Prediction method, prediction system, prediction program, and novel peptide and dipeptidyl peptidase-iv inhibitory composition

Abstract

To reduce man-hour required for identifying a peptide having predetermined physiologically activity.SOLUTION: In a prediction method, one or more computers execute the steps of: reading out a combination of a physical property value of a peptide and an evaluation index regarding physiological activity of the peptide for a plurality of peptides to be leaned; machine-learning a relation between the physical property value and the evaluation index and creating a prediction model; and predicting an evaluation index regarding the physiological activity of a peptide to be predicted by using the created prediction model and a physical property value of the peptide to be predicted. Further, the physical property value of a peptide is at least any one of isoelectric point, hydrophobicity, molecular weight, presence / absence of polarity and aromatic ring of each residue constituting the peptide.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present technology relates to a prediction method, a prediction system and a prediction program, as well as a novel peptide, and a peptide having dipeptidyl peptidase-IV inhibitory activity. [Background technology]

[0002] Previously, methods have been proposed to identify peptides containing features that are positively associated with natural endogenous or exogenous cellular processing, transport, and major histocompatibility complex (MHC) presentation (e.g., Patent Document 1). In addition, a method has been proposed in which logistic regression is performed using 120 physicochemical properties as features to predict peptides with high FFAR agonist activity (e.g., Non-Patent Document 1).

[0003] Dipeptidyl peptidase-IV (hereinafter, also referred to as "DPP-IV") is a multifunctional transmembrane glycoprotein with N-terminal dipeptidase activity. DPP-IV is present on the cells of various tissues, such as the liver, kidney, small intestine, salivary gland, blood cells, and plasma, in most mammals. DPP-IV is thought to play various roles in the body, one of which is the function of glucagon-like peptide-1 (GL), an incretin. It is known that this enzyme is an enzyme that breaks down glucose-dependent insulinotropic polypeptide (hereinafter referred to as "GIP") and glucose-dependent insulinotropic polypeptide (hereinafter referred to as "P-1").

[0004] GLP-1 is released after a meal and has pleiotropic effects, including glucose-induced stimulation of insulin synthesis and secretion, suppression of glucagon secretion, modulation of gene expression, trophic effects on β-cells, suppression of food intake, and slowing of gastric emptying. GIP is also released after a meal and has the effect of promoting insulin secretion from the pancreas in a blood glucose concentration-dependent manner. Inhibition of DPP-IV suppresses the decomposition of GLP-1 and GIP, and increases the concentration of these incretins in the blood. As a result, it is known that insulin secretion is promoted and blood glucose levels are reduced. Since the condition for the promotion of insulin secretion by incretins is high blood glucose levels, it is believed that inhibition of DPP-IV is less likely to cause the side effect of hypoglycemia, which occurs with conventional insulin secretion promoters, in type 2 diabetes caused by reduced insulin secretion.

[0005] Thus, DPP-IV inhibitors that have the effect of inhibiting DPP-IV activity are expected to be active ingredients of new types of diabetes treatment drugs, and several medicines have already been put on the market. In addition, the search for ingredients that can become further DPP-IV inhibitors is also continuing (Patent Document 2, etc.).

[0006] In addition, dietary therapy and exercise therapy are the first-choice treatment for diabetes, and diabetes drugs are usually used when blood glucose control is insufficient. In such a situation, there is a demand for DPP-IV inhibitors to be ingested through the diet in the form of supplements or food additives. Therefore, various searches have been conducted for ingredients that have DPP-IV inhibitory activity and can be used as food and beverages. For example, some oligopeptides derived from milk proteins have been found to have DPP-IV inhibitory activity (Patent Documents 3 to 6). In particular, DPP-IV inhibitory activity has been confirmed for tripeptides derived from casein (Patent Documents 7 to 9). [Prior art documents] [Patent documents]

[0007] [Patent Document 1] International Publication No. 2017 / 186959 [Patent Document 2] Special Publication No. 2008-527011 [Patent Document 3] US Patent Application Publication No. 2009 / 0075904 [Patent Document 4] Patent No. 6026720 [Patent Document 5] Patent No. 4915833 [Patent Document 6] International Publication No. 2006 / 068480 [Patent Document 7] Patent No. 1453270 [Patent Document 8] Patent No. 5832049 [Patent Document 9] International Publication No. 2022 / 186290 [Non-patent literature]

[0008] [Non-Patent Document 1] Keitaro Yoshioka et al., Biochemical and Biophysical Research Communications, 2021, Volume 550, p.177-183 Summary of the Invention [Problem to be solved by the invention]

[0009] In order to identify a peptide having a desired physiological activity, screening is generally performed to measure the degree of activity of each of peptides having various amino acid sequences, which generally requires many steps. In one aspect, the present invention aims to reduce the steps required to identify a peptide having a predetermined physiological activity.

[0010] In another aspect, an objective of the present invention is to provide a peptide having excellent DPP-IV inhibitory activity. [Means for solving the problem]

[0011] A prediction method according to one aspect of the present invention is realized by the following configuration. (Aspect 1) Reading out combinations of peptide physical property values ​​and evaluation indices related to the biological activity of the peptides for a plurality of peptides to be learned; Creating a prediction model by machine learning the relationship between the physical property value and the evaluation index; predicting an evaluation index regarding a physiological activity of the peptide to be predicted using the prediction model created and physical property values ​​of the peptide to be predicted; The prediction method is implemented by one or more computers. The physical property values ​​of the peptide are at least any one of the isoelectric point, hydrophobicity, molecular weight, polarity, and the presence or absence of an aromatic ring of each residue constituting the peptide. (Aspect 2) The prediction model is one or more models including SVR (Support Vector Regression). R 2. The prediction method according to embodiment 1. (Aspect 3) The predictive model further comprises a linear model. 3. The prediction method according to embodiment 2. (Aspect 4) The physiological activity is an activity against a factor related to the prevention, treatment, or improvement of a disease or the like. 4. The prediction method according to any one of aspects 1 to 3. (Aspect 5) The physiological activity is dipeptidyl peptidase-IV (DPP-IV) inhibitory activity. 5. The prediction method according to any one of aspects 1 to 4. (Aspect 6) The physical properties of the peptide include the isoelectric point and hydrophobicity of each residue constituting the peptide. 6. The prediction method according to any one of aspects 1 to 5. (Aspect 7) The peptide is a tripeptide. 7. The prediction method according to any one of aspects 1 to 6. (Aspect 8) The multiple peptides to be learned contain all 20 types of amino acids in their amino acid sequences. 8. The prediction method according to any one of aspects 1 to 7.

[0012] The contents described in the Means for Solving the Problems can be combined as much as possible without departing from the problem and technical idea of ​​the present invention. The contents of the means for solving the problem can be provided as an apparatus such as a computer or a system including multiple apparatuses, a method executed by a computer, or a program executed by a computer. The program can also be executed on a network. A recording medium holding the program may also be provided.

[0013] Furthermore, as a result of intensive research conducted by the present inventors to solve the above problems, they discovered that an oligopeptide containing a specific amino acid sequence has high DPP-IV inhibitory activity, leading to the completion of the present invention.

[0014] That is, a first embodiment according to another aspect of the present invention is an oligopeptide comprising any of the sequences described below. Ala-Pro-Val Pro-Pro-Ile Pro-Pro-Val Ala-Pro-Ile Leu-Pro-Ile Met-Pro-Val Val-Pro-His Glu-Pro-Ile In this embodiment, preferably the oligopeptide consists of said sequence.

[0015] A second aspect of the present invention is a composition for inhibiting dipeptidyl peptidase-IV (DPP-IV), comprising one or more oligopeptides selected from the group consisting of oligopeptides having any of the sequences described below. Ala-Pro-Val Pro-Pro-Ile Pro-Pro-Val Ala-Pro-Ile Leu-Pro-Ile Lys-Pro-Ile Met-Pro-Val Val-Pro-His Val-Pro-Glu Glu-Pro-Ile In this embodiment, preferably the oligopeptide consists of said sequence. The composition of this embodiment is preferably a medicine or a food or drink.

[0016] A second aspect of the present invention is a composition for suppressing an increase in blood glucose level, comprising as an active ingredient one or more oligopeptides selected from those having any of the sequences described below. Ala-Pro-Val Pro-Pro-Ile Pro-Pro-Val Ala-Pro-Ile Leu-Pro-Ile Lys-Pro-Ile Met-Pro-Val Val-Pro-His Val-Pro-Glu Glu-Pro-Ile In this embodiment, preferably the oligopeptide consists of said sequence. The composition of this embodiment is preferably used to suppress an increase in blood glucose level after a meal or during fasting. The composition of this embodiment is preferably a medicine or a food or drink. Effect of the Invention

[0017] According to one aspect of the present invention, it is possible to reduce the number of steps required to identify a peptide having a given physiological activity.

