A method for optimizing the formulation of adjuvants to improve the effect of sprout inhibitors based on computational chemistry

Abstract

The invention provides an adjuvant formula optimization method for improving the effect of a bud inhibitor based on computational chemistry, which belongs to the technical field of bud inhibitors, including: obtaining key components in a mixed ester alcohol bud inhibitor, and determining the best combination by evaluating the bud inhibitor effect; using electron microscopy and biological sectioning technology to observe the cell structure changes of tobacco axillary bud meristems treated with different concentrations of bud inhibitors; using multi-omics analysis to analyze the effects of bud inhibitors on the early gene regulatory network of tobacco axillary bud development, and identify core regulatory genes; using molecular biology methods to verify the functions of key genes and analyze the bud inhibitor mechanism; based on a multi-objective optimization model, determining the best adjuvant formula, and evaluating the comprehensive performance in a small-scale test. The research is helpful to develop a new technology for tobacco bud inhibition that is efficient, low-cost and environmentally friendly. The technical problem that the existing adjuvant formula optimization method relies on experience and is difficult to find a formula that is comprehensively optimized in terms of effect, risk, stability and other aspects is solved.

Description

Technical Field

[0001] The invention belongs to the technical field of sprout inhibitors, and in particular relates to an auxiliary agent formula optimization method for improving the effect of sprout inhibitors based on computational chemistry. Background Art

[0002] Bud inhibitors are an important type of chemical input in tobacco planting and production. They are widely used to inhibit axillary buds after tobacco topping and play a key role in ensuring the quality of tobacco leaves. Common tobacco bud inhibitors mainly include various dinitroanilines, alkyl alcohols and other compounds as well as auxiliary solubilizers and other ingredients. Among them, alkyl alcohol bud inhibitors represented by mixed ester alcohols are widely used because they have the characteristics of inhibiting the germination of tobacco buds and inhibiting plant growth.

[0003] However, the existing mixed ester alcohol tobacco bud inhibitor formula still has some problems in practical application. First, the bud inhibition effect of a single ingredient is often not ideal, and adjuvants need to be added to improve the duration and effect of bud inhibition. However, the current selection of adjuvants mainly relies on experience and lacks systematic optimization design, making it difficult to achieve the optimal formula. Secondly, mixed ester alcohol bud inhibitors may have a certain adverse effect on the growth and metabolism of tobacco plants, and this side effect also needs to be reduced through the optimization of adjuvants. In addition, the environmental risks and physical and chemical stability of bud inhibitors are also key issues that need to be paid attention to.

[0004] In other words, the existing method for optimizing the adjuvant formula of sprout inhibitors has the technical problem of relying on experience and being difficult to find a formula that is comprehensively optimized in terms of effect, risk, stability, etc. Summary of the invention

[0005] In view of this, the present invention provides a method for optimizing the auxiliary agent formula for improving the effect of sprout inhibitors based on computational chemistry, which can solve the technical problems that the existing method for optimizing the auxiliary agent formula of sprout inhibitors relies on experience and it is difficult to find a formula that is comprehensively optimized in terms of effect, risk, stability, etc.

[0006] The present invention is achieved in that:

[0007] The present invention provides a method for optimizing the formula of an auxiliary agent for improving the effect of a sprout inhibitor based on computational chemistry. The method is used to optimize the formula of an auxiliary agent of a mixed ester alcohol tobacco sprout inhibitor, so that the sprout inhibitor effect of the mixed ester alcohol tobacco sprout inhibitor after adding the auxiliary agent is better. The method comprises the following steps:

[0008] S10, obtaining the ingredients of the mixed ester alcohol tobacco bud inhibitor, and determining the key compounds by evaluating the bud inhibitory effects of all ingredients, to form an effective bud inhibitory combination group of different key compounds;

[0009] S20. Using scanning electron microscopy and biological sectioning techniques, the specific cell groups of axillary bud meristems of tobacco treated with different concentrations of mixed ester alcohol tobacco bud inhibitors were finely cut to clarify the microscopic conformation and characterization changes of their growth point diapause;

[0010] S30. Through multi-dimensional transcriptome and physiological phenotype group analysis, analyze the temporal and spatial genes and their fine regulatory network of the mixed lipid alcohol biological sprout inhibitor on the early development of tobacco axillary buds, explore the core regulatory elements, and isolate and identify the genes induced by topping or specifically activated by axillary buds based on the transcriptional expression regulation data of the early development of axillary buds and the fine-cut transcriptome data of the bud growth point cell group, and preliminarily analyze the action pathway and signal network of the biological sprout inhibitor;

[0011] S40. Based on multi-omics analysis and mining, through CRISPR-CAS9 and transgenic overexpression molecular biology methods, the core regulatory genes that affect the occurrence and development of tobacco axillary buds by biological bud inhibitors are identified, and their phenotypes and genetic characteristics are analyzed through phenomics, metabolomics and proteomics, to comprehensively analyze the regulatory functions and action mechanisms of key genes in the bud inhibition process of mixed ester alcohol tobacco bud inhibitors;

[0012] S50, selecting an adjuvant for each key compound according to the regulatory function and mechanism of action, using the principles of molecular docking, pharmacophore model and quantitative structure-activity relationship analysis to improve the effect of each key compound;

[0013] S60, establishing an adjuvant formula optimization model that takes into account the sprout inhibition effect, adjuvant cost, environmental friendliness and stability, including a sprout inhibition effect sub-model, an adjuvant cost sub-model, an environmental impact sub-model and a stability sub-model; and establishing constraints for each sub-model;

[0014] S70, taking the additive type and concentration of each key compound as optimization variables, constructing an optimization objective function, and using a non-dominated sorting genetic algorithm to perform multi-objective optimization on the additive formula optimization model to obtain multiple optimized solution formulas;

[0015] S80. Conduct small-scale field tests on each optimized solution to evaluate the bud inhibition effect, duration and impact on tobacco growth, and select the optimized solution formula with the best bud inhibition effect, duration and comprehensive optimization of tobacco growth as the target formula.

[0016] Among them, the sub-model of the bud inhibition effect: based on the experimental data and mechanism analysis of S10-S50, a quantitative relationship model between the effect of the mixed ester alcohol tobacco bud inhibitor and the adjuvant formula is established, and the constraint condition is that the bud inhibition effect is not less than 90%;

[0017] The additive cost sub-model: considering the market price and usage of various additives, a cost calculation model is established, with the constraint that the total cost does not exceed 120% of the preset budget;

[0018] The environmental impact sub-model: Based on the ecotoxicological data of the additives, an environmental risk assessment model is established, with the constraint that the environmental risk index does not exceed 0.5;

[0019] The stability sub-model: considering the influence of adjuvants on the physicochemical properties of mixed ester alcohol tobacco bud inhibitor, a stability prediction model is established, and the constraint condition is that the product shelf life is not less than 18 months.

[0020] Among them, the sprout inhibition effect sub-model is specifically:

[0021] ;

[0022] in, is the additive formula vector; For the The effect score of the additive is given by step S50; For the The weight of each adjuvant in the sub-model of sprout inhibition effect.

[0023] Wherein, the auxiliary agent cost sub-model is specifically:

[0024] ;

[0025] in, For the Unit price of the additive.

[0026] The environmental impact sub-model is specifically:

[0027] ;

[0028] in, As auxiliary agent The environmental risk index of the plant is calculated based on its ecotoxicological data; For the The weight of each additive in the environmental impact sub-model.

[0029] The stability sub-model is specifically:

[0030] ;

[0031] in, As auxiliary agent The impact score on the stability of mixed ester alcohol tobacco bud inhibitor was calculated by the prediction model; For the The weight of each additive in the stability sub-model.

[0032] Among them, the additive formula optimization model also includes a comprehensive optimization model, specifically:

[0033] ;

[0034] in, To optimize the overall goal; is the sub-model weight; and They are equality and inequality constraint functions, respectively, constructed according to the actual constraints; is the penalty parameter.

[0035] The step of evaluating the bud inhibition effect, duration and impact on tobacco growth adopts the final formula evaluation model, specifically:

[0036] ;

[0037] in, To optimize the solution formula The comprehensive score of is the weight coefficient, determined by expert scoring method; is the standard deviation of the scores; is the volatility penalty coefficient, which is determined through sensitivity analysis.

[0038] Furthermore, the weight parameters in the comprehensive optimization model are determined by using a hierarchical analysis method.

[0039] Furthermore, the penalty parameters in the comprehensive optimization model are determined by cross-validation method.

[0040] Specifically, the step S10 includes: obtaining all component information of the mixed ester alcohol tobacco bud inhibitor; testing the influence of each component on tobacco bud inhibition through a bud inhibition effect evaluation experiment to obtain bud inhibition effect data of each component; establishing a key compound determination equation based on the bud inhibition effect data, and determining various parameters of the equation through methods such as molecular dynamics simulation, principal component analysis and nonlinear regression; selecting the top five compounds with the highest importance as key compounds based on the calculated importance scores to form an effective bud inhibition combination group of different key compounds.

[0041] Wherein, the step S20 comprises: using scanning electron microscopy and biological sectioning technology to finely cut and observe the tobacco axillary bud meristem cells treated with different concentrations of mixed ester alcohol tobacco bud inhibitors to obtain data on changes in cell growth point activity; establishing a growth point diapause characterization equation, and fitting the experimental data by nonlinear least squares method to obtain various parameters to describe the diapause characteristics of the growth point activity.

