculated with cell growth as the goal; Obtain the variation of protein concentration with the maximum product production rate; Identify the enzyme concentration as the target product production rate increases, and determine the up-regulation strategy based on the monotonically continuous increase in enzyme concentration as the production rate increases; determine the down-regulation strategy based on the monotonically continuous decrease in enzyme concentration as the production rate increases.

[0011] In some embodiments, the algorithm for obtaining the maximum production rate in the overexpressing cells and dividing the gradient into N parts comprises the following equation: Steady-state material balance constraint equation; Equations for upper and lower bounds on reaction fluxes; Thermodynamic driving force equation for the reaction; Thermodynamic minimum driving force constraint equation; Enzyme concentration summation equation; Enzyme catalytic constant equation; Molecular mass equation of the enzyme.

[0012] In some embodiments, after fixing the target product production rate gradient value, the following constraints are used to solve the problem with the goal of maximizing cell growth: Minimize the thermodynamic driving force constraints of the reaction; Minimize enzyme concentration constraints; In some embodiments, the variation of enzyme concentration with target product production rate is obtained by the following steps: Calculate the maximum specific growth rate of cells at different target product production rates; Obtaining the enzyme concentration at the above maximum specific growth rate from the optimal solution; Analyze the changing trend of enzyme concentration under the gradient of target product production rate.

[0013] In some embodiments, the prediction method is used to determine targets for strengthening or weakening to increase the production rate of amino acid compounds.

[0014] In some embodiments, the process of minimizing the enzyme concentration constraint also takes into account the additive effect of enzyme concentration when each enzyme catalyzes multiple reactions.

[0015] In some embodiments, the conditions constraining the thermodynamic driving force of a reaction include imposing thermodynamic constraints on pathways participating in the reaction to ensure that reactions not in the pathways can escape the thermodynamic constraints.

[0016] The second purpose of the present application is to provide a metabolic engineering system using the above-mentioned metabolic engineering target prediction method, including 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 transformation strategies.

[0017] The third purpose of the present application is to provide a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by one or more processors, the one or more processors can execute 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 the present application significantly improves the prediction accuracy of metabolic engineering strategies, especially in the production of important metabolites such as amino acids, by introducing enzyme-heat constraints and gradient calculation methods; the metabolic engineering target prediction method described in the present application constructs an algorithm with total protein as the core by adding the enzyme concentrations required for all reactions catalyzed by the same enzyme, thereby avoiding the contradiction between the same enzyme reactions that may occur in traditional methods; the metabolic engineering target prediction method described in the present application determines the modification target by comparing the monotonicity of the enzyme concentration, thereby avoiding the problem of the strategy being infeasible due to the reaction flux crossing the zero point, and reducing the workload and cost of the experiment.

[0019] 2. The metabolic engineering target prediction method described in this application significantly improves the production efficiency of target metabolites through accurate metabolic engineering strategy prediction and transformation, and meets the needs of industrialized production. The metabolic engineering target prediction method described in this application has high prediction accuracy and low infeasibility, can guide more efficient experimental design and resource allocation, and reduces waste. The application of the metabolic engineering target prediction method described in this application not only improves the experimental technology level in the field of biotechnology, but also provides strong theoretical support and experimental tools for related research, and promotes the in-depth development of scientific research. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0021] Figure 1 A schematic diagram for identifying key enzymes in metabolic pathways using the metabolic engineering target prediction method of the present application; Figure 2 This is a schematic diagram of the statistical results of indicators for the production of five products including lysine, valine, isoleucine, alanine, and glutamic acid using Corynebacterium glutamicum. DETAILED DESCRIPTION

[0022] In order to facilitate the understanding of those skilled in the art, the present invention is further described below in conjunction with embodiments, and the contents mentioned in the implementation modes are not intended to limit the present invention.

[0023] As used herein, "and / or" includes the term of all combinations of any one or more associated listed items. The terms used herein are only used to describe specific embodiments and are not intended to limit the present 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. It is further understood that "including", when used in this specification, specifies the stated features, integers, steps, operations, elements and / or compositions, but does not exclude the existence or addition of one or more other features, integers, steps, operations, elements, compositions and / or combinations thereof.

