CN109411032B - Method for evaluating functional components of edible and medicinal plants and machine-readable storage medium - Google Patents
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Abstract
The embodiment of the invention provides a functional component evaluation method of an edible and medicinal plant and a machine-readable storage medium, belongs to the technical field of functional component evaluation of the edible and medicinal plant, and solves the problem that functional components aiming at specific efficacies in the edible and medicinal plant cannot be effectively identified in the prior art. The method comprises the following steps: acquiring relevant information of the edible and medicinal plants to be evaluated and health problems to be researched; and obtaining functional components aiming at the health problems to be researched contained in the edible and medicinal plants to be evaluated according to the related information of the edible and medicinal plants to be evaluated, the health problems to be researched and a preset evaluation model corresponding to the edible and medicinal plants to be evaluated. The embodiment of the invention is suitable for the process of analyzing the functional components contained in the edible and medicinal plants aiming at the health problems to be researched.
Description
Technical Field
The invention relates to the technical field of evaluation of functional ingredients of edible and medicinal plants, in particular to a method for evaluating the functional ingredients of the edible and medicinal plants and a machine-readable storage medium.
Background
As society develops, a number of nutrition-related health issues arise. Nutritional health foods help to reduce the risk of some chronic diseases. However, the problems of unstable efficacy and uncontrollable quality generally exist in the current commercially available nutritional health products, and consumers are difficult to trust. The reason for this is that no scientific definition of functional ingredients is available, and there is no standardization basis in the processes of raw material selection, processing, storage, etc.
For the research of functional components, the prior art focuses on the separation and identification of single active components, and cannot effectively identify the composition of the functional components of a complex system.
Disclosure of Invention
An object of the embodiments of the present invention is to provide a method for evaluating functional ingredients of edible and medicinal plants and a machine-readable storage medium, which solve the problem in the prior art that functional ingredients for specific efficacy in edible and medicinal plants cannot be identified effectively, and provide functional ingredients that exert specific health effects in edible and medicinal plants.
In order to achieve the above object, an embodiment of the present invention provides a method for evaluating functional ingredients of edible and medicinal plants, the method comprising: acquiring relevant information of the edible and medicinal plants to be evaluated and health problems to be researched; and obtaining functional components aiming at the health problems to be researched contained in the edible and medicinal plants to be evaluated according to the related information of the edible and medicinal plants to be evaluated, the health problems to be researched and a preset evaluation model corresponding to the edible and medicinal plants to be evaluated.
Further, a preset evaluation model corresponding to the edible and medicinal plants to be evaluated is established in the following way: searching to obtain documents related to the edible and medicinal plants to be evaluated and the health problems to be researched; according to the types and the designated functional components of the edible and medicinal plants to be evaluated, counting to obtain a first proportion of the number of documents related to the designated functional components to the number of documents retrieved by the edible and medicinal plants to be evaluated in the corresponding types, and a second proportion of the number of documents related to different types of the edible and medicinal plants to be evaluated to the number of documents retrieved by the health problems to be researched; and obtaining the correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the first proportion and the second proportion.
Further, a preset evaluation model corresponding to the edible and medicinal plants to be evaluated is established in the following way: establishing an activity evaluation model according to the health problem to be researched, the pathological pathway and the corresponding known therapeutic drug components; obtaining the activity characteristic score of the edible and medicinal plants to be evaluated and/or the appointed functional components thereof according to the activity evaluation model; obtaining the content of the specified functional components in the edible and medicinal plants to be evaluated; and obtaining the correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the activity feature scores of the edible and medicinal plants to be evaluated and/or the appointed functional components thereof and the content of the appointed functional components.
Further, a preset evaluation model corresponding to the edible and medicinal plants to be evaluated is established in the following way: obtaining the binding free energy between a designated functional component in the edible and medicinal plant to be evaluated and the target protein by utilizing molecular docking software according to the target protein and the active site with known structures; obtaining the association between the target protein and the health problem to be researched according to a open source database DAG; and obtaining the correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the target protein.
Further, a preset evaluation model corresponding to the edible and medicinal plants to be evaluated is established in the following way: searching to obtain documents related to the edible and medicinal plants to be evaluated and the health problems to be researched; counting the ratio of the number of documents related to the specified functional component to the number of documents retrieved by the corresponding type of the edible and medicinal plants to be evaluated according to the type of the edible and medicinal plants to be evaluated and the specified functional component; determining specific functional components in the edible and medicinal plants to be evaluated according to the proportion and the corresponding preset proportion range; establishing an activity evaluation model according to the health problem to be researched, the pathological pathway and the corresponding known therapeutic drug components; obtaining the activity characteristic score of the edible and medicinal plants to be evaluated and/or the specific functional components according to the activity evaluation model; obtaining the content of the specific functional components in the edible and medicinal plants to be evaluated; and obtaining a correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the activity feature scores of the edible and medicinal plants to be evaluated and/or the specific functional components and the content of the specified functional components.