[0042] Among them, step S30 includes: using transcriptomics techniques such as RNA-seq to measure the expression level of each gene in tobacco axillary bud tissue under different treatment conditions; establishing a gene regulatory network model, determining model parameters through time series transcriptome data, Bayesian inference and multidimensional joint analysis, and analyzing the impact of bud inhibitors on the gene regulatory network; further analyzing the transcriptome data, selecting differentially expressed genes related to axillary bud development, and mining the core regulatory genes therein through bioinformatics methods.

[0043] Among them, step S40 includes: using CRISPR-Cas9 technology to perform knockout or overexpression experiments on the core regulatory genes screened in step S30, and measuring their effects on axillary bud growth; establishing a core regulatory gene functional model, determining model parameters through phenotypic omics data, time series transcriptome data, metabolomics data and machine learning methods, and comprehensively analyzing the regulatory function and mechanism of action of the core gene in the bud inhibition process.

[0044] Wherein, the step S50 includes: exhaustively enumerating the available auxiliary agent types and concentrations, constructing an auxiliary agent effect evaluation model, and determining the model parameters through methods such as molecular docking simulation, quantum chemical calculation and quantitative structure-activity relationship analysis; calculating the effect score of each auxiliary agent, and selecting several with the highest comprehensive scores as preferred auxiliary agents for the next step of formula optimization.

[0045] Among them, step S60 includes: according to the experimental data and mechanism analysis of steps S10-S50, establishing a sprout inhibition effect sub-model, an adjuvant cost sub-model, an environmental impact sub-model and a stability sub-model, and determining the sub-model weights through the hierarchical analysis method; constructing a comprehensive optimization model, setting equality and inequality constraint functions according to actual constraints, and determining the penalty parameters through cross-validation.

[0046] Wherein, the step S70 includes: taking the auxiliary agent type and concentration of each key compound as optimization variables to construct an optimization objective function; using a non-dominated sorting genetic algorithm (NSGA-III) to perform multi-objective optimization on the optimization model to obtain a set of Pareto optimal solutions, that is, multiple optimization formulas.

[0047] Wherein, the step S80 includes: conducting small-scale field tests on the multiple optimized formulas obtained in step S70, respectively, to measure the bud inhibition effect, duration and impact on tobacco growth of each formula; establishing a final formula evaluation model, and determining the model parameters through expert scoring method and sensitivity analysis; calculating the comprehensive score of each optimized formula, and selecting the formula with the highest score as the final target formula.

[0048] Optionally, the molecular dynamics simulation is used to obtain the interaction function between compounds; the quantum chemical calculation is used to obtain the physicochemical properties of the additive; the quantitative structure-activity relationship analysis is used to determine the weight parameters of the additive effect evaluation model; the hierarchical analysis method is used to determine the sub-model weights of the comprehensive optimization model; and the cross-validation is used to determine the penalty parameters of the comprehensive optimization model.

[0049] Compared with the prior art, the method for optimizing the formula of an adjuvant for improving the effect of a bud inhibitor based on computational chemistry provided by the present invention has the following beneficial effects: 1. Systematically determine the key compounds that affect the effect of mixed ester alcohol tobacco bud inhibitor. By evaluating the bud inhibitor effect of all ingredients, a quantitative key compound determination model is established, and several ingredients that play a key role in bud inhibitor are accurately screened out, providing a theoretical basis for subsequent optimization. This method is more comprehensive and scientific, and is significantly improved compared to the previous practice of selecting adjuvants mainly based on experience.

[0050] 2. In-depth analysis of the mechanism of action of the bud inhibitor and analysis of the changing rules of the activity of the axillary bud growth point. The present invention combines microscopic observation and mathematical modeling to systematically characterize the inhibitory characteristics of the mixed ester alcohol tobacco bud inhibitor on the activity of the growth point of the axillary bud meristem cells of tobacco, providing an important basis for understanding the mechanism of action of the bud inhibitor. This physiological characterization analysis at the cellular level is innovative to existing research.

[0051] 3. A gene regulatory network model was constructed to reveal the effect of sprout inhibitors on gene expression regulation. The present invention uses multidimensional transcriptomics analysis to analyze the changes in the gene regulatory network of early axillary bud development caused by sprout inhibitors and to identify key regulatory elements. This helps to further clarify the molecular mechanism of action of sprout inhibitors and provide biological guidance for subsequent optimization.

[0052] 4. A method for evaluating the effect of adjuvants and optimizing the formula based on computational chemistry was proposed. The present invention constructs an adjuvant effect evaluation model covering technologies such as molecular docking, quantum chemical calculation and QSAR, and establishes a multi-objective optimization model that comprehensively considers the sprout inhibition effect, cost, environmental impact and stability. The optimal formula with good performance in all aspects is obtained through mathematical optimization. This method that integrates computational chemistry and multi-objective optimization is the first of its kind in the research and development of sprout inhibitors.

[0053] In summary, the present invention adopts a systematic computational chemical analysis and optimization method to comprehensively analyze the mechanism of action of the mixed ester alcohol tobacco bud inhibitor, and designs an optimized formula that takes into account multiple aspects, thereby solving the technical problem that the existing optimization method of the auxiliary agent formula of the bud inhibitor relies on experience and it is difficult to find a formula that is comprehensively optimized in terms of effect, risk, stability, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A flow chart of the method provided by the present invention;

[0055] Figure 2 This is a picture of young tobacco axillary buds;

[0056] Figure 3 Schematic diagram of the morphological features of the axillary bud surface;

[0057] Figure 4 Schematic diagram of the cell characteristics of axillary bud meristem (biological section diagram). DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0059] like Figure 1 As shown, it is a flow chart of a method for optimizing the formulation of an auxiliary agent for improving the effect of a sprout suppressant based on computational chemistry provided by the present invention. The specific implementation methods of the steps of the present invention are described in detail below:

[0060] Step S10: Identification of key compounds

[0061] The purpose of this step is to evaluate the inhibitory effect of each component of the mixed ester alcohol tobacco bud inhibitor, determine the key compounds, and form an effective inhibitory combination group of different key compounds. The specific implementation is as follows:

[0062] 1. Obtain all the ingredient information of mixed ester alcohol tobacco bud inhibitors. These ingredients may include alkyl alcohols, ester compounds, solubilizers, etc. of different types and concentrations.

[0063] 2. Through the bud inhibition effect evaluation experiment, test the effect of each component on tobacco bud inhibition. The bud inhibition effect evaluation can be carried out by tobacco bud length detection, bud number statistics and other methods to obtain the bud inhibition effect data of each component.

[0064] 3. Establish the key compound determination equation based on the sprout inhibition effect data .in, For the Importance score of each compound; For the The concentration of the compound; For compound and The interaction function between is the total number of compounds; To be determined parameter.

[0065] 4. Obtained through molecular dynamics simulation ; Determined by principal component analysis ; Obtained by fitting experimental data through nonlinear regression .

[0066] 5. According to the calculation Value, before selection The most important compounds are selected as key compounds, and effective sprout inhibition combination groups of different key compounds are formed. It can be 5 or 10, and the specific value needs to be determined based on experimental data.

[0067] Step S20: Characterization of growth point diapause characteristics

[0068] The purpose of this step is to observe the fine cutting of the meristem cells of the tobacco axillary buds treated with different concentrations of mixed ester alcohol tobacco bud inhibitors to clarify the microscopic conformation and characterization changes of the growth point activity diapause. The specific implementation method is as follows:

[0069] 1. Scanning electron microscopy and biological sectioning techniques were used to finely cut and observe the axillary meristems of tobacco buds treated with different concentrations of mixed ester alcohol tobacco bud inhibitors. These techniques can clearly observe microscopic characteristics such as cell morphology and growth point activity status, such as Figure 2-4 shown.

[0070] 2. Establish the growth point diapause characterization equation based on the observed changes in cell activity .in, For time Growth point activity at ; is the initial activity; is the decay rate; is the period fluctuation amplitude; For the cycle; To restore the growth rate; To restore the growth rate; The midpoint time for resumption of growth; are polynomial fitting parameters; is the highest degree of the polynomial.

[0071] 3. Cell activity data was obtained by measuring the cell activity data through biological slice technology ; The nonlinear least squares method was used to fit the experimental data to obtain These parameter values ​​can reflect the diapause characteristics of growth point activities and provide experimental basis at the cellular level for subsequent analysis.

[0072] Step S30: Gene regulatory network analysis

[0073] The purpose of this step is to analyze the gene regulatory network of mixed ester alcohol tobacco bud inhibitor on the early development of tobacco axillary buds through multi-dimensional transcriptome and physiological phenotype analysis, and to explore the core regulatory elements. The specific implementation method is as follows:

[0074] 1. Use RNA-seq and other transcriptomics techniques to measure the expression levels of various genes in tobacco axillary bud tissues under different treatment conditions .

[0075] 2. Establishing a gene regulatory network model .in, is the maximum expression rate; is the regulation threshold; is the Hill coefficient; is the degradation rate; For external signals; is the control parameter.

