A Design Matching Method for Detecting the Freshness of Royal Jelly Based on a Structural Diffusion Model

Abstract

The present invention discloses a method for detecting and designing matching of the freshness of royal jelly based on a structural diffusion model, which relates to the technical field of food quality detection. A method for detecting and designing matching of the freshness of royal jelly based on a structural diffusion model includes: obtaining samples, peptide segment analysis, reverse peptide targeting, binding energy calculation, and constructing a detection and design matching system. Through the method of reverse peptide targeting and using mass spectrometry analysis and database matching technology, the present invention can quickly identify the proteins and their degradation products related to the freshness of royal jelly, greatly shortening the time for finding relevant protein markers and significantly improving the efficiency of freshness detection; by accurately matching protein sequences and identifying degradation products, it ensures that the selected markers are closely related to the degradation process of royal jelly, thereby improving the scientificity and accuracy of freshness detection; by utilizing the proteomic information of royal jelly, the efficiency and cost-effectiveness of data analysis are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of food quality detection, and particularly to a design matching method for detecting the freshness of royal jelly based on a structural diffusion model. Background Art

[0002] Royal jelly is a nutrient secreted by bees and is widely used in the fields of health products, cosmetics, and food. It has high nutritional value and bioactive components. However, the components of royal jelly are easily affected by environmental conditions. Especially during storage, the proteins, amino acids, and other active substances in royal jelly will degrade or denature, resulting in a decrease in its freshness, thereby affecting its use effect. Therefore, how to scientifically and accurately detect the freshness of royal jelly has become an urgent problem to be solved.

[0003] Currently, the methods for detecting the freshness of royal jelly include: Patent ZL202010049812.0 provides a monoclonal antibody that recognizes the specific sensitive protein MRJP4 and develops it into an ELISA kit; CN103059135B provides a specific antibody that recognizes MRJP1 and ELISA quantitative detection; CN202110248185.8 provides a biomarker for detecting the freshness of royal jelly and its screening method, and its principle is basically the same as the above two methods. Although these methods can achieve quantitative analysis of the freshness of royal jelly compared with non-destructive detection (Raman spectroscopy, infrared spectroscopy), the entire process consumes a lot of money and costs. Especially, these methods all require enzymatic pretreatment and rely on a single labeled peptide segment combined with a database to screen for labeled proteins, and then prepare monoclonal antibodies through immunized animal mouse experiments. Therefore, there is an urgent need to develop a rapid detection method and detection system for the freshness of royal jelly. Summary of the Invention

[0004] Based on this, this paper proposes a design matching method for detecting the freshness of royal jelly based on a structural diffusion model. By analyzing the protein degradation of royal jelly under different storage conditions, combining mass spectrometry analysis, proteomics databases, and structural prediction models, this method designs a detection design matching method that can accurately identify the freshness of royal jelly, providing a new solution for the quality control of royal jelly.

[0005] A design matching method for detecting the freshness of royal jelly based on a structural diffusion model includes the following steps:

[0006] Step S1: Collect royal jelly samples under different storage conditions, including fresh royal jelly of the same kind and its products, and royal jelly samples under storage conditions of storing at -20°C, 4°C, and 25°C for 0, 1, 3, 6, 12, and 24 months respectively. After mixing each sample with pure water according to a set ratio, perform extraction, centrifugation, and filtration treatments to obtain the supernatant of each royal jelly sample;

[0007] Step S2: Analyze the supernatant of each royal jelly sample to obtain the identified peptide segments, molecular weight data, and amino acid sequence data of the peptide segments in each royal jelly sample. The analysis steps include peptide segment separation, mass spectrometry analysis, data analysis, spectrum generation, and peptide segment identification;

[0008] Step S3: Perform peptide reverse targeting. Obtain the royal jelly proteome information from the UniPort database. The proteome information contains protein sequences. Match each identified peptide segment with the royal jelly protein sequences one by one to obtain the sequence sites where each peptide segment is located. Obtain the protein regions where the proteins are located according to the sequence sites where the peptide segments are located. Calculate the rate of increase in peptide segments RPIR generated by degradation of each protein region, and obtain the relative degradation rate PRDR of each protein by weighting the rate of increase in each peptide segment;

