An antimicrobial peptide, yuce 4, and its mutants designed based on artificial intelligence.

Abstract

This invention, based on deep learning and molecular dynamics simulations, yielded a novel antimicrobial peptide, yuce 4, designed using artificial intelligence, exhibiting highly efficient and broad-spectrum antimicrobial activity. The existence of the α-helix in yuce 4 was confirmed by nuclear magnetic resonance (NMR), circular dichroism (CD) chromatography, and Alphafold 2 three-dimensional structure prediction. Furthermore, mutants of yuce 4 were designed, showing a 4-8 fold increase in activity against Stenotrophomonas maltophilia WH006 and Pseudomonas aeruginosa PAO1, with several analogs achieving a MIC of 8 μg / mL. This invention also demonstrates the potential of deep learning and molecular dynamics simulations in accelerating the discovery of antimicrobial peptides and shows promise as an important tool for developing novel antimicrobial peptides.

Description

Technical Field

[0001] This invention belongs to the field of antimicrobial drug technology, specifically relating to the application of antimicrobial peptides and their mutants in inhibiting bacterial growth. Background Technology

[0002] Antibiotics are the most common drugs used clinically to treat bacterial infections. However, with the overuse of antibiotics in recent years, more and more drug-resistant bacteria have emerged, becoming one of the greatest threats to human health and safety. Developing new antibacterial drugs is therefore urgently needed. In the drug development and design process, traditional methods often face problems such as long development time, high cost, and low error tolerance. Artificial intelligence (AI), on the other hand, can quickly collect drug molecule information, construct a large database, and utilize machine learning to analyze their structural or physicochemical characteristics, drug-forming patterns, cytotoxicity, and other properties to generate new drug molecules. These new molecules can then be used for candidate screening, accelerating the drug design process and reducing costs.

[0003] Existing antimicrobial drugs are partly derived from microbial metabolites. Among these, antimicrobial peptides have attracted widespread attention due to their low toxicity, broad-spectrum antimicrobial activity, and low likelihood of inducing (or even reversing) drug resistance. After billions of years of evolution, antimicrobial peptides (AMPs) have become increasingly important.

[0004] Antimicrobial peptides often appear in organisms as part of the immune system, acting as a defense system to resist invading pathogens through various mechanisms, thus exerting antibacterial effects. Antimicrobial peptides have small molecular weights, mostly composed of 6 to 50 amino acids. Due to the presence of a relatively large number of lysine and arginine residues, their net charge number ranges from +2 to +9. Most antimicrobial peptides can act on the cell membrane, forming transmembrane ion channels, thereby disrupting the cell membrane's integrity and causing bacterial cell contents to leak out, leading to cell death. Evidence has shown that the bioactivity of antimicrobial peptides is significantly correlated with factors such as positive charge, hydrophobicity, amphiphilicity, and secondary structure, providing a feasible basis for further modification of antimicrobial peptides. Due to their broad-spectrum antimicrobial activity and low toxicity, antimicrobial peptides have become a viable alternative to antibiotics for treating bacterial diseases, showing promising development and application prospects. Summary of the Invention

[0005] To address the shortcomings of existing antibiotics, this invention utilizes deep learning and molecular dynamics simulations to design a novel antimicrobial peptide, yuce 4, based on an artificial intelligence-designed sequence, which exhibits highly efficient and broad-spectrum antimicrobial activity.

[0006] The first objective of this invention is to provide an antimicrobial peptide, yuce 4, designed based on artificial intelligence, the amino acid sequence of which is shown in SEQ ID NO.1.

[0007] To further enhance the bioactivity of the antimicrobial peptide yuce 4, a second objective of this invention is to provide a mutant based on the modified antimicrobial peptide yuce 4, which contains one or more of the following amino acid mutations based on SEQ ID NO.1: D1A, T2A, F3A, G4A, R5A, R7A, R8A, W9A, W10A, L13A, G14A, R17A, R18A, D1R, T2R, F3R, G4R, W9R, W10R, A11R, A12R, L13R, G14R, A15R.

[0008] Preferably, the amino acid sequence of the mutant is as shown in any one of SEQ ID NO.2-36.

