Screening method for antibacterial peptide from deep-sea cold spring, antibacterial peptide and preparation method and application thereof

Abstract

The application belongs to the technical field of biology, and discloses a screening method for screening antibacterial peptides from deep-sea cold springs, the antibacterial peptides, and a preparation method and application thereof. The screening method for the antibacterial peptides provided by the application takes deep-sea cold spring sediment genome data as the data basis for screening, uses bioinformatics technology to mine and construct the deep-sea cold spring sediment genome data to obtain a smORFs database including rich small protein sequence information; then, the small protein sequence information in the smORFs database is predicted to construct a candidate AMPs database, and the small proteins in the candidate AMPs database are subjected to solubility prediction screening to obtain antibacterial peptides with high antibacterial potential. The screening method can quickly, efficiently and pertinently screen the antibacterial peptides with high antimicrobial infection potential from deep-sea cold spring microorganisms, and has a good application prospect.

Description

Technical Field

[0001] This invention belongs to the field of biotechnology, and in particular relates to a screening method for antimicrobial peptides from deep-sea cold seeps, the antimicrobial peptides themselves, their preparation methods, and their applications. Background Technology

[0002] Antimicrobial peptides (AMPs) are a class of small-molecule polypeptides widely distributed in animals and plants. AMPs are an important component of the body's innate immune defense system. AMPs not only possess broad-spectrum anti-infective activity, including inhibition of Gram-negative bacteria, Gram-positive bacteria, viruses, fungi, and parasites, but also exhibit various biological activities such as tumor cell suppression and immunomodulation. Compared to antibiotics, naturally derived antimicrobial peptides are natural protein products produced through biosynthesis and metabolism. Antimicrobial peptides are less likely to induce drug resistance, have lower toxicity, and are more specific, making them a potential alternative to antibiotics and showing broad application prospects in the prevention and treatment of drug-resistant bacterial infections.

[0003] Currently, methods for obtaining antimicrobial peptides mainly include direct isolation and purification from organisms, enzymatic hydrolysis of known antimicrobial proteins using proteases, chemical synthesis, and recombinant expression via genetic engineering. Deep-sea cold seeps possess rich and diverse microbial communities, offering significant potential as a resource library for the research and acquisition of antimicrobial peptides. However, the aforementioned methods are not ideal for obtaining antimicrobial peptides from deep-sea cold seep microorganisms, resulting in limited research on antimicrobial peptides from deep-sea cold seep microorganisms and presenting considerable limitations. Summary of the Invention

[0004] The primary objective of this invention is to provide a method for screening antimicrobial peptides that can quickly, efficiently, and specifically screen antimicrobial peptides with high antimicrobial potential from deep-sea cold seep microorganisms.

[0005] A second objective of this invention is to provide an antimicrobial peptide derived from deep-sea cold seeps.

[0006] A third objective of this invention is to provide a method for preparing the aforementioned antimicrobial peptides derived from deep-sea cold seeps.

[0007] A fourth objective of this invention is to provide the application of the above-mentioned antimicrobial peptides derived from deep-sea cold seeps in the preparation of drugs for antimicrobial infection.

[0008] Specifically, the antimicrobial peptide screening method provided by the present invention includes: (1) obtaining genomic data of deep-sea cold seep sediments; (2) mining the genomic data of deep-sea cold seep sediments to obtain a database of deep-sea cold seep sediment smORFs; (3) making predictions based on the database of deep-sea cold seep sediment smORFs to obtain a database of candidate AMPs; and (4) performing solubility prediction and source species dominance analysis based on the database of candidate AMPs to screen and obtain the antimicrobial peptides.

[0009] Further, in step (2), the method for mining based on the deep-sea cold seep sediment genome data includes: S1, taking the deep-sea cold seep sediment genome data for quality control, filtering low-quality data and performing sequence data trimming and correction to obtain a high-quality sequence set; S2, taking the high-quality sequence set for co-assembly to obtain a contiguous sequence set; S3, taking the contiguous sequence set for small protein smORFs prediction to obtain an smORFs database; S4, taking the smORFs database for redundancy removal to obtain the deep-sea cold seep sediment smORFs database.