[0009] Step S4: Fit the relative degradation rate of each protein with the storage time of the royal jelly sample to obtain the relative degradation rate equation of each protein at different storage temperatures. Use R 2 as an evaluation parameter to construct a labeled protein degradation database, sort the R 2 values of each protein, and use the proteins with higher rankings as candidate marker proteins;

[0010] Step S5: Obtain the binding antibody information of the candidate marker proteins. Use a structure prediction model to construct the three-dimensional structure of the candidate marker proteins. Use a structure diffusion model to design binding proteins based on the binding antibody information. Use the Rosetta energy function to calculate the binding energy between each binding protein and the candidate marker proteins to construct a binding antibody library;

[0011] Step S6: Construct a matching system for detecting the freshness of royal jelly according to the relative degradation rate equation and the binding antibody information.

[0012] As a preferred technical solution of the present invention, the supernatant analysis includes:

[0013] Peptide segment separation: Separate the peptide segments by high-performance liquid chromatography according to the hydrophobicity of different peptide segments;

[0014] Mass spectrometry analysis: Perform mass spectrometry analysis on the separated peptide segments to determine the mass-to-charge ratio of each peptide segment and generate a mass spectrometry map of the peptide segments;

[0015] Data analysis: Perform data analysis on the mass spectrometry analysis results, compare them with a known protein database to determine the amino acid sequence of the peptide segment;

[0016] Spectrum generation: Generate a peptide spectrum showing the mass distribution of the peptide segment based on the mass spectrometry analysis results;

[0017] Peptide segment identification: Identify the peptide segment according to the average local confidence level of the peptide segment. When the average confidence level is higher than 85%, the peptide segment identification is successful.

[0018] As a preferred technical solution of the present invention, the acquisition of the average confidence level includes:

[0019] Calculate the LDF score by calculating the similarity between the peptide segment to be identified and the peptides in the protein database to evaluate the matching quality of the peptide spectrum, convert the LDF score to a P value. The P value represents the probability that the score obtained by a wrong match is greater than the set threshold. The smaller the P value, the higher the matching quality of the peptide spectrum; convert the P value to -10*log10(P), take the score greater than the set threshold as the credible confidence level, and calculate the proportion of the credible confidence level as the average confidence level.

[0020] As a preferred technical solution of the present invention, the acquisition of the relative protein degradation rate PRDR includes:

[0021] The calculation formula for the rate of increase in peptide segments RPIR generated by degradation in the region where each protein is located is: where Pet represents the content of the peptide, Pet p represents the content of the peptide in the royal jelly samples with different storage times under different conditions, Pet u represents the content of the peptide in the royal jelly sample without any treatment;

[0022] The calculation formula for the relative degradation rate PRDR of each protein is: where n represents the total number of peptide segments in the target protein; PRIR K is the rate of increase in peptide segments generated by degradation in the kth region, and also represents the degree of degradation of this region of the protein.

[0023] As a preferred technical solution of the present invention, the acquisition of the relative degradation rate equation includes:

[0024] Select a mathematical model according to the natural degradation characteristics of royal jelly proteins to describe the degradation process of proteins over time. The formula of the exponential model is: R(t) = Ae ―at ; where R(t) represents the relative degradation rate at time t, A is the initial degradation rate, and a is the degradation rate constant;

[0025] Using the method of mathematical fitting, the relative degradation rate data of each protein is fitted with an exponential model to obtain the model parameters. After transforming the model, the parameters are solved to obtain the relative degradation rate equation.

[0026] Using R 2 As an evaluation parameter, the fitting effect of the model is tested. Select the mathematical model with the R 2 value closest to 1 to obtain the relative degradation rate equation; construct a labeled protein degradation database, assign labels to each protein, and record its degradation equation, parameters, and evaluation indicators in the database.