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

[0025] The exemplary invention described herein may appropriately lack any one or more element limitations that are not specifically disclosed herein. Therefore, the terms "comprise", "include", "contain", etc. should be understood broadly and non-restrictively. In addition, the terminology used herein is used as a description, not a limitation, and it is unintentional to use these terminology expressions that do not include any equivalent characteristics, but only describe some of their characteristics, but various modifications are possible within the scope of the present invention according to the rights. Therefore, although the present invention has been specifically disclosed by preferred embodiments and optional features, the modifications disclosed herein to embody the changes of the present invention may be recorded by those skilled in the art, and such modifications and changes will be considered to be within the scope of the present invention.

[0026] Terminology explanation: Metabolic Engineering is a science that changes the metabolic pathways of organisms through genetic engineering and molecular biology techniques to improve their ability to produce specific chemicals (such as drugs, enzymes, fuels, etc.). It usually involves the expression or inhibition of specific genes in the genome of an organism to optimize metabolic flux.

[0027] Enzyme-thermodynamic constraints refer to the consideration of both the catalytic efficiency and thermodynamic constraints of enzymes in the design of metabolic engineering strategies. The catalytic efficiency of enzymes determines the reaction rate, while thermodynamic constraints (such as the change 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-heat constraints. It optimizes enzyme expression in the metabolic pathway by analyzing the monotonic change trend of enzyme concentration as the production rate of the target product increases, thereby increasing the production rate of the target product.

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

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

[0031] Maximum Thermodynamic Driving Force (MDF) refers to the maximum thermodynamic driving force that drives a chemical reaction. It is equal to the inverse of the change in Gibbs free energy. In metabolic engineering, MDF is used to evaluate the spontaneity and directionality of a reaction.

[0032] Enzyme Catalytic Constant (kcat) refers to the rate constant of an enzyme-catalyzed chemical reaction, which indicates the catalytic efficiency of the enzyme on the substrate. The larger the kcat, the faster the enzyme-catalyzed reaction.

[0033] The stoichiometric matrix is ​​a matrix that describes the transformation relationship between various substances in the metabolic reaction network in an organism. It is usually used to represent the metabolic flux balance equation and is the basis of metabolic network analysis.

[0034] Flux Value refers to the rate or flow rate of each reaction in a metabolic network. In the design of metabolic engineering strategies, the metabolic pathway is optimized by adjusting the flux value, thereby increasing the production rate of the target metabolite.

[0035] Downregulation strategy refers to reducing the impact of a gene on a metabolic pathway by reducing the expression of a gene or the concentration of an enzyme. In metabolic engineering, this can be used to optimize metabolic flux distribution and increase the production rate of the target product.

[0036] Upregulation strategy refers to increasing the expression of a gene or the concentration of an enzyme to enhance its contribution to the metabolic pathway. In metabolic engineering, this is usually used to improve the flux or efficiency of the metabolic pathway.

[0037] Target prediction refers to predicting key genes or enzymes in metabolic engineering through calculation and analysis. In metabolic engineering, accurate prediction of targets is crucial to improving transformation efficiency.

[0038] The original algorithm of traditional FSEOF has the following disadvantages: 1. Failure to consider the catalytic efficiency and thermodynamic limitations of enzymes Traditional FSEOF algorithms are mainly based on metrological models and cannot fully consider the catalytic efficiency and thermodynamic limitations of enzymes, which play a vital role in actual biological metabolic processes. Therefore, the prediction accuracy of the algorithm is limited.

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

[0040] 3. Limited forecast accuracy Due to the above-mentioned defects, the prediction accuracy of the traditional FSEOF algorithm is limited and cannot accurately guide the design and optimization of metabolic engineering strategies.

[0041] 4. Strategic feasibility issues Due to the failure to fully consider the thermodynamic constraints and catalytic efficiency of enzymes, the transformation strategies proposed by the traditional FSEOF algorithm may have feasibility issues in practical applications, resulting in experimental failure or poor results.

