A small molecule sweet peptide DSTT and its application

Abstract

This invention relates to the field of bioactive peptide technology, specifically to a small molecule sweet peptide DSTT and its applications. The amino acid sequence of the small molecule sweet peptide DSTT is shown in SEQ ID NO:1. A machine learning model was used to predict the sweetness of the peptides obtained from hydrolysis. Molecular docking analysis was performed on the screened peptides using AutoDock Vina to determine the interaction mode and binding site between the peptides and receptors. Electronic tongue experiments verified that the sweet peptide DSTT has promising applications as a sweet food additive.

Description

Technical Field

[0001] This invention relates to the field of bioactive peptide technology, specifically to a small molecule sweet peptide DSTT and its applications. Background Technology

[0002] Sugar is a fundamental component of the modern diet, primarily due to its ability to satisfy the innate human preference for sweetness. However, chronic excessive sugar intake has been linked to adverse health outcomes, including obesity, type 2 diabetes, hypertension, and cardiovascular disease. Consequently, various sweeteners have been widely incorporated into food and beverage products as sugar substitutes. Nevertheless, their safety profile remains under ongoing scientific investigation. Recent evidence suggests that some artificial sweeteners may disrupt glucose homeostasis, alter gut microbiota composition, and may contribute to neurotoxicity and carcinogenicity. While sugar alcohols elicit a lower glycemic response, high intake is often associated with gastrointestinal discomfort. Furthermore, some natural sweeteners have been linked to endocrine disruption and hepatotoxicity and nephrotoxicity. Therefore, sweeteners cannot be considered inherently safe substitutes for sugar, and their long-term consumption—especially at high levels or in susceptible populations—requires comprehensive evaluation. This underscores the urgent need to develop safer, lower-calorie sweeteners to effectively replace traditional sugars without inducing negative health effects.

[0003] Short peptides, generally possessing good affinity and low biotoxicity, have begun to attract attention. Sweet peptides, a type of short peptide, can induce a pronounced sweet taste. They activate intracellular signaling cascades by binding to sweet taste receptors—primarily T1R2 / T1R3 heterodimers—leading to sweet taste perception. Compared to traditional glycosyl sweeteners, sweet peptides exhibit higher sweetness intensity, minimal caloric contribution, and inherent biodegradability, making them attractive candidates for sugar substitution. These characteristics have garnered increasing attention in food chemistry, sensory science, and peptide-based molecular engineering. Milk, a staple food in the diet, is rich in functional proteins and bioactive peptides. Through enzymatic hydrolysis, microbial fermentation, or simulated gastrointestinal digestion, these proteins can be cleaved into peptides, some of which possess sweetness and sensory modulating properties.

[0004] Therefore, in response to the problems mentioned in the background art, the present invention screens short peptides with obvious sweetness characteristics from a milk peptide library, which can be used as sugar substitutes in the food industry. Summary of the Invention

[0005] The purpose of this invention is to provide a small molecule sweet peptide DSTT and its applications to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A small molecule sweet peptide DSTT, the amino acid sequence of which is shown in SEQ ID NO:1.

[0008] Application of small molecule sweet peptide DSTT in the preparation of food additives.

[0009] Compared with the prior art, the beneficial effects of the present invention are:

[0010] This invention utilizes computer technology to simulate enzymatic hydrolysis and employs a machine learning model to predict the sweetness of the resulting peptides. Molecular docking analysis was performed on the screened peptides using AutoDock Vina to determine the interaction modes and binding sites between the peptides and receptors. Electronic tongue experiments verified that the sweet peptide DSTT has promising applications as a sweet food additive. Attached Figure Description

[0011] Figure 1 Experimental diagram showing the molecular docking results of the sweet peptide DSTT;

[0012] Figure 2 This is a diagram of the sweetness activity experiment of the sweet peptide DSTT. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] Please see Figures 1 to 2 The present invention provides:

[0015] Example 1: Rational design and molecular docking of the sweet peptide DSTT