[0027] As a preferred technical solution of the present invention, constructing the three-dimensional structure of the candidate marker protein includes:

[0028] Obtain a structure prediction model. The initial structure prediction model is constructed based on the ESMFold model. Obtain the structure information of all proteins in royal jelly and form a prediction data set. Use the prediction data set to train the initial structure prediction model, evaluate the parameters and adjust the hyperparameters to obtain an optimized structure prediction model; add an evaluation layer to the optimized structure prediction model. The evaluation layer matches the output result of the optimized structure prediction model with the prediction data set, and adds a weight label to the output result according to the matching result. The output of the optimized structure prediction model with the evaluation layer added is used as the final structure prediction model.

[0029] Send the amino acid sequence of the candidate marker protein into the structure prediction model to obtain the three-dimensional structure of the candidate marker protein.

[0030] As a preferred technical solution of the present invention, the design of the binding protein includes:

[0031] Obtain the binding site information and target antigen information in the three-dimensional structure of the marker protein. Use the structure diffusion model to obtain the initial antibody structure that conforms to the target antigen information from random noise, and evolve the initial antibody structure to generate an antibody structure that matches the binding site information in the three-dimensional structure of the candidate marker protein.

[0032] The evolution is guided based on the binding site information of the target protein, so that the generated antibody is specific. By fine-tuning the binding interface between the antibody and the target protein, the binding site of the antibody is highly matched with the geometric shape and chemical properties of the candidate marker protein.

[0033] Obtain the predicted binding energy of all generated antibody structures, and select the three antibody structures with the lowest predicted binding energy as the binding proteins.

[0034] As a preferred technical solution of the present invention, the acquisition of the binding energy includes:

[0035] For the antibody structure generated by the structure prediction model, match it with the antibody structures in the binding energy prediction database to obtain the preset binding energy of the corresponding antibody structure in the binding energy prediction database;

[0036] For the binding protein, calculating the binding energy of each binding protein and the candidate marker protein using the Rosetta energy function includes: obtaining the three-dimensional structures of the binding protein and the marker protein and performing structure optimization; constructing the binding protein and the marker protein into a complex; calculating the binding energy of the complex by separating the two parts of the complex and calculating the individual energies of the binding protein and the marker protein respectively; calculating the total energy of the complex; the binding energy is the total energy of the complex minus the energy of the binding protein and the energy of the marker protein, optimizing the structure of the complex, and screening the structure of the complex with the lowest binding energy.

[0037] As a preferred technical solution of the present invention, the royal jelly freshness detection design matching system includes:

[0038] An input module for inputting a target peptide segment or a target protein as a retrieval condition;

[0039] An acquisition module for obtaining its relative degradation rate equation, associated R 2 value, and binding antibody library based on the input target peptide segment or target protein;

[0040] A recommendation module for screening out the antibody with the optimal binding energy based on the binding energy from the antibodies in the obtained binding antibody library and providing its binding energy value and structural information.

[0041] The present invention has the following advantages:

[0042] 1. By the method of peptide segment reverse target searching and using mass spectrometry analysis and database matching technology, the present invention can quickly identify the proteins and their degradation products related to the freshness of royal jelly, greatly shortening the time for finding relevant protein markers and significantly improving the efficiency of freshness detection; by accurately matching protein sequences and identifying degradation products, it ensures that the screened markers are closely related to the degradation process of royal jelly, thereby improving the scientificity and accuracy of freshness detection; by utilizing the proteome information of royal jelly, unnecessary experimental repetitions are avoided, and the efficiency and cost-effectiveness of data analysis are improved.

[0043] 2. The present invention develops a complete freshness detection design matching system for royal jelly by establishing a dataset of relative protein degradation rates and combining it with a structural diffusion model. This system can not only calculate the relative degradation rates of various proteins in royal jelly but also design antibodies that highly match the target protein using a structure prediction and energy calculation model, thereby achieving precise freshness detection. Based on the relative degradation rate dataset, the system can comprehensively cover the degradation laws of royal jelly under different storage conditions, ensuring the comprehensiveness of the freshness detection results.

