A method for qualitative prediction of the aroma compound enhancement effect

Abstract

The application relates to a qualitative prediction method for the flavor-enhancing effect of aroma compounds, which comprises the following steps: constructing an aroma compound database; obtaining the 3D structure of umami receptor protein T1R1 / T1R3 from a protein molecule database to obtain a receptor protein dataset, obtaining the structure of aroma compounds and monosodium glutamate from a ZINC database as a ligand molecule dataset, then performing molecular docking, and saving the binding energy data record; taking the binding energy data corresponding to each aroma compound and the molecular descriptor as characteristic variables, and taking whether the aroma compound has the flavor-enhancing effect as a target variable to construct a logistic regression model; inputting aroma compound information into the constructed logistic regression model to obtain a qualitative output result of the prediction of the flavor-enhancing effect of the aroma compound. Compared with the prior art, the application predicts whether the aroma compound has the flavor-enhancing effect by constructing a logistic regression model by using the binding energy and the molecular descriptor and other properties, the method is simple and fast, the result is intuitive and reliable, and the method is widely applicable.

Description

Technical Field

[0001] This invention relates to the field of food flavor chemistry, and in particular to a qualitative prediction method for the flavor-enhancing effect of aroma compounds. Background Technology

[0002] In recent years, salt-related health concerns have led to extensive research into low-sodium foods. International authorities such as the World Health Organization (WHO) advocate for reducing sodium in food. Lowering sodium content in food without compromising consumer acceptability has become a goal of the food industry, leading to the hypothesis that aroma can compensate for reduced salt content. Taste enhancers, which are generally tasteless but enhance basic flavors, are gaining increasing attention in the food industry due to their unique properties. For example, non-calorie sweeteners can replace some sugar in foods and beverages, making them suitable for people with health problems such as obesity and diabetes. Salt enhancers, by replacing some sodium salts, can reduce the risk of cardiovascular disease while maintaining the food's palatability. Therefore, healthy, natural taste enhancers have significant health and commercial value.

[0003] There is an urgent need to develop a qualitative prediction method for the flavor-enhancing effect of aroma compounds in the existing technology, so as to quickly predict whether a certain aroma compound has a flavor-enhancing effect on monosodium glutamate (MSG) and provide effective guidance for the development of healthy and natural flavor enhancers. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a qualitative prediction method for the flavor-enhancing effect of aroma compounds, with a prediction rate of up to 83.33%, which can quickly predict whether a certain aroma compound has a flavor-enhancing effect on monosodium glutamate.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] This invention provides a qualitative prediction method for the flavor-enhancing effect of aroma compounds, comprising the following steps:

[0007] S1: Construct an aroma compound database;

[0008] S2: Obtain the 3D structure of umami receptor protein T1R1 / T1R3 from the protein molecular database to obtain the receptor protein dataset. Obtain the structure of aroma compound and monosodium glutamate from the ZINC database as the ligand molecule dataset. Then, use PyRx software to perform molecular docking on the elements in the receptor protein dataset and ligand molecule dataset, and record and save the binding energy data.

[0009] S3: Using the binding energy data and molecular descriptors of each aroma compound as feature variables, and whether it has an umami enhancement effect as the target variable, a logistic regression model is constructed.

[0010] S4: Input the aroma compound information to be predicted into the logistic regression model to obtain the qualitative output result of the prediction of the aroma compound's flavor-enhancing effect.

[0011] Furthermore, in S1, the aroma compound database includes a variety of aroma compounds, the molecular formulas of the aroma compounds, the CAS numbers of the aroma compounds, and the common names and scientific names of the aroma compounds.

[0012] Furthermore, in S1, the aroma compound database includes the following compounds:

[0013] Phenylacetaldehyde, 2,5-dimethylpyrazine, dimethyl trisulfide, 2-methyl-3-tetrahydrofuranthiol, HDMF, HEMF, fenugreek lactone, 3-methylthiopropanol, 3-methylthiopropanol, furfuryl mercaptan, maltol, citric acid, ethyl fenugreek lactone, and 1-octen-3-ol.

[0014] Furthermore, in S2, the process of obtaining the receptor protein dataset includes:

[0015] The 3D structures of umami receptor proteins T1R1 / T1R3 obtained from the protein molecular database were imported into the AutodockTool software. The umami receptor proteins T1R1 / T1R3 were preprocessed, and then the docking sites were predicted using the AutodockTool software. Based on the feasibility of docking, it was determined whether to retain water molecules or metal ions near the docking pocket. The processed umami receptor protein T1R1 / T1R3 data were obtained and saved to a PDBQT file.