[0016] This invention simulates enzymatic hydrolysis of milk proteins to obtain 3336 short peptides, from which sweet-tasting polypeptides are systematically screened. Data was derived from 75 milk protein sequences in the UniProt database, including major components such as casein and whey protein. Trypsin and thermophilic protease were used to simulate the generation of short peptides. The simulated hydrolysis was performed using the ExPASyPeptideCutter tool, with the enzyme specificity set to trypsin's lysine and arginine C-terminal cleavage and thermophilic protease's hydrophobic residue cleavage. This generated 3336 unique peptides with lengths of 2-10 amino acids, and the molecular weight, isoelectric point, lipophilicity, solubility, and other physicochemical properties of each peptide were fully recorded.

[0017] During the screening process, this invention employed the VirtuousSweetBitter model based on machine learning to predict the sweetness potential of peptides in the database. This model utilizes deep learning algorithms to extract information from features such as the amino acid composition, sequence order, and secondary structure of peptides, and generates feature vectors using five feature encoding methods: AAC, DPC, CTDC, CTDD, and CTDT, with a total feature vector length of 843 dimensions. The training dataset included known sweet and non-sweet peptides, and through five-fold cross-validation, the model achieved a prediction accuracy of over 90%. By performing batch predictions on a database containing 3336 peptides, the model calculated the sweetness score for each peptide, and finally selected the top 10 peptides with the highest sweetness scores as candidate peptides for subsequent validation and experimental stages. Simultaneously, BitterGNNs and iBitter-GRE models were used to exclude peptides with bitterness potential, ensuring the purity of the sensory characteristics of the candidate peptides.

[0018] Using the VirtuousSweetBitter model, short peptides with scores greater than 0.95 were screened from the top 10 peptides with the highest sweetness ratings, and further molecular docking analysis was performed. Among them, DSTT (SEQ ID NO:1), short for Asp-Ser-Thr-Thr, is a peptide characterized by the presence of serine and threonine residues, which contribute to the formation of hydrogen bond interactions.

[0019] To further evaluate the potential of the screened sweet peptides, this invention also conducted molecular characterization. The screened sweet peptides were analyzed using the SwissADME tool for ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction, with a focus on evaluating water solubility, lipophilicity, gastrointestinal absorption, blood-brain barrier (BBB) ​​permeability, and bioavailability. The evaluation results showed that the sweet peptides screened by DSTT et al. exhibited good water solubility (ESOL model Log S value in the range of -0.5 to -1.5, indicating easy solubility in water); low lipophilicity (Log P value of -2.0 to -1.0, indicating strong hydrophilicity, which is beneficial for bioavailability); high gastrointestinal absorption (>90%, easily absorbed orally); low blood-brain barrier permeability (No, reducing central nervous system side effects); and moderate to high bioavailability. Furthermore, toxicity predictions showed no significant hepatotoxicity, nephrotoxicity, or carcinogenicity (negative AMES test and hERG blockade), no inhibition of cytochrome P450 enzymes, and no significant side effects. Furthermore, the metabolic pathways of these peptides were predicted using the ADMETlab 2.0 platform, and the results showed that they did not participate in other metabolic pathways and did not exhibit significant side effects.

[0020] This invention utilizes AutoDock Vina to perform molecular docking analysis on screened peptides, predicting their binding sites on target receptors. The molecular docking process includes: First, obtaining the crystal structure of the human sweet taste receptor T1R2 / T1R3 (PDB ID: 8JJP) from the PDB database. Then, using AutoDock Tools, the receptor and ligand files are prepared, including adding polar hydrogen, calculating Gasteiger charges, and setting the docking grid size to 40×40×40 Å, with the center coordinates representing the receptor's active site. Candidate peptides such as DSTT are used as ligands, and their structures are optimized using the MMFF94 force field. The docking mode is set to exhaustiveness=8, generating nine conformations. The conformation with the lowest binding affinity is selected as the optimal result. The docking results are analyzed and visualized using Pymol 2.6. The docking results are shown in Table 1.