[0044] 3. The present invention simulates the natural structural evolution process of proteins through a structural diffusion model, generates a three-dimensional protein structure with biological significance from initial random noise, and accurately predicts the three-dimensional structure of the target protein, especially for the complex protein degradation products in royal jelly, thereby improving the accuracy of structure prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a flowchart of a method for detecting and designing a match for the freshness of royal jelly based on a structural diffusion model according to the present invention;

[0046] Figure 2 is a degradation diagram of the top 2 marker proteins associated with the freshness of royal jelly visualized based on the reverse target searching technique;

[0047] Figure 3 is a degradation diagram of the 3rd and 4th marker proteins associated with the freshness of royal jelly visualized based on the reverse target searching technique;

[0048] Figure 4 is a degradation diagram of the 5th marker protein associated with the freshness of royal jelly visualized based on the reverse target searching technique;

[0049] Figure 5 is a fitting diagram of the top 2 marker proteins highly associated with the freshness of royal jelly obtained based on the reverse target searching technique;

[0050] Figure 6 is a fitting diagram of the 3rd and 4th marker proteins highly associated with the freshness of royal jelly obtained based on the reverse target searching technique;

[0051] Figure 7 is a fitting diagram of the 5th marker protein highly associated with the freshness of royal jelly obtained based on the reverse target searching technique;

[0052] Figure 8 is a structural diagram of an antibody that binds to a marker protein highly associated with the freshness of royal jelly designed based on the structural diffusion model. DETAILED DESCRIPTION OF THE INVENTION

[0053] To enable those skilled in the art to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention.

[0054] Embodiment, a method for designing and matching the freshness detection of royal jelly based on a structural diffusion model, see Figures 1 to 8 as shown, including the following steps:

[0055] Step S1: Collect royal jelly samples under different storage conditions, including royal jelly samples of the same fresh royal jelly and its products stored at -20°C, 4°C, and 25°C for 0, 1, 3, 6, 12, and 24 months respectively. After mixing each sample with pure water according to a set ratio, perform extraction, centrifugation, and filtration treatments to obtain the supernatant of each royal jelly sample;

[0056] Filter and purify the royal jelly supernatant through a special sieve to remove impurities and insoluble components therein;

[0057] Step S2: Analyze the supernatant of each royal jelly sample to obtain the identified peptide segments, molecular weight data, and amino acid sequence data of the peptide segments in each royal jelly sample. The analysis steps include peptide segment separation, mass spectrometry analysis, data analysis, spectrum generation, and peptide segment identification;

[0058] The supernatant analysis includes:

[0059] Peptide segment separation: Separate the peptide segments by high-performance liquid chromatography (HPIC) according to the hydrophobicity of different peptide segments;

[0060] The separation of peptide segments by high-performance liquid chromatography (HPLC) includes:

[0061] Select an appropriate chromatographic column: Select a suitable chromatographic column according to the properties of the peptide segments, a reverse-phase C18 chromatographic column, which is suitable for the separation of most hydrophobic peptide segments; Prepare the mobile phase: Configure a mobile phase containing an appropriate proportion of organic solvent and water, and a mixed solution of acetonitrile-water or methanol-water can be used, where the proportion of acetonitrile can gradually increase from 5% to 70%; Set the gradient elution program: Set a time gradient to gradually increase the proportion of the organic solvent during the elution process and separate according to the hydrophobicity difference of the peptide segments; Optimize the flow rate and temperature: Select a suitable flow rate (usually 1-2 ml / min) and an appropriate temperature to obtain the best resolution; Collect the fractions: Collect the eluted fractions at regular intervals and mix the fractions at different time periods for subsequent mass spectrometry analysis;

[0062] Mass spectrometry analysis: Perform mass spectrometry analysis on the separated peptide segments to determine the mass-to-charge ratio of each peptide segment and generate a mass spectrometry map of the peptide segments. Since different peptide segments have different amino acid compositions and sequences, their m / z values in the mass spectrometry will also be different. By comparing the m / z values obtained in the experiment with the theoretical m / z values of known proteins in the database, identify the peptide segments in the sample and the proteins from which they are derived. It can be carried out using electrospray ionization (ESI) and matrix-assisted laser desorption / ionization (MALDI).