[0018] Since the oligopeptide according to another aspect of the present invention has excellent DPP-IV inhibitory activity, it can be suitably contained in a pharmaceutical composition as a DPP-IV inhibitor, or can be made into a composition for suppressing postprandial blood glucose increase in the form of food, beverage, or the like. [Brief description of the drawings]

[0019] [Figure 1] FIG. 1 is a functional block diagram illustrating an example of a prediction device. [Diagram 2] FIG. 2 is a process flow diagram illustrating an example of the prediction process. [Diagram 3] FIG. 3 is a diagram illustrating an example of data stored in the storage device. [Figure 4] Figure 4 is a graph showing the influence of explanatory variables in the Lasso regression model. [Diagram 5] FIG. 5 shows the results of evaluating the accuracy of the Lasso regression model. [Figure 6] Figure 6 is a graph showing the influence of explanatory variables in the Lasso regression model. [Figure 7] FIG. 7 shows the results of comparing the accuracy of the Laso regression model depending on the presence or absence of molecular weight. [Figure 8] FIG. 8 shows the results of comparing the accuracy of prediction models with and without standardization of sequence data. [Figure 9] FIG. 9 shows tripeptides predicted to have high activity rates by the linear model, tripeptides predicted to have high activity rates by the nonlinear model, and tripeptides predicted to have high activity rates in common with both models. [Figure 10] FIG. 10 is a graph showing the results of measuring the evaluation index for tripeptides predicted to have high activity rates. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] Hereinafter, an embodiment according to one aspect of the present invention will be described with reference to the drawings. Note that the embodiment is merely an example, and the present invention is not limited to the following configurations, etc.

[0021] In this embodiment, screening for identifying peptides having a certain physiological activity is performed by prediction. The screening is performed by using the physical properties of the peptides to be learned (training data). A prediction model is created by performing machine learning on the relationship between the physical property value and the evaluation index of physiological activity, and the prediction model is used to calculate the evaluation index according to the physical property value of the peptide to be predicted. Compared to the case where the degree of activity is actually measured for each of peptides having various amino acid sequences, screening by prediction makes it possible to extract candidates for peptides having the desired physiological activity with fewer man-hours.

[0022] The peptide is, for example, a tripeptide, but the number of amino acid residues is not limited to three. The physiological activity is, for example, dipeptidyl peptidase-IV (DPP-IV) inhibitory activity, but is not limited thereto, and can be applied to, for example, peptides having activity on factors related to the prevention, treatment, or improvement of diseases. The prediction model may be a linear model or a nonlinear model. The linear model may be linear regression in which a regularization term is added to a loss function such as Ridge regression or Lasso regression. The nonlinear model may be support vector regression (SVR), a decision tree (regression tree), etc. Also, The physical property values ​​are, for example, the isoelectric point and hydrophobicity of each residue. However, the physical property values ​​are not limited to this, and at least some of the isoelectric point, hydrophobicity, molecular weight, polarity (hydrophilicity), and the presence or absence of an aromatic ring can be used. The peptide to be learned can be created, for example, using a peptide array, but is not limited to this. The peptide array is provided with a plurality of unit areas in which peptides are immobilized on a solid phase carrier. The type of peptide can be changed for each unit area arranged on the solid phase carrier, and the peptides can be spot-synthesized by a peptide solid-phase synthesis method. The peptide to be learned is preferably a sample whose amino acid sequence contains all 20 types of amino acids. The peptide to be learned is also assumed to have an evaluation index of physiological activity measured in advance based on an existing method. The evaluation index may be the inhibitory activity rate (also simply called the inhibition rate). The evaluation index may be IC 50 , pIC 50 , E.C. 50 The DPP-IV inhibitory activity, inhibition rate, IC 50can be confirmed by a conventional method. In this way, the prediction model created using, for example, about 900 tripeptides as training data and test data can predict the degree of physiological activity for 8,000 tripeptides. The prediction accuracy of the prediction model can be evaluated by actually measuring the activity rate, etc., for peptides predicted by the prediction model to have a high desired physiological activity.

[0023] <Device configuration> FIG. 1 is a functional block diagram showing an example of a prediction device 1. The prediction device 1 is a computer, and includes a processor 11, a storage device 12, and an input / output device 13. The processor 11 is an arithmetic processing device such as a CPU (Central Processing Unit) and executes a program. The processor 11 performs the processing according to the embodiment by executing a program. In the example of FIG. 1, functional blocks are shown in the processor 11. That is, the processor 11 functions as a learning unit 111, a prediction unit 112, and an evaluation unit 113 by executing a predetermined program. The storage device 12 includes a main storage device such as a random access memory (RAM) or a read only memory (ROM), and an auxiliary storage device (secondary storage device) such as a hard disk drive (HDD), a solid state drive (SSD), or a flash memory. The main storage device temporarily stores the program read by the processor 11 and secures a working area for the processor 11. The auxiliary storage device stores the program executed by the processor 11 and information read and written by the program. The storage device 12 is assumed to store in advance the physical property values ​​and physiological activity evaluation indexes of the peptides used as training data. The input / output device 13 is, for example, a user interface such as an input device such as a keyboard or a mouse, an output device such as a monitor, or an input / output device such as a touch panel.

[0024] The learning unit 111 creates a prediction model using training data stored in the storage device 12. The training data is information about peptides whose inhibition rates have been measured. The learning unit 111 uses the inhibition rate of a peptide as a response variable and the amino acid sequence contained in the peptide as a response variable. A prediction model is created using the physical property values ​​of each of the amino acids as explanatory variables. The prediction unit 112 predicts the inhibition rate of the peptide using the created prediction model and the physical property values ​​of the amino acids that constitute the peptide to be predicted. Note that prediction may be performed particularly for peptides whose actual measured values ​​are unknown. The evaluation unit 113 calculates a predetermined evaluation index using test data for the prediction model created by the learning unit 111. The evaluation unit 113 may also calculate the influence of each of the explanatory variables included in the prediction model to support the user in selecting explanatory variables.

[0025] <Prediction processing> FIG. 2 is a process flow diagram showing an example of prediction processing. The process of FIG. 2 is started based on a user's operation. In this embodiment, an example of predicting the DPP-IV inhibitory activity of a tripeptide will be described. In addition, it is assumed that information on a plurality of peptides with known inhibition rates is stored in the storage device 12. The plurality of peptides with known inhibition rates are used as training data and test data. Note that the plurality of peptides may be divided into training data and test data, and cross-validation may be performed. The inhibition rate is, for example, a value actually measured using a plurality of peptides created on a peptide array.

[0026] FIG. 3 is a diagram showing an example of data stored in the storage device 12. The table shown in FIG. 3 includes attributes of "amino acid sequence," "feature amount," and "activity." The attribute of "amino acid sequence" further includes attributes of "1st," "2nd," and "3rd," and in each field, identification information (abbreviation) indicating the type of residue is stored. For example, the first residue is called the first residue to the third residue in the order from the N-terminus to the C-terminus. The attribute of "feature amount" stores physical property values ​​related to three residues. In the example of FIG. 3, isoelectric point (pl) and hydrophobicity (pH2A) are exemplified as physical property values. In addition to or instead of these pieces of information, information such as molecular weight, polarity, and the presence or absence of aromatic rings may be stored. The value of the feature amount is determined according to the type of amino acid constituting the peptide. In the field of "activity," information indicating the inhibition rate of a predetermined physiological activity measured for each peptide is stored. Furthermore, it is preferable that a plurality of peptides to be used as training data and test data (hereinafter also referred to as "training data, etc.") are created with a combination that comprehensively includes 20 types of amino acids at each position from residue 1 to residue 3. In other words, it is preferable that 20 types of amino acids at each residue are covered by the plurality of training data.

[0027] The learning unit 111 of the prediction device 1 reads out training data, etc. from the storage device 12, and performs a predetermined pre-processing (FIG. 2: S1). The pre-processing is a process of converting the training data, etc. into a format suitable for machine learning. For example, the learning unit 111 may convert an amino acid sequence, which is categorical data, into a dummy variable. The learning unit 111 may also standardize the physical property value or the inhibition rate so that the average is 0 and the variance is 1. The learning unit 111 may also perform a process of removing outliers that satisfy a predetermined condition.