[0076] 3. Obtained through time series transcriptome data and Bayesian inference ; Obtained through joint analysis of multidimensional transcriptome and physiological phenotype group These parameters reflect the effects of sprout inhibitors on gene regulatory networks.

[0077] 4. Further analysis of transcriptome data, selection of differentially expressed genes related to axillary bud development, and mining of core regulatory genes through bioinformatics methods. These core genes play a key role in the bud inhibition process and are the focus of subsequent optimization of adjuvant formulas.

[0078] Step S40: core gene function analysis

[0079] The purpose of this step is to clarify the core regulatory genes that affect the development of axillary buds by mixed ester alcohol tobacco bud inhibitors through molecular biology experiments and multi-omics analysis, and to analyze their regulatory functions and mechanisms of action. The specific implementation methods are as follows:

[0080] 1. Using CRISPR-Cas9 technology, perform knockout or overexpression experiments on the core regulatory genes screened in step S30 to measure their effects on axillary bud growth. Through these experiments, the expression levels of core genes can be obtained. .

[0081] 2. Establish a functional model of core regulatory genes .in, For the Functional scores of core regulatory genes; is the phenotype-related function; is the time-integrated function; is a function of metabolite concentration; is the Hilbert transform; is the weight parameter.

[0082] 3. Construction through phenotypic data ;Constructed using time series transcriptome data ; Obtained through metabolomics data ;Use random forest and other machine learning methods to train .

[0083] 4. Analyze the functional scores of core genes , determine which genes play a leading role in the sprout inhibition process, and conduct in-depth research on their regulatory mechanisms, providing an important basis for subsequent optimization of adjuvants.

[0084] Step S50: Evaluation of additive effect

[0085] The purpose of this step is to evaluate the optional adjuvants by computational chemistry methods based on the analysis results of steps S10-S40 to improve the sprout inhibition effect of each key compound. The specific implementation is as follows:

[0086] 1. Types and concentrations of available additives Exhaustively enumerate and build an additive effect evaluation model for each additive .in, For adjuvant and receptor The binding energy of As auxiliary agent physical and chemical properties; The target physical and chemical properties; is the weight parameter.

[0087] 2. Obtained through molecular docking simulation ; Obtained by quantum chemical calculation ; Determined based on the pharmacophore model ; Obtained through quantitative structure-activity relationship analysis .

[0088] 3. Calculate the effect score of each additive , and select the ones with the highest comprehensive scores as the preferred additives for the next step of formula optimization.

[0089] Step S60: additive formulation optimization modeling

[0090] The purpose of this step is to establish a comprehensive optimization model that takes into account the sprout inhibition effect, adjuvant cost, environmental friendliness and stability, and provide a basis for subsequent optimization of the formula. The specific implementation method is as follows:

[0091] 1. Establish a sub-model of the bud inhibition effect: Based on the experimental data and mechanism analysis of steps S10-S50, establish a quantitative relationship model between the effect of the mixed ester alcohol tobacco bud inhibitor and the auxiliary agent formula, with the constraint that the bud inhibition effect is not less than 90%.

[0092] 2. Establish an additive cost sub-model: Consider the market price and usage of various additives, and establish a cost calculation model with the constraint that the total cost does not exceed 120% of the preset budget.

[0093] 3. Establish an environmental impact sub-model: Based on the ecotoxicological data of the additives, establish an environmental risk assessment model, with the constraint that the environmental risk index does not exceed 0.5.

[0094] 4. Establish a stability sub-model: Considering the influence of adjuvants on the physicochemical properties of mixed ester alcohol tobacco bud inhibitors, a stability prediction model is established with the constraint that the product shelf life is not less than 18 months.

[0095] 5. Establish a comprehensive optimization model .in, To optimize the overall goal; is the additive formula vector; is the objective function of the four sub-models; is the sub-model weight; and are the equality and inequality constraint functions respectively; is the penalty parameter.

[0096] 6. Determine through analytic hierarchy process ;Build according to actual constraints ; Determined by cross validation . This comprehensive optimization model provides a mathematical framework for subsequent optimization.

[0097] Step S70: Multi-objective optimization solution

[0098] The purpose of this step is to use the improved non-dominated sorting genetic algorithm to perform multi-objective optimization on the additive formula optimization model established in step S60 to obtain multiple optimized solution formulas. The specific implementation method is as follows:

[0099] 1. The type and concentration of additives for each key compound As optimization variables, construct optimization objective function .

[0100] 2. Using the improved non-dominated sorting genetic algorithm (NSGA-III) Optimize, where is the population size. The main steps of the NSGA-III algorithm include: (1) Initializing the population ;(2) Perform non-dominated sorting on the population;(3) Calculate the reference point;(4) Perform selection, crossover and mutation operations;(5) Repeat steps (2)-(4) until the termination condition is met.

[0101] 3. After multiple generations of evolutionary optimization, the algorithm finally obtains a set of Pareto optimal solutions, that is, multiple optimization formulas These formulations strike a balance between multiple objectives including sprout suppression, cost, environmental impact and stability.

[0102] Step S80: Final formulation evaluation

[0103] The purpose of this step is to conduct a comprehensive evaluation of the multiple optimized formulas obtained in step S70 through a small-scale field test and select the best optimized solution formula. The specific implementation is as follows:

[0104] 1. Multiple recipes obtained by optimizing step S70 , respectively, to conduct small-scale field trials. In the trial, the sprout inhibition effect of each formula was measured. , Duration and its impact on tobacco growth .here Indicates Repeat the test.

[0105] 2. Establish the final formula evaluation model .in, To optimize the solution formula The comprehensive score of is the number of test repetitions; is the weight coefficient; is the standard deviation of the scores; is the volatility penalty coefficient.

[0106] 3. Determined by expert scoring method ; Determined based on sensitivity analysis .here They respectively reflect the bud inhibition effect, duration and importance weight to tobacco growth.

[0107] 4. Calculate the comprehensive score for each optimized formula ,The formula with the highest score was selected as the final target formula.,The comprehensive score takes into account multiple aspects of performance, which not only meets the bud inhibition effect requirements, but also takes into account the duration and the impact on tobacco growth, and is a relatively balanced ,optimization solution.

[0108] This series of methodologies provides a comprehensive and systematic solution for the optimization of the adjuvant formula of mixed ester alcohol tobacco bud inhibitors. It has both experimental basis and computational support, and can effectively improve the bud inhibition effect.

[0109] Specifically, the principle of the present invention is: relying on computational chemistry technology, by establishing a series of mathematical models, comprehensively analyzing and optimizing the adjuvant formula of mixed ester alcohol tobacco bud inhibitor. The core of this methodology is to use objective and quantitative calculation methods to replace the traditional empirical research and development model in order to obtain a better formula solution.

[0110] First, the method for determining key compounds of the present invention makes full use of technologies such as molecular dynamics simulation, principal component analysis and nonlinear regression. By testing the sprout inhibition effect of each component, a quantitative key compound determination model is established, which not only considers the contribution of a single compound, but also reflects the interaction between compounds. This comprehensive and quantitative analysis method can more accurately screen out the key components that play a decisive role in sprout inhibition, providing a reliable theoretical basis for subsequent optimization.

[0111] Secondly, the present invention deeply analyzes the activity change rules of tobacco axillary bud growth points under the action of sprout inhibitors through fine microscopic observation and mathematical modeling. Using advanced technologies such as electron microscopy and laser capture microdissection, the time series data of cell activity were obtained, and a growth point diapause characteristic characterization model was established. This model can not only quantitatively describe the dynamic evolution of growth point activity, but also depict complex behaviors such as periodic fluctuations and recovery growth induced by sprout inhibitors. This physiological characterization at the cellular level provides an important basis for subsequent gene regulatory network analysis.

[0112] Thirdly, the present invention uses multidimensional transcriptomics analysis to establish a gene regulatory network model, analyzing the spatiotemporal dynamics of the regulation of gene expression in the early stage of axillary bud development by mixed ester alcohol sprout inhibitors. This model not only reflects the direct effect of exogenous signals (i.e. sprout inhibitors) on gene expression, but also describes the mutual regulatory relationship between genes. Through this systematic analysis, the core regulatory genes that play a key role in the sprout inhibition process can be excavated, providing important clues for subsequent research on molecular regulatory mechanisms.

[0113] Based on the above in-depth analysis of key compounds, cell behavior and gene regulation, the present invention further proposes a method for adjuvant effect evaluation and formulation optimization based on computational chemistry. First, an adjuvant effect evaluation model was constructed through molecular docking, quantum chemical calculation and QSAR technology to quantitatively predict the effects of different adjuvants on the sprout inhibition effect of each key compound. Then, a comprehensive optimization model was established, integrating multiple objectives such as sprout inhibition effect, adjuvant cost, environmental impact and stability. The improved NSGA-III algorithm was used for multi-objective optimization, and a set of optimal formulation solutions that took into account various factors were obtained. This methodology combining computational chemistry with multi-objective optimization provides a powerful tool support for the systematic optimization of sprout inhibitor formulations.

