Antibacterial peptide L-AMPs of yellow croaker
Through screening and computer identification methods of 136 amino acid sequences in yellow croaker protein, three antibacterial peptides with antibacterial activity against a variety of pathogenic bacteria were discovered and verified, which solved the problems of extended food shelf life and drug resistance, and achieved efficient and safe food preservation effects.
Patent Information
- Application Number
- CN202510255401.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-05
- Publication Date
- 2025-06-24
AI Technical Summary
The prior art is difficult to effectively utilize natural preservatives to extend the shelf life of food, and chemical preservatives have drug resistance problems.
Through computer discrimination methods, 136 amino acid sequences in yellow croaker protein were screened, and three antibacterial peptides with antibacterial activity against Staphylococcus aureus, E. coli, and Porphyromonas gingivalis were discovered and verified.
Effective inhibition of the three pathogenic bacteria has been achieved, the shelf life of food is extended, and due to its high biosafety, it is not easy to develop drug resistance.
Smart Images
Figure CN120192364A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of antimicrobial peptides, and particularly relates to the amino acid sequences of multiple antimicrobial peptides L-AMPs derived from yellow croaker. Background Art
[0002] Microbial contamination is one of the main causes of food safety problems and can trigger foodborne diseases that threaten life and health. Using chemical preservatives is an effective means of food preservation. However, in recent years, the development and application of natural preservatives have become an important topic for extending the shelf life of food and ensuring food safety. Antimicrobial peptides are a type of small molecule peptides, and their specific composition and structure endow them with a unique antibacterial mechanism. Antimicrobial peptides can bind to bacterial biofilms through various actions such as electrostatic adsorption, change their conformation and penetrate the membrane structure, resulting in the outflow of cell contents, having broad-spectrum bacteriostasis, or binding to cell ribosomes to hinder DNA synthesis and other ways to play a bactericidal role, and are not easily resistant to drugs. In the present invention, through a computer discrimination method, the target antimicrobial peptides are compared and analyzed with the existing antimicrobial peptide database, and the antimicrobial properties of antimicrobial peptides are discriminated using support vector machine (SVM), random forest (RF), and artificial neural network (ANN) algorithms. And based on the obtained data set, its own discrimination model is established, verifying that the distribution of specific amino acids in the sequence has a relatively obvious positive impact on the antimicrobial properties of antimicrobial peptides, with a certain degree of accuracy. The present invention first discovered three effective natural antimicrobial peptide fragments in yellow croaker protein. These three antimicrobial peptides can all bind to peptidoglycan glycosyltransferase of the pathogenic bacterium Staphylococcus aureus, outer membrane lipopolysaccharide of Escherichia coli, and outer membrane RagAB peptide transporter of Porphyromonas gingivalis, and eliminate pathogenic bacteria from the level of destroying the cell wall and cell membrane of pathogenic bacteria to the level of affecting the physiological metabolism of pathogenic bacteria, providing an effective new solution for extending the food preservation period. Summary of the Invention
[0003] The purpose of the present invention is to provide three natural antimicrobial peptides derived from yellow croaker that have good antibacterial activity against Staphylococcus aureus, Escherichia coli, and Porphyromonas gingivalis and have relatively high toxicological safety for absorption and metabolism by the body. The present invention uses support vector machine (SVM), random forest (RF), and artificial neural network (ANN) discrimination algorithms to discriminate and screen 136 protein amino acids derived from yellow croaker, and obtains three antimicrobial peptides that have antibacterial activity against Staphylococcus aureus, Escherichia coli, and Porphyromonas gingivalis.
[0004] In order to achieve the above purpose, the present invention provides the following technical solutions: Yellow croaker antimicrobial peptides L-AMPs. The three yellow croaker antimicrobial peptides L-AMPs all come from yellow croaker protein fragments and include at least one of the following small molecule peptides: The amino acid sequence is KNYKY, SEQ ID NO.1; The amino acid sequence is KMVAR, SEQ ID NO.2; The amino acid sequence is FAMRK, SEQ ID NO.3.
[0005] Preferably, the three yellow croaker antimicrobial peptides are obtained by screening and comparison from 136 protein peptide fragments derived from yellow croaker. The three yellow croaker antimicrobial peptides L-AMPs all have high antibacterial properties in the discriminant algorithms of support vector machine (SVM), random forest (RF), and artificial neural network (ANN).
[0006] Preferably, the three yellow croaker antimicrobial peptides L-AMPs simultaneously meet the following conditions: SVM antimicrobial peptide discrimination rate > 0.98, RF antimicrobial peptide discrimination rate > 0.534, and are discriminated as antimicrobial peptides by the ANN algorithm.