[0009] This invention also provides a chemical preparation method for the antimicrobial peptide yuce 4 and its mutants, comprising the following steps: synthesizing the full sequence of yuce 4 and its mutants using a polypeptide solid-phase synthesis method based on their amino acid sequences; protecting the thiol groups on the two cysteine ​​residues with ACM protecting groups; and finally linking the disulfide bonds using iodine oxidation; through C... 18 Purification was performed using a reverse-phase column chromatography, molecular weight was determined using electrospray mass spectrometry, purity was identified using high-performance liquid chromatography, and secondary structure was characterized using circular dichroism chromatography.

[0010] A third objective of this invention is to provide the application of the antimicrobial peptide yuce 4 and its mutants in inhibiting bacterial growth.

[0011] Furthermore, the bacteria are either Gram-negative or Gram-positive.

[0012] Furthermore, the Gram-negative bacteria include enzyme-producing lysobacterium YC36, Escherichia coli ATCC8739, Stenotrophomonas maltophilia WH006, Pseudomonas aeruginosa PAO1, and Pseudomonas aeruginosa SM45; the Gram-positive bacteria include Bacillus subtilis 168, Bacillus thuringiensis BNCC 336393, and Staphylococcus aureus SYZX101.

[0013] Preferably, the antimicrobial peptide yuce 4 mutant is used to inhibit Stenotrophomonas maltophilia WH006 and Pseudomonas aeruginosa PAO1.

[0014] The beneficial effects of this invention are:

[0015] The advantage of this invention is that it generates a novel antimicrobial peptide sequence through deep learning. Preliminary predictions using Alphafold 2 and molecular dynamics simulations indicated the presence of an α-helix and predetermined stability, which was subsequently confirmed by nuclear magnetic resonance (NMR) technology. It exhibits significant broad-spectrum antibacterial activity. Site-directed mutagenesis of yuce 4 revealed a substantial increase in the activity of the mutant, demonstrating strong inhibitory effects against both Gram-positive and Gram-negative bacteria. In particular, the yuce 4 mutant showed a 4-8 fold increase in activity against Stenotrophomonas maltophilia WH006 and Pseudomonas aeruginosa PAO1. This provides a new approach for the further development of novel antimicrobial drugs to replace existing antibiotics. Attached Figure Description

[0016] Figure 1 The secondary structure of the antimicrobial peptide yuce 4 was predicted by Alphafold 2.

[0017] Figure 2 The secondary structure of the antimicrobial peptide yuce 4 was determined by nuclear magnetic resonance (NMR) technology.

[0018] Figure 3 These are the results of circular dichroism characterization of yuce 4; Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] Example 1: Generation and prediction of antimicrobial peptides based on artificial intelligence technology

[0021] An antimicrobial peptide generation model was trained using an existing antimicrobial peptide database to generate candidate antimicrobial peptides. The candidate antimicrobial peptides were then screened and predicted again using the online website CAMP, resulting in five antimicrobial peptides with the highest probability. Their sequences are shown in Table 1.

[0022] Table 1. Generation and prediction of antimicrobial peptides

[0023]

[0024] a: The two cysteine ​​residues within the antimicrobial peptide backbone molecule shown in the table form a disulfide bond.

[0025] Example 2: Alphafold 2 prediction of the secondary structure of the generated antimicrobial peptide

[0026] The secondary structures of five antimicrobial peptides were predicted using alphafold 2, and the results showed that all of them had the potential for the presence of α-helices. The secondary structure model for generating yuce 4 was as follows: Figure 1 As shown.

[0027] Example 3: Chemical Synthesis of Antimicrobial Peptides

[0028] All antimicrobial peptides used in the experiment were synthesized using solid-phase Fmoc chemical synthesis. Besides various amino acids with protecting groups, other required raw materials included RinkAmide resin, piperidine, DMF, DCM, HCTU, DIPEA, trifluoroacetic acid, triisopropylsilane, and anhydrous diethyl ether. The direction of peptide synthesis proceeded from the C-terminus to the N-terminus, with C-terminal amidation modification.

[0029] Specific implementation steps: First, RinkAmide resin was swollen overnight in a DMF:DCM = 1:1 solution. Then, 20% piperidine solution was added and reacted for 30 minutes to remove the Fmoc protecting group from the resin. Next, the correct amino acids were added sequentially according to the desired amino acid sequence. DMF was used as the solvent for amino acid coupling, and HTCU and DIPEA acted as condensing agents and activators of the amino acids, respectively. The reaction time was 1 hour. Then, the Fmoc protecting group on the amino acids was removed again, and the next amino acid was coupled, repeating the cycle until the last amino acid. After the peptide sequence was synthesized, the peptide was cleaved from the resin using a lysis buffer of trifluoroacetic acid:triisopropylsilane:water = 9:0.5:0.5 for 3 hours. The synthesized antimicrobial peptide was then precipitated using diethyl ether. The peptide was dissolved in a 1 mg / mL iodine solution to oxidize the Acm-protected cysteine, reacting for 30 minutes. Ascorbic acid was then added to terminate the oxidation reaction, followed by C... 18 Separation and purification were performed using a reverse-phase column chromatography. Electrospray ionization mass spectrometry verified the correct molecular weight of the peptide, and high-performance liquid chromatography identified a purity >95%.