[0114] In short, the technical solution of the present invention makes full use of computational chemistry technologies such as molecular dynamics simulation, quantum chemical calculation, mathematical modeling, and multi-objective optimization, deeply explores the key factors affecting the bud inhibition effect, and establishes a series of quantitative analysis and optimization models based on this. Compared with the traditional research and development model that relies on experience and single test evaluation, this methodology is more systematic and quantitative, which not only helps to better understand the mechanism of action of bud inhibitors, but also can obtain the optimal formula that takes into account multiple performance indicators, providing a more efficient and environmentally friendly bud inhibitor solution for tobacco production.

[0115] In order to better understand and implement the present invention, a specific embodiment 1 of the present invention is provided below. The steps included in this embodiment 1 are specifically described as follows:

[0116] Step S10: Identification of key compounds

[0117] The purpose of this step is to evaluate the inhibitory effect of each component of the mixed ester alcohol tobacco bud inhibitor, determine the key compounds, and form an effective inhibitory combination group of different key compounds. The specific implementation is as follows:

[0118] First, obtain all the component information of mixed ester alcohol tobacco bud inhibitor, which may include alkyl alcohols, ester compounds, solubilizers, etc. of different types and concentrations. Indicates The concentration of the compound, , is the total number of compounds.

[0119] Through the bud inhibition effect evaluation experiment, the effect of each component on tobacco bud inhibition was tested. The bud inhibition effect evaluation can be carried out by tobacco bud length detection, bud number statistics and other methods to obtain the bud inhibition effect data of each component. Represents compound and The interaction function between them is obtained through molecular dynamics simulation.

[0120] According to the sprout inhibition effect data, the key compound determination equation was established:

[0121] ;

[0122] in, For the Importance score of each compound. It is a pending parameter, and the acquisition method is as follows:

[0123] Determined by principal component analysis (PCA). PCA is a commonly used unsupervised learning algorithm that can extract the most important variation pattern in the data, thereby determining the contribution of each variable (i.e., each compound) to the overall variation, i.e. The value of .

[0124] It is obtained by fitting the experimental data through nonlinear regression. Nonlinear regression is a commonly used parameter estimation method, which can fit the parameters of complex function models containing nonlinear terms, thereby determining the values ​​of the above-mentioned unknown parameters.

[0125] According to the calculated Value, before selection The most important compounds are selected as key compounds, and effective sprout inhibition combination groups of different key compounds are formed. It can be 5 or 10, and the specific value needs to be determined based on experimental data.

[0126] Step S20: Characterization of growth point diapause characteristics

[0127] The purpose of this step is to observe the fine cutting of the meristem cells of the tobacco axillary buds treated with different concentrations of mixed ester alcohol tobacco bud inhibitors to clarify the microscopic conformation and characterization changes of the growth point activity diapause. The specific implementation method is as follows:

[0128] Scanning electron microscopy and biological sectioning techniques were used to finely cut and observe the axillary meristems of tobacco buds treated with different concentrations of mixed ester alcohol tobacco bud inhibitors. These techniques can clearly observe microscopic characteristics such as cell morphology and growth point activity status.

[0129] According to the observed changes in cell activity, the growth point diapause characterization equation was established:

[0130] ;

[0131] in, For time Growth point activity at ; is the initial activity; is the decay rate; is the period fluctuation amplitude; For the cycle; To restore the growth rate; To restore the growth rate; The midpoint time for resumption of growth; are polynomial fitting parameters; is the highest degree of the polynomial.

[0132] Cell viability data were obtained by laser capture microdissection ; The nonlinear least squares method was used to fit the experimental data to obtain These parameter values ​​can reflect the diapause characteristics of growth point activities and provide experimental basis at the cellular level for subsequent analysis.

[0133] Step S30: Gene regulatory network analysis

[0134] The purpose of this step is to analyze the gene regulatory network of mixed ester alcohol tobacco bud inhibitor on the early development of tobacco axillary buds through multi-dimensional transcriptome and physiological phenotype analysis, and to explore the core regulatory elements. The specific implementation method is as follows:

[0135] First, transcriptomic techniques such as RNA-seq were used to measure the expression levels of various genes in tobacco axillary bud tissues under different treatment conditions. ,in , is the total number of genes.

[0136] Then, the gene regulatory network model was established:

[0137] ;

[0138] in, For gene The maximum expression rate of For gene Gene Thresholds of regulation; is the Hill coefficient, which describes the nonlinear characteristics of regulation; For gene The degradation rate; For external signals; is the control parameter.

[0139] Obtained through time series transcriptome data and Bayesian inference ; Obtained through joint analysis of multidimensional transcriptome and physiological phenotype group These parameters reflect the effects of sprout inhibitors on gene regulatory networks.

[0140] Further analysis of transcriptome data, the differentially expressed genes related to axillary bud development were selected, and the core regulatory genes were mined through bioinformatics. These core genes play a key role in the bud inhibition process and are the focus of subsequent optimization of adjuvant formula.

[0141] Step S40: core gene function analysis

[0142] The purpose of this step is to clarify the core regulatory genes that affect the development of axillary buds by mixed ester alcohol tobacco bud inhibitors through molecular biology experiments and multi-omics analysis, and to analyze their regulatory functions and mechanisms of action. The specific implementation methods are as follows:

[0143] First, the CRISPR-Cas9 technology was used to knock out or overexpress the core regulatory genes screened in step S30, and their effects on axillary bud growth were measured to obtain the core gene expression levels. .

[0144] Then, establish the core regulatory gene function model:

[0145] ;

[0146] in, For the Functional scores of core regulatory genes; is the phenotype-related function; is the time-integrated function; is a function of metabolite concentration; is the Hilbert transform; is the weight parameter.

[0147] Constructed through phenomics data; Constructed using time series transcriptome data; Obtained through metabolomics data; It is obtained by training using machine learning methods such as Random Forest.

[0148] Analysis of functional scores of core genes , determine which genes play a leading role in the sprout inhibition process, and conduct in-depth research on their regulatory mechanisms, providing an important basis for subsequent optimization of adjuvants.

[0149] Step S50: Evaluation of additive effect

[0150] The purpose of this step is to evaluate the optional adjuvants by computational chemistry methods based on the analysis results of steps S10-S40 to improve the sprout inhibition effect of each key compound. The specific implementation is as follows:

[0151] The types and concentrations of additives that can be used Exhaustively enumerate and build an additive effect evaluation model for each additive:

[0152] ;

[0153] in, As auxiliary agent With receptor The binding energy of As auxiliary agent No. physical and chemical properties; The target physical and chemical properties; is the weight parameter.

[0154] Obtained through molecular docking simulation ; Obtained by quantum chemical calculation ; Determined based on the pharmacophore model ; Obtained through quantitative structure-activity relationship analysis .

[0155] Calculate the effectiveness score of each additive , and select the ones with the highest comprehensive scores as the preferred additives for the next step of formula optimization.

[0156] Step S60: additive formulation optimization modeling

[0157] The purpose of this step is to establish a comprehensive optimization model that takes into account the sprout inhibition effect, adjuvant cost, environmental friendliness and stability, and provide a basis for subsequent optimization of the formula. The specific implementation method is as follows:

[0158] First, establish the sprout inhibition effect sub-model:

[0159] ;

[0160] in, is the additive formula vector; For the The effect score of the additive is given by step S50; For the The weight of the adjuvant in the sprout inhibition effect sub-model. The constraint condition is that the sprout inhibition effect is not less than 90%.

[0161] Establish the additive cost sub-model:

[0162] ;

[0163] in, For the The unit price of the additive is 120%. The constraint is that the total cost does not exceed 120% of the preset budget.

[0164] Build the environmental impact sub-model:

[0165] ;

[0166] in, As auxiliary agent The environmental risk index of the plant is calculated based on its ecotoxicological data; For the The weight of the additive in the environmental impact sub-model. The constraint condition is that the environmental risk index does not exceed 0.5.

[0167] Build the stability submodel:

[0168] ;

[0169] in, As auxiliary agent The impact score on the stability of mixed ester alcohol tobacco bud inhibitor was calculated by the prediction model; For the The weight of each additive in the stability sub-model. The constraint is that the product shelf life is not less than 18 months.

[0170] Finally, a comprehensive optimization model is constructed:

[0171] ;

[0172] in, To optimize the overall goal; is the sub-model weight, determined by the analytic hierarchy process; and They are equality and inequality constraint functions, respectively, constructed according to the actual constraints; is the penalty parameter determined by cross-validation.

[0173] Step S70: Multi-objective optimization solution

[0174] The purpose of this step is to use the improved non-dominated sorting genetic algorithm (NSGA-III) to perform multi-objective optimization on the additive formulation optimization model established in step S60 to obtain multiple optimized solution formulations. The specific implementation method is as follows:

[0175] The type and concentration of additives for each key compound As optimization variables, construct optimization objective function . Using NSGA-III algorithm Optimize, where is the population size.

[0176] After multiple generations of evolutionary optimization, the algorithm finally obtains a set of Pareto optimal solutions, that is, multiple optimization formulas These formulations strike a balance between sprout suppression, cost, environmental impact and stability.

[0177] Step S80: Final formulation evaluation

[0178] The purpose of this step is to conduct a comprehensive evaluation of the multiple optimized formulas obtained in step S70 through a small-scale field test and select the best optimized solution formula. The specific implementation is as follows:

[0179] Multiple recipes optimized in step S70 , respectively, to conduct small-scale field trials. In the trial, the sprout inhibition effect of each formula was measured. , Duration and its impact on tobacco growth ,in , is the number of test repetitions.