[0028] Furthermore, the learning unit 111 creates a prediction model using the read test data (FIG. 2: S2). In this step, one or more prediction models are created. The prediction model can be created using an existing machine learning method, and may be a linear model or a nonlinear model. For example, four types of prediction models may be created using Ridge regression, Lasso regression, SVR, and a regression tree. The objective variable of the prediction model is the inhibition rate of the peptide. Furthermore, the explanatory variables of the prediction model are the physical property values ​​of the amino acids that constitute the peptide. The specific types of physical property values ​​and their combinations are set by the user.

[0029] Then, the evaluation unit 113 evaluates the performance of the created prediction model (FIG. 2: S3). In this step, the evaluation unit 113 performs a prediction process using test data and obtains the following as an evaluation index: For example, the root mean square error (RMSE), the coefficient of determination (R 2 ), and other indices are calculated. The calculated evaluation indices are output via the input / output device 13, for example.

[0030] The evaluation unit 113 also determines whether the accuracy of the prediction model is sufficient (FIG. 2: S4). In this step, the determination is made based on, for example, a user operation input via the input / output device 13. The user can appropriately determine the criteria for whether the prediction accuracy is sufficient. If an input is received indicating that the accuracy of the prediction model is not sufficient (S4: NO), the learning unit 111 accepts an instruction to correct the prediction model based on the user operation (FIG. 2: S5), and returns to the processing of S1. In S5, for example, the feature amount used as an explanatory variable in the prediction model is changed based on the user operation.

[0031] On the other hand, if the prediction unit 112 receives an input indicating that the accuracy of the prediction model is sufficient in S4 (S4: YES), the prediction unit 112 performs a process of predicting an evaluation index related to a predetermined physiological activity for the peptide to be predicted using the prediction model created in S2 (FIG. 2: S6-S8). The peptide to be predicted is, for example, all combinations containing 20 types of amino acids at each position from the first residue to the third residue (i.e., 8000 types of tripeptides).

[0032] First, the prediction unit 112 performs a predetermined pre-processing (FIG. 2: S6). The processing in this step is basically the same as S1. That is, the prediction unit 112 may convert the amino acid sequence of the peptide to be predicted into a dummy variable. In addition, the prediction unit 112 may standardize the physical property values ​​of the amino acids that constitute the peptide to be predicted.

[0033] In addition, the prediction unit 112 predicts an evaluation index related to a predetermined physiological activity for each of the peptides to be predicted (FIG. 2: S7). If multiple prediction models are created in S2, in this step, the prediction results for each of the peptides to be predicted are calculated by each prediction model. Therefore, the prediction results may differ for each prediction model.

[0034] Then, the prediction unit 112 outputs the prediction result (FIG. 2: S8). In this step, the prediction result is displayed, for example, via the input / output device 13. The prediction result may be configured to output a predetermined number of peptides with the top predicted values ​​of the inhibition rate. Furthermore, if multiple prediction models are created in S2, in this step, peptides whose predicted values ​​are determined to belong to the top predetermined number of sets in common with the multiple prediction models, or peptides whose predicted values ​​are determined to belong to the top predetermined number of sets by some of the prediction models, may be output in an identifiable manner.

[0035] The output in S8 is a candidate peptide having a desired physiological activity. The user can reduce the number of steps required to identify a suitable peptide by preferentially measuring the inhibition rate of peptides with higher prediction values.

[0036] <Example of prediction method> For 907 tripeptides created using a peptide array, the inhibition rate of DPP-IV inhibitory activity, which is the objective variable, was measured and used as training data and test data. In addition, Lasso regression was performed using the isoelectric point (PI), hydrophobicity (PH2A), molecular weight (MW), polarity (Po), and the presence or absence of an aromatic ring (Aroma) of each of the first, second, and third residues as explanatory variables, and the influence of the feature was calculated, and the results shown in Figure 4 were obtained. Note that the type of amino acid of each residue was not distinguished in the explanatory variables. In addition, the explanatory variables were appropriately standardized. Figure 4 is a graph showing the influence of the explanatory variables in the Lasso regression model. As shown in Figure 4, it can be said that the influence of the isoelectric point (PI), hydrophobicity (PH2A), and molecular weight (MW) is relatively high.

[0037] In addition, when the accuracy of the prediction model was evaluated for the following explanatory variables: when only isoelectric point (PI), when only hydrophobicity (PH2A), when only molecular weight (MW), and when all of isoelectric point (PI), hydrophobicity (PH2A), and molecular weight (MW) were used, the results shown in Figure 5 were obtained. Figure 5 shows the results of evaluating the accuracy of the Lasso regression model using test data. In particular, the prediction accuracy was particularly high when the prediction model was created using all of isoelectric point (PI), hydrophobicity (PH2A), and molecular weight (MW).

[0038] Next, a Lasso regression model was created to predict the inhibition rate of DPP-IV inhibitory activity using the isoelectric point (PI), hydrophobicity (PH2A), and molecular weight (MW) of each of the first, second, and third residues, as well as the type of amino acid as explanatory variables, and the influence of each explanatory variable was calculated, resulting in the results shown in FIG. 6. Standardization was performed for each physical property value. FIG. 6 is a graph showing the influence of explanatory variables in the Lasso regression model. According to the example of FIG. 6, an amino acid sequence having proline (P) at the second residue is predicted to have a high inhibition rate of DPP-IV inhibitory activity. In addition, explanatory variables with a high influence among physical properties include the hydrophobicity of the first residue, the isoelectric point of the first residue, the hydrophobicity of the third residue, the isoelectric point of the third residue, the hydrophobicity of the second residue, and the molecular weight of the second residue.

[0039] FIG. 7 is a diagram showing the results of comparing the accuracy of the Lasso regression model with and without molecular weight. As shown in FIG. 7, the accuracy of the prediction model was slightly improved when the isoelectric point (PI), hydrophobicity (PH2A), and molecular weight (MW), and the type of amino acid of the first, second, and third residues were used as explanatory variables, compared to when the isoelectric point (PI), hydrophobicity (PH2A), and molecular weight (MW), and the type of amino acid of the first, second, and third residues were used as explanatory variables. In addition, there is an advantage in that the amount of calculation required to create a prediction model is reduced by the amount of molecular weight excluded from the explanatory variables. From the above, the physical property values ​​used as explanatory variables are preferably isoelectric point (PI) and hydrophobicity (PH2A).

[0040] In addition, the accuracy of the prediction model was evaluated when the sequence data was further standardized. FIG. 8 shows the results of comparing the accuracy of the prediction model with and without standardization of the sequence data. In the case where the sequence data was also standardized, the entire explanatory variables, including the numerical values ​​and physical property values ​​created by encoding the amino acid sequence, were standardized. By performing standardization including the sequence data, the accuracy of the prediction model was slightly improved. In addition, although not shown, the scale (difference) of the influence of the explanatory variables was reduced, and the diversity of the prediction results was improved.

[0041] Based on the above, among the top 15 tripeptides predicted to have high inhibition rates in the prediction results by Ridge regression, Lasso regression, and SVR using isoelectric point and hydrophobicity, there were 10 sequences (LPI, LPH, LPF, LPD, LPR, LPK, MPV, VPV) whose inhibition rates were unknown (i.e., not included in the training data or test data) and were found in duplicate by multiple prediction models. In addition, there were 8 sequences (PPV, PPI, APV, IPV, LPA, MPV, API, VPH) whose inhibition rates were unknown and found by any one of the prediction models. In addition, among the tripeptides included in the top 15 sequences whose inhibition rates were known (i.e., included in the training data and test data), there were 7 sequences (VPI, IPI, LPV, APF, PPQ, PPT, IPT, VPA) that were predicted to have high inhibition rates by any of the prediction models.

[0042] The above prediction results, when the inhibition rates were unknown from the perspectives of the linear model (Ridge regression and Lasso regression) and the nonlinear model (SVR), were summarized in Figure 9. Figure 9 shows tripeptides predicted to have high inhibition rates by the linear model, tripeptides predicted to have high inhibition rates by the nonlinear model, and tripeptides that had high inhibition rates in common with both models. FIG. 1 shows tripeptides predicted to

[0043] Figure 10 is a graph showing the results of measuring the inhibition rate of tripeptides predicted to have a high inhibition rate. As shown in Figure 10, of the 17 tripeptides predicted to have a high inhibition rate, 10 had an inhibition rate of over 40% at a concentration of 50 μM. As described above, unknown peptides with relatively high DPP-IV inhibitory activity were extracted.