[0030] Example 4: Determination of the bioactivity of antimicrobial peptides

[0031] The bioactivity of antimicrobial peptides is expressed by testing the minimum inhibitory concentration (MIC) of the antimicrobial peptide. The MIC refers to the minimum concentration at which the drug can inhibit the growth of bacteria in the culture medium. The bacteria tested in this invention include Gram-positive bacteria such as Bacillus subtilis 168, Bacillus thuringiensis BNCC 336393, and Staphylococcus aureus SYZX101, as well as Gram-negative bacteria such as Bacillus lysozyme YC36, Escherichia coli ATCC8739, Stenotrophomonas maltophilia WH006, Pseudomonas aeruginosa PAO1, and Pseudomonas aeruginosa SM45.

[0032] After reviving the bacterial strain, it was subcultured once, and single colonies were picked and cultured in MH liquid medium until it reached the logarithmic growth phase. The appropriate culture medium was prepared, sealed, and sterilized in an autoclave for later use. The antimicrobial peptide to be tested was prepared into a solution of a specific concentration. In a 96-well plate, 100 μL of culture medium was added to each well. In rows A, B, and C, the first column added 100 μL of the first antimicrobial peptide to be tested. The plate was diluted using a micro-dilution method until the last well. 100 μL of the diluted bacterial suspension was added (final CFU / mL approximately 5 x 10⁻⁶). 6 To ensure a final volume of 200 μL per well, negative and positive controls were set up to guarantee bacterial growth. The 96-well plates were then incubated at 37°C for 18 hours, and colony growth was observed. The results showed that only yuce 4 among the five candidate antimicrobial peptides exhibited significant broad-spectrum antimicrobial activity, and its MIC values ​​are shown in Table 2.

[0033] Table 2 Bioactivity of antimicrobial peptide yuce 4

[0034]

[0035] Example 5: Selection and synthesis of yuce 4 mutant

[0036] 5.1 Selection and Synthesis of Yuce 4 Single Mutants

[0037] The activity of antimicrobial peptides mainly depends on factors such as positive charge, hydrophobicity, amphiphilicity, and secondary structure. Based on this, each amino acid on yuce 4 was subjected to site-directed mutagenesis with alanine (Ala) and arginine (Arg) to obtain different single mutants to study their structure-activity relationship. Their sequences are shown in Table 3.

[0038] Table 3 Selection of yuce 4 single mutants

[0039]

[0040] a: The two cysteine ​​residues within the antimicrobial peptide backbone molecule shown in the table form a disulfide bond.

[0041] 5.2 Selection and Synthesis of Multiple Mutants of yuce 4

[0042] Based on the bioactivity assay results of the antimicrobial peptide yuce 4, we found that the bioactivity was significantly enhanced when amino acids at positions 1, 11, 14, and 15 were replaced. Therefore, we selected two or more sites for mutation to obtain different multiple mutants. In addition, we also wanted to determine the role of disulfide bonds in the antimicrobial peptide, so we replaced Cys in the sequence with Ala. The sequences of the multiple mutants are shown in Table 4.

[0043] Table 4 Selection of multiple mutants of yuce 4

[0044]

[0045] a: The two cysteine ​​residues within the antimicrobial peptide backbone molecule shown in the table form a disulfide bond.

[0046] Example 6: Bioactivity test of yuce 4 mutant

[0047] 6.1 Bioactivity assay of yuce 4 single mutant

[0048] Yuce 4 exhibited broad-spectrum antibacterial activity against both Gram-positive and Gram-negative bacteria, particularly against *Bacillus subtilis* YC36, *Bacillus subtilis* 168, *Stenotrophomonas maltophilia* WH006, and *Pseudomonas aeruginosa* PAO1. We focused on alanine scanning against these four bacteria to determine the effect of each amino acid residue on physicochemical properties and bioactivity. In bioactivity assays, yuce 4[D1A] showed a 2-fold increase in activity against *Bacillus subtilis* YC36 and *Stenotrophomonas maltophilia* WH006. Simultaneously, the MIC against *Pseudomonas aeruginosa* decreased significantly from 64 μg / mL to 16 μg / mL. This change in activity is likely due to the reduction of a negative charge in yuce 4[D1A]. The increase in positive charge is intended to more effectively exert its antibacterial activity against bacterial pathogens. However, the alanine substitution in yuce 4 did not significantly alter its activity against Gram-positive bacteria such as *Bacillus subtilis* 168.