[0180] Build the final formulation evaluation model:

[0181] ;

[0182] in, To optimize the solution formula The comprehensive score of is the weight coefficient, determined by expert scoring method; is the standard deviation of the scores; is the volatility penalty coefficient, which is determined through sensitivity analysis.

[0183] Calculate a comprehensive score for each optimized recipe ,The formula with the highest score was selected as the final target formula.,The comprehensive score takes into account multiple aspects of performance, which not only meets the bud inhibition effect requirements, but also takes into account the duration and the impact on tobacco growth, and is a relatively balanced ,optimization solution.

[0184] In summary, the specific steps of this embodiment 1 are summarized as follows:

[0185] 1. Through molecular dynamics simulation, principal component analysis and nonlinear regression methods, a key compound determination model was established, and the key components affecting the sprout inhibition effect were determined.

[0186] 2. Using electron microscopy observation and nonlinear least squares method, a model for characterizing the characteristics of growth point diapause was established, revealing the changing patterns of axillary bud growth point activity under the action of sprout inhibitors.

[0187] 3. A gene regulatory network model was constructed, and the effects of sprout inhibitors on gene expression regulation were analyzed by combining time series transcriptome data and Bayesian inference.

[0188] 4. Establish a core regulatory gene functional model, integrate multi-omics data, and systematically analyze the action mechanism of key genes in sprout inhibition regulation.

[0189] 5. A model for evaluating the effect of additives was constructed using molecular docking, quantum chemical calculations and QSAR technology, providing a theoretical basis for the selection of additives.

[0190] 6. Construct a comprehensive optimization model that integrates multiple factors such as sprout inhibition effect, adjuvant cost, environmental impact and stability, providing a mathematical framework for formula optimization.

[0191] 7. The improved NSGA-III algorithm was used for multi-objective optimization, and a set of alternative optimization formulas were obtained.

[0192] 8. The final optimized formula was selected through small-scale field trials and expert experience evaluation.

[0193] In order to further better understand and implement the present invention, the following provides Example 2 and Example 3 of two specific application scenarios of the present invention.

[0194] Example 2 describes in detail how to use the method of the present invention to extract mixed fat alcohol tobacco sprout inhibitor using palm oil, coconut oil, rapeseed oil, rice bran oil and kitchen waste oil as raw materials, and optimize its auxiliary agent formula.

[0195] 1. Raw material preparation and pretreatment

[0196] First, gather the following ingredients:

[0197] Palm oil: from Malaysia, acid value 0.5mgKOH / g;

[0198] Coconut oil: from the Philippines, acid value 0.3mgKOH / g;

[0199] Rapeseed oil: from Heilongjiang, China, acid value 0.2mgKOH / g;

[0200] Rice bran oil: from Hunan, China, acid value 1.2mgKOH / g;

[0201] Waste cooking oil: recycled locally, acid value 5.6mgKOH / g;

[0202] Pretreatment of raw materials:

[0203] a) Filtration: Filter through a 100-mesh sieve to remove visible impurities.

[0204] b) Deacidification: The rice bran oil and waste cooking oil with high acid value are treated with alkaline deacidification to reduce the acid value to below 1.0 mgKOH / g.

[0205] c) Decolorization: Use activated white clay for decolorization to remove pigments and some impurities.

[0206] d) Deodorization: Deodorization was carried out at 160°C and vacuum degree 0.6 kPa for 3 hours.

[0207] 2. Mixed alcohol extraction

[0208] High pressure hydrogenation method is used to extract mixed fat alcohol:

[0209] a) Mix the pretreated oils in the following proportions: 30% palm oil, 20% coconut oil, 25% rapeseed oil, 15% rice bran oil, and 10% waste cooking oil.

[0210] b) Add the mixed oil and catalyst (copper-chromium catalyst, 1% of the oil mass) into a high-pressure reactor.

[0211] c) introducing hydrogen, raising the pressure to 25 MPa, and the temperature to 280° C., and the reaction time to 4 hours.

[0212] d) After the reaction is completed, the mixture is cooled to room temperature and separated by vacuum distillation to obtain crude mixed fatty alcohol.

[0213] e) distilling the crude mixed fat alcohol to obtain a mixed fat alcohol product with a purity of more than 98%.

[0214] 3.S10: Obtain the ingredients of mixed fat alcohol tobacco bud inhibitor and identify the key compounds

[0215] The mixed alcohol components were analyzed by gas chromatography-mass spectrometry (GC-MS). The main components and contents are as follows:

[0216] Hexadecanol (C16H34O): 35.2%;

[0217] Octadecanol (C18H38O): 28.7%;

[0218] Tetradecanol (C14H30O): 15.6%;

[0219] Eicosanol (C20H42O): 10.3%;

[0220] Dodecanol (C12H26O): 5.8%;

[0221] Other higher alcohols: 4.4%

[0222] In order to identify the key compounds, an experiment to evaluate the sprout inhibition effect was conducted. Five main ingredients were selected and prepared into a 0.5% aqueous dispersion. Each treatment was repeated 3 times, with 100 tobacco seedlings treated each time. The sprout inhibition rate was calculated after 7 days, and the results are shown in Table 1:

[0223] Table 1 Statistical table of germination inhibition rate

[0224] Compound Bud inhibition rate (%) Cetyl Alcohol 87.3 Octadecanol 82.1 Tetradecanol 75.6 Eicosanol 68.9 Dodecanol 62.4

[0225] Determine the equation based on the key compound in step S10:

[0226] ;

[0227] Through molecular dynamics simulation and experimental data fitting, the following parameters are obtained:

[0228] ;

[0229] ;

[0230] The importance score of each compound was calculated:

[0231] Hexadecanol: E1=8.76;

[0232] Octadecanol: E2=8.12;

[0233] Tetradecanol: E3=7.45;

[0234] Eicosanol: E4=6.81;

[0235] Dodecanol: E5=6.23;

[0236] According to the scoring results, hexadecanol, octadecyl alcohol and tetradecyl alcohol were determined to be the key compounds, forming an effective sprout inhibition combination group.

[0237] 4.S20: Analysis of the microscopic conformation and characterization changes of specific cell populations in the axillary bud meristem

[0238] Hexadecanol was selected as a representative compound, and three concentrations of 0.1%, 0.3% and 0.5% aqueous dispersions were prepared to treat tobacco seedlings. Samples were taken at 0h, 24h, 48h and 72h after treatment, and the axillary bud meristems were observed and analyzed using scanning electron microscopy (SEM) and biological sectioning techniques.

[0239] SEM observation results showed that with the increase of treatment concentration and time, the surface cells of axillary bud meristem gradually shrank and the intercellular space increased. After 72h of treatment with 0.5% concentration, the cell surface showed obvious shrinkage and depression.

[0240] TEM observation results showed that after treatment, a large number of vacuoles appeared in the cytoplasm and the organelle structure gradually became blurred. After 72 hours of treatment with 0.5% concentration, the nucleus was condensed, the mitochondria were expanded, and the endoplasmic reticulum was fragmented.

[0241] RNA extraction and sequencing of specific cell population samples isolated by LCM technology revealed a significant decrease in the expression of genes related to cell division and growth. For example, the expression of the cell cycle regulatory gene CDK1 decreased by 78.3% after 72 hours of treatment at a concentration of 0.5%.

[0242] According to the growth point diapause characterization equation in step S20:

[0243] ;

[0244] The experimental data were fitted by nonlinear least squares method, and the following parameters were obtained:

[0245] ;

[0246] ;

[0247] This equation well describes the changing trend of axillary bud meristem activity over time and provides a quantitative basis for subsequent research.

[0248] 5.S30: Analysis of the gene regulatory network of mixed alcohol biological sprout inhibitor on the early development of tobacco axillary buds

[0249] The whole transcriptome sequencing of tobacco seedlings treated with 0.5% hexadecanol was performed, and samples were taken three times at each of the six time points of 0h, 6h, 12h, 24h, 48h, and 72h. Through differentially expressed gene analysis, 2,876 significantly changed genes were identified.

[0250] Using the WGCNA (Weighted Gene Co-expression Network Analysis) method, a gene co-expression network was constructed and 8 functional modules were identified. Among them, the module M1 related to cell division and growth contained 423 genes, the module M2 related to hormone signal transduction contained 315 genes, and the module M3 related to oxidative stress response contained 287 genes.

[0251] According to the gene regulatory network model in step S30:

[0252] ;

[0253] Taking the key gene CYCD3 in module M1 as an example, the following parameters are obtained through Bayesian inference:

[0254] ;

[0255] ;

[0256] This model successfully describes the temporal changes in CYCD3 gene expression levels and its interactions with other genes and external signals.

[0257] Through similar analysis, the action pathway and signal network of mixed fat alcohol biological sprout inhibitor were preliminarily analyzed. The results showed that mixed fat alcohol mainly achieved the sprout inhibition effect by inhibiting the cytokinin signaling pathway, activating the ABA signaling pathway, and inducing oxidative stress response.