[0007] Preferably, the amino acid sequences such as SEQ ID NO.1, SEQ ID NO.2, and SEQ ID NO.3 can all bind to the peptidoglycan glycosyltransferase / transpeptidase of Staphylococcus aureus, affecting the formation of its cell wall.
[0008] Preferably, the amino acid sequences such as SEQ ID NO.1, SEQ ID NO.2, and SEQ ID NO.3 can all bind to the lipopolysaccharide on the cell membrane of Escherichia coli, affecting the integrity of its inner membrane.
[0009] Preferably, the amino acid sequences such as SEQ ID NO.1, SEQ ID NO.2, and SEQ ID NO.3 can all bind to the outer membrane RagAB peptide transporter of Porphyromonas gingivalis, affecting its protein transport physiological activity.
[0010] Preferably, the amino acid sequences such as SEQ ID NO.1, SEQ ID NO.2, and SEQ ID NO.3 can be applied to oral care compositions for inhibiting periodontitis caused by Porphyromonas gingivalis, wherein the composition contains 0.1% - 5% by weight of yellow croaker antimicrobial peptide L-AMPs.
[0011] Preferably, the yellow croaker antimicrobial peptide L-AMPs can be prepared by the following steps: (1) Extract the total protein from yellow croaker tissues and obtain candidate peptide segments by enzymatic hydrolysis; (2) Use the SVM-RF-ANN triple discriminant model to screen out peptide segments with an antimicrobial peptide discrimination rate ≥ 0.98; (3) Purify the target peptide by HPLC with a purity ≥ 95%.
[0012] In the above technical solution, the yellow croaker antimicrobial peptide L-AMPs provided by the present invention. The present invention first discovered three safe and effective antimicrobial peptide fragments in the yellow croaker protein fragments. Under the three antimicrobial peptide discrimination methods of support vector machine (SVM), random forest (RF), and artificial neural network (ANN), the three antimicrobial peptides simultaneously satisfy that the SVM antimicrobial peptide discrimination rate > 0.98, the RF antimicrobial peptide discrimination rate > 0.534, and the ANN algorithm discriminates as antimicrobial peptides. Moreover, the drug-induced liver injury (DILI), mutagenicity (AMES), carcinogenicity, and skin sensitization of these three antimicrobial peptides are all negative, so they have good biological safety. And these three antimicrobial peptides can bind to the peptidoglycan glycosyltransferase 3DWK of the Staphylococcus aureus cell wall, the lipopolysaccharide 4RHB of the Escherichia coli cell membrane, and the RagAB peptide transporter 6LSN of the Porphyromonas gingivalis cell membrane. The docking energy with 3DWK is less than -80.9499 kcal / mol, the docking energy with 4RHB is less than -69.3665 kcal / mol, and the docking energy with 6SLN is less than -52.7364 kcal / mol. Therefore, the three antimicrobial peptides can all bind well to the pathogenic bacteria and play an antibacterial role. BRIEF DESCRIPTION OF THE DRAWINGS
[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.
[0014] Figure 1 It is an analysis relationship model diagram of amino acid sequence score and antimicrobial peptide activity; Figure 2 It is a heat map of the confusion matrix of the SVM model of the 136-peptide training set; Figure 3 It is a heat map of the confusion matrix of the BP neural network model of the 136-peptide training set; Figure 4 It is a heat map of the confusion matrix of the random forest (RF) model of the 136-peptide training set; Figure 5 The sequence is a schematic diagram of KNYKY (SEQ ID NO.1); Figure 6 The sequence is a schematic diagram of KMVAR (SEQ ID NO.2); Figure 7 The sequence is a schematic diagram of FAMRK (SEQ ID NO.3). DETAILED DESCRIPTION OF THE EMBODIMENTS
[0015] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.
[0016] The present invention will be further described below by way of specific embodiments.
[0017] Example 1 Antimicrobial Peptide Screening In the antimicrobial peptide screening database of the present invention, 136 amino acid sequences derived from yellow croaker were used, which are respectively: The peptide fragments derived from yellow croaker with 136 known sequences were discriminated for antimicrobial peptides. The support vector machine (SVM), random forest (RF), and artificial neural network (ANN) algorithms in the APD3 database were respectively used to analyze and discriminate the 136 protein peptide fragments from the existing known antimicrobial peptides. Among them, seven antimicrobial peptides with better antimicrobial activity that simultaneously satisfied the three algorithms were found.
[0018] In addition, in order to illustrate the influence of the different amino acid distributions in the 136 peptide segments with the serial number 5 in the present invention on the antimicrobial properties of the peptide segments, the present invention also established a discrimination model for the relationship between the amino acid sequence score and the antimicrobial properties of antimicrobial peptides.