[0016] Furthermore, in S2, the pretreatment operation includes dehydrating, hydrogenating, and charging the umami receptor proteins T1R1 / T1R3.

[0017] Furthermore, in S2, the molecular docking process using PyRx software includes:

[0018] Import the energy-minimized ligand molecule file and the processed umami receptor protein T1R1 / T1R3 data into PyRx simultaneously, set up the docking pocket, and run the AutoDock Vina module for docking.

[0019] Furthermore, in S2, the energy-minimized ligand molecule file is obtained by performing energy minimization processing on the ligand molecule dataset through the AutoDock Vina module.

[0020] Furthermore, in S2, the binding energy of all permutations and combinations of the receptor protein, aroma compound, and monosodium glutamate is obtained as the binding energy dataset.

[0021] Furthermore, in S3, the process of obtaining the molecular descriptor is as follows:

[0022] The SDF files of aroma compounds were imported into the ChemDes server to collect molecular descriptor information of aroma compounds. The Simpleimputer function in Python was used to fill in the missing values ​​in the acquired data.

[0023] The molecular descriptor information includes:

[0024] Molecular weight, LogP, refractive index, molar refractive index, molar volume, isothermal specific volume, surface tension, polarizability, XlogP, number of hydrogen bond donors, number of hydrogen bond acceptors, number of rotatable bonds, number of tautomers, topological polar surface area, number of heavy atoms, complexity, number of undetermined stereocenters, Chi0n, Chi0v, Chi1n, Chi1v, Chi2n, Chi2v, Chi3n, Chi3v, Chi4n, Chi4v, HallKierAlpha, HeavyAtomCount, Kappa1, Kappa2, Kappa3, number of aliphatic rings, number of aromatic heterocycles, number of aromatic rings, NumHeteroatoms, number of electrons, TPSA.

[0025] Furthermore, in S4, specifically, the following is included: using the binding energy dataset and molecular descriptors as feature variables, and whether or not it has an umami-enhancing effect as the target variable, a logistic regression model is constructed to predict whether aroma substances have the ability to enhance the perception of MSG umami.

[0026] Compared with the prior art, the present invention has the following technical advantages:

[0027] This invention relates to a research method for predicting whether aroma substances have an umami-enhancing effect by constructing a logistic regression model based on properties such as binding energy and molecular descriptors. This method is simple, fast, and produces intuitive and reliable results with wide applicability. Attached Figure Description

[0028] Figure 1 This is a flowchart for predicting aroma substances used to enhance umami perception in this invention. Detailed Implementation

[0029] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Component models, material names, connection structures, control methods, algorithms, and other features not explicitly described in this technical solution are considered common technical features disclosed in the prior art.

[0030] This embodiment includes the following steps:

[0031] 1) Establish an aroma compound database containing 14 aroma compounds;

[0032] 2) Obtain the 3D structure of umami receptors T1R1 / T1R3 from the protein molecular database (http: / / www.rcsb.org / ), and obtain the structure of aroma substances and flavor substance MSG from the ZINC database. Use them as receptor proteins and ligand molecules respectively for molecular docking through the AutoDock Vina module in PyRx. Record and save the binding energy data.

[0033] 3) Obtain 39 molecular descriptors for aroma compounds through the ChemDes server;

[0034] 4) The logistic regression algorithm from the Python package sklearn was used for modeling, with the binding energy of each aroma substance and 38 molecular descriptors as feature variables. The logistic regression model was constructed to predict the aroma substances that can enhance the umami effect of MSG.

[0035] To further illustrate the research methods for predicting aroma compounds used to enhance umami perception, a more detailed explanation is provided below with reference to examples.

[0036] See Figure 1 This embodiment mainly uses a logistic regression model to predict aroma compounds used to enhance umami perception. The specific steps are as follows:

[0037] In this embodiment, 14 reported aroma compounds with sweetness-enhancing effects were used, namely phenylacetaldehyde, 2,5-methylpyrazine, dimethyl trisulfide, 2-methyl-3-tetrahydrofuranthiol, HDMF, HEMF, fenugreek lactone, 3-methylthiopropanol, 3-methylthiopropanol, furfuryl mercaptan, maltol, citric acid, ethyl fenugreek lactone, and 1-octen-3-ol.

[0038] In this embodiment, the molecular docking process is as follows: The PDB file is imported into the AutodockTool software. Pre-processing operations are performed on the umami receptor proteins T1R1 / T1R3, including dehydration, hydrogenation, and charge addition. Then, the docking site is predicted using a program, and the retention of water molecules or metal ions near the docking pocket is considered. The results are saved to a PDBQT file for later use. Fourteen small molecule ligand files for aroma compounds are prepared. These 14 PDB files are imported into PyRx, and energy minimization processing is performed on the small molecules with default parameters. The files are saved in PDBQT format. Utilizing PyRx's ability to batch process molecular docking between small and large molecules, the 14 energy-minimized ligand molecule files are imported into PyRx. The processed umami receptor protein file is also imported into the software. Docking pockets are set, and the Vina module is run for docking. After the process is complete, the binding energies of the 14 aroma compounds to the umami receptor proteins are recorded.