[0063] Data analysis: Perform data analysis on the results of mass spectrometry analysis, compare it with a known protein database to determine the amino acid sequence of the peptide segments. In the implementation process, use Peak to generate an antibody structure that matches the binding site information in the three-dimensional structure of the candidate marker protein, and use Studio 8.0 to generate an antibody structure that matches the binding site information in the three-dimensional structure of the candidate marker protein to analyze the molecular weight and amino acid sequence of the obtained peptide segment data.

[0064] Spectrum generation: According to the results of mass spectrometry analysis, generate a peptide spectrum map showing the mass distribution of the peptide segments, which is used to infer the primary structure of the protein.

[0065] Peptide segment identification: Perform peptide segment identification according to the average local confidence of the peptide segments. When the average confidence is higher than 85%, the peptide segment identification is successful.

[0066] The acquisition of the average confidence includes:

[0067] Calculate the LDF score by calculating the similarity between the peptide segment to be identified and the peptides in the protein database to evaluate the matching quality of the peptide spectrum map. Convert the LDF score to a P value. The P value represents the probability that the score obtained from a false match is greater than the set threshold. The smaller the P value, the higher the matching quality of the peptide spectrum map. Convert the P value to -10*log10(P), take the score greater than the set threshold as the credible confidence, and calculate the proportion of the credible confidence as the average confidence.

[0068] Step S3: Perform peptide reverse targeting. Obtain the royal jelly proteome information from the UniPort database. The proteome information contains protein sequences. Match each identified peptide segment with the royal jelly protein sequences one by one to obtain the sequence sites where each peptide segment is located. Obtain the protein regions where the proteins are located according to the sequence sites where the peptide segments are located. Calculate the rate of increase in peptide segments generated by degradation in each protein region, RPIR. Obtain the relative degradation rate of each protein, PRDR, by weighting the rate of increase in peptide segments of each type.

[0069] The acquisition of the relative degradation rate of protein, PRDR, includes:

[0070] The calculation formula for the rate of increase in peptide segments generated by degradation in each protein region, RPIR, is: where Pet represents the peptide content, Pet p represents the peptide content of royal jelly samples with different storage times under different conditions, Pet u represents the peptide content of royal jelly samples without any treatment;

[0071] For each protein, the relative degradation rate PRDR is calculated by the formula: where n represents the total number of peptide segments in the target protein; PRIR K is the increase rate of peptide segments generated by degradation in the k-th region, and also represents the degradation degree of this region of the protein.

[0072] Step S4: Fit the relative degradation rate of each protein with the storage time of the royal jelly sample to obtain the relative degradation rate equation of each protein at different storage temperatures. Using R 2 as the evaluation parameter, construct a labeled protein degradation database, sort the R 2 values of each protein, and take the proteins with higher rankings as candidate marker proteins; The degradation diagrams and fitting diagrams of the top five candidate marker proteins stored under different conditions are shown in Figure 2 and Figure 3 as shown;

[0073] The acquisition of the relative degradation rate equation includes:

[0074] According to the natural degradation characteristics of royal jelly proteins, select a mathematical model to describe the degradation process of proteins over time. The formula of the exponential model is: R(t) = Ae ―at ; where R(t) represents the relative degradation rate at time t, A is the initial degradation rate, and a is the degradation rate constant;

[0075] Use the method of mathematical fitting to fit the relative degradation rate data of each protein with the exponential model, obtain the model parameters, solve the parameters by transforming the model, and obtain the relative degradation rate equation;

[0076] For example, perform a linear regression fit on the logarithmically transformed data of the exponential model: lnR = β0 + β1t; where β0 = lnA, β1 = -a; Obtain the values of β0 and β1 through linear regression analysis, and thus calculate the values of the parameters A and a of the exponential model;

[0077] Use R 2 as the evaluation parameter, test the fitting effect of the model, and select the mathematical model with the R 2 value closest to 1 to obtain the relative degradation rate equation; Construct a labeled protein degradation database, assign labels to each protein, and record its degradation equation, parameters, and evaluation indicators in the database.