Further, a preset evaluation model corresponding to the edible and medicinal plants to be evaluated is established in the following way: searching to obtain documents related to the edible and medicinal plants to be evaluated and the health problems to be researched; counting the ratio of the number of documents related to the specified functional component to the number of documents retrieved by the corresponding type of the edible and medicinal plants to be evaluated according to the type of the edible and medicinal plants to be evaluated and the specified functional component; determining specific functional components in the edible and medicinal plants to be evaluated according to the proportion and the corresponding preset proportion range; obtaining the binding free energy between the specific functional component and the target protein in the edible and medicinal plant to be evaluated by utilizing molecular docking software according to the target protein and the active site with known structures; obtaining the association between the target protein and the health problem to be researched according to a open source database DAG; and obtaining the correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the target protein.
Further, a preset evaluation model corresponding to the edible and medicinal plants to be evaluated is established in the following way: establishing an activity evaluation model according to the health problem to be researched, the pathological pathway and the corresponding known therapeutic drug components; obtaining the activity characteristic score of the edible and medicinal plants to be evaluated and/or the appointed functional components thereof according to the activity evaluation model; obtaining the content of the specified functional components in the edible and medicinal plants to be evaluated; determining specific functional components in the edible and medicinal plants to be evaluated according to the active characteristic scores of the edible and medicinal plants to be evaluated and/or the specified functional components, the content of the specified functional components, and the corresponding preset score range and content range; obtaining the binding free energy between the specific functional component and the target protein according to the target protein and the active site with known structures by using molecular docking software; obtaining the association between the target protein and the health problem to be researched according to a open source database DAG; and obtaining the correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the target protein.
Further, a preset evaluation model corresponding to the edible and medicinal plants to be evaluated is established in the following way: searching to obtain documents related to the edible and medicinal plants to be evaluated and the health problems to be researched; counting the ratio of the number of documents related to the specified functional component to the number of documents retrieved by the corresponding type of the edible and medicinal plants to be evaluated according to the type of the edible and medicinal plants to be evaluated and the specified functional component; determining specific functional components in the edible and medicinal plants to be evaluated according to the proportion and the corresponding preset proportion range; establishing an activity evaluation model according to the health problem to be researched, the pathological pathway and the corresponding known therapeutic drug components; obtaining the activity characteristic score of the edible and medicinal plants to be evaluated and/or the specific functional components according to the activity evaluation model; obtaining the content of the specific functional components in the edible and medicinal plants to be evaluated; determining the functional components to be estimated in the edible and medicinal plants to be estimated according to the activity characteristic scores of the edible and medicinal plants to be estimated and/or the specific functional components, the content of the specified functional components, and the corresponding preset score range and content range; obtaining the binding free energy between the functional component to be estimated and the target protein in the edible and medicinal plant to be estimated by utilizing molecular docking software according to the target protein with a known structure and an active site; obtaining the association between the target protein and the health problem to be researched according to a open source database DAG; and obtaining the correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the target protein.
Further, the obtaining of the functional components of the edible and medicinal plants to be evaluated comprises: obtaining the functional components of the edible and medicinal plants to be evaluated and reducing the disease risk.
Accordingly, the embodiment of the present invention also provides a machine-readable storage medium, which stores instructions for causing a machine to execute the above-mentioned method for evaluating functional ingredients of edible and medicinal plants.
According to the technical scheme, after the relevant information of the edible and medicinal plants to be evaluated and the health problems to be researched are obtained, the functional components aiming at the health problems to be researched contained in the edible and medicinal plants to be evaluated are obtained according to the relevant information of the edible and medicinal plants to be evaluated, the health problems to be researched and the preset evaluation model corresponding to the edible and medicinal plants to be evaluated. The embodiment of the invention solves the problem that functional components aiming at specific efficacies in edible and medicinal plants cannot be effectively identified in the prior art, provides the functional components playing specific health roles in the edible and medicinal plants, and the functional components are used as key elements required to be controlled by the edible and medicinal plants as functional foods or further developed into the functional foods and are also material bases for the final products to play corresponding health roles.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
FIG. 1 is a schematic flow chart of a method for evaluating functional ingredients of edible and medicinal plants according to an embodiment of the present invention;
FIG. 2 is an exemplary table of a first ratio of the number of documents referred to in specifying a functional ingredient to the number of documents retrieved for the corresponding type of said edible-medicinal plant to be evaluated provided by an embodiment of the present invention;
FIG. 3 is an exemplary table of the number of documents involved in different types of the phytopharmaceutical plant to be assessed and the second ratio thereof to the number of documents retrieved for the health issue to be studied provided by an embodiment of the present invention;
FIG. 4 is an example of the functional component research data of tea leaves for improving sugar metabolism in the description of "functional characteristics-chemical characterization" provided by the embodiment of the present invention;
FIG. 5 is an example of data of a functional component research on improving sugar metabolism of tea leaves by a mass spectrometry technology combined with a stoichiometric chemical method and a molecular docking method provided by an embodiment of the invention;
FIG. 6 is a comparative example of the pathway of action of flavonoids with non-flavonoids polyphenols provided by an embodiment of the present invention;
FIG. 7 is a diagram illustrating a comparison of results of functional components obtained by establishing a preset evaluation model according to the first, second, and fifth ways provided in the embodiment of the present invention;
fig. 8 is a diagram illustrating a comparison of results of functional components obtained by establishing the obtained preset evaluation model in the first and seventh ways according to the embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating embodiments of the invention, are given by way of illustration and explanation only, not limitation.