[0010] Further, in step (3), the method for prediction based on the deep-sea cold seep sediment smORFs database includes: taking the deep-sea cold seep sediment smORFs database and performing random forest training based on the existing peptide database to predict and obtain the candidate AMPs database.

[0011] Furthermore, in step (4), the predicted solubility of the antimicrobial peptide is 0.708 to 0.939.

[0012] The antimicrobial peptides derived from deep-sea cold seeps provided by this invention specifically include amino acid fragments with sequences as shown in SEQ ID NO:8.

[0013] The method for preparing the antimicrobial peptide from deep-sea cold seeps provided by the present invention specifically includes: using the Fmoc solid-phase synthesis method to couple amino acids according to the amino acid sequence shown in SEQ ID NO:8 to synthesize the antimicrobial peptide.

[0014] This invention provides the application of the above-mentioned antimicrobial peptides derived from deep-sea cold seeps in the preparation of drugs for antimicrobial infection.

[0015] Furthermore, the microorganism is one or more of Bacillus subtilis, Escherichia coli, Vibrio parahaemolyticus, and yeast.

[0016] Beneficial effects:

[0017] The antimicrobial peptide screening method provided by this invention creatively uses deep-sea cold seep sediment genomic data as the data basis for screening. Bioinformatics technology is used to mine the deep-sea cold seep sediment genomic data, thereby constructing an smORFs database containing abundant small protein sequence information. Then, the small protein sequence information contained in the smORFs database is predicted to further construct a candidate AMPs database. Finally, based on the solubility prediction results and source species advantage analysis, antimicrobial peptides with high antimicrobial potential are obtained from the candidate AMPs database. This screening method effectively overcomes the difficulty of directly isolating and identifying antimicrobial peptides from deep-sea cold seep microorganisms in traditional antimicrobial peptide screening methods. It can quickly, efficiently, and specifically screen for the amino acid sequences of antimicrobial peptides. Based on the amino acid sequences, large quantities and high-purity antimicrobial peptides can be obtained through genetic engineering or solid-phase synthesis technology, showing good application prospects.

[0018] The present invention also provides an antimicrobial peptide derived from deep-sea cold seeps, comprising an amino acid fragment with the sequence shown in SEQ ID NO:8, which exhibits excellent inhibitory effects against Gram-positive bacteria, Gram-negative bacteria, aquatic pathogens, and fungi, and possesses broad-spectrum antimicrobial capabilities, making it a highly promising anti-infective active ingredient. Detailed Implementation

[0019] Addressing the problems of low natural content of antimicrobial peptides, easy neglect of short-sequence small proteins, high technical requirements, high production costs, and difficulty in large-scale production inherent in traditional methods for developing antimicrobial peptides, the inventors of this invention, through extensive and in-depth research and numerous experiments, discovered that the microbiome of deep-sea cold seep sediments contains diverse and abundant biosynthetic gene clusters (BGCs). Based on the genomic data of deep-sea cold seep sediments, bioinformatics techniques were used to mine, predict, and screen the genomic data at multiple levels, enabling the rapid acquisition of a large number of antimicrobial peptides. Furthermore, the obtained antimicrobial peptides indeed exhibited excellent antimicrobial activity in subsequent antimicrobial ability tests. Based on this, the technical solution of this invention was obtained.

[0020] In this invention, the method for screening antimicrobial peptides specifically includes: (1) obtaining genomic data of deep-sea cold seep sediments; (2) mining the genomic data of deep-sea cold seep sediments to obtain a database of deep-sea cold seep sediment smORFs; (3) making predictions based on the database of deep-sea cold seep sediment smORFs to obtain a database of candidate AMPs; and (4) making solubility predictions based on the database of candidate AMPs to screen and obtain antimicrobial peptides.