[0258] 6.S40: Identify the core regulatory genes that affect the occurrence and development of tobacco axillary buds by biological bud inhibitors

[0259] Based on the results of S30, 10 candidate core regulatory genes were selected for CRISPR-Cas9 gene editing and overexpression experiments, including cell cycle regulators (CYCD3, E2Fa), hormone signal transduction factors (ARR1, ABI5) and transcription factors (DREB2A, NAC29).

[0260] CRISPR-Cas9 knockout vectors and overexpression vectors of these 10 genes were constructed and transformed into tobacco by Agrobacterium-mediated method. The transgenic plants obtained were verified by PCR and sequencing, and then subjected to the mixed alcohol treatment experiment.

[0261] The experimental results showed that knockout of CYCD3 and ARR1 genes significantly enhanced the sprout inhibition effect of mixed alcohol, with the sprout inhibition rate increased by 15.3% and 12.7%, respectively. Overexpression of ABI5 and NAC29 plants showed stronger sensitivity to mixed alcohol treatment, with the sprout inhibition rate increased by 18.6% and 16.9%, respectively.

[0262] Comprehensive phenotypic, metabolomic and proteomic analyses were performed on these transgenic plants. Taking NAC29 overexpressing plants as an example, metabolomic analysis showed that the content of proline and γ-aminobutyric acid (GABA) increased significantly, by 2.3 times and 1.8 times, respectively. Proteomic analysis found that the expression of protease inhibitors and chitinases related to cell wall remodeling increased by 2.7 times and 2.1 times, respectively.

[0263] According to the core regulatory gene function model in step S40:

[0264] ;

[0265] Taking NAC29 as an example, the following parameters are obtained through machine learning training:

[0266] ;

[0267] in, They represent the functions of germination inhibition rate, biomass reduction and hormone level changes, respectively; Represent the concentration functions of proline and GABA, respectively.

[0268] Through this model, the functional score of NAC29 in the germination inhibition process of mixed lipid alcohol can be quantitatively evaluated, providing a basis for subsequent adjuvant screening.

[0269] 7.S50: Additive selection

[0270] Based on the results of S10-S40, three key directions for improving the sprout inhibition effect of mixed lipid alcohol were determined: enhancing cell division inhibition, promoting ABA signal transduction, and strengthening oxidative stress response. Based on these directions, the following categories of potential adjuvants were screened:

[0271] a) Cell cycle inhibitor: Roscovitine;

[0272] b) ABA analogs: Pyrabactin;

[0273] c) Oxidative stress inducer: salicylic acid;

[0274] d) Surfactant: Tween-20;

[0275] e) Osmotic regulator: Glycerol;

[0276] Molecular docking, pharmacophore model analysis and quantitative structure-activity relationship (QSAR) analysis were performed on these excipients. Taking roscomycin as an example, the AutoDockVina software was used to perform molecular docking between it and the CYCD3-CDK complex. The results showed that the binding energy of roscomycin and the CYCD3-CDK complex was -8.7 kcal / mol, forming three hydrogen bonds and multiple hydrophobic interactions.

[0277] Evaluation model based on additive effect in S50 step:

[0278] ;

[0279] For roscomycin, the following parameters were obtained:

[0280] ;

[0281] ;

[0282] ;

[0283] ;

[0284] in, and Represent CYCD3-CDK complex and ARR1 protein, respectively. and represent the octanol / water partition coefficient (LogP) and polar surface area (PSA), respectively.

[0285] Through this model, the effect score of roscomycin at different concentrations can be calculated to determine the optimal addition concentration. Similarly, the same analysis was performed for other adjuvants.

[0286] 8.S60: Establishing an additive formulation optimization model

[0287] Based on the previous analysis results, a comprehensive optimization model including four sub-models: sprout inhibition effect, adjuvant cost, environmental friendliness and stability was established.

[0288] a) Bud suppression effect sub-model:

[0289] ;

[0290] Among them, IR is the germination inhibition rate, GI is the growth inhibition index, and HR is the hormone response index. Through multiple regression analysis, we get:

[0291] ;

[0292] Constraints: ;

[0293] b) Additive cost sub-model:

[0294] ;

[0295] in, is the unit price of the i-th additive, Add the amount to it.

[0296] Constraints: , B is the preset budget.

[0297] c) Environmental impact sub-model:

[0298] ;

[0299] in, is the median effect concentration, BCF is the bioconcentration factor, is the environmental half-life. Through principal component analysis, we get:

[0300] ;

[0301] Constraints: ;

[0302] d) Stability sub-model:

[0303] ;

[0304] Among them, ST is the storage temperature tolerance range, pH is the pH stability range, and VS is the viscosity stability. Through the fuzzy comprehensive evaluation method, we get:

[0305] ;

[0306] Constraints: Product shelf life is not less than 18 months.

[0307] 9.S70: Multi-objective optimization solution

[0308] The improved non-dominated sorting genetic algorithm (NSGA-III) is used for multi-objective optimization. The optimization objective function is:

[0309] ;

[0310] in, ;

[0311] Set the population size to 200 and the maximum number of iterations to 1000. The crossover probability is 0.8 and the mutation probability is 0.1.

[0312] After optimization, a series of non-dominated solutions were obtained, among which three typical formulations are as follows:

[0313] Recipe A:

[0314] Mixed alcohol (main ingredient): 0.5%; Roscomycin: 50ppm; Pyrabensulfuron: 30ppm; Salicylic acid: 100ppm; Tween-20: 0.1%; Glycerin: 1%;

[0315] Recipe B:

[0316] Mixed alcohol (main ingredient): 0.4%; Roscomycin: 40ppm; Pyrabensulfuron: 40ppm; Salicylic acid: 150ppm; Tween-20: 0.15%; Glycerin: 1.5%;

[0317] Recipe C:

[0318] Mixed alcohol (main ingredient): 0.6%; Roscomycin: 60ppm; Pyrabensulfuron: 20ppm; Salicylic acid: 80ppm; Tween-20: 0.08%; Glycerin: 0.8%;

[0319] 10.S80: Verify and optimize final formulation

[0320] A small-scale field trial was conducted under greenhouse conditions to evaluate the bud suppression effect, duration and effect on tobacco growth of the three formulations. Each formulation was used to treat 300 tobacco seedlings, with three replicates. A water control group and a lipid alcohol-only treatment group were also set up.

[0321] The test results are shown in Table 2:

[0322] Table 2 Experimental results

[0323] formula Bud inhibition rate (%) Duration (days) Relative reduction in biomass (%) A 95.3 28 12.5 B 93.8 32 10.2 C 97.1 25 15.8 Mixed alcohol 87.6 21 8.7 Comparison 5.2

[0324] The model was evaluated based on the final recipe in step S80:

[0325] ;

[0326] Determined by expert scoring: ;

[0327] The comprehensive scores of the three recipes are calculated:

[0328] Formula A: S(A)=8.76;

[0329] Formula B: S(B)=8.92;

[0330] Formula C: S(C)=8.45;

[0331] Based on these results, formulation B was selected as the final optimized formulation.

[0332] To further verify the effect of Formulation B, the following additional tests were performed:

[0333] a) Accelerated stability test: The sample of formula B was stored at 45°C and 75% relative humidity for 3 months, and the physical and chemical properties and biological activity were tested every half month. The results showed that after 3 months, the sprout inhibition activity remained above 92% of the original effectiveness, and the physical and chemical indicators such as pH value and viscosity were within acceptable ranges.

[0334] b) Ecotoxicology test:

[0335] Impact on soil microorganisms: At the recommended dose, the inhibition rate of soil dehydrogenase activity is 8.3%, which is a low toxicity level.

[0336] Effects on non-target plants: At the recommended dose, the growth inhibition rate of the legume crop alfalfa is 5.7%, which is within the safe range.

[0337] Impact on aquatic organisms: The 96h-LC50 for zebrafish is 78.5 mg / L, which is at a low toxicity level.

[0338] c) Field test: Field tests were conducted at three tobacco planting bases in different geographical locations, with three replicates in each location and an area of ​​50 square meters in each plot. The results showed that formula B showed a stable sprout inhibition effect under different environmental conditions, with an average sprout inhibition rate of 92.7% and a duration of 30-34 days.

[0339] Based on these comprehensive verification results, Formula B was confirmed as the final optimized mixed fat alcohol tobacco bud inhibitor adjuvant formula. This formula not only significantly improved the bud inhibition effect and duration, but also had good stability and environmental safety.

[0340] Example 3: A tobacco research institute decided to develop a new type of mixed alcohol tobacco bud inhibitor to improve the efficiency and quality of tobacco planting. The research team carried out a series of experiments and analysis according to the method of the present invention.

[0341] Step S10: Obtaining the ingredients of the mixed ester alcohol tobacco bud inhibitor and determining the key compounds

[0342] First, mixed alcohol was extracted from palm oil, coconut oil, rapeseed oil, rice bran oil and kitchen waste oil. The main components of mixed alcohol were determined by gas chromatography-mass spectrometry (GC-MS) analysis as shown in Table 3:

[0343] Table 3 Main ingredients

[0344] Compound content(%) Cetyl Alcohol 35.2 Octadecanol 28.7 Eicosanol 15.6 Docosanol 10.3 Tetracosanol 5.8 Other fatty alcohols 4.4

[0345] A series of experiments were conducted to evaluate the sprout inhibition effect. By applying the key compound determination equation of the present invention, the importance score of each compound was calculated. The results showed that hexadecanol and octadecanol had the highest scores and were determined as key compounds. The study found that different proportions of these two compounds would produce different sprout inhibition effects.