For the health problems related to the nutrition of the edible and medicinal plants, repeated demonstration of experimental data is required. However, it is difficult to explain the problem only with data from one laboratory. With the development of information technology, it is possible to perform stoichiometric analysis based on high-resolution mass spectrometry data and predict characteristics such as component activity, metabolism, and the like from chemical structures. Meanwhile, by using a text mining method, multi-party research information can be acquired from published documents to be proved and predicted. The application of big data is a necessary trend in the development of nutritional health foods. The embodiment of the invention just utilizes the existing known technologies of computer reptile, high-resolution mass spectrometry, activity evaluation, statistical analysis and the like to analyze the influence of functional components in the edible and medicinal plants on specific health problems. Through the superposition use of a plurality of analysis methods, the functional components and the composition characteristics thereof which are repeatedly verified in different methods are considered to have higher reliability and are better than the conclusion obtained by 1 method. Specific embodiments will be described in detail below.
FIG. 1 is a schematic flow chart of a method for evaluating functional ingredients of edible and medicinal plants according to an embodiment of the present invention. As shown in fig. 1, the method comprises the steps of:
102, obtaining a functional component aiming at the health problem to be researched, which is contained in the edible and medicinal plant to be evaluated, according to the relevant information of the edible and medicinal plant to be evaluated, the health problem to be researched and a preset evaluation model corresponding to the edible and medicinal plant to be evaluated.
Wherein the information related to the edible medicinal plant to be evaluated can be the name of the edible medicinal plant to be evaluated, such as tea, and the health problem to be researched can be the name of a certain disease, such as diabetes. By utilizing the preset evaluation model, the functional components of the edible and medicinal plants to be evaluated and the reduced disease risk of the edible and medicinal plants can be obtained.
The preset evaluation model corresponding to the plant to be evaluated in step 102 can be established in the following ways:
the first one is to utilize text mining technology:
1) searching to obtain documents related to the edible and medicinal plants to be evaluated and the health problems to be researched;
2) according to the types and the designated functional components of the edible and medicinal plants to be evaluated, counting to obtain a first proportion of the number of documents related to the designated functional components to the number of documents retrieved by the edible and medicinal plants to be evaluated in the corresponding types, and a second proportion of the number of documents related to different types of the edible and medicinal plants to be evaluated to the number of documents retrieved by the health problems to be researched;
3) and obtaining the correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the first proportion and the second proportion.
For example, the relevant information of the edible and medicinal plants to be evaluated is tea, the health problem to be researched is diabetes, and research papers related to tea and diabetes are searched at https:// www.ncbi.nlm.nih.gov/pubmed/by using data mining technology and using tea and diabetes as keywords, and corresponding documents are obtained. And the computer performs interpretation analysis on each research paper based on the texts of the documents according to the keywords provided by the tea type, the specified functional components and the like. When the tea types are "green tea", "white tea", "oolong tea", "black tea", and the specified functional components are "tea polyphenol", "theaflavin", "thearubigin", etc., the number of documents containing the above information in the documents thus found is shown in fig. 2. Taking the tea polyphenol of green tea as an example, the distribution of the article is counted according to the following formula:
the first ratio of a specific functional component of green tea to improve diabetes mellitus is the number of documents related to a specific functional component of green tea/total number of documents in green tea
According to the existing research literature, the main functional components (sorted by importance) of different types of tea leaves for improving diabetes are: (1) green tea: the catechin in tea polyphenol is the main functional component, and Epigallocatechin (EGC) and gallate (EGCG) are the most important, and caffeine is the second most important. (2) White tea: catechins are the most important functional components of concern to date. (3) Oolong tea: catechin, theaflavin and thearubigin in tea polyphenol and gallic acid are all functional components of oolong tea which are concerned to improve diabetes. (4) Black tea: catechin, theaflavin and thearubigin in tea polyphenol are functional components which should be paid attention to the improvement of diabetes by black tea, and theanine and gallic acid also get a certain attention. (5) Black tea: tea polyphenol, caffeine and tea polysaccharide are functional components of black tea for improving diabetes.
Catechin is a characteristic functional component which is different from other tea types for green tea, white tea, theaflavin, thearubigin, oolong tea and black tea and tea polysaccharide for black tea. Catechins are significant for different tea types, but are particularly significant for green tea.
In the same manner, as shown in fig. 3, a second ratio of the number of documents involved in the different tea leaf types to the documents relevant to the prevention or amelioration of diabetes was obtained. The results indicate that most types of tea studies are concerned with type 2 diabetes and its associated symptoms. Green tea is different concerns about the insulin system, white tea is for the pre-diabetes, oolong tea and black tea are for the postprandial blood sugar, and black tea is different concerns about the postprandial blood sugar and the insulin system for different tea types, so that the difference of action mechanisms is suggested. And (3) performing relevant subdivision on the data shown in the figures 2 and 3 to obtain a relevant evaluation model of the tea leaves, the functional components and the diabetes. But also further suggests that the association of catechin and insulin functions should be concerned among the functional components of different tea leaves; the association of theaflavin and thearubigin with postprandial blood glucose. The difference of the functional components of the tea is the reason that different tea types have different mechanisms and are suitable for different people when diabetes is prevented or improved.