[0049] Table 5. Minimum inhibitory concentrations (MICs) of the yuce 4 alanine mutant (unit: μg / mL)

[0050]

[0051] The increased activity of yuce 4[D1A] against Gram-negative bacteria led us to conclude that this was likely due to the addition of a positive charge. We then performed an arginine scan on yuce 4, a common peptide modification strategy. Positively charged residues play a crucial role in biological activity, and the addition of a positive charge facilitates the binding of AMPs to the bacterial membrane, thereby enhancing antibacterial activity. Although the activity against Stenotrophomonas maltophilia WH006 did not increase as expected during the arginine scan, we found that the activity against Pseudomonas aeruginosa PAO1 increased 4–8-fold when amino acids at positions 1, 3, 11, and 12 were replaced with arginine. Pseudomonas aeruginosa is a common clinical pathogen, and infections caused by it have a high mortality rate and are difficult to treat. This change demonstrates that increasing the positive charge by altering amino acids at certain positions can indeed increase activity. Therefore, the introduction of positively charged Lys, amino acids with similar properties such as 2,4-diaminobutyric acid (Dab) and 2,3-diaminopropionic acid (Dap) may also enhance biological activity. The MIC value test results are shown in Table 6.

[0052] Table 6. Minimum inhibitory concentrations (MICs) of the yuce 4 arginine mutant (unit: μg / mL)

[0053]

[0054] 6.1 Bioactivity assay of multiple mutants of yuce 4

[0055] In designing the yuce 4 multiple mutants, we continued our strategy of increasing the positive charge. We selected amino acids at positions 1, 11, 14, and 15 that could induce changes in activity. For *Pseudomonas aeruginosa* PAO1, once the activity reached a certain value, further increasing the number of positive charges did not increase activity. On the contrary, the activity decreased to some extent, possibly because excessive positive charges alter the peptide's amphiphilicity. However, these multiple mutants showed better activity against *Stenotrophomonas maltophilia* WH006, with a MIC reaching 8 μg / mL. This indicates that increasing the number of charges is beneficial in inhibiting the growth of *Stenotrophomonas maltophilia* WH006.

[0056] Table 7. Minimum inhibitory concentrations (MICs) of yuce 4 multiple mutants (unit: μg / mL)

[0057]

[0058] Example 7: Analysis of the secondary structure of yuce 4 using nuclear magnetic resonance (NMR) technology

[0059] We used nuclear magnetic resonance (NMR) to analyze the antimicrobial peptide yuce 4. The NMR results showed that the antimicrobial peptide yuce 4 possesses an α-helix, and its structural model is shown below. Figure 2 As shown.

[0060] Example 8: Analysis of the structure of antimicrobial peptide yuce 4 using circular dichroism spectroscopy

[0061] We used circular dichroism spectroscopy to detect the antimicrobial peptide yuce 4 and found negative absorption at 208 nm and 222 nm, and positive absorption at 190 nm, confirming the presence of an α-helix. The results are as follows: Figure 3 As shown.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A mutant of the antimicrobial peptide yuce 4, characterized in that, The amino acid sequence of the mutant is shown in SEQ ID NO. 29, and two cysteine ​​residues in its backbone molecule form a disulfide bond.

2. A method for chemical preparation of a mutant of the antibacterial peptide yuce 4 according to claim 1, characterized by, The procedure includes the following steps: based on the amino acid sequence of the mutant antimicrobial peptide yuce 4, the full sequence is synthesized using a polypeptide solid-phase synthesis method; the thiol groups on the two cysteine ​​residues are protected with ACM protecting groups; and finally, disulfide bonds are linked by iodine oxidation. The peptide is purified by C18 reverse-phase chromatography, the molecular weight is determined by electrospray mass spectrometry, the purity is identified by high-performance liquid chromatography, and the secondary structure is characterized by circular dichroism spectroscopy.

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