[0044] As can be seen from FIG. 10, the prediction accuracy of the nonlinear model was relatively high. Therefore, it can be said that it is preferable to use SVR as the prediction model. In addition, since tripeptides predicted to have a high inhibition rate only by the nonlinear model also include those with a relatively high inhibition rate, it is expected that screening will be performed without omission, for example, by taking the union of the prediction results of the linear model and the prediction results of the nonlinear model. In addition, the action of the peptides extracted by screening may be verified and used as a novel peptide according to another aspect of the present invention, a composition for inhibiting DPP-IV, etc.

[0045] <Other> The configurations in the embodiments are merely examples, and additions, omissions, and modifications of the configurations are possible without departing from the spirit of the present invention. In addition, the aspects disclosed in this specification can be combined with other aspects disclosed in this specification.

[0046] The configuration shown in the embodiment can be expected to predict relatively high physiological activities other than DPP-IV inhibitory activity. The configuration shown in the embodiment can be applied to peptides in general, not limited to tripeptides. The physiological activity in the embodiment includes so-called biological activity.

[0047] In addition, some of the functions of the prediction device 1 may be distributed and implemented in multiple computers included in one system, or the same function may be processed in parallel by multiple computers. In addition, the prediction device 1 may be a server connected to a network. In this case, the prediction device 1 performs processing in response to a request from another computer and transmits the processing result to the requesting computer. In addition, some of the functions of the prediction device 1 may be provided on a so-called cloud. In addition, the processes from S1 to S8 shown in FIG. 2 do not need to be executed continuously, and only the processes from S5 to S8 may be performed by a computer that is previously equipped with a prediction model created in S1 to S4.

[0048] Furthermore, the data structure of the table shown in FIG. 3 is just an example, and the information may be normalized and stored separately in multiple tables, or denormalized and stored collectively in a single table, as appropriate.

[0049] The present invention includes a computer program for executing the processing according to the embodiment, and a computer-readable recording medium having the program recorded thereon. The processing according to the embodiment can be performed by a computer executing the program recorded on the recording medium.

[0050] A computer-readable recording medium is a recording medium that stores information such as data and programs through electrical, magnetic, optical, mechanical, or chemical action and can be read by a computer. Among such recording media, those that can be removed from a computer include flexible disks, magneto-optical disks, optical disks, magnetic tapes, memory cards, etc. Also, recording media that are fixed to a computer include HDDs, SSDs, ROMs, etc.

[0051] <New peptides and peptides with DPP-IV inhibitory activity> Next, embodiments according to other aspects of the present invention will be described in detail. However, the present invention is not limited to the following embodiments and can be freely modified within the scope of the present invention.

[0052] The present invention provides an oligopeptide comprising any one of the following sequences, which have been newly discovered in natural proteins: Ala-Pro-Val Pro-Pro-Ile Pro-Pro-Val Ala-Pro-Ile Leu-Pro-Ile Met-Pro-Val Val-Pro-His Glu-Pro-Ile

[0053] The present invention also provides an oligopeptide comprising any one of the sequences described below, which is contained in a composition described below. The oligopeptide comprising any one of the eight sequences described above and the two sequences below is hereinafter also referred to as the "oligopeptide of the present invention." These oligopeptides of the present invention include peptides containing amino acid sequences extracted as having DPP-IV inhibitory activity by the above-mentioned prediction method of the present invention. Lys-Pro-Ile Val-Pro-Glu

[0054] Here, Ala (A) represents an alanine residue, Pro (P) represents a proline residue, Val (V) represents a valine residue, Ile (I) represents an isoleucine residue, Leu (L) represents a leucine residue, Met (M) represents a methionine residue, His (H) represents a histidine residue, Glu (E) represents a glutamic acid residue, and Lys (K) represents a lysine residue. Each of the amino acids is preferably an L-amino acid.

[0055] The oligopeptide of the present invention is not particularly limited as long as it contains any of the above-mentioned 10 types of sequences, and may have any amino acid residue added to the N-terminus and / or C-terminus of the above sequence. In the present invention, the oligopeptide preferably has an amino acid length of 3 to 10 residues, more preferably 3 to 7 residues, and even more preferably 3 to 4 residues.

[0056] As the oligopeptide of the present invention, a tripeptide consisting of any one of the sequences described below (hereinafter also referred to as "the tripeptide of the present invention") is particularly preferred. A peptide consisting of Ala-Pro-Val (also called "APV peptide") A peptide consisting of Pro-Pro-Ile (also called a "PPI peptide") A peptide consisting of Pro-Pro-Val (also called "PPV peptide") A peptide consisting of Ala-Pro-Ile (also called an "API peptide") A peptide consisting of Leu-Pro-Ile (also called an "LPI peptide") A peptide consisting of Lys-Pro-Ile (also called "KPI peptide") A peptide consisting of Met-Pro-Val (also called "MPV peptide") A peptide consisting of Val-Pro-His (also called "VPH peptide") A peptide consisting of Val-Pro-Glu (also called "VPE peptide") A peptide consisting of Glu-Pro-Ile (also called an "EPI peptide")

[0057] The oligopeptide containing any of the ten types of sequences described above may be in the free form or in the form of a salt thereof. Examples of the salt include alkali metals such as potassium and sodium. alkaline earth metals such as calcium, magnesium, etc. Also, it may be in the form of an acid addition salt such as hydrochloride, lactate, ascorbate, malate, etc.

[0058] The oligopeptides of the present invention can be obtained, for example, by (1) hydrolyzing a protein or peptide containing a desired sequence among the aforementioned 10 types of amino acid sequences with a hydrolase or the like, and isolating and purifying the resulting hydrolyzate; (2) synthesizing the oligopeptide of the present invention by a peptide synthesis method, and then isolating and purifying the desired oligopeptide of the present invention from the resulting crude synthesis product; or (3) extracting the oligopeptide of the present invention from a plant, animal, or microorganism that produces the oligopeptide of the present invention according to the present invention, and then isolating and purifying the resulting extract. Methods (1) and (2) will be specifically explained below.

[0059] (1) Hydrolysis The oligopeptide of the present invention can be obtained by hydrolyzing a protein or peptide containing a desired sequence among the above-mentioned 10 types of amino acid sequences with a protease, acid, alkali, etc., and isolating and purifying the oligopeptide of the present invention from the obtained hydrolysate. Examples of raw material proteins and peptides include proteins derived from milk, soybeans, peas, rice, wheat, barley, potatoes, sweet potatoes, corn, eggs, livestock meat, fish meat, seafood, etc.

[0060] A method for obtaining the oligopeptide of the present invention by treatment with hydrolase will be described below using a typical protein as a raw material as an example, but the oligopeptide can be obtained by appropriately modifying the method depending on the type of protein raw material. The protein raw material is a protein that contains the oligopeptide of the present invention in its primary structure and is capable of producing the oligopeptide of the present invention when digested with an appropriate hydrolase. First, before hydrolysis with an enzyme, the raw protein is dispersed and dissolved in water or warm water to prepare a protein aqueous solution. In order to solubilize the protein, the pH may be appropriately adjusted. The concentration of the protein aqueous solution is not particularly limited, but is usually set to a concentration of 2% by mass or more, more preferably about 5 to 30% by mass in terms of protein concentration. Furthermore, it is preferable to desalt the aqueous protein solution by an ion exchange method using a sodium or potassium type cation exchange resin (preferably a strongly acidic cation exchange resin), an electrodialysis method, an ultrafiltration membrane method, a loose reverse osmosis membrane method, or the like, and to appropriately adjust the pH and calcium concentration. Either a column method or a batch method may be used for desalting. In addition, the aqueous protein solution may be appropriately heat sterilized before desalting, etc.

[0061] The aqueous protein solution is then subjected to a hydrolysis treatment, for example, an enzyme treatment, an acid treatment, an alkali treatment, a heat treatment, etc., and two or more of these treatments may be appropriately combined. For the enzyme treatment, protease derived from plants, animals, microorganisms, etc. can be used, and one or more of these can be used in combination. As the protease, endoprotease is preferred. Examples of the endoprotease include serine proteases, metalloproteases, cysteine ​​proteases, and aspartic acid proteases, and one or more of these may be selected for use. It is preferred to use a protease. Proteases are further classified into alkaline proteases, neutral proteases and acidic proteases, of which neutral proteases are preferred.