[0019] There are already many research results showing that when the amino acid sequences of K, R, H, D, Y, F, and W are in the peptide segment, especially the amino acid K, it has an obvious positive linear influence on the antimicrobial properties of the peptide segment; when the amino acid sequences of L, I, Q, and E are in the peptide segment, it has an obvious negative linear influence on the antimicrobial properties in the peptide segment; In the discrimination model of the present invention, the following parameters were respectively set for the establishment of the model.
[0020] Positive amino acid score = (number of amino acid K + 2) * (number of amino acid R) * (number of amino acid H) * (number of amino acid D) * (number of amino acid Y) * (number of amino acid F) * (number of amino acid W); Negative amino terminal score = (number of amino acids L, I, Q, E in the peptide segment * 3); Amino acid sequence score X = positive amino acid score + negative amino acid score; For the calculation process of the peptide segment antimicrobial property Y, Assign Y1 = the possible probability value P1 of AMP discriminated by the support vector machine method in the APD3 database; Assign Y2 = the NAMP value P2 discriminated by the random forest method in the APD3 database. When it is discriminated as NAMP, its value is the NAMP probability value P2. When it is discriminated as AMP, P2 is 0. Assign Y3 = the discrimination value P3 discriminated by the artificial neural network method in the APD3 database. When it is discriminated as NAMP, its value is -0.5. When it is discriminated as AMP, its value is 0. Define the peptide antibacterial property as Y, and Y = Y1 + Y2 + Y3; For the training set containing 136 kinds of peptides, it can be obtained that: Finally, an analysis relationship model between the amino acid sequence score and the antibacterial peptide activity is established. From the model, it can be concluded that in the training set composed of 136 kinds of peptides, when the amino acid sequence score is greater than X > 5, the antibacterial activity of the peptide also satisfies 0.98 < Y ≤ 1; attached Figure 1 as shown: In addition, the antibacterial peptide discrimination in the present invention was carried out on 136 kinds of peptides, and the support vector machine discrimination model, BP neural network discrimination model, and random forest discrimination model in the present invention were respectively established; Among them, the parameters of the support vector machine SVM discrimination model for 136 kinds of peptides are shown in Table 4.
[0021] The heat map of the confusion matrix of the SVM model for the 136-peptide training set is as attached Figure 2 shown.
[0022] The evaluation results of the SVM model for 136 kinds of peptides are shown in Table 5, and the model accuracy > 0.9. Therefore, this model is effective and reliable.
[0023] Among them, the parameters of the BP neural network model for 136 kinds of peptides are shown in Table 6.
[0024] The heat map of the confusion matrix of the BP neural network model for the 136-peptide training set is as attached Figure 3 shown.
[0025] The evaluation results of the BP neural network for 136 kinds of peptides are shown in Table 7, and the model accuracy > 0.9. Therefore, this model is effective and reliable.
[0026] Among them, the parameters of the random forest RF model for 136 kinds of peptides are shown in Table 8.
[0027] The heat map of the confusion matrix of the random forest RF model for 136 peptide segments is shown in the appendix Figure 4 as follows
[0028] The evaluation results of the random forest RF model for 136 peptide segments are shown in Table 9. The accuracy of this model is >0.9. Therefore, this model is accurate and reliable
[0029] Example 2 Antimicrobial Peptide Toxicity Analysis Based on the arrangement structure of the amino acid sequence of the antimicrobial peptide, seven physicochemical properties of the antimicrobial peptide such as its molecular weight, net charge number, hydrophilicity, and protein binding potential can be calculated, as shown in Table 10
[0030] In Tables 11 and 12, ADMETLAB was used to evaluate the absorption, distribution, metabolism, clearance, and toxicity (ADMET) properties of seven antimicrobial peptides
[0031] In Tables 11 and 12, the AMES mutagenicity of four antimicrobial peptides, namely KKSKM, KRKKK, RKKKK, and RTWCK, showed positive. Although they have good antibacterial properties, they are not recommended to be used as antimicrobial peptides in food and medicine
[0032] Preferably, in Tables 11 and 12, the data results of each index show that among them, the drug-induced liver injury DILI, mutagenicity AMES, carcinogenicity, and skin sensitization of three antimicrobial peptides, namely KNYKY, KMVAR, and FAMRK, are all negative. Considering other indicators, these three antimicrobial peptides not only have good antibacterial properties but also have good biosafety
[0033] Therefore, the three peptides with the sequence of KNYKY (SEQ ID NO.1), the sequence of KMVAR (SEQ ID NO.2), and the sequence of FAMRK (SEQ ID NO.3) are the safe antimicrobial peptides with good antibacterial properties screened from 136 peptide fragments
[0034] Example 3 Mechanism of Action Test of Antimicrobial Peptides against Three Pathogenic Bacteria The cell wall and cell membrane of bacteria are important components of their growth and metabolism. A large number of research results show that antimicrobial peptides directly act on the surface of the microbial outer membrane, causing lysis and death of the organism. At the same time, another research result shows that bacteriostatic peptides can enter the organism to inhibit DNA, RNA, and protein synthesis, interfere with the cell metabolism of the flora, and thus contain and kill pathogenic microorganisms.