[0042] In order to overcome these shortcomings, the present application provides a protein-centric metabolic engineering target prediction method based on the concentration change trend of reference type to high-yield type enzyme, comprising the following steps: Step 1, obtaining the maximum production rate in the overexpressing cells and gradient splitting into N parts; Step 2, calculating the maximum specific growth rate of cells at different target product production rates; Step 3, obtaining the change of enzyme concentration at the above maximum specific growth rate from the optimal solution; Step 4: Identify the enzyme concentration as the target product production rate increases, and determine the up-regulation strategy for the enzyme based on the monotonically continuous increase in enzyme concentration as the production rate increases, and determine the down-regulation strategy for the enzyme based on the monotonically continuous decrease in enzyme concentration as the production rate increases.

[0043] In some embodiments, in step 1, the algorithm for obtaining the maximum production rate in the overexpressing cells and dividing the gradient into N parts comprises the following equation: Steady-state material balance constraint equation; Equations for upper and lower bounds on reaction fluxes; Thermodynamic driving force equation for the reaction; Thermodynamic minimum driving force constraint equation; Enzyme concentration summation equation; Enzyme catalytic constant equation; Molecular mass equation of the enzyme.

[0044] Specifically, the gradient is divided into 10 parts, and the formula involved is as follows:

[0045] Where S represents the stoichiometric matrix of the model, v represents the flux value of each reaction in the metabolic network, and 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) indicates the determination of the reaction in the set. The thermal driving force is equal to the negative value of Gibbs free energy; is the coefficient of metabolite i in reaction j, is the standard Gibbs free energy of reaction j, R is the gas constant (8.3145 J / mol / K), T is the default temperature (298 K), is the logarithmic concentration of metabolite i in reaction j. Formula (4) Formula (5) represents the thermodynamic constraints imposed on the pathways involved in the reaction, where B represents The minimum value of the thermodynamic driving force among all reactions in the set, K is a constant, and a sufficiently large value is used to ensure that reactions not in the pathway can escape thermodynamic constraints; since thermodynamic constraints should only apply to reactions participating in the pathway, it is necessary to distinguish between participating and non-participating reactions by using integer variables (binary variables). If a reaction does not participate, then = 0, and = 0. Formula (6) represents the sum of enzyme concentrations of multiple reactions catalyzed by an enzyme, represents the enzyme cost assigned to reaction j; where j belongs to the set of reactions catalyzed by enzyme p; formula (7) where represents the enzyme catalytic constant of reaction j; Formula (7) defines that the maximum flux of reaction j is determined by the enzyme catalytic constant of reaction j and the enzyme concentration assigned to reaction j; Formula (8) (g / mmol) is the molecular mass of enzyme p, and Epool represents the mass fraction of enzyme in the 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 dividing the maximum production rate gradient, the response of cellular metabolism 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 concentrations 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 segmentation is to divide the solved maximum production rate into 10 parts equally. Each part represents a production rate gradient. It is necessary to ensure that the interval between each gradient is appropriate so as to fully reflect the sensitivity and adaptability of cell metabolism to changes in production rate. For example, the maximum production rate is 25mmol / gDW / h, which is divided 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 embodiments, in step 2, after fixing the gradient value, the solution is performed with the following constraints with cell growth as the goal: Reaction thermodynamic driving force constraints; Minimize enzyme concentration constraints; Cell growth objective function.

[0050] Specifically, the constraints of the algorithm are:

[0051] The algorithm constraints used to solve the minimum total enzyme concentration under constraints involve the formula (10):

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

[0053] Specifically, thermodynamic driving force is the force that drives chemical reactions, which determines whether the reaction can proceed spontaneously. In metabolic networks, some reactions may be subject to thermodynamic constraints, that is, insufficient thermodynamic driving force leads to a decrease in reaction rate or even cessation. The introduction of MDF constraints can ensure that thermodynamic factors are fully considered during the optimization process, thereby avoiding the design of thermodynamically infeasible metabolic pathways; The synthesis and maintenance of enzymes consume cellular resources, including energy and nutrients. In metabolic engineering design, excessively increasing enzyme concentration will not only increase the burden on cells, but may also affect the normal operation of other metabolic pathways. Therefore, introducing the constraint of minimizing enzyme concentration during the optimization process can reduce the use of enzyme concentration as much as possible 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 goals of cell metabolic activities and an important indicator for measuring the success of metabolic engineering strategies. Optimizing solutions with cell growth as the goal can ensure that the basic physiological functions of cells are not damaged during the optimization process, while increasing the production rate of the target product.