[0039] In this embodiment, the process of obtaining molecular descriptors for aroma compounds is as follows: The SDF file of the aroma compounds is imported into the ChemDes server, and information on 38 molecular descriptors of aroma compounds is collected (molecular weight, LogP, refractive index, molar refractive index, molar volume, isotonic specific volume, surface tension, polarizability, XlogP, number of hydrogen bond donors, number of hydrogen bond acceptors, number of rotatable bonds, number of tautomers, topological polar surface area, number of heavy atoms, complexity, number of undetermined stereocenters, Chi0n, Chi0v, Chi1n, Chi1v, Chi2n, Chi2v, Chi3n, Chi3v, Chi4n, Chi4v, HallKierAlpha, HeavyAtomCount, Kappa1, Kappa2, Kappa3, number of aliphatic rings, number of aromatic heterocycles, number of aromatic rings, NumHeteroatoms, number of electrons, TPSA). The Simpleimputer function in Python is used to fill in the missing values ​​in the acquired data, and the mean is used for filling in all cases.

[0040] In this embodiment, the predictive model is established as follows: using the binding energy of each aroma substance and 38 molecular descriptors as feature variables, and whether it has an umami-enhancing effect as the target variable, a logistic regression model is constructed to predict whether the aroma substances have the ability to enhance the perception of MSG umami. The accuracy of the logistic regression model constructed using properties such as binding energy and molecular descriptors to predict whether aroma substances have an umami-enhancing effect can reach 83.33%, which can quickly and effectively predict whether aroma substances have an umami-enhancing effect.

[0041] 1) Obtain the structures of phenylacetaldehyde, 2,5-methylpyrazine, dimethyl trisulfide, 2-methyl-3-tetrahydrofuranthiol, HDMF, HEMF, fenugreek lactone, 3-methylthiopropanol, 3-methylthiopropanol, furfuryl mercaptan, maltol, citric acid, ethyl fenugreek lactone, 1-octen-3-ol, and umami receptor proteins T1R1 / T1R3 from protein molecular databases and Zinc database.

[0042] 2) Using molecular docking technology, the specific steps are as follows: Import the PDB file into the software AutodockTool, perform pretreatment operations on the umami receptor proteins T1R1 / T1R3, such as dehydration, hydrogenation, and charge addition, and then predict the docking site and consider whether to retain water molecules or metal ions near the docking pocket through the program, and save it to a PDBQT file for later use; prepare 14 small molecule ligand files of aroma compounds, import the 14 PDB files into PyRx respectively, perform energy minimization processing on the small molecules, set the parameters by default, and save the files in PDBQT format; taking advantage of the characteristic of PyRx software to batch process molecular docking between small molecules and macromolecules, import the 14 energy-minimized ligand molecule files into PyRx, and then import the processed umami receptor protein file into the software in the same way, set the docking pocket, and run the Vina module for docking; after the operation is completed, record the binding energy of the 14 aroma compounds and umami receptor proteins.

[0043] 3) After data processing, the original dataset was divided into a training set and a test set. The training set was used for actual training, while the test set was used to verify the effectiveness of the prediction model. Finally, the model score was calculated to evaluate the model, which was constructed using a logistic regression mathematical model built using Python. As shown in Table 1 below, substituting the test set into the model and calling the fit(x,y) method reveals that the model's accuracy in predicting whether aroma compounds can enhance umami perception is 83.33%, where 1 represents enhancement of umami perception and 0 represents no enhancement of umami perception. It can be inferred that the logistic regression model has relatively good predictive performance and has certain application value in predicting whether aroma compounds can enhance umami perception.

[0044] Table 1 Comparison of actual and predicted values ​​of the six aroma substances in this embodiment.

[0045]

[0046]

[0047] Therefore, the method for predicting aroma substances used to enhance umami perception in this invention is simple, quick, and yields intuitive and reliable results.

[0048] The above description of the embodiments is provided to enable those skilled in the art to understand and use the invention. It will be apparent to those skilled in the art that various modifications can be made to these embodiments, and the general principles described herein can be applied to other embodiments without inventive effort. Therefore, the present invention is not limited to the above embodiments, and any improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the invention should be within the protection scope of the present invention.