[0062] The protease may be a commercially available product. Examples of the protease include Sumiteam LP (manufactured by Shin Nippon Chemical Industry Co., Ltd.) and Bioprase (manufactured by Nagase Chemtech Co., Ltd.). (manufactured by Amano Enzyme Co., Ltd.), Proleather (manufactured by Amano Enzyme Co., Ltd.), Protease S (manufactured by Amano Enzyme Co., Ltd.), PTN6.0S (manufactured by Novozymes Co., Ltd.), Savinase (manufactured by Novozymes Co., Ltd.), GODOB.AP (manufactured by Godo Shusei Co., Ltd.), Protease N (manufactured by Amano Enzyme Co., Ltd.), GODO B. Examples of enzymes include NP (manufactured by Godo Shusei Co., Ltd.), Neutrase (manufactured by Novozymes), Alcalase (manufactured by Novozymes), trypsin (manufactured by Novozymes), chymotrypsin (manufactured by Novozymes), subtilisin (manufactured by Novozymes), papain (manufactured by Amano Enzymes), and bromelain (manufactured by Amano Enzymes). One or more of these enzymes may be selected and used.

[0063] The amount of endoprotease used relative to the protein is not particularly limited and may be appropriately adjusted depending on the substrate concentration, enzyme titer, reaction temperature, reaction time, etc., but it is generally preferable to add the endoprotease at a ratio of 100 to 30,000 activity units per gram of protein in the protein. By appropriately adjusting the conditions for hydrolysis with the protease, the desired peptide can be obtained.

[0064] Before hydrolysis with the protease, the pH of the raw material protein solution can be adjusted to the optimum pH for the enzyme to be used using food-safe salts such as potassium carbonate, sodium hydroxide, etc. The pH of the raw material protein solution is adjusted to preferably 5 to 10, more preferably 7 to 10.

[0065] The reaction temperature of the protease is desirably within the optimum temperature range for the enzyme used, preferably 30 to 70°C, and more preferably 40 to 60°C. The reaction time for the protease may be, for example, 0.5 to 24 hours, preferably 1 to 15 hours, and more preferably 3 to 10 hours, by continuing the reaction while monitoring the decomposition rate of the enzyme reaction until the desired decomposition rate is reached. The decomposition rate is not particularly limited, but is preferably 10 to 40%.

[0066] The decomposition rate of the raw material protein is calculated by measuring the total nitrogen content of the sample using the Kjeldahl method (Food Analysis Methods, edited by the Japan Society of Food Industry, p. 102, Korin Co., Ltd., 1984) and measuring the formol nitrogen content of the sample using the formol titration method (Mitsuda et al., eds., Food Engineering Experiment Book, Vol. 1, p. 547, Yokendo, 1970), and then calculating the decomposition rate from these measured values ​​using the following formula. Decomposition rate (%) = (formol nitrogen amount / total nitrogen amount) x 100

[0067] Hydrolysis by the protease may be terminated by heating and inactivating the enzyme. The heating temperature and holding time of the heat inactivation treatment may be appropriately set to conditions that allow sufficient inactivation, taking into consideration the thermal stability of the enzyme used. For example, it is preferable to carry out the treatment for 1 to 3 seconds when inactivating the enzyme at 100°C or higher (preferably 120 to 140°C), and for 3 to 40 minutes when inactivating the enzyme at 60°C or higher but lower than 100°C. The heat treatment may be carried out by either a batch method or a continuous method. As the continuous method, a plate heat exchange method, an infusion method, an injection method, or the like may be used. The above-mentioned heat inactivation treatment can also be used in combination with a sterilization treatment of the hydrolysate, and a conventional heat treatment method or the like can be used. After completion of the hydrolysis, the pH of the hydrolysis solution is adjusted, if necessary, to preferably 6 to 8, more preferably 7.0±0.5, and further preferably 7.0±0.3.

[0068] In the production of the protein hydrolysate according to the present invention, a solution with an unadjusted calcium concentration is In the case of hydrolysis, the resulting decomposition liquid may be desalted as described above to adjust the calcium concentration. The enzyme is then inactivated by heating in a conventional manner. The reaction heating temperature and reaction holding time can be appropriately set to conditions that allow sufficient inactivation, taking into consideration the thermal stability of the enzyme used. After heat inactivation, the enzyme is cooled in a conventional manner and can be used as is, or can be concentrated to obtain a concentrated liquid as necessary, and the concentrated liquid can be further dried to obtain a powder product.

[0069] Furthermore, when the aqueous protein solution is hydrolyzed by acid treatment or alkali treatment, the pH of the aqueous protein solution may be adjusted before treatment. In the case of treatment by pH adjustment, the pH of the aqueous protein solution is preferably 5 or less or 9 or more, more preferably 4 or less or 10 or more. The aqueous solution thus pH-treated can be left or stirred at room temperature for several minutes or more, preferably 5 minutes to 1 hour, to obtain an acid- or alkali-treated hydrolysate. Here, "room temperature" refers to about 4 to 40°C, preferably 10 to 30°C. The protein aqueous solution may be hydrolyzed by heat treatment. The pH of the protein aqueous solution may be unadjusted or may be pH adjusted (specifically, acidic (pH 5 or less), neutral (pH 6 to 8), or alkaline (pH 8 or more)). The heat treatment may be performed at about 4 to 100° C. under the same conditions as the acid-alkali treatment.

[0070] The content of the oligopeptide of the present invention in the protein hydrolysate is not particularly limited, but the lower limit is preferably 0.001% by mass or more, more preferably 0.005% by mass or more, and even more preferably 0.01% by mass or more, from the viewpoint of better exerting the efficacy of the present invention, and the upper limit is preferably 5% by mass or less, more preferably 3% by mass or less, even more preferably 2% by mass or less, and even more preferably 1.5% by mass or less, from the viewpoint of the production efficiency of the hydrolysate of the present invention.

[0071] In the present invention, the peptide content of a subject can be measured by the following method. (a) The sample powder is diluted and dissolved in 0.2% formic acid aqueous solution to a concentration of 1.0 mg / mL, and then ultrasonically crushed for 10 minutes, filtered through a 0.22 μm PVDF filter (Millipore) to prepare a powder solution, and LC / MS analysis is performed under the following measurement conditions. Meanwhile, solutions of chemically synthesized standard peptides (Peptide Institute) of the peptide to be measured are prepared at several concentrations, and LC / MS analysis is performed under the following measurement conditions to create a calibration curve. Among the peaks in the analysis of the powder solution, those having the same molecular weight and retention time as the standard peptide are identified as having the same sequence as the standard peptide. The content of the target peptide in the powder solution is determined by comparing the peak area of ​​the standard peptide with the peak area of ​​the sample powder.

[0072] (b) Target peptide content (mg / g protein hydrolysate) Target peptide content (mg / g of protein hydrolysate) = [measured target peptide content in the obtained protein hydrolysate (mg)] / [mass of the obtained protein hydrolysate (g)] [Measured amount (mg) of target peptide in the obtained protein hydrolysate] is the measured amount of the target peptide in the sample by "LC / MS" described below.

[0073] (c)LC / MS equipment used Mass spectrometer: TSQ Quantum Discovery MAX (Thermo Fisher Scientific). High performance liquid chromatograph: Prominence (Shimadzu Corporation), column: XBridgeBEH300 C18 φ2.1 mm×250 mm, 3.5 μm (Waters Corporation).

[0074] (d) LC / MS measurement conditions Mobile phase A: 0.2% by weight formic acid in water Mobile phase B: 0.2% by weight formic acid-acetonitrile solution Time program: 2%B (0.0 min)-25%B (5.0 min)-65%B (5.1 min)-65%B (10 min)-85%B (10.1 min)-85%B (13.0%)-2%B (13.1 min)-STOP (30.0 min). Sample injection volume: 10 μL, column temperature: 40°C, liquid flow rate: 200 μL / min Analysis mode: SRM measurement. Product Mass: m / z=260.10(Parent m / z = 375.21)

[0075] The obtained protein hydrolysate can exert its efficacy even when used in an unpurified state. That is, the protein hydrolysate may be ingested or administered in the form of a food or beverage or pharmaceutical composition described below. Furthermore, the obtained protein hydrolysate may be appropriately subjected to known separation and purification. For example, the obtained protein hydrolysate may be subjected to molecular weight fractionation to obtain a protein hydrolysate containing a fraction corresponding to the molecular weight of the oligopeptide of the present invention according to the present invention. The fraction containing the target substance may be determined using the DPP-IV inhibitory activity described below as an index. For molecular weight fractionation, for example, methods such as ultrafiltration and gel filtration can be used, which can increase the removal rate of peptides and free amino acids with unnecessary molecular weights. In the case of ultrafiltration, a desired ultrafiltration membrane may be used, and in the case of gel filtration, a desired gel filtration agent for size exclusion chromatography may be used. Furthermore, in order to desalt, remove impurities, or increase purity, known separation and purification methods, such as various types of chromatography (e.g., ion exchange chromatography, adsorption chromatography, reverse phase chromatography, partition chromatography, etc.), solvent precipitation, salting out, partition between two liquid phases, etc., may be used.