In addition, during text mining, the names, designated functional components, functional characteristics, research types and the like of the edible and medicinal plants can be used as key words, and information such as product names, functional descriptions, functional names/English names, product classifications, target groups, unsuitable groups, usage amount, usage range, regulations, domestic patent conditions, domestic patent numbers, foreign patent conditions, foreign patent numbers and the like of the edible and medicinal plants and related articles, and information such as functional component names, chemical structure information, biological activity marks and links, pharmaceutical bank numbers, sources, activity, toxicity, absorption and metabolism properties, action mechanisms, regulations and the like of the edible and medicinal plants and related articles can be obtained from globally published information. The messages can be stored as basic information in a local database to provide data support for the association evaluation model.
When the information is processed, the crawler controller can be used for sending requests one by using a Chrome browser and obtaining character string results of detailed information of relevant documents. And specifying functional component keywords, judging whether each document contains the specific keywords by using a web crawler analyzer, and storing the obtained information into a local database after carrying out structural processing on the information.
After the correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched is obtained in the first mode, the functional components aiming at the health problems to be researched contained in the edible and medicinal plants to be evaluated can be obtained when the relevant information of the edible and medicinal plants to be evaluated and the health problems to be researched are obtained.
Second, it is described by "functional characteristics-chemical characterization":
1) establishing an activity evaluation model according to the health problem to be researched, the pathological pathway and the corresponding known therapeutic drug components;
2) obtaining the activity characteristic score of the edible and medicinal plants to be evaluated and/or the appointed functional components thereof according to the activity evaluation model;
3) obtaining the content of the specified functional components in the edible and medicinal plants to be evaluated;
4) and obtaining the correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the activity feature scores of the edible and medicinal plants to be evaluated and/or the appointed functional components thereof and the content of the appointed functional components.
Wherein, aiming at the health problem to be researched, the pathological pathway is analyzed from the aspects of physiology, medicine and biology. Aiming at the key link which is already used as the screening target of the known therapeutic medicine components and functional components, an activity evaluation model is established by using the forms of enzyme biochemical tests, animal models and the like. And (3) taking the edible and medicinal plants to be evaluated and/or the appointed functional components thereof as research objects, testing by using the activity evaluation model, describing activity characteristics and obtaining an activity characteristic score. For example, the activity inhibition rate of the plant for food and medicine to be studied against a specific enzyme, the degree of improvement of the animal's indices such as blood sugar and blood fat, and the like. The characterization of the activity profile can be quantitative data or can be a result of assigning semi-qualitative and qualitative results. Then, the content of the specified functional components in the edible and medicinal plants to be evaluated is detected by using a laboratory standardized experimental method (quantitative research), and the requirements are that batch-to-batch stability is realized and the result can be repeated. Or identifying the content of the specified functional component in the medicinal and edible plant to be evaluated by using a non-targeted screening technology combining high-resolution mass spectrometry and a metrological method (qualitative research or semi-quantitative research).
In addition, the PCA method can be used for finding different substances according to the content and activity characteristic data of semi-qualitative or qualitative specified functional components, and the change characteristics of the compounds consistent with the change direction of the activity can be predicted through the correlation analysis of the substances and the activity. Thereby obtaining the correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated.
Take black tea as an example. Reducing starch digestion and absorption is one of the pathological pathways that stabilize postprandial blood glucose. Alpha-glucosidase, alpha-amylase is the major starch digestive enzyme; the DPP-4 enzyme inactivates glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP). Therefore, the alpha-glucosidase and the DPP-4 enzyme are main targets for developing diabetes drugs; alpha-amylase is a main target point in the food field related to sugar metabolism health research. And carrying out an enzyme biochemical experiment, obtaining activity data, and grading the activity characteristics. Meanwhile, tea polyphenol, catechin, theaflavin, thearubigin, theabrownin, tea polysaccharide, tea flavone, gallic acid, caffeine and other specified functional components in the tea are detected. And analyzing the functional components of the black tea for regulating the postprandial blood sugar by using a statistical analysis method to obtain a correlation evaluation model of the health problems of the tea leaves, the functional components and the stable postprandial blood sugar. As shown in fig. 4, the synergistic effect of catechins and theaflavins in black tea is the most important factor in regulating blood glucose; caffeine and thearubigin are also active ingredients.
After the model for evaluating the association of tea leaves, functional components, and health problems with stable postprandial blood glucose obtained by the second method is used, when information on certain types of tea leaves, such as the name of black tea and health problems to be studied, such as health problems with stable postprandial blood glucose, is obtained, the functional components, catechin and theaflavin for the health problems with stable postprandial blood glucose contained in black tea can be obtained.
Third, molecular docking is used in conjunction with cyber pharmacology:
1) retrieving to obtain the functional component information of the edible and medicinal plant to be researched and the potential action target spot on the physiological path of the health problem to be researched, and obtaining the binding free energy between the specified functional component in the edible and medicinal plant to be evaluated and the target protein by utilizing molecular docking software according to the target protein and the active site with known structures;
2) obtaining the association between the target protein and the health problem to be researched according to a open source database DAG;
3) and obtaining the correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the target protein.