[0346] Step S20: Analyzing the changes in tobacco axillary bud meristem after mixed ester alcohol treatment

[0347] Three different concentrations of mixed ester alcohol solutions (0.5%, 1% and 2%) were selected to treat tobacco plants, and samples were collected on the 3rd, 7th and 14th days after treatment. Scanning electron microscopy (SEM) was used to observe the microstructural changes of axillary bud meristems.

[0348] SEM observation results showed that with the increase of mixed ester alcohol concentration and the extension of treatment time, the growth points of axillary bud meristems gradually shrank and the cells became loosely arranged. TEM observation further revealed the changes in the internal structure of cells, including the degradation of organelles and the thickening of cell walls.

[0349] Using biological section technology, the affected axillary bud meristem cell population was precisely isolated. Real-time quantitative PCR was used to analyze the gene expression of these cells, and it was found that the expression levels of multiple genes related to cell division and growth were significantly reduced.

[0350] The growth point diapause characterization equation of the present invention was used to fit the change curve of the growth point activity of axillary buds over time under treatment with different concentrations of mixed ester alcohol. The results showed that the treatment effect of 2% mixed ester alcohol was the best, and the growth point activity of axillary buds could be reduced to less than 20% of the initial value within 7 days after treatment.

[0351] Step S30: Multidimensional transcriptome and physiological phenotype analysis

[0352] Comprehensive transcriptome sequencing and physiological phenotype analysis were performed on the treated tobacco samples. Expression data of more than 20,000 genes were obtained through RNA-seq technology. At the same time, a series of physiological indicators were measured, including chlorophyll content, photosynthesis rate, transpiration rate, etc.

[0353] Using the gene regulatory network model of the present invention, a 500-node axillary bud development regulatory network was constructed. By analyzing the network topology and dynamic characteristics, 50 core regulatory genes that may be involved in the bud inhibition process were identified.

[0354] Among them, the most striking is a gene encoding TCP transcription factor NtTCP15, whose expression level drops rapidly after mixed alcohol treatment. Promoter analysis revealed that the promoter region of NtTCP15 contains multiple cis-acting elements related to fatty alcohol response.

[0355] Step S40: Verification of core regulatory gene function

[0356] In order to verify the role of NtTCP15 in the bud inhibition process, CRISPR-Cas9 technology was used to create transgenic tobacco plants with NtTCP15 gene knockout. At the same time, a NtTCP15 overexpression vector was constructed and transformed into wild-type tobacco.

[0357] Phenotypic analysis results showed that NtTCP15 knockout plants showed significant inhibition of axillary bud growth, and even without the application of sprout inhibitors, a certain sprout inhibition effect could be achieved. In contrast, NtTCP15 overexpression plants showed stronger axillary bud growth ability and reduced sensitivity to mixed ester alcohol sprout inhibitors.

[0358] Detailed metabolomics and proteomics analysis revealed that NtTCP15 regulates a series of downstream genes related to cell division, cell wall synthesis and hormone signal transduction. Through the core regulatory gene function model of the present invention, the functional score of NtTCP15 was calculated to be 0.92 (full score is 1), confirming its key role in the bud inhibition process.

[0359] Step S50: Selection of additives

[0360] Based on the in-depth understanding of NtTCP15 and its related pathways, we began to screen for adjuvants that can enhance the sprout inhibition effect of mixed ester alcohol. We mainly focus on the following potential adjuvants:

[0361] 1. Surfactant: improve the permeability of mixed ester alcohol;

[0362] 2. Antioxidants: extend the shelf life of mixed ester alcohol;

[0363] 3. Chelating agent: reduce the interaction between mixed ester alcohol and metal ions;

[0364] 4. pH regulator: optimize the stability and activity of mixed ester alcohol;

[0365] Using molecular docking technology, the interaction between different adjuvants and NtTCP15 protein and its related receptors was simulated. Through the adjuvant effect evaluation model of the present invention, the researchers calculated the effect score of each adjuvant.

[0366] Finally, the following additives were selected for further testing:

[0367] 1. Surfactant: Polysorbate 80 (Tween80);

[0368] 2. Antioxidants: Vitamin E;

[0369] 3. Chelating agent: disodium EDTA;

[0370] 4. pH adjuster: citric acid;

[0371] Step S60: Establishing an additive formulation optimization model

[0372] An optimization model was established that comprehensively considered the sprout inhibition effect, adjuvant cost, environmental friendliness and stability. The researchers conducted a large number of experiments and collected the necessary data to build and verify each sub-model.

[0373] a) Bud suppression effect sub-model: The effects of different concentrations of mixed ester alcohol and adjuvant combinations on tobacco bud suppression were tested. The researchers found that adding an appropriate amount of Tween80 can significantly improve the permeability of mixed ester alcohol, thereby enhancing the bud suppression effect. Through regression analysis, a relationship model between the bud suppression effect and the concentrations of mixed ester alcohol and Tween80 was established.

[0374] b) Additive cost sub-model: The market price data of various additives were collected and the costs at different dosages were considered. The researchers found that although the unit price of disodium EDTA was higher, its impact on the total cost was limited due to its small usage.

[0375] c) Environmental impact sub-model: By consulting literature and conducting ecotoxicology experiments, the environmental risks of various additives were evaluated. The results showed that among the selected additives, Tween80 and vitamin E were more environmentally friendly, while the use of disodium EDTA required strict control of dosage.

[0376] d) Stability submodel: Accelerated aging tests were conducted to test the effects of different adjuvant combinations on the stability of mixed ester alcohol sprout inhibitors. The researchers found that adding an appropriate amount of vitamin E and adjusting the pH value can significantly improve the storage stability of the product.

[0377] Based on the above analysis, the constraints of each sub-model were determined: sprout inhibition effect: ≥90%; total cost: ≤120% of the budget; environmental risk index: ≤0.5; product shelf life: ≥18 months;

[0378] Step S70: Multi-objective optimization

[0379] The improved non-dominated sorting genetic algorithm (NSGA-III) was used to perform multi-objective optimization of the additive formula. The researchers took the concentration of each additive as the optimization variable and constructed the optimization objective function.

[0380] Algorithm parameters: population size: 100; number of iterations: 500; crossover probability: 0.8; mutation probability: 0.1.

[0381] After multiple runs and tuning, the algorithm finally converged and obtained a series of non-dominated solutions. Five representative optimization formulations were selected for further evaluation, as shown in Table 4:

[0382] Table 4 Formulation evaluation table

[0383] Recipe Number Mixed ester alcohol(%) Tween80(%) Vitamin E(%) Disodium EDTA(%) Citric acid(%) A 1.5 0.2 0.05 0.01 0.1 B 1.8 0.15 0.08 0.005 0.08 C 2.0 0.18 0.06 0.008 0.12 D 1.6 0.22 0.07 0.012 0.09 E 1.7 0.17 0.09 0.007 0.11

[0384] Step S80: Small-scale field trial evaluation

[0385] A small-scale field trial was conducted in three different tobacco growing areas, with 500 tobacco plants planted in each location, using five optimized formulations and a control group (using only the same concentration of mixed ester alcohol without adding adjuvants). The trial lasted for 8 weeks, during which the following indicators were recorded:

[0386] 1. Bud inhibition effect: calculate the proportion of tobacco plants that inhibit axillary bud growth;

[0387] 2. Duration: record the number of weeks the sprout inhibition effect lasts;

[0388] 3. Effects on tobacco growth: measuring plant height, leaf area and dry matter accumulation;

[0389] The test results are shown in Table 5:

[0390] Table 5 Experimental results of Example 2

[0391] formula Bud suppression effect (%) Duration (weeks) Plant height reduction (%) Leaf area reduction (%) Dry matter reduction (%) Comparison 82.5 5 5.2 4.8 3.9 A 92.3 7 3.8 3.5 2.8 B 94.7 7 4.1 3.9 3.2 C 96.5 8 4.5 4.2 3.5 D 93.8 7 3.6 3.3 2.6 E 95.2 8 4.3 4.0 3.3

[0392] The final formula evaluation model of the present invention was used to calculate the comprehensive score of each formula. The results showed that formula C obtained the highest comprehensive score, followed by formula E and B.

[0393] Formula C not only showed the best bud inhibition effect (96.5%) and the longest duration (8 weeks), but also had an acceptable impact on normal tobacco growth. Although its impact on plant height, leaf area and dry matter accumulation was slightly higher than that of other formulas, this was considered an acceptable trade-off considering its excellent bud inhibition performance.

[0394] To further verify the effect of Formula C, a large-scale field trial was conducted in the following growing season. The researchers selected five different tobacco growing areas, planted 5,000 tobacco plants in each area, and compared the effect of Formula C with traditional bud suppression methods.

[0395] Large-scale test results further confirmed the superiority of Formulation C:

[0396] 1. Bud inhibition effect: The average bud inhibition effect of formula C reaches 95.8%, while the traditional method is only 85.3%.

[0397] 2. Labor saving: Using Formula C can reduce the number of manual bud removal from an average of 3 times to 1 time, significantly reducing labor costs.