[0054] In some embodiments, in step 3, the variation of the enzyme concentration with the maximum production rate of the target product is obtained by the following steps: Calculate the maximum specific growth rate of cells at different target product production rates; Obtaining the enzyme concentration at the above maximum specific growth rate from the optimal solution; Analyze the changing trend of enzyme concentration under the gradient of target product production rate.

[0055] Specifically, according to metabolic engineering theory, the concentration of enzymes often has 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 continue to increase (these enzymes may be the limiting factor for the increase in production rate), while the concentration of other enzymes may remain unchanged or decrease; by identifying this monotonicity, it is possible to determine which enzymes play a key role in the process of increasing production rate. Therefore, through step 3, by analyzing the changes in protein concentration under different production rate gradients, we can identify enzymes that have a significant impact on the increase in production rate, and further determine which enzymes need to be strengthened (i.e., increase their expression level) and which need to be weakened (i.e., reduce their expression level), thereby providing precise targets for subsequent metabolic engineering transformation.

[0056] Specifically, in the process of cell metabolism, the concentration of enzymes is often closely related to the production rate of products. When the concentration of an enzyme increases continuously with the increase of production rate, it indicates that the enzyme may be the bottleneck of the production rate increase, so it is necessary to further increase the production rate by strengthening the enzyme (such as increasing its expression or improving its catalytic efficiency). On the contrary, if the concentration of an enzyme decreases continuously with the increase of production rate, it may mean that the flux of the reaction catalyzed by the enzyme decreases during the process of increasing production rate. At this time, it is possible to consider weakening 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 effect on the production rate, and select those enzymes that change significantly with the increase or decrease of production rate as targets for metabolic engineering transformation; for enzymes that need to be strengthened, methods such as overexpression, gene knockout (removal of inhibitory regulatory elements) or introduction of efficient mutants can be adopted; for enzymes that need to be weakened, it can be achieved by reducing their expression, introducing negative regulatory elements or knocking out key genes.

[0057] Therefore, the metabolic engineering target prediction method shown in the present application adds together different reactions catalyzed by the same enzyme, avoids the contradictions of metabolic engineering transformation strategies, and further adds enzyme thermal constraints. This method significantly improves the feasibility of metabolic engineering strategies. Considering that the transformation strategy of metabolic engineering is enzymes, a strategy algorithm for enzyme concentration is developed to avoid the problem of strategy infeasibility caused by changes in the direction of reaction flux, thereby reducing the workload and cost of the experiment, and is used to determine the strengthening or weakening of targets to increase the production rate of amino acid compounds.

[0058] The second purpose of the present application is to provide a metabolic engineering system using the above-mentioned method, including a data acquisition module, a data processing module and a strategy output module, wherein 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 transformation strategies.

[0059] The third purpose of the present application is to provide a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by one or more processors, the one or more processors can execute the metabolic engineering target prediction method.

[0060] The following embodiments and application examples further illustrate the content and use of the metabolic engineering target prediction method described in this application; Embodiment 1: as Figure 1 As shown, a protein-centric metabolic engineering target prediction method based on the concentration change trend of reference type to high-yield type enzymes includes the following steps: Step 1, by solving the maximum production rate of the target product in the overexpression cells, and gradient splitting into 10 parts; Step 2: Fix the gradient value, solve the problem with cell growth as the goal under the constraints of MDF (reaction thermodynamic driving force) and minimizing enzyme concentration, and obtain the enzyme concentration at the above maximum specific growth rate from the optimal solution; Step 3, analyzing the change trend of enzyme concentration under the gradient of target product production rate change; Step 4: Identify the enzyme concentration as the target product production rate increases, and determine the up-regulation strategy for the enzyme based on the monotonically continuous increase in enzyme concentration as the production rate increases, and determine the down-regulation strategy for the enzyme based on the monotonically continuous decrease in enzyme concentration as the production rate increases.