The method comprises the steps of selecting a designated functional component in the edible and medicinal plant to be evaluated and a target protein to perform virtual activity evaluation based on related properties such as the target protein with known structures, an active site and the like by utilizing molecular docking software such as DOCK, AutoDock and the like, evaluating the interaction between the designated functional component and the target protein, and predicting possible binding sites, binding stable conformations and binding free energies between the designated functional component and the target protein. In addition, the interaction visualization and molecular structure analysis of the specified functional components and the target protein can be realized.
On the basis of molecular docking, selecting specified functional components and target proteins which are most likely to interact, and acquiring the association between the target proteins and the health problems to be researched by utilizing a open source database DAG. And analyzing the correlation network of 'functional components-diseases-targets' by using Cytoscape software, and simultaneously establishing a correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched.
Taking green tea as an example, 10 targets related to the health of sugar metabolism are focused, and among them, 5 human targets with structural information (alpha-glucosidase, DPP4, GLP-1R, pancreatetic alpha-amylase, SGLT-2). And (3) finishing the characteristic chemical component structure of the green tea, and acquiring the free energy of the combination of the chemical components and the target protein by utilizing molecular docking. Sorting the chemical components of the tea according to the principle that the tea has stronger binding capacity with a plurality of target proteins and the principle that the chemical components of the tea have stronger binding capacity aiming at a single target protein, and finally screening out (selecting according to scores) the main functional components which possibly influence the glycometabolism as catechin components. Disease pathways that utilize cyber-pharmacological approaches to assess the potential role of catechins are primarily associated with insulin regulation.
After the correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched is obtained by the third mode, the functional components aiming at the health problems to be researched contained in the edible and medicinal plants to be evaluated can be obtained when the relevant information of the edible and medicinal plants to be evaluated and the health problems to be researched are obtained. Taking green tea as an example, after acquiring green tea and health problems of sugar metabolism, functional components aiming at the health problems of sugar metabolism contained in green tea, such as catechins, can be obtained.
Fourth, the first and second modes are combined:
1) searching to obtain documents related to the edible and medicinal plants to be evaluated and the health problems to be researched;
2) counting the ratio of the number of documents related to the specified functional component to the number of documents retrieved by the corresponding type of the edible and medicinal plants to be evaluated according to the type of the edible and medicinal plants to be evaluated and the specified functional component;
3) determining specific functional components in the edible and medicinal plants to be evaluated according to the proportion and the corresponding preset proportion range;
4) establishing an activity evaluation model according to the health problem to be researched, the pathological pathway and the corresponding known therapeutic drug components;
5) obtaining the activity characteristic score of the edible and medicinal plants to be evaluated and/or the specific functional components according to the activity evaluation model;
6) obtaining the content of the specific functional components in the edible and medicinal plants to be evaluated;
7) and obtaining a correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the activity feature scores of the edible and medicinal plants to be evaluated and/or the specific functional components and the content of the specified functional components.
Wherein, the specific functional components obtained in the first mode can be used as the basis for determining the activity characteristic score and the chemical analysis in the second mode, and the specific implementation steps can refer to the contents described in the first and second modes.
Fifth, the first and third modes are combined:
1) searching to obtain documents related to the edible and medicinal plants to be evaluated and the health problems to be researched;
2) counting the ratio of the number of documents related to the specified functional component to the number of documents retrieved by the corresponding type of the edible and medicinal plants to be evaluated according to the type of the edible and medicinal plants to be evaluated and the specified functional component;
3) determining specific functional components in the edible and medicinal plants to be evaluated according to the proportion and the corresponding preset proportion range;
4) obtaining the binding free energy between the specific functional component and the target protein in the edible and medicinal plant to be evaluated by utilizing molecular docking software according to the target protein and the active site with known structures;
5) obtaining the association between the target protein and the health problem to be researched according to a open source database DAG;
6) and obtaining the correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the target protein.
Wherein, the specific functional components obtained in the first mode can be used as the basis for determining the binding free energy in the third mode, and the specific implementation steps can refer to the contents described in the first and third modes.
Sixth, the second and third modes are combined:
1) establishing an activity evaluation model according to the health problem to be researched, the pathological pathway and the corresponding known therapeutic drug components;
2) obtaining the activity characteristic score of the edible and medicinal plants to be evaluated and/or the appointed functional components thereof according to the activity evaluation model;
3) obtaining the content of the specified functional components in the edible and medicinal plants to be evaluated;
4) determining specific functional components in the edible and medicinal plants to be evaluated according to the active characteristic scores of the edible and medicinal plants to be evaluated and/or the specified functional components, the content of the specified functional components, and the corresponding preset score range and content range;
5) obtaining the binding free energy between the specific functional component and the target protein according to the target protein and the active site with known structures by using molecular docking software;
6) obtaining the association between the target protein and the health problem to be researched according to a open source database DAG;
7) and obtaining the correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the target protein.
The specific implementation steps of the third mode of molecular docking and network pharmacology research by combining the high-resolution mass spectrometry method in the second mode with specific functional components analyzed by a database, a ChemSpider and the like can refer to the contents described in the second and third modes.