[0078] Step S5: Obtain the binding antibody information of the candidate biomarker protein, use the structure prediction model to construct the three-dimensional structure of the candidate biomarker protein, and the three-dimensional structure of the candidate biomarker protein is as Figure 4 shown; use the structure diffusion model to design the binding protein based on the binding antibody information, calculate the binding energy of each binding protein to the candidate biomarker protein using the Rosetta energy function, and construct the binding antibody library; the binding energy of a candidate biomarker protein to the corresponding binding protein in the binding antibody library is shown in Table 1;

[0079] Table 1 Binding energy table of target proteins highly related to the freshness of royal jelly and binding proteins

[0080]

[0081] Constructing the three-dimensional structure of the candidate biomarker protein includes:

[0082] Obtain the structure prediction model. The initial structure prediction model is constructed based on the ESMFold model. Obtain the structure information of all proteins in royal jelly and form a prediction structure dataset;

[0083] Among them, the structure prediction is based on the FASTA format sequence of the protein, which is uploaded to ESMfold for structure prediction, and one or more PDB files are output, and the files contain the three-dimensional structure of the predicted protein;

[0084] The design of the binding protein includes:

[0085] Obtain the binding site information and target antigen information in the three-dimensional structure of the biomarker protein, use the structure diffusion model to obtain the initial antibody structure that conforms to the target antigen information from random noise, and use ProteinMPNN to optimize and design the initial antibody structure, and at the same time generate an antibody structure complex that matches the three-dimensional structure of the biomarker protein based on ESMfold;

[0086] Obtain the predicted binding energies of all generated antibody structures, and select the three antibody structures with the lowest predicted binding energies as candidate binding antibody proteins.

[0087] The specific steps of the implementation process include:

[0088] Generation of the initial structure: Based on the RFdiffusion diffusion model which is usually used to generate the structure of the binding protein from scratch. First, the diffusion model starts from a random noise structure, and then gradually evolves it into a three-dimensional protein structure that conforms to physical and chemical laws. This model generates an antibody structure that matches the binding site information of the target protein; through the reverse diffusion process, a reasonable structure is gradually constructed from a random distribution, and finally an antibody structure that matches the target antigen is generated;

[0089] Evaluation of binding energy: The generated antibody structures are further evaluated by calculating the binding energy, which is a key parameter for measuring the binding stability between an antibody and a target protein. The structural diffusion model can screen multiple generated antibody structures and select those with the lowest binding energy for subsequent verification; predict which antibody structures are most suitable for binding to the labeled protein, thus providing a basis for subsequent antibody engineering;

[0090] Structure verification and iteration: Computational tools (such as Rosetta, etc.) are used to further verify and optimize the generated antibody structures. If the generated structures fail to achieve the expected binding effect, the diffusion model can be used again to regenerate improved antibody structures by adjusting the guiding signals or input data;

[0091] The acquisition of binding energy includes:

[0092] For the antibody structures generated by the structure prediction model, they are matched with the antibody structures in the binding energy prediction database to obtain the preset binding energy of the corresponding antibody structures in the binding energy prediction database;

[0093] For the binding protein, calculating the binding energy of each binding protein and the candidate labeled protein using the Rosetta energy function includes: obtaining the three-dimensional structures of the binding protein and the labeled protein and performing structure optimization; constructing the binding protein and the labeled protein into a complex; calculating the binding energy of the complex by separating the two parts of the complex and calculating the individual energies of the binding protein and the labeled protein respectively; calculating the total energy of the complex; the binding energy is the total energy of the complex minus the energies of the binding protein and the labeled protein, and optimizing the structure of the complex to screen the structure of the complex with the lowest binding energy.

[0094] Step S6: Construct a freshness detection design matching system for royal jelly according to the relative degradation rate equation and the binding antibody information.

[0095] The freshness detection design matching system for royal jelly includes:

[0096] An input module, which is used to input the target peptide segment or target protein as a retrieval condition;

[0097] An acquisition module, which is used to obtain the relative degradation rate equation, the associated R 2 value, and the binding antibody library based on the input target peptide segment or target protein;

[0098] A recommendation module, which is used to screen out the antibody with the optimal binding energy based on the binding energy from the antibodies in the obtained binding antibody library and provide its binding energy value and structural information.