[0398] 3. Tobacco leaf quality: The tobacco leaf quality score of tobacco plants treated with formula C increased by an average of 8.5%, mainly in terms of leaf uniformity and maturity.

[0399] 4. Environmental impact: Compared with traditional methods, Formula C reduces the amount of chemicals used by 30% and reduces potential pollution to soil and groundwater.

[0400] Economic benefit analysis shows that although the initial cost of Formulation C is slightly higher than the traditional method, considering the labor savings, yield increase and quality improvement it brings, the net income per hectare of tobacco field increases by about 15%.

[0401] Based on these comprehensive evaluation results, Formula C was finally determined as the best formula for the new mixed fat alcohol tobacco bud inhibitor. The researchers then proceeded to optimize the product formula and develop the production process.

[0402] Product formula optimization:

[0403] In order to further improve the stability and ease of use of the product, formula C was fine-tuned:

[0404] 1. Optimization of emulsification system: By adjusting the dosage of Tween80 and adding a small amount of castor oil polyoxyethylene ether (HCO-40), the dispersibility and stability of mixed alcohol in water were improved.

[0405] 2. pH buffer system: On the basis of retaining citric acid, an appropriate amount of sodium citrate is added to construct a more stable pH buffer system, so that the product can maintain the optimal pH value under different water conditions.

[0406] 3. Thickener: Adding a small amount of xanthan gum increases the viscosity of the product and improves its adhesion on the surface of the tobacco plant.

[0407] 4. Antifreeze: Considering the need for winter storage in some areas, an appropriate amount of propylene glycol is added to lower the freezing point of the product.

[0408] The final optimized formula is shown in Table 6:

[0409] Table 6 Final formulation of Example 2

[0410] Element content(%) Mixed alcohol 2.0 Tween80 0.15 HCO-40 0.05 Vitamin E 0.06 Disodium EDTA 0.008 Citric acid 0.10 Sodium Citrate 0.05 Xanthan gum 0.02 Propylene glycol 1.0 water 96.552

[0411] Through this Example 2, it is shown in detail how to use the method of the present invention to develop a new type of highly efficient and environmentally friendly mixed fat alcohol tobacco bud inhibitor. The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered within the protection scope of the present invention.

Claims

1. A method for optimizing the formulation of an auxiliary agent for improving the effect of a sprout suppressant based on computational chemistry, characterized in that: The method is used to optimize the formula of the auxiliary agent of the mixed ester alcohol tobacco bud inhibitor, so that the mixed ester alcohol tobacco bud inhibitor has a better bud inhibition effect after the auxiliary agent is added. The method comprises the following steps: S10, obtaining the ingredients of the mixed ester alcohol tobacco bud inhibitor, and determining the key compounds by evaluating the bud inhibitory effects of all ingredients, to form an effective bud inhibitory combination group of different key compounds; S20. Using scanning electron microscopy and biological sectioning techniques, the specific cell groups of axillary bud meristems of tobacco treated with different concentrations of mixed ester alcohol tobacco bud inhibitors were finely cut to clarify the microscopic conformation and characterization changes of their growth point diapause; S30. Through multi-dimensional transcriptome and physiological phenotype group analysis, analyze the temporal and spatial genes and their fine regulatory network of the mixed lipid alcohol biological bud inhibitor on the early development of tobacco axillary buds, explore the core regulatory elements, and isolate and identify the genes induced by topping or specifically activated by axillary buds based on the transcriptional expression regulation data of the early development of axillary buds and the fine-cut transcriptome data of the bud growth point cell group, and preliminarily analyze the action pathway and signal network of the biological bud inhibitor; S40. Based on multi-omics analysis and mining, through CRISPR-CAS9 and transgenic overexpression molecular biology methods, the core regulatory genes that affect the occurrence and development of tobacco axillary buds by biological bud inhibitors are identified, and their phenotypes and genetic characteristics are analyzed through phenomics, metabolomics and proteomics, to comprehensively analyze the regulatory functions and action mechanisms of key genes in the bud inhibition process of mixed ester alcohol tobacco bud inhibitors; S50, selecting an adjuvant for each key compound according to the regulatory function and mechanism of action, using the principles of molecular docking, pharmacophore model and quantitative structure-activity relationship analysis to improve the effect of each key compound; S60, establishing an adjuvant formula optimization model that takes into account the sprout inhibition effect, adjuvant cost, environmental friendliness and stability, including a sprout inhibition effect sub-model, an adjuvant cost sub-model, an environmental impact sub-model and a stability sub-model; and establishing constraints for each sub-model; S70, taking the additive type and concentration of each key compound as optimization variables, constructing an optimization objective function, and using a non-dominated sorting genetic algorithm to perform multi-objective optimization on the additive formula optimization model to obtain multiple optimized solution formulas; S80. Conduct a small-scale field test on each optimized solution to evaluate the bud inhibition effect, duration and impact on tobacco growth, and select the optimized solution formula with the best bud inhibition effect, duration and comprehensive optimization on tobacco growth as the target formula; The step S10 includes: obtaining all component information of the mixed ester alcohol tobacco bud inhibitor; testing the effect of each component on tobacco bud inhibition through a bud inhibition effect evaluation experiment; and establishing a key compound determination equation: ； in, For the Importance score of each compound; For the The concentration of the compound; For compound and The interaction function between is the total number of compounds; is a parameter to be determined; Afterwards, according to the calculated Value, before selection The most important compounds were selected as key compounds, and effective sprout inhibition combination groups of different key compounds were formed; The step S20 includes: using a scanning electron microscope and biological sectioning technology to finely cut and observe the axillary bud meristems of tobacco treated with mixed ester alcohol tobacco bud inhibitors of different concentrations, and establishing a growing point diapause characterization equation based on the observed changes in cell activity: ； in, For time Growth point activity at ; is the initial activity; is the decay rate; is the period fluctuation amplitude; For the cycle; To restore the growth rate; To restore the growth rate; The midpoint time for resumption of growth; are polynomial fitting parameters; is the highest degree of the polynomial; The step S30 includes: using RNA-seq transcriptomics technology to measure the expression level of each gene in tobacco axillary bud tissue under different treatment conditions ; Establish a gene regulatory network model: ； in, is the maximum expression rate; is the regulation threshold; is the Hill coefficient; is the degradation rate; For external signals; as regulatory parameters; analyze transcriptome data, screen out differentially expressed genes related to axillary bud development, and mine the core regulatory genes through bioinformatics methods; The step S40 includes: using CRISPR-Cas9 technology to perform a knockout or overexpression experiment on the core regulatory gene screened in step S30, measuring its effect on axillary bud growth, and obtaining the core gene expression level ; Establish a core regulatory gene function model: ； in, For the Functional scores of core regulatory genes; is the phenotype-related function; is the time-integrated function; is a function of metabolite concentration; is the Hilbert transform; is the weight parameter; The step S50 includes: Exhaustively enumerate and build an additive effect evaluation model for each additive: ;in, For adjuvant and receptor The binding energy of As auxiliary agent physical and chemical properties; The target physical and chemical properties; is the weight parameter; Among them, the bud-inhibiting effect sub-model: based on the experimental data and mechanism analysis of S10-S50, a quantitative relationship model between the effect of mixed ester alcohol tobacco bud-inhibiting agent and the adjuvant formula is established; the adjuvant cost sub-model: considering the market price and dosage of various adjuvants, a cost calculation model is established; the environmental impact sub-model: based on the ecotoxicological data of adjuvants, an environmental risk assessment model is established; the stability sub-model: considering the influence of adjuvants on the physicochemical properties of mixed ester alcohol tobacco bud-inhibiting agent, a stability prediction model is established; Among them, the sprout inhibition effect sub-model is specifically: ； in, is the additive formula vector; For the The effect score of the additive is given by step S50; For the The weight of the adjuvant in the sub-model of sprout inhibition effect; Wherein, the auxiliary agent cost sub-model is specifically: ； in, For the Unit price of the additive; The environmental impact sub-model is specifically: ； in, As auxiliary agent The environmental risk index of the plant is calculated based on its ecotoxicological data; For the The weight of each adjuvant in the environmental impact sub-model; The stability sub-model is specifically: ； in, As auxiliary agent The impact score on the stability of mixed ester alcohol tobacco bud inhibitor was calculated by the prediction model; For the The weight of each additive in the stability submodel; The additive formulation optimization model also includes a comprehensive optimization model, specifically: ； in, To optimize the overall goal; is the sub-model weight; and They are equality and inequality constraint functions, respectively, constructed according to the actual constraints; is the penalty parameter; The target recipe evaluation model is specifically: in, To optimize the solution formula The comprehensive score of is the weight coefficient, determined by expert scoring method; is the standard deviation of the scores; is the volatility penalty coefficient, which is determined through sensitivity analysis.

2. The method for optimizing the auxiliary agent formula for improving the effect of the sprout suppressant based on computational chemistry according to claim 1, characterized in that: The weight parameters in the comprehensive optimization model are determined by using the hierarchical analysis method.

3. A method for optimizing the formulation of an auxiliary agent for improving the effect of a sprout suppressant based on computational chemistry according to claim 2, characterized in that: The penalty parameters in the comprehensive optimization model are determined by cross-validation method.

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