The seventh mode is to combine the first, second and third modes:
1) searching to obtain documents related to the edible and medicinal plants to be evaluated and the health problems to be researched;
2) counting the ratio of the number of documents related to the specified functional component to the number of documents retrieved by the corresponding type of the edible and medicinal plants to be evaluated according to the type of the edible and medicinal plants to be evaluated and the specified functional component;
3) determining specific functional components in the edible and medicinal plants to be evaluated according to the proportion and the corresponding preset proportion range;
4) establishing an activity evaluation model according to the health problem to be researched, the pathological pathway and the corresponding known therapeutic drug components;
5) obtaining the activity characteristic score of the edible and medicinal plants to be evaluated and/or the specific functional components according to the activity evaluation model;
6) obtaining the content of the specific functional components in the edible and medicinal plants to be evaluated;
7) determining the functional components to be estimated in the edible and medicinal plants to be estimated according to the activity characteristic scores of the edible and medicinal plants to be estimated and/or the specific functional components, the content of the specified functional components, and the corresponding preset score range and content range;
8) obtaining the binding free energy between the functional component to be estimated and the target protein in the edible and medicinal plant to be estimated by utilizing molecular docking software according to the target protein with a known structure and an active site;
9) obtaining the association between the target protein and the health problem to be researched according to a open source database DAG;
10) and obtaining the correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the target protein.
Wherein, the functional components of the edible and medicinal plants are comprehensively evaluated based on literature data, statistical analysis data of 'functional characteristics-chemical characterization', molecular docking and network pharmacology calculation data. For example, functional components for diabetes contained in certain types of tea leaves can be obtained, and quantitative indexes such as whether the functional components have the functional characteristics of antioxidation, postprandial blood glucose stabilization, uric acid synthesis reduction, fat absorption reduction, insulin sensitivity improvement, lipid deposition reduction and the like can also be obtained, and half effective dose, half inhibitory concentration, maximum inhibitory rate, maximum activation rate, percentage of functional improvement and the like can be calculated or summarized.
For example, the reported chemical structure information is extracted by using a data mining technology in https:// www.ncbi.nlm.nih.gov/pubmed/searching research papers related to the research of the material components of the tea leaves. Meanwhile, the high-resolution mass spectrometry data is combined with a principal component analysis method, and the component changes of the post-fermented tea before and after fermentation are compared, so that the catechin is considered to be mainly present in the tea leaves with lighter fermentation degree, such as green tea, and the content and the types of flavonoid glycoside and saponin substances in the post-fermented tea are increased. Functional components analyzed by mass spectrometry and functional components reported in the literature are incorporated into subsequent molecular docking and network pharmacology analysis links.
According to reports on action targets of tea and sugar metabolism in the literature, the targets which are closely related to sugar metabolism processes and have resolved protein structures, such as alpha-glucosidase, alpha-amylase, DPP4 enzyme, GLP-1 receptor, SGLT-2 receptor and the like, are focused for molecular docking. The functional components of the tea are sequenced according to the conditions that the tea has stronger binding capacity with a plurality of target proteins and the tea has stronger binding capacity aiming at a single target protein. Finally, 23 functional components which can influence the carbohydrate metabolism (selected according to scores) in the tea leaves are screened out, and the specific result is shown in figure 5, wherein delta G is less than or equal to-12 kcal/mol, which indicates that the binding capacity is strong (white); -12kcal/mol <. DELTA.G ≦ -8kcal/mol indicating stronger binding capacity (light grey); -8kcal/mol <. DELTA.G.ltoreq.4 kcal/mol means a weak binding capacity (medium grey); -4kcal/mol <. DELTA.G indicates poor binding capacity (dark grey). The results show that the components such as catechin, flavonoid glycoside, saponin and the like in the tea are related to the effect of regulating the blood sugar. Wherein, the flavonoid glycoside component has the most acting targets and can play a role in stabilizing postprandial blood sugar through multiple channels such as inhibiting starch digestion, reducing glucose transport, promoting insulin secretion and the like. The method is mainly suitable for tea components with small molecular weight and capable of accurately describing molecular structures, and is not suitable for macromolecular substances such as thearubigin, theabrownin, tea polysaccharide and the like.
For the characteristic that the polyphenol compounds found in the research are superior to the saponin compounds, aiming at the former two compounds, the open source database DAG is utilized to obtain the association between the target genes and diseases. The cytoscape3.2.1 is used for analyzing the association network of the compound-disease-target spot to find key components and target spots. Setting node colors and graph sizes based on the number of edges, and analyzing an interaction network of the target protein by using a development database String10.0. The disease pathway where the protein is located is found by a KEGG pathway identity algorithm, and the metabolic disease-related disease pathway with P < 1 × 10-4 is obtained to be used as an important pathway for further analysis. As shown in fig. 6, in terms of improving metabolic diseases, there is a difference in the dominant pathways of action between flavonoids and polyphenols, and thus there may be a synergistic effect.
The results of the above calculations show that the flavonoids in tea have a strong function of improving glycometabolism and are a comprehensive function of multiple channels. The action of macromolecular components such as polymeric polyphenols has to be demonstrated by other research means.