[0076] The separated and purified peptide fractions can be subjected to peptide identification by mass spectrometry to confirm whether they contain the oligopeptide of the present invention. The oligopeptide of the present invention thus obtained can be used as it is in the form of a peptide solution, or, if necessary, the solution can be concentrated by a known method to be used as a concentrate, or the concentrate can be dried by a known method to be used as a powder.

[0077] (2) Synthesis The oligopeptides of the present invention can also be produced by chemical synthesis or biosynthesis. Chemical synthesis of peptides can be carried out by the liquid phase method or solid phase method commonly used for the synthesis of oligopeptides. The synthesized peptide can be deprotected as necessary, and the oligopeptide of the present invention can be isolated by removing unreacted reagents and by-products. Such peptide synthesis can be carried out using a commercially available peptide synthesizer. Peptide biosynthesis can be carried out by a conventional method, such as by introducing a peptide expression vector into a host organism and allowing the organism to produce and secrete the peptide. Whether or not the desired peptide has been obtained can be confirmed using the DPP-IV inhibitory activity described below as an indicator.

[0078] The oligopeptide of the present invention has the effect of inhibiting DPP-IV activity. Here, having DPP-IV inhibitory activity means that the DPP-IV activity in the presence of the oligopeptide of the present invention is smaller than the activity in the absence of the oligopeptide, and The inhibition rate is preferably 10% or more, more preferably 30% or more, and even more preferably 50% or more. The 50% inhibitory concentration (IC 50 ) is preferably 1000 μM or less, more preferably 500 μM or less, and even more preferably 100 μM or less. In addition, DPP-IV inhibitory activity, inhibition rate, IC 50 can be confirmed by standard methods.

[0079] Therefore, the oligopeptide of the present invention can be preferably contained as an active ingredient of a DPP-IV inhibitor. Here, the oligopeptide of the present invention may be contained in the agent in the form of a protein hydrolysate, in other words, a protein hydrolysate containing one or more oligopeptides of the present invention can be preferably contained as an active ingredient of a DPP-IV inhibitor.

[0080] DPP-IV degrades substances involved in physiological functions in the body, which can cause various diseases and symptoms. Therefore, by inhibiting DPP-IV, the degradation of substances involved in physiological functions in the body that are usually degraded by DPP-IV is suppressed, and their life span is extended, making it possible to prevent, improve, or treat diseases and symptoms caused by DPP-IV. Substances involved in the physiological functions include, for example, incretins such as GLP-1 and GIP. Inhibition of the degradation of GLP-1 leads to glucose-induced stimulation of insulin biosynthesis and secretion, suppression of glucagon secretion, regulation of gene expression, nutritional effect on β-cells, inhibition of food intake, and slowing of gastric emptying. In addition, inhibition of the degradation of GIP leads to glucose-induced stimulation of insulin secretion from the pancreas. These can contribute to normalization of elevated blood glucose levels and improvement and adjustment of hunger and body weight.

[0081] It is also known that the action of DPP-IV reduces or damages the function of endothelial cells in blood vessels. This reduced function or damage of endothelial cells in blood vessels leads to vascular disorders such as vasoconstriction due to increased vascular tension, arteriosclerosis, and thrombus formation, which in turn leads to impaired blood flow in organs, resulting in organ dysfunction and diabetic complications. In recent years, many reports have shown that administration of DPP-IV inhibitors improves endothelial cell function (e.g., Endocrine Journal 2011, 58 (1), 69-73, J Am Coll Cardiol. 2012, 59(3), 265-76, Diabetes Care. 2011, 34(9), 2072-7, Cardiovascular Diabetology 2011, 10(85), etc.). It is believed that the improvement in endothelial cell function is not only due to the reduction in blood glucose levels, but also due to the protective effect of incretin on blood vessels. When hyperglycemia reduces the function of endothelial cells and blood vessels lose their flexibility, blood pressure rises, and the increased blood pressure further damages blood vessels, creating a vicious cycle that has adverse effects on organs such as the heart, kidneys, and brain. Therefore, DPP-IV inhibitors are thought to play an important role in the treatment of the circulatory system.

[0082] From the above, the oligopeptide of the present invention, the protein hydrolysate containing one or more of the oligopeptides of the present invention, and the DPP-IV inhibitor of the present invention containing these have DPP-IV inhibitory activity, and thereby exhibit an effect of suppressing blood glucose rise, an effect of improving hyperglycemia, an effect of suppressing vascular endothelial dysfunction, an effect of suppressing vascular endothelial damage, an effect of suppressing vascular damage, an effect of protecting vascular endothelial cells, an appetite suppressant effect, etc. In this specification, "suppression of blood glucose level rise" includes the meaning of lowering blood glucose level, and particularly means "capable of lowering blood glucose levels that have risen above normal or above necessary levels." Normal blood glucose levels can be determined by referring to the diagnostic criteria of the Japan Diabetes Society (revised in 2012). Blood glucose level rise may refer to the rise that occurs after a meal. Furthermore, the oligopeptide of the present invention and the DPP-IV inhibitor of the present invention containing the same have DPP-IV inhibitory activity, and therefore, it is considered that the oligopeptide of the present invention and the DPP-IV inhibitor of the present invention containing the same can be used in a method for preventing, improving and / or treating diseases and symptoms caused by DPP-IV by ingesting or administering the oligopeptide of the present invention and the DPP-IV inhibitor of the present invention containing the same to animals including humans. Here, prevention of a disease, symptom, etc. includes preventing the onset of a disease or symptom, delaying the onset, and reducing the risk of the onset in a subject that is not suffering from (has not developed) the disease, symptom, etc.

[0083] The subject to which the oligopeptide of the present invention is administered (recipient) and the subject to which the oligopeptide is ingested (recipient) are not particularly limited as long as they are animals, but are usually humans. They may be adults, children, infants, newborns, etc. Furthermore, there are no particular limitations on the gender.

[0084] Another aspect of the present invention is the use of an oligopeptide of the present invention in the manufacture of a DPP-IV inhibitor or a composition for the prevention, amelioration and / or treatment of a disease or condition caused by DPP-IV. Another aspect of the present invention is the use of the oligopeptides of the present invention in the inhibition of DPP-IV or in the prevention, amelioration and / or treatment of diseases or conditions caused by DPP-IV. Another aspect of the present invention is the oligopeptide of the present invention for use in inhibiting DPP-IV, or for preventing, ameliorating and / or treating a disease or condition caused by DPP-IV. Another aspect of the present invention is a method for inhibiting DPP-IV or preventing, ameliorating and / or treating a disease or condition caused by DPP-IV, comprising administering to an animal an oligopeptide of the present invention.

[0085] In this specification, "administering the oligopeptide of the present invention to an animal" may be synonymous with "allowing an animal to ingest the oligopeptide of the present invention." Ingestion may be voluntary (free intake) or forced (forced intake). That is, the administration step may specifically be, for example, a step of blending the oligopeptide of the present invention into food, drink, or feed and supplying it to a subject, thereby allowing the subject to ingest the oligopeptide of the present invention freely.

[0086] The timing of intake (administration) of the oligopeptide of the present invention is not particularly limited, and can be appropriately selected depending on the condition of the subject to be administered.

[0087] The intake (administration) amount of the oligopeptide of the present invention is appropriately selected depending on the age, sex, condition, other conditions, etc. of the subject to be ingested (administered). The intake (administration) amount of the oligopeptides of the present invention is preferably in the range of 1 μg / day to 10 mg / day, and more preferably in the range of 5 μg / day to 5 mg / day for adults, for each type of oligopeptide of the present invention. Regardless of the amount or period of ingestion (administration), the oligopeptide of the present invention can be administered once a day or in divided doses.

[0088] The period of ingestion (administration) of the oligopeptide of the present invention is not particularly limited, but is preferably 12 weeks or more, more preferably 24 weeks or more, in order to obtain the desired effect. In addition, there is no particular upper limit to the period of ingestion (administration), and continuous, long-term ingestion (administration) is possible.

[0089] The oligopeptide of the present invention may be administered orally or parenterally, preferably orally. Parenteral administration includes transdermal, intravenous, rectal, and inhalation.