Claims

1. A qualitative prediction method for the flavor-enhancing effect of aroma compounds, characterized in that, Includes the following steps: S1: Construct an aroma compound database; S2: Obtain the 3D structure of umami receptor protein T1R1 / T1R3 from the protein molecular database to obtain the receptor protein dataset. Obtain the structure of aroma compound and monosodium glutamate from the ZINC database as the ligand molecule dataset. Then, use PyRx software to perform molecular docking on the elements in the receptor protein dataset and ligand molecule dataset, and record and save the binding energy data. S3: Using the binding energy data and molecular descriptors of each aroma compound as feature variables, and whether it has an umami enhancement effect as the target variable, a logistic regression model is constructed. S4: Input the aroma compound information to be predicted into the logistic regression model to obtain the qualitative output result of the prediction of the aroma compound's flavor-enhancing effect.

2. The qualitative prediction method for the flavor-enhancing effect of aroma compounds according to claim 1, characterized in that, In S1, the aroma compound database includes a variety of aroma compounds, their corresponding molecular formulas, their corresponding CAS numbers, their common names, and their scientific names.

3. The qualitative prediction method for the flavor-enhancing effect of aroma compounds according to claim 1, characterized in that, In S1, the aroma compound database includes the following compounds: Phenylacetaldehyde, 2,5-dimethylpyrazine, dimethyl trisulfide, 2-methyl-3-tetrahydrofuranthiol, HDMF, HEMF, fenugreek lactone, 3-methylthiopropanol, 3-methylthiopropanol, furfuryl mercaptan, maltol, citric acid, ethyl fenugreek lactone, and 1-octen-3-ol.

4. The qualitative prediction method for the flavor-enhancing effect of aroma compounds according to claim 1, characterized in that, In S2, the process of obtaining the receptor protein dataset includes: The 3D structures of umami receptor proteins T1R1 / T1R3 obtained from the protein molecular database were imported into the AutodockTool software. The umami receptor proteins T1R1 / T1R3 were preprocessed, and then the docking sites were predicted using the AutodockTool software. Based on the feasibility of docking, it was determined whether to retain water molecules or metal ions near the docking pocket. The processed umami receptor protein T1R1 / T1R3 data were obtained and saved to a PDBQT file.

5. The qualitative prediction method for the flavor-enhancing effect of aroma compounds according to claim 4, characterized in that, In S2, the pretreatment operation includes dehydrating, hydrogenating, and charging the umami receptor proteins T1R1 / T1R3.

6. The qualitative prediction method for the flavor-enhancing effect of aroma compounds according to claim 4, characterized in that, In S2, the molecular docking process using PyRx software includes: Import the energy-minimized ligand molecule file and the processed umami receptor protein T1R1 / T1R3 data into PyRx simultaneously, set up the docking pocket, and run the AutoDock Vina module for docking.

7. The qualitative prediction method for the flavor-enhancing effect of aroma compounds according to claim 6, characterized in that, In S2, the energy-minimized ligand molecule file is obtained by performing energy minimization processing on the ligand molecule dataset through the AutoDock Vina module.

8. The qualitative prediction method for the flavor-enhancing effect of aroma compounds according to claim 6, characterized in that, In S2, the binding energy of all permutations and combinations of receptor protein, aroma compound, and monosodium glutamate is obtained as the binding energy dataset.

9. The qualitative prediction method for the flavor-enhancing effect of aroma compounds according to claim 6, characterized in that, In S3, the process of obtaining molecular descriptors is as follows: The SDF files of aroma compounds were imported into the ChemDes server to collect molecular descriptor information of aroma compounds. The Simpleimputer function in Python was used to fill in the missing values ​​in the acquired data. The molecular descriptor information includes: Molecular weight, LogP, refractive index, molar refractive index, molar volume, isothermal specific volume, surface tension, polarizability, XlogP, number of hydrogen bond donors, number of hydrogen bond acceptors, number of rotatable bonds, number of tautomers, topological polar surface area, number of heavy atoms, complexity, number of undetermined stereocenters, Chi0n, Chi0v, Chi1n, Chi1v, Chi2n, Chi2v, Chi3n, Chi3v, Chi4n, Chi4v, HallKierAlpha, HeavyAtomCount, Kappa1, Kappa2, Kappa3, number of aliphatic rings, number of aromatic heterocycles, number of aromatic rings, NumHeteroatoms, number of electrons, TPSA.

10. A qualitative prediction method for the flavor-enhancing effect of aroma compounds according to claim 8, characterized in that, S4 specifically includes: using the binding energy dataset and molecular descriptors as feature variables, and whether it has an umami-enhancing effect as the target variable, constructing a logistic regression model to predict whether aroma substances have the ability to enhance the perception of MSG umami.

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