In addition, as shown in fig. 7, the preset evaluation models obtained by the first, second and fifth ways are respectively established, and the results of the obtained functional components are compared. Wherein, the functional components of the tea (black tea) for improving the sugar metabolism are briefly described.
Through comprehensive judgment, the key functional components for improving the sugar metabolism of the optimized black tea are as follows: the tea polyphenols (such as theaflavin and thearubigin), catechin and theaflavin have synergistic effect, and caffeine can enhance the effect of tea polyphenols. From the functional characteristics, the black tea for preventing or improving the glycometabolism health is characterized by stabilizing the postprandial blood sugar, and the most main characteristic functional components of the black tea are theaflavin and thearubigin; black tea also has some effect on the insulin system and may be associated with catechins.
In addition, as shown in fig. 8, results of functional ingredients obtained by establishing the obtained preset evaluation model (post-fermented tea) by the above-described first and seventh modes, respectively, were compared.
Through comprehensive judgment, the key functional components for improving the sugar metabolism of the optimized post-fermented tea are as follows: tea polysaccharide, tea polyphenol, caffeine, flavonoid glycosides, and saponin. Wherein tea polysaccharide, flavonoid glycoside and saponin are increased in the tea fermentation process, so that the tea is a marker functional component for improving sugar metabolism of post-fermented tea, which is different from green tea and black tea. These ingredients are of particular concern when preparing food products from later fermented teas.
In addition, in an extensible way, after the functional ingredients aiming at the health problem to be researched and contained in the edible and medicinal plants to be evaluated are obtained by using the preset evaluation model, the functional ingredients can be used as the edible and medicinal plants to be used as functional foods or further developed into key elements required to be controlled by the functional foods, and are also material bases for the final products to play corresponding health roles. The functional components are characterized by differentiation advantages of the edible and medicinal plants and the products thereof different from other similar products without special control, and are embodied in that the beneficial components and the health effect are stable and can be described. Based on the functional components obtained in the embodiments of the present invention, the value of the functional extract obtained by separation and purification, and the feasibility of the functional extract as a marker and a label in functional food development and functional publicity are determined according to patent information and regulatory states described in a database.
In addition, through the preset evaluation model, the corresponding edible and medicinal plant information can be obtained by utilizing the provided functional components and the health problems to be researched.
Accordingly, embodiments of the present invention provide a machine-readable storage medium having stored thereon instructions for causing a machine to execute the method for evaluating a functional ingredient of a medicinal plant according to the above embodiments.
Although the embodiments of the present invention have been described in detail with reference to the accompanying drawings, the embodiments of the present invention are not limited to the details of the above embodiments, and various simple modifications can be made to the technical solutions of the embodiments of the present invention within the technical idea of the embodiments of the present invention, and the simple modifications all belong to the protection scope of the embodiments of the present invention.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, the embodiments of the present invention do not describe every possible combination.
Those skilled in the art will understand that all or part of the steps in the method according to the above embodiments may be implemented by a program, which is stored in a storage medium and includes several instructions to enable a single chip, a chip, or a processor (processor) to execute all or part of the steps in the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In addition, any combination of various different implementation manners of the embodiments of the present invention is also possible, and the embodiments of the present invention should be considered as disclosed in the embodiments of the present invention as long as the combination does not depart from the spirit of the embodiments of the present invention.
Claims (3)
1. A method for evaluating functional ingredients of an edible or medicinal plant, the method comprising:
acquiring relevant information of the edible and medicinal plants to be evaluated and health problems to be researched;
obtaining functional components aiming at the health problems to be researched contained in the edible and medicinal plants to be evaluated according to the related information of the edible and medicinal plants to be evaluated, the health problems to be researched and a preset evaluation model corresponding to the edible and medicinal plants to be evaluated,
wherein, the preset evaluation model corresponding to the edible and medicinal plants to be evaluated can be established by one of the following modes:
searching to obtain documents related to the edible and medicinal plants to be evaluated and the health problems to be researched;
according to the types and the designated functional components of the edible and medicinal plants to be evaluated, counting to obtain a first proportion of the number of documents related to the designated functional components to the number of documents retrieved by the edible and medicinal plants to be evaluated in the corresponding types, and a second proportion of the number of documents related to different types of the edible and medicinal plants to be evaluated to the number of documents retrieved by the health problems to be researched;
obtaining a correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the first proportion and the second proportion;
or,
establishing an activity evaluation model according to the health problem to be researched, the pathological pathway and the corresponding known therapeutic drug components;
obtaining the activity characteristic score of the edible and medicinal plants to be evaluated and/or the appointed functional components thereof according to the activity evaluation model;
obtaining the content of the specified functional components in the edible and medicinal plants to be evaluated;
obtaining an association evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the activity feature scores of the edible and medicinal plants to be evaluated and/or the appointed functional components thereof and the content of the appointed functional components;
or,
searching to obtain documents related to the edible and medicinal plants to be evaluated and the health problems to be researched;