[0090] As described above, the DPP-IV inhibitor of the present invention can be suitably used for the prevention, amelioration, or treatment of diseases or symptoms caused by DPP-IV, and therefore, it is preferable to incorporate the DPP-IV inhibitor in a pharmaceutical composition. That is, a pharmaceutical composition for the prevention, amelioration, or treatment of diseases or symptoms caused by DPP-IV is also an embodiment of the present invention. Examples of diseases and symptoms caused by DPP-IV include, but are not limited to, hyperglycemia, diabetes, diabetic complications, vascular endothelial disorder, vascular disorder, etc. Diabetes preferably includes type 2 diabetes, more preferably type 2 diabetes caused by decreased insulin secretion. In addition, diseases and symptoms caused by DPP-IV may include those in which DPP-IV is directly involved, as well as those in which DPP-IV is indirectly involved. Furthermore, the pharmaceutical composition of the present invention may be used to treat various diseases and conditions caused by hyperglycemia, diabetes, and hyperglycemic states, including, for example, diabetic microangiopathy (e.g., retinopathy, nephropathy, neuropathy, etc.) and macrovascular complications (e.g., ischemic heart disease such as angina pectoris and myocardial infarction, cerebral infarction, arteriosclerosis obliterans, gangrene, etc.).

[0091] The pharmaceutical composition may be administered orally or parenterally, preferably orally. Parenteral administration includes transdermal, intravenous, rectal, and inhalation. The pharmaceutical composition can be formulated into a desired dosage form according to the administration method.For example, in the case of oral administration, it can be formulated into solid preparations such as powders, granules, tablets, and capsules; liquid preparations such as solutions, syrups, suspensions, and emulsions.In addition, in the case of parenteral administration, it can be formulated into suppositories, ointments, injections, and the like. In the formulation, in addition to the oligopeptide of the present invention, ingredients that are usually used in formulations, such as excipients, pH adjusters, colorants, flavoring agents, etc., can be used. It is also possible to use other pharmaceutical ingredients, or other medicines, such as ingredients that can prevent, ameliorate, and / or treat diseases or symptoms caused by DPP-IV, which are known or will be discovered in the future. Furthermore, other peptides may be present in combination so long as they do not impede the effects of the present invention. In addition, formulation can be carried out by a known method according to the dosage form. When formulating, a carrier usually used in formulation may be appropriately mixed. Such carriers include excipients, binders, disintegrants, lubricants, stabilizers, flavoring agents, etc.

[0092] Examples of excipients include sugar derivatives such as lactose, sucrose, glucose, mannitol, and sorbitol; starch derivatives such as corn starch, potato starch, α-starch, dextrin, and carboxymethyl starch; cellulose derivatives such as crystalline cellulose, hydroxypropyl cellulose, hydroxypropylmethyl cellulose, carboxymethyl cellulose, and calcium carboxymethyl cellulose; gum arabic; dextran; pullulan; silicate derivatives such as light anhydrous silicic acid, synthetic aluminum silicate, and magnesium aluminometasilicate; phosphate derivatives such as calcium phosphate; carbonate derivatives such as calcium carbonate; and sulfate derivatives such as calcium sulfate.

[0093] Examples of binders include gelatin, polyvinylpyrrolidone, macrogol, and the like, in addition to the above-mentioned excipients.

[0094] Examples of disintegrants include the above-mentioned excipients, as well as chemically modified starch or cellulose derivatives such as croscarmellose sodium, sodium carboxymethyl starch, and crosslinked polyvinylpyrrolidone.

[0095] Examples of lubricants include talc; stearic acid; metal stearates such as calcium stearate and magnesium stearate; colloidal silica; waxes such as pea gum and galangal; boric acid; glycol; carboxylic acids such as fumaric acid and adipic acid; sodium carboxylates such as sodium benzoate; sulfates such as sodium sulfate; leucine; lauryl sulfates such as sodium lauryl sulfate and magnesium lauryl sulfate; silicic anhydride, silicic acid hydrate, etc. starch derivatives, etc.

[0096] Examples of the stabilizer include paraoxybenzoic acid esters such as methylparaben and propylparaben; alcohols such as chlorobutanol, benzyl alcohol, and phenylethyl alcohol; benzalkonium chloride; acetic anhydride; and sorbic acid.

[0097] Examples of flavoring agents include sweeteners, acidulants, and fragrances. In the case of a liquid preparation for oral administration, examples of the carrier used include solvents such as water.

[0098] The timing of taking the pharmaceutical composition of the present invention is not particularly limited, and may be, for example, before meals, after meals, between meals, or before going to bed, but before meals is preferable.

[0099] When the oligopeptide of the present invention is formulated as a composition to be orally ingested, it is preferably in the form of a food or drink.

[0100] As described above, the oligopeptide of the present invention has DPP-IV inhibitory activity, and thereby exhibits effects such as suppressing blood glucose level rise, improving hyperglycemia, suppressing vascular endothelial dysfunction, suppressing vascular endothelial disorder, suppressing vascular disorder, protecting vascular endothelial cells, and suppressing appetite, and can be used as a composition intended to achieve these effects. A particularly preferred embodiment is a composition for suppressing postprandial blood glucose level rise. The oligopeptide of the present invention can be used in the form of food or drink without any particular limitation on its use. It can also be used as food or drink for the above-mentioned various actions, and is particularly preferably used as food or drink for suppressing postprandial blood glucose level increase.

[0101] There are no particular limitations on the form or properties of the food or beverage product as long as it does not impair the effects of the present invention and can be taken orally. Other than containing the oligopeptide of the present invention, the food or beverage product can be produced by conventional methods using raw materials normally used for food or beverage products.

[0102] Food and drink products include any of the following forms: liquid, paste, gel, solid, powder, etc. For example, tablet confectionery; liquid food (nutritional food for tube feeding); wheat flour products such as bread, macaroni, spaghetti, noodles, cake mix, fried chicken powder, breadcrumbs, etc.; instant noodles, cup noodles, retort / prepared foods, canned foods, microwave foods, instant soup / stew, instant miso soup / cleansing liquid, canned soup, freeze-dried foods, other instant foods, etc.; canned agricultural products, canned fruit processed agricultural products such as jams and marmalades, pickles, boiled beans, dried agricultural goods, cereals (processed grain products); processed marine products such as canned seafood, fish ham and sausages, fish paste products, marine delicacies, and tsukudani; processed livestock products such as canned livestock pastes, livestock ham and sausages; dairy products such as processed milk, milk drinks, yogurt, lactic acid bacteria drinks, cheese, ice cream, modified milk powder, cream, other dairy products, etc.; fats and oils such as butter, margarines, vegetable oils, etc. Basic seasonings such as soy sauce, miso, sauces, processed tomato seasonings, mirin, vinegar, etc.; complex seasonings and foods such as cooking mixes, curry bases, sauces, dressings, noodle soups, spices, and other complex seasonings; frozen foods such as raw frozen foods, semi-cooked frozen foods, and cooked frozen foods; caramel, candy, chewing gum, chocolate, cookies, biscuits, cakes, pies, snacks, crackers, Japanese sweets, rice snacks, bean snacks, and dessert snacks. , jellies, and other sweets; beverages such as carbonated drinks, natural fruit juice, fruit juice drinks, soft drinks with fruit juice, fruit pulp drinks, fruit drinks with fruit particles, vegetable drinks, soy milk, soy milk drinks, coffee drinks, tea drinks, powdered drinks, concentrated drinks, sports drinks, nutritional drinks, alcoholic drinks, and other beverages; other commercially available foods such as baby food, furikake, and ochazuke nori; infant formula; enteral nutrition; functional foods (foods for specified health uses, foods with nutritional functions), etc.

[0103] In addition, one aspect of the food and drink may be feed, such as pet food, livestock feed, and fish feed. The form of the feed is not particularly limited, and may contain, in addition to the oligopeptide of the present invention, for example, grains such as corn, wheat, barley, rye, milo, etc.; vegetable oil cakes such as soybean oil cake, rapeseed oil cake, palm oil cake, linseed oil cake, etc.; bran such as wheat bran, wheat bran, rice bran, defatted rice bran, etc.; manufacturing residues such as corn gluten meal and corn jam meal; animal feeds such as fish meal, skim milk powder, whey, yellow grease, tallow, etc.; yeasts such as torula yeast and brewer's yeast; mineral feeds such as calcium phosphate and calcium carbonate; oils and fats; simple amino acids; sugars, etc.

