Artificial intelligence designed antibacterial peptides and uses thereof

Abstract

The application discloses artificial intelligence designed antibacterial peptides and application thereof, and belongs to the field of biological medicine. Through the self-developed artificial intelligence model, based on the random starting sequence DGWTMVKARYIERFMRGLRGFHIKAA, 7 mutations are carried out step by step, and a series of 7 antibacterial peptides are obtained. Tests show that the 7 new antibacterial peptides can effectively inhibit bacterial growth, especially gram-positive bacteria Staphylococcus aureus and Bacillus subtilis, wherein one of the antibacterial peptides has excellent inhibitory effect on gram-negative bacteria and gram-positive bacteria, and the average minimum inhibitory concentration is less than 3 muM. The antibacterial peptides can provide basis and foundation for the development of a new generation of antibacterial drugs and antibacterial products.

Description

TECHNICAL FIELD

[0001] The present application relates to an artificially intelligent designed antibacterial peptide and its application, in particular to an artificially intelligent designed antibacterial peptide and its application in the preparation of a medicament, and belongs to the field of biological medicine. BACKGROUND

[0002] In the "post-antibiotic era", more than 700,000 people die from drug-resistant bacterial infections every year, and this number is growing at a rate of 100,000 people per year. Antimicrobial peptides are a class of short peptides with broad-spectrum inhibitory effects on pathogens such as bacteria. They mainly exert antibacterial effects by destroying bacterial cell membranes, and have the characteristics of rapid killing of bacteria and not easily leading to pathogen resistance compared to small molecule antibiotics. Although the number of antimicrobial peptides recorded in existing databases has reached 30,000, compared to the vast theoretical sequence space, known antimicrobial peptides are still very scarce, and not all have high antibacterial activity. SUMMARY

[0003] In order to efficiently obtain more antimicrobial peptides with high antibacterial activity, the present application uses an independently developed artificial intelligence model to gradually mutate the random starting sequence DGWTMVKARYIERFMRGLRGFHIKAA (SEQ ID No: 1) 7 times to obtain a series of 7 antimicrobial peptides.

[0004] The names and sequences of the series of antimicrobial peptides provided by the present application are as follows:

[0005] Perceptide24-1: DGWTMVKARYIKRFMRGLRGFHIKAA (SEQ ID No: 2)

[0006] Perceptide24-2: RGWTMVKARYIKRFMRGLRGFHIKAA (SEQ ID No: 3)

[0007] Perceptide24-4: RGWFMVKARYIKRFMRGLRGFHIKAA (SEQ ID No: 4)

[0008] Perceptide24-5: RGWFKVKARYIKRFMRGLRGFHIKAA (SEQ ID No: 5)

[0009] Perceptide24-6: RGWFKVKMRYIKRFMRGLRGFHIKAA (SEQ ID No: 6)

[0010] Perceptide24-7: RGWFKVKERYIKRFMRGLRGFHIKAA (SEQ ID No: 7)

[0011] Perceptide 24: RGWFKVKRRYIKRFMRGLRGFHIKAA (SEQ ID No: 8)

[0012] These antibacterial peptides can be synthesized by chemical methods such as polypeptide solid-phase synthesis, and applied in antibacterial drugs and other antibacterial products (such as disinfectants, detergents).

[0013] The 7 new antibacterial peptides provided by the application can effectively inhibit bacterial growth, especially Gram-positive bacteria Staphylococcus aureus and Bacillus subtilis. Among them, Perceptide 24 has excellent inhibitory effect on Gram-negative bacteria and Gram-positive bacteria, and the average minimum inhibitory concentration is less than 3 μM. These antibacterial peptides can provide basis and foundation for the development of a new generation of antibacterial drugs. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 The mutation process of the 7 antibacterial peptides obtained by the application. DETAILED DESCRIPTION

[0015] The application will be further described below by examples. However, it should be understood that these descriptions are only for further illustrating the features and advantages of the application, but not for limiting the claims of the application.

[0016] Example 1 Source and physicochemical properties of antibacterial peptides

[0017] The application obtains 7 antibacterial peptides by gradually mutating the random starting sequence DGWTMVKARYIERFMRGLRGFHIKAA 7 times through an artificial intelligence model independently developed by the application. The mutation process is as shown in Figure 1 , wherein the sequence wheel chart of each antibacterial peptide is drawn using modlAMP. In the sequence wheel chart, black text on white background is non-polar amino acid, and white text on black background is polar amino acid. The artificial intelligence model gradually introduces E12K, D1R, T4F, M5K, A8M, M8E, and E8R mutations to the starting sequence, and introduces arginine (R) and lysine (K) to increase the net charge of the antibacterial peptide, which is consistent with the characteristic of antibacterial peptides with multiple positive charges. Table 1 shows the physicochemical properties of each antibacterial peptide, including molecular weight, net charge, isoelectric point, proportion of aromatic amino acids, hydrophobic moment, and proportion of hydrophobic amino acids. With the introduction of mutations, the hydrophobic moment of the antibacterial peptide gradually increases, showing more amphiphilic characteristics.