[0035] Peptidoglycan glycosyltransferase 3DWK is the direct acting enzyme for peptidoglycan synthesis in the cell wall of Staphylococcus aureus. Lipopolysaccharide 4RHB is an important component on the cell membrane of Escherichia coli. RagAB peptide transporter 6LSN is an important transporter on the cell membrane of Porphyromonas gingivalis that affects the physiological metabolism of the organism.
[0036] In this embodiment, three antimicrobial peptides are respectively subjected to CDOCKER molecular docking with the corresponding active sites. All three antimicrobial peptides have key binding sites for 3DWK, 4RHB, and 6LSN. As shown in Table 13, Table 14, and Table 15, the CDOCKER docking energies of the three antimicrobial peptides with 3DWK are all less than -80.9499 kcal / mol, with 4RHB are all less than -69.3665 kcal / mol, and with 6SLN are all less than -52.7364 kcal / mol. Therefore, the three antimicrobial peptides can all bind well to 3DWK of Staphylococcus aureus, 4RHB of Escherichia coli, and 6LSN of Porphyromonas gingivalis to exert bacteriostatic effects.
[0037]
[0038] Only some exemplary embodiments of the present invention have been described by way of illustration above. Undoubtedly, for those of ordinary skill in the art, without departing from the spirit and scope of the present invention, the described embodiments can be modified in various different ways. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of the claims of the present invention.
Claims
1. Yellow croaker antimicrobial peptide L-AMPs, characterized in that: The three yellow croaker antimicrobial peptides L-AMPs are all derived from yellow croaker protein fragments, including at least one of the following small molecule peptides: The amino acid sequence is KNYKY, SEQ ID NO. 1; The amino acid sequence is KMVAR, SEQ ID NO. 2; The amino acid sequence is FAMRK, SEQ ID NO.
3.
2. The yellow croaker antimicrobial peptide L-AMPs according to claim 1, characterized in that: The three yellow croaker antimicrobial peptides were obtained after screening and comparison of 136 protein peptide fragments derived from yellow croaker. The three yellow croaker antimicrobial peptides L-AMPs all have high antibacterial properties in support vector machine (SVM), random forest (RF), and artificial neural network (ANN) discrimination algorithms.
3. The yellow croaker antimicrobial peptide L-AMPs according to claim 1, characterized in that: The three yellow croaker antimicrobial peptides L-AMPs simultaneously meet the following requirements: SVM antimicrobial peptide discrimination rate>0.98, RF antimicrobial peptide discrimination rate>0.534, and are discriminated as antimicrobial peptides by ANN algorithm.
4. The yellow croaker antimicrobial peptide L-AMPs according to claim 1, characterized in that: The amino acid sequences such as SEQ ID NO.1, SEQ ID NO.2 and SEQ ID NO.3 can all bind to the peptidoglycan glycosyltransferase / transpeptidase of Staphylococcus aureus to affect the cell wall formation thereof.
5. The yellow croaker antimicrobial peptide L-AMPs according to claim 2, characterized in that: The amino acid sequences such as SEQ ID NO.1, SEQ ID NO.2 and SEQ ID NO.3 can bind to lipopolysaccharide on the cell membrane of Escherichia coli and affect the integrity of its inner membrane.
6. The yellow croaker antimicrobial peptide L-AMPs according to claim 3, characterized in that: The amino acid sequences such as SEQ ID NO.1, SEQ ID NO.2 and SEQ ID NO.3 can bind to the RagAB peptide transporter protein in the outer membrane of Porphyromonas gingivalis and affect its protein transport physiological activity.
7. The yellow croaker antimicrobial peptide L-AMPs according to claim 1, characterized in that: The amino acid sequences such as SEQ ID NO.1, SEQ ID NO.2, and SEQ ID NO.3 can be applied to oral care compositions for inhibiting periodontitis caused by Porphyromonas gingivalis, wherein the composition contains 0.1%-5% by weight of yellow croaker antimicrobial peptides L-AMPs.
8. The yellow croaker antimicrobial peptide L-AMPs according to claim 1, characterized in that: The yellow croaker antimicrobial peptide L-AMPs can be prepared by the following steps: (1) Extracting total protein from yellow croaker tissue and obtaining candidate peptides through enzymatic hydrolysis; (2) The SVM-RF-ANN triple discriminant model was used to screen peptides with an antimicrobial peptide discrimination rate of ≥ 0.98; (3) The target peptide was purified by HPLC with a purity of ≥95%.