[0061] Among them, in step 1, the maximum production rate in the overexpression cells is solved and the gradient is divided into 10 parts. The formula algorithm involved is:

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

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

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

[0065] Under the constraints of the above conditions, the maximum specific growth rate of cells under 10 production rate gradients was obtained; Step 3: Find the changes in enzyme concentration as the production rate gradient changes at the maximum specific growth rate of the cell. Step 4: Identify the enzyme concentration as the target product production rate increases, and determine the up-regulation strategy for the enzyme based on the monotonically continuous increase in enzyme concentration as the production rate increases, and determine the down-regulation strategy for the enzyme based on the monotonically continuous decrease in enzyme concentration as the production rate increases.

[0066] Therefore, after determining the minimum total enzyme concentration through the above equation, 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 increasing or decreasing absolute flux values ​​are determined as targets for enhancement or attenuation.

[0067] Application example: Taking the production of lysine by Corynebacterium glutamicum as an example, Figure 2 As shown in Table 1, the strategies for producing lysine that need to be modified predicted by the metabolic engineering target prediction method (ET-FSEOF algorithm) have been experimentally verified; Table 1 Correct transformation targets for lysine production predicted by ET-FSEOF algorithm

[0068] Thus, the accuracy of the metabolic engineering target prediction method of the present invention is improved by 35.48% on average compared with FSEOF, and the minimum accuracy rate is increased by 63.51%. Among them, hom, thrA and thrB may be involved in the biosynthesis pathway of threonine (Thr); ilvB and ilvN may be involved in the synthesis of branched-chain amino acids (such as valine Val, leucine Leu, isoleucine Ile); asd is involved in the synthesis of aspartic acid (Asp); lysC and lysA are involved in the synthesis of lysine (Lys); dapA is involved in the synthesis of diaminopimelic acid (DAP), which is one of the precursors of lysine synthesis.

[0069] The above embodiments are preferred implementation schemes of the present invention. In addition, the present invention may also be implemented in other ways. Any obvious replacement without departing from the concept of the present invention is within the protection scope of the present invention.

Claims

1. A protein-centric metabolic engineering target prediction method based on the concentration change trend of reference to high-yield enzymes, characterized in that: The following steps are involved: Obtain the maximum production rate in the overexpressing cells and divide the gradient into N parts; Fixed gradient value, calculated with cell growth as the goal; Obtain the variation of protein concentration with the maximum product production rate; Identify the enzyme concentration as the target product production rate increases, and determine the up-regulation strategy based on the monotonically continuous increase in enzyme concentration as the production rate increases; determine the down-regulation strategy based on the monotonically continuous decrease in enzyme concentration as the production rate increases.

2. The metabolic engineering target prediction method according to claim 1, characterized in that: The algorithm for obtaining the maximum production rate in the overexpressing cells and dividing the gradient into N parts includes the following equations: Steady-state material balance constraint equation; Equations for upper and lower bounds on reaction fluxes; Thermodynamic driving force equation for the reaction; Thermodynamic minimum driving force constraint equation; Enzyme concentration summation equation; Enzyme catalytic constant equation; Molecular mass equation of the enzyme.

3. The metabolic engineering target prediction method according to claim 2, characterized in that The formula algorithm involved in the equation is: 。 4. The metabolic engineering target prediction method according to claim 1, characterized in that: After fixing the gradient value, the following constraints are used to solve the problem with cell growth as the goal: Reaction thermodynamic driving force constraints; Minimize enzyme concentration constraints; Cell growth objective function.

5. The metabolic engineering target prediction method according to claim 1, characterized in that: The variation of protein concentration with the maximum product production rate is obtained by the following steps: Calculate the maximum specific growth rate of cells under different production rate gradients; Analyze the changing trend of enzyme concentration under different production rate gradients.

6. The metabolic engineering target prediction method according to claim 1, characterized in that: The prediction method is used to determine the strengthening or weakening target to increase the production rate of amino acid compounds.

7. The metabolic engineering target prediction method according to claim 4, characterized in that: The additive effect of enzyme concentration when each enzyme catalyzes multiple reactions is also considered in the process of minimizing the enzyme concentration constraint.

8. The metabolic engineering target prediction method according to claim 4, characterized in that: The conditions for constraining the thermodynamic driving force of a reaction include imposing thermodynamic constraints on the pathways involved in the reaction and ensuring that reactions not in the pathways can escape the thermodynamic constraints.

9. A metabolic engineering system using the metabolic engineering target prediction method according to any one of claims 1 to 8, 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 transformation strategies.

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

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