[0099] It should be understood that those of ordinary skill in the art can make improvements or modifications based on the above description, and all such improvements and modifications shall fall within the protection scope of the appended claims of the present invention. The parts not described in detail in this specification belong to the prior art well-known to those of ordinary skill in the art.

Claims

1. A royal jelly freshness detection design and matching method based on a structural diffusion model, characterized in that: The following steps are involved: Step S1: collecting royal jelly samples under different storage conditions, including royal jelly samples of the same type of fresh royal jelly and its products stored at -20°C, 4°C and 25°C for 0, 1, 3, 6, 12 and 24 months, respectively, mixing each sample with pure water according to a set ratio, extracting, centrifuging and filtering, and obtaining the supernatant of each royal jelly sample; Step S2: analyzing the supernatant of each royal jelly sample to obtain the identified peptides in each royal jelly sample, the molecular weight data and the amino acid sequence data of the peptides, and the analysis steps include peptide separation, mass spectrometry analysis, data analysis, spectrum generation and peptide identification; Step S3: Perform peptide reverse targeting, obtain royal jelly proteome information from the UniPort database, the proteome information contains protein sequences, match all identified peptides one by one with the royal jelly protein sequence, obtain the sequence site of each peptide, obtain the protein region according to the sequence site of the peptide, calculate the increase rate RPIR of peptides produced by degradation of each protein region, and obtain the relative degradation rate PRDR of each protein by weighting the increase rate of each peptide; Step S4: Fit the relative degradation rate of each protein to the storage time of the royal jelly sample to obtain the relative degradation rate equation of each protein at different storage temperatures, expressed as R 2 As an evaluation parameter, a protein degradation database was constructed and the R 2 The values ​​are sorted, and the proteins with the highest ranking are taken as candidate marker proteins; Step S5: obtaining the binding antibody information of the candidate marker protein, constructing the three-dimensional structure of the candidate marker protein using the structure prediction model, designing the binding protein based on the binding antibody information using the structure diffusion model, calculating the binding energy of each binding protein with the candidate marker protein using the Rosetta energy function, and constructing a binding antibody library; Step S6: Based on the relative degradation rate equation and combined with antibody information, a royal jelly freshness detection design and matching system is constructed.

2. A royal jelly freshness detection design and matching method based on a structural diffusion model according to claim 1, characterized in that: Supernatant analysis includes: Peptide separation: Peptides are separated by high performance liquid chromatography according to the hydrophobicity of different peptides; Mass spectrometry analysis: Perform mass spectrometry analysis on the separated peptides to determine the mass-to-charge ratio of each peptide and generate a mass spectrum of the peptides; Data analysis: Perform data analysis on the mass spectrometry results and compare them with the known protein database to determine the amino acid sequence of the peptide; Spectrum generation: Generate a peptide spectrum showing the mass distribution of peptides based on the results of mass spectrometry analysis; Peptide identification: Peptide identification is performed based on the average local confidence of the peptides. When the average confidence is higher than 85%, the peptide identification is successful.

3. A royal jelly freshness detection design and matching method based on a structural diffusion model according to claim 2, characterized in that: The acquisition of average confidence includes: The LDF score is calculated by comparing the similarity between the peptide to be identified and the peptides in the protein database to evaluate the matching quality of the peptide spectrum. The LDF score is converted into a P value, which indicates the probability that a score obtained by a false match is greater than the set threshold. The smaller the P value, the higher the matching quality of the peptide spectrum. The P value is converted to -10*log10(P), and the score greater than the set threshold is used as the credible confidence. The proportion of credible confidence is calculated as the average confidence.

4. A royal jelly freshness detection design and matching method based on a structural diffusion model according to claim 1, characterized in that: The acquisition of protein relative degradation rate PRDR includes: The calculation formula for the increase rate RPIR of peptides produced by degradation in each protein region is: Where Pet represents the content of peptide, Pet p Represents the peptide content of royal jelly samples stored for different periods of time under different conditions, Pet u It represents the peptide content of royal jelly sample without any treatment; The relative degradation rate PRDR of each protein is calculated as follows: Where n represents the total number of peptides in the target protein; PRIR K The increase rate of peptides generated by degradation of the kth region also indicates the degree of degradation of this region of the protein.