[0104] When the oligopeptide of the present invention is in the form of a food or drink (including feed), it can be provided or sold as a food or drink labeled with a use for improving diseases or symptoms caused by DPP-IV, or for use in preventing brain function decline, preventing memory decline, etc. Furthermore, the oligopeptide of the present invention according to the present specification can be used for the production of such a food or drink.

[0105] Such "indication" acts include all acts for informing consumers of the above-mentioned uses, and any expression that can recall or infer the above-mentioned uses falls under the category of "indication" acts in this invention, regardless of the purpose of the indication, the content of the indication, the object or medium on which it is displayed, etc. Furthermore, it is preferable that the "labeling" be done in an expression that allows consumers to directly recognize the above-mentioned uses.Specific examples include the act of transferring, delivering, displaying for the purpose of transferring or delivering, or importing food and beverage products or product packaging on which the above-mentioned uses are written, displaying or distributing advertisements, price lists, or transaction documents related to the products and writing the above-mentioned uses in information containing the above-mentioned uses and providing them by electromagnetic means (such as the Internet), etc.

[0106] On the other hand, the content of the labeling is preferably a labeling approved by the government etc. (for example, a labeling approved based on various systems established by the government and made in a manner based on such approval, etc.) In addition, it is preferable that such a labeling content is affixed to the packaging, containers, catalogs, pamphlets, POP and other sales site promotional materials, other documents, etc.

[0107] In addition, "labeling" also includes labeling as health food, functional food, enteral nutrition food, special purpose food, health functional food, food for specified health uses, nutrient functional food, functional food, functional labeling food, medical drug, etc. Among these, in particular, labeling approved by the Consumer Affairs Agency, for example, labeling approved under the system related to food for specified health uses, nutrient functional food, or functional labeling food, or a similar system, etc., can be mentioned. Specifically, labeling as food for specified health uses, labeling as food for conditional specified health uses, labeling that affects the structure or function of the body, labeling that reduces the risk of disease, labeling of functionality based on scientific evidence, etc., can be mentioned, and more specifically, labeling as food for specified health uses (especially labeling of health uses) and similar labeling as specified in the Cabinet Office Ordinance on the Permission of Labeling for Special Uses Prescribed in the Health Promotion Act (Cabinet Office Ordinance No. 57 of August 31, 2009) are typical examples.

[0108] Examples of such displays include "Slows the rise in blood glucose levels after meals", "Suppresses rise in blood glucose levels after meals", "For those concerned about blood glucose levels after meals", "Lowers high blood glucose levels", "For those concerned about high blood glucose levels", "Lowers high fasting blood glucose levels", "For those concerned about high fasting blood glucose levels", "Lowers high HbA1c (glycated hemoglobin) levels", "For those concerned about high HbA1c (glycated hemoglobin) levels", "For improving hyperglycemia", "Protects vascular endothelial cells", "When you want to suppress your appetite", etc. EXAMPLES

[0109] The present invention will be described in more detail below with reference to examples, but the present invention is not limited to these examples. It is not something that can be determined.

[0110] <Evaluation of DPP-IV inhibitory activity of tripeptides> The DPP-IV inhibitory activity of the tripeptides of the present invention was evaluated by IC 50 For comparison, other tripeptides containing the same amino acid residues as the tripeptide of the present invention but different sequences were also evaluated in the same manner. The DPP-IV inhibitory activity was evaluated according to the method of Kato et al. (Kato, T. et al., Biochem. Med. 19, p351, 1978). That is, recombinant human DPPIV (Enzo Life Sciences) was used as the enzyme (DPP-IV), and Gly-Pro-AMC (Enzo Life Sciences) was used as the substrate. DPP-IV inhibitor was placed in each well of a 384-well black plate (Nunc). Add 5 μL of P-IV solution (10.4 μU / μL) and 5 μL of each peptide solution. The mixture was incubated at 37°C for 10 minutes. After mixing, the mixture was incubated for 15 minutes at 37° C. A microplate reader (SH-9000, manufactured by Corona Electric Co., Ltd.) was used to measure the fluorescence intensity (Ex. 380 nm / Em. 460 nm). The inhibition rate was calculated using the following formula. Inhibition rate (%)=[1-(Y / X)]×100% X: Fluorescence intensity value of "water + enzyme + substrate" Y: Fluorescence intensity value of "test substance (peptide) + enzyme + substrate" I C 50 To calculate the inhibition rate, the peptide concentration was diluted stepwise, and the inhibition rate was calculated. Based on the results, the relationship between the peptide concentration and the inhibition rate was calculated. The peptide concentration at which the inhibition rate reached 50% was calculated using this relationship, and the IC 50 It was decided.

[0111] The results are shown in Table 1. 50The tripeptides of the present invention, APV peptide, PPI peptide, PPV peptide, API peptide, LPI peptide, KPI peptide, MPV peptide, VPH peptide, VPE peptide, and EPI peptide, showed high DPP-IV inhibitory activity. On the other hand, other tripeptides containing the same amino acid residues as the tripeptides of the present invention but with different sequences all showed IC 50 The DPP-IV inhibitory activity was significantly low.

[0112] [Table 1] [Explanation of symbols]

[0113] 1: Prediction device 11: Processor 111: Learning Department 112: Prediction Department 113: Evaluation Department 12: Storage device 13: Input / output device

Claims

1. Reading out combinations of peptide physical property values ​​and evaluation indices related to the biological activity of the peptides for a plurality of peptides to be learned; Creating a prediction model by machine learning the relationship between the physical property value and the evaluation index; predicting an evaluation index regarding a physiological activity of the peptide to be predicted using the prediction model created and physical property values ​​of the peptide to be predicted; A prediction method implemented by one or more computers, comprising: The physical property values ​​of the peptide are at least any one of the isoelectric point, hydrophobicity, molecular weight, polarity, and the presence or absence of an aromatic ring of each residue constituting the peptide. Forecasting methods.

2. The prediction model is one or more models including SVR (Support Vector Regression). R The prediction method according to claim 1 .

3. The predictive model further comprises a linear model. The prediction method according to claim 2 .

4. The physiological activity is an activity against a factor related to the prevention, treatment, or improvement of a disease or the like. The prediction method according to claim 1 .

5. The physiological activity is dipeptidyl peptidase-IV (DPP-IV) inhibitory activity. The prediction method according to claim 1 .

6. The physical properties of the peptide include the isoelectric point and hydrophobicity of each residue constituting the peptide. The prediction method according to claim 1 .

7. The peptide is a tripeptide. The prediction method according to claim 1 .

8. The plurality of peptides to be learned contain all 20 types of amino acids in their amino acid sequences. The prediction method according to claim 1 .

9. A prediction system comprising one or more computers that executes the prediction method according to any one of claims 1 to 8.

10. A prediction program for causing one or more computers to execute the prediction method according to any one of claims 1 to 8.

11. An oligopeptide comprising any of the sequences described below. Ala-Pro-Val Pro-Pro-Ile Pro-Pro-Val Ala-Pro-Ile Leu-Pro-Ile Met-Pro-Val Val-Pro-His Glu-Pro-Ile

12. The oligopeptide of claim 11 consisting of the above sequence.

13. A composition for inhibiting dipeptidyl peptidase-IV (DPP-IV), comprising one or more oligopeptides selected from those having any of the sequences described below. Ala-Pro-Val Pro-Pro-Ile Pro-Pro-Val Ala-Pro-Ile Leu-Pro-Ile Lys-Pro-Ile Met-Pro-Val Val-Pro-His Val-Pro-Glu Glu-Pro-Ile

14. The composition for inhibiting DPP-IV according to claim 13, wherein the oligopeptide consists of the sequence.

15. The composition for inhibiting DPP-IV according to claim 13 or 14, which is a pharmaceutical.

16. The composition for inhibiting DPP-IV according to claim 13 or 14, which is a food or drink.

17. A composition for suppressing blood glucose level increase, comprising as an active ingredient one or more oligopeptides selected from those having any of the sequences described below. Ala-Pro-Val Pro-Pro-Ile Pro-Pro-Val Ala-Pro-Ile Leu-Pro-Ile Lys-Pro-Ile Met-Pro-Val Val-Pro-His Val-Pro-Glu Glu-Pro-Ile

18. The composition for suppressing blood glucose level increase described in claim 17, wherein the oligopeptide consists of the above sequence.

19. The composition according to claim 17 or 18, which is used to suppress an increase in blood glucose level after a meal or when fasting.

20. The composition according to claim 17 or 18, which is a food or drink.

21. 19. The composition of claim 17 or 18, which is a pharmaceutical agent.

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