counting the ratio of the number of documents related to the specified functional component to the number of documents retrieved by the corresponding type of the edible and medicinal plants to be evaluated according to the type of the edible and medicinal plants to be evaluated and the specified functional component;
determining specific functional components in the edible and medicinal plants to be evaluated according to the proportion and the corresponding preset proportion range;
establishing an activity evaluation model according to the health problem to be researched, the pathological pathway and the corresponding known therapeutic drug components;
obtaining the activity characteristic score of the edible and medicinal plants to be evaluated and/or the specific functional components according to the activity evaluation model;
obtaining the content of the specific functional components in the edible and medicinal plants to be evaluated;
obtaining an association evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the activity feature scores of the edible and medicinal plants to be evaluated and/or the specific functional components and the content of the specified functional components;
or,
searching to obtain documents related to the edible and medicinal plants to be evaluated and the health problems to be researched;
counting the ratio of the number of documents related to the specified functional component to the number of documents retrieved by the corresponding type of the edible and medicinal plants to be evaluated according to the type of the edible and medicinal plants to be evaluated and the specified functional component;
determining specific functional components in the edible and medicinal plants to be evaluated according to the proportion and the corresponding preset proportion range;
obtaining the binding free energy between the specific functional component and the target protein in the edible and medicinal plant to be evaluated by utilizing molecular docking software according to the target protein and the active site with known structures;
obtaining the association between the target protein and the health problem to be researched according to a open source database DAG;
obtaining a correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the target protein;
or,
establishing an activity evaluation model according to the health problem to be researched, the pathological pathway and the corresponding known therapeutic drug components;
obtaining the activity characteristic score of the edible and medicinal plants to be evaluated and/or the appointed functional components thereof according to the activity evaluation model;
obtaining the content of the specified functional components in the edible and medicinal plants to be evaluated;
determining specific functional components in the edible and medicinal plants to be evaluated according to the active characteristic scores of the edible and medicinal plants to be evaluated and/or the specified functional components, the content of the specified functional components, and the corresponding preset score range and content range;
obtaining the binding free energy between the specific functional component and the target protein according to the target protein and the active site with known structures by using molecular docking software;
obtaining the association between the target protein and the health problem to be researched according to a open source database DAG;
obtaining a correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the target protein;
or,
searching to obtain documents related to the edible and medicinal plants to be evaluated and the health problems to be researched;
counting the ratio of the number of documents related to the specified functional component to the number of documents retrieved by the corresponding type of the edible and medicinal plants to be evaluated according to the type of the edible and medicinal plants to be evaluated and the specified functional component;
determining specific functional components in the edible and medicinal plants to be evaluated according to the proportion and the corresponding preset proportion range;
establishing an activity evaluation model according to the health problem to be researched, the pathological pathway and the corresponding known therapeutic drug components;
obtaining the activity characteristic score of the edible and medicinal plants to be evaluated and/or the specific functional components according to the activity evaluation model;
obtaining the content of the specific functional components in the edible and medicinal plants to be evaluated;
determining the functional components to be estimated in the edible and medicinal plants to be estimated according to the activity characteristic scores of the edible and medicinal plants to be estimated and/or the specific functional components, the content of the specified functional components, and the corresponding preset score range and content range;
obtaining the binding free energy between the functional component to be estimated and the target protein in the edible and medicinal plant to be estimated by utilizing molecular docking software according to the target protein with a known structure and an active site;
obtaining the association between the target protein and the health problem to be researched according to a open source database DAG;
and obtaining the correlation evaluation model of the edible and medicinal plants to be evaluated, the functional components and the health problems to be researched, which correspond to the edible and medicinal plants to be evaluated, according to the target protein.
2. The method of claim 1, wherein said obtaining functional components of said edible-medicinal plants to be evaluated comprises:
obtaining the functional components of the edible and medicinal plants to be evaluated and reducing the disease risk.
3. A machine-readable storage medium having stored thereon instructions for causing a machine to perform the method of assessing a functional composition of a medicinal plant according to any one of claims 1-2.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101120969A (en) * | 2006-08-08 | 2008-02-13 | 李仲才 | Medicine for treating diabetes and its complications and preparing method thereof |
| CN101615222A (en) * | 2008-06-23 | 2009-12-30 | 中国医学科学院放射医学研究所 | A kind of Chinese prescription designing technique based on the Chinese medicine effective component group |
| CN102749348A (en) * | 2012-06-18 | 2012-10-24 | 河南省科高植物天然产物开发工程技术有限公司 | Method for identifying active components in medicinal plant |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101120969A (en) * | 2006-08-08 | 2008-02-13 | 李仲才 | Medicine for treating diabetes and its complications and preparing method thereof |
| CN101615222A (en) * | 2008-06-23 | 2009-12-30 | 中国医学科学院放射医学研究所 | A kind of Chinese prescription designing technique based on the Chinese medicine effective component group |
| CN102749348A (en) * | 2012-06-18 | 2012-10-24 | 河南省科高植物天然产物开发工程技术有限公司 | Method for identifying active components in medicinal plant |
Non-Patent Citations (2)
| Title |
|---|
| Studies on identification of medicinal plant variety based on NIR spectroscopy using plant leaves;P. Kelina Sahaya Rajesh等;《IEEE》;20140116;第1-4页 * |
| 分子生药学在蒙药研究中的应用;李虔全等;《中国中药杂志》;20111001;第36卷(第19期);第2615-2619页 * |
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