[0021] Table 1: Predicted affinity results of screened peptides

[0022] Ranking polypeptide sequence Binding affinity (kcal / mol) Ranking polypeptide sequence Binding affinity (kcal / mol) 1 DSTT -6.90 6 STTG -6.50 2 CDSS -6.80 7 CSSG -6.30 3 TCDA -6.80 8 MKG -6.30 4 FCG -6.60 9 CTSG -6.20 5 MDG -6.60 10 TSG -6.20

[0023] The sweet receptor binding affinity of the small molecule peptide DSTT in this invention is -6.90 kcal / mol, and its binding sites with T1R2 / T1R3 are as follows: Figure 1 As shown in Table 1, binding affinity is typically negative; a lower (more negative) value indicates a more stable complex formed between the peptide and receptor. This stable binding enhances the peptide's activation potential for the T1R2 / T1R3 sweet taste receptor: when the peptide firmly occupies the active site of the receptor, it induces conformational changes in the receptor, activates downstream G protein signaling pathways, and ultimately triggers intracellular calcium ion release and neural signal transduction, leading to the perception of sweetness in humans. The data in Table 1 show that the sweet peptide DSTT has the highest affinity score, indicating its optimal potential as a sweetener. Combined with molecular docking results, the interaction between the peptide and receptor can be understood in detail. DSTT mainly binds to key residues D151, S153, S174, I176, S177, and D287 via hydrogen bonds, and forms a carbon-hydrogen bond with A175. These interactions include hydrogen bonds formed between serine residues of DSTT and receptors S174 and S177, and electrostatic interactions between threonine residues and D151 and D287, ensuring a stable binding conformation.

[0024] Example 2: Experiment to verify the sweetness activity of the sweet peptide DSTT

[0025] The milk peptides involved in this invention were synthesized and provided by Liaoning Newgene Biochemical Technology Co., Ltd., with a purity >98%. They were obtained by Fmoc solid-phase peptide synthesis, which involves loading Wang resin starting from the C-terminal Thr and sequentially coupling Thr, Ser, and Asp amino acids (using HBTU / DIEA activation). Each step is deprotected by Fmoc (20% piperidine / DMF), and finally lysed with a TFA mixture and purified by HPLC.

[0026] Sweetness was evaluated using an electronic tongue system (SA402B, Insent, Japan). The electronic tongue is equipped with multi-channel sensors, including sensors for sweetness, bitterness, and sourness, to simulate human taste perception. Sample preparation: DSTT was dissolved in deionized water to a concentration of 0.1 g / L, sonicated for 5 minutes to ensure complete dissolution, and then filtered to remove insoluble matter. Testing procedure: The sample was tested in four cycles, each including a 30-second taste measurement phase, followed by a 3-second wash with a standard reference solution and a 30-second aftertaste evaluation phase. The sensor was calibrated before each cycle; the data from the first round was discarded due to calibration adjustments, and the final result was the average of the remaining three rounds. The sweetness value was calculated using the sensor response voltage and quantified with reference to the sucrose standard curve. Electronic tongue analysis showed that the sweetness value of DSTT was 16.09, indicating significant sweetness characteristics. The sweetness activity verification diagram of the sweet peptide DSTT is shown below. Figure 2 As shown.

[0027] This invention simulates enzymatic hydrolysis using computer technology. A machine learning model was used to predict the sweetness of the hydrolyzed peptides. Molecular docking analysis was performed on the screened peptides using AutoDock Vina to determine the interaction mode and binding site between the peptides and receptors. Electronic tongue experiments verified that the sweet peptide DSTT has promising applications as a sweet food additive.

[0028] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A small molecule sweet peptide DSTT, characterized in that, Its amino acid sequence is shown in SEQ ID NO:

1.

2. The application of the small molecule sweet peptide DSTT as described in claim 1 in the preparation of food additives.

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