[0018] Table 1. Physicochemical properties of 7 antibacterial peptides

[0019]

[0020] Example 2 Determination of minimum inhibitory concentration of antibacterial peptides

[0021] The bacteria used in the minimum inhibitory concentration determination experiment include two standard strains of gram-negative bacteria and two standard strains of gram-positive bacteria, which are Escherichia coli ATCC 25922, Pseudomonas aeruginosa ATCC 27853, Staphylococcus aureus ATCC 6538 and Bacillus subtilis ATCC 6633. Escherichia coli ATCC 25922, Pseudomonas aeruginosa Pseudomonas aeruginosa ATCC 27853, Staphylococcus aureus Staphylococcus aureus ATCC 6538, Bacillus subtilis Bacillus subtilis ATCC 6633.

[0022] The minimum inhibitory concentration of the antibacterial peptide was determined using the Hancock broth dilution method, and the experimental steps were as follows.

[0023] (1) Preparation of bacterial solution. The freeze-dried strains of the four standard strains were recovered, and were cultured overnight for activation, and were streaked on LB (Hibio, #HB0129-2) plates for culture. Four single colonies with similar morphology were picked into 5 mL of LB medium (Hibio, #HB0218) and cultured at 37°C in a shaking incubator at 220 rpm for about 3 hours, then diluted to an OD value of 0.08-0.13. 625

[0024] (2) Preparation of antibacterial peptide solution. The antibacterial peptide powder was synthesized by Wuhan Danggang Biotechnology Co., Ltd. by solid-phase chemical synthesis method, and the mass spectrometry identified the correct molecular weight. The antibacterial peptide sample was purified by high-performance liquid chromatography, and the purity was more than 95%. Each 0.1 mg of antibacterial peptide powder was dissolved in sterile PBS buffer (pH 7.4) to prepare an antibacterial peptide stock solution with a concentration of 512 μM.

[0025] ​(3) Antimicrobial test. The assay was performed using sterilized polypropylene 96-well culture plates (Greiner, #655201). Three parallel controls were set for each antimicrobial peptide. Columns 1-10 were used for bacterial solution and gradient diluted antimicrobial peptide solution, column 11 was used for growth control (without antimicrobial peptide solution), and column 12 was used for sterility control (only medium was added). First, 100 μL of LB medium was added to each of columns 1-11, and 200 μL of LB medium was added to column 12. The stock solution of antimicrobial peptide was diluted 8-fold with LB medium, and 100 μL of the diluted solution was added to column 1. Then, 100 μL of the solution in column 1 was transferred to column 2, mixed, and 100 μL of the solution in column 2 was transferred to column 3, mixed, and the procedure was repeated to achieve a two-fold gradient dilution (the concentrations were 128, 64, 32, 16, 8, 4, 2, 1, 0.5, and 0.25 μM, respectively). After mixing in column 10, 100 μL of the solution was discarded. 400 μL of the bacterial solution was diluted 100-fold in 39.6 mL of LB medium, and 100 μL of the diluted solution was added to each of columns 1-11. The bacterial solution used for the growth control was diluted 1:100, and 100 μL of the diluted solution was spread on an LB plate without antimicrobial peptide to check the bacterial growth density. The 96-well culture plate and the plate were placed in a 37°C incubator for 16 hours.

[0026] (4) Result observation. The bacterial growth in each well of the cell culture plate was observed directly. The lowest antimicrobial peptide solution concentration value at which no bacteria were observed to grow was determined to be the minimum inhibitory concentration of the antimicrobial peptide under the given conditions. The results are shown in Table 2. The random initial sequence had no antimicrobial activity against the four strains, but the antimicrobial activity of the antimicrobial peptide increased as mutations were introduced.

[0027] Table 2. Minimum inhibitory concentrations of seven antimicrobial peptides against four strains (unit: μM)

[0028]

Claims

1. An antibacterial peptide, characterized in that, The amino acid sequence of the antibacterial peptide is as follows: Perceptide24: RGWFKVKRRYIKRFMRGLRGFHIKAA.

2. Use of the antibacterial peptide of claim 1 in the preparation of an antibacterial medicament against one or more of the following bacteria: Escherichia coli 、 Pseudomonas aeruginosa 、 Staphylococcus aureus 、 Bacillus subtilis.

3. Use according to claim 2, wherein the compound is ###0002### The effective concentration of the antibacterial peptide in the antibacterial drug is greater than or equal to 0.5 μM.

4. Use according to claim 3, wherein the compound is ###0002### The effective concentration of the antibacterial peptide in the antibacterial drug is greater than or equal to 3 μM.

5. An antibacterial agent, characterized by, The antibacterial peptide as claimed in claim 1.

6. The antibacterial medicament according to claim 5, wherein The antibacterial drug contains the antibacterial peptide Perceptide24 with an effective concentration greater than or equal to 3 μM.

7. Use of the antibacterial peptide of claim 1 in the manufacture of an antibacterial product directed against one or more of the following bacteria: Escherichia coli 、 Pseudomonas aeruginosa 、 Staphylococcus aureus 、 Bacillus subtilis.

8. Use according to claim 7, wherein the compound is ###0002### The antibacterial product is a disinfectant or a detergent.

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