5. A royal jelly freshness detection design and matching method based on a structural diffusion model according to claim 1, characterized in that: The relative degradation rate equation is obtained by: According to the natural degradation characteristics of royal jelly protein, a mathematical model is selected to describe the degradation process of protein over time, where the formula of the exponential model is expressed as: R(t) = Ae ―at ; Where R(t) represents the relative degradation rate at time t, A is the initial degradation rate, and a is the degradation rate constant; Using a mathematical fitting method, the relative degradation rate data of each protein is fitted with an exponential model to obtain model parameters, and the parameters are obtained by transforming the model and solving it to obtain a relative degradation rate equation; Using R 2 As the evaluation parameter, we can test the model’s fitting effect and select R 2 The mathematical model with the value closest to 1 obtains the relative degradation rate equation; a labeled protein degradation database is constructed, a label is assigned to each protein, and its degradation equation, parameters and evaluation indicators are recorded in the database.

6. A royal jelly freshness detection design and matching method based on a structural diffusion model according to claim 1, characterized in that: Constructing the three-dimensional structure of candidate marker proteins includes: Obtain a structure prediction model. The initial structure prediction model is constructed based on the ESMFold model. The structural information of all royal jelly proteins is obtained and a prediction data set is formed. The prediction data set is used to train the initial structure prediction model, evaluate parameters, and adjust hyperparameters to obtain an optimized structure prediction model. An evaluation layer is added to the optimized structure prediction model. The evaluation layer matches the output result of the optimized structure prediction model with the prediction data set, adds a weight tag to the output result according to the matching result, and outputs the optimized structure prediction model with the added evaluation layer as the final structure prediction model. The amino acid sequence of the candidate marker protein is sent to the structure prediction model to obtain the three-dimensional structure of the candidate marker protein.

7. A royal jelly freshness detection design and matching method based on a structural diffusion model according to claim 1, characterized in that: The design of binding proteins includes: Obtaining the binding site information and target antigen information in the three-dimensional structure of the marker protein, using the structural diffusion model to obtain the initial antibody structure that matches the target antigen information from random noise, and evolving the initial antibody structure to generate an antibody structure that matches the binding site information in the three-dimensional structure of the candidate marker protein; Evolution is guided by the binding site information of the target protein, making the generated antibodies specific. By fine-tuning the binding interface between the antibody and the target protein, the antibody binding site is highly matched with the geometry and chemical properties of the candidate marker protein. The predicted binding energies of all generated antibody structures were obtained, and the three antibody structures with the lowest predicted binding energies were selected as binding proteins.

8. A royal jelly freshness detection design and matching method based on a structural diffusion model according to claim 7, characterized in that: The acquisition of binding energy includes: For the antibody structure generated by the structure prediction model, match it with the antibody structure in the binding energy prediction database to obtain the binding energy preset in the binding energy prediction database for the corresponding antibody structure; For binding proteins, the Rosetta energy function is used to calculate the binding energy of each binding protein with the candidate marker protein, including: obtaining the three-dimensional structure of the binding protein and the marker protein, and optimizing the structure; constructing the binding protein and the marker protein into a complex; calculating the binding energy of the complex, by separating the two parts of the complex, and calculating the individual energies of the binding protein and the marker protein respectively; calculating the total energy of the complex; the binding energy is the total energy of the complex minus the energy of the binding protein and the energy of the marker protein, optimizing the structure of the complex, and screening the structure of the complex with the lowest binding energy.

9. A royal jelly freshness detection design and matching method based on a structural diffusion model according to claim 1, characterized in that: Royal jelly freshness detection design and matching system includes: Input module, used for input module refers to inputting target peptide or target protein as search condition; The acquisition module is used to obtain the relative degradation rate equation and the associated R based on the input target peptide or target protein. 2 Values ​​and binding antibody libraries; The recommendation module is used to screen out antibodies with the best binding energy based on the binding energy according to the antibodies in the obtained binding antibody library, and provide their binding energy values ​​and structural information.

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