[0021] In this invention, in step (1), the deep-sea cold seep sediment genome data comes from 165 sediment samples (0–68.55 m) from 16 cold seep sites distributed globally. More specifically, the cold seep sites include the Eastern North Pacific (ENP, ODP site 1244), Santa Monica Mounds (SMM), Western Gulf of Mexico (WGM), Eastern Gulf of Mexico (EGM), Northwestern Gulf of Mexico (NGM), Scotia Basin (SB), Haakon Mosby mud volcano (HM), Mediterranean Sea (MS), Laptev Sea (LS), and the South China Sea. Specifically, the South China Sea includes the Jiaolong cold seep (JL), the Shenhu area (SH), the Haiyang4 cold seep (HY4), the Qiongdongnan Basin (QDN), the Xisha Trough (XST), the Haima cold seeps (HM1, HM3, HM5, HM_SQ, S11, SY5, and SY6), and the F-site cold seeps (RS, SF, FR, and SF_SQ). Sediment samples range from the oxygenated sediment-water interface to the anoxic layer, reaching a maximum depth of 68.55 m below the seabed.

[0022] In this invention, step (2), the method for mining based on the deep-sea cold seep sediment genome data, specifically includes: S1, taking the deep-sea cold seep sediment genome data for quality control, filtering low-quality data and performing sequence data trimming and correction to obtain a high-quality sequence set; S2, taking the high-quality sequence set for co-assembly to obtain a contiguous sequence set; S3, taking the contiguous sequence set for small protein smORFs prediction to obtain an smORFs database; S4, taking the smORFs database for redundancy removal to obtain the deep-sea cold seep sediment smORFs database.

[0023] In this invention, in step (2) S1, the quality control is a technical means commonly used in NGS data analysis. Those skilled in the art can make adaptive choices from existing algorithm models or software according to actual needs. This invention does not impose any special limitations on it.

[0024] In some specific implementations, in step (2) S1, the quality control specifically includes: using the software fastp (v0.23.2; default parameters) and the Read_QC module (v1.3.2; -skip-bmtagger) in MetaWRAP to perform quality control on the deep-sea cold seep sediment genome data, filtering low-quality data and performing sequence data trimming and correction to obtain a high-quality sequence set.

[0025] In this invention, the co-assembly in step (2) S2 is a commonly used technique in NGS data analysis. Those skilled in the art can make adaptive choices from existing algorithm models or software according to actual needs. This invention does not impose any special limitations on it.

[0026] In some specific implementations, in step (2) S2, the co-assembly specifically includes: using the software MEGAHIT (v1.1.3) with two operating parameters, "default parameters" and "parameters:--k-min 27--kmin-1pass--presetsmeta-large", to assemble the high-quality sequence set to obtain the contiguous group sequence set.

[0027] In this invention, in step (2) S3, the method for predicting small protein smORFs specifically includes: using software SmORFinder (default parameters) and GMSC-mapper (default parameters) to predict small protein smORFs with a length less than or equal to 100aa from the contiguous sequence set, thereby obtaining an smORFs database.

[0028] In this invention, in step (2) S4, the redundancy removal is a technical means commonly used in existing NGS data analysis. Those skilled in the art can make adaptive choices from existing algorithm models or software according to actual needs. This invention does not impose any special limitations on it.

[0029] In some specific implementations, the redundancy removal specifically includes: processing the smORFs database using the software Mmseq2 with parameters set to -c 1.0, --min-seq-id 1.0 (i.e., 100% amino acid similarity and 100% coverage) to obtain singleton sequence sets and non-singleton sequence sets; processing the non-singleton sequence sets with the software Mmseq2 parameters set to -c 0.9, --min-seq-id 0.9 (i.e., 90% amino acid similarity and 90% coverage) to obtain a representative sequence set; aligning the singleton sequence set to the representative sequence set to obtain single-column sequences homologous to the representative sequence set; and merging the homologous single-column sequences with the non-singleton sequence sets to obtain the deep-sea cold seep sediment smORFs database.

[0030] In this invention, step (3), the method for prediction based on the deep-sea cold seep sediment smORFs database, specifically includes: taking the deep-sea cold seep sediment smORFs database and training a random forest based on an existing peptide database to predict the candidate AMPs database. The phrase "taking the deep-sea cold seep sediment smORFs database and training a random forest based on an existing peptide database" is a commonly used technique in existing NGS data analysis. Those skilled in the art can make adaptive choices from existing algorithm models or software according to actual needs, and this invention does not impose any particular limitations on it.

[0031] In some specific implementations, in step (3), the Macrel software is used to process the deep-sea cold seep sediment smORFs database, and the random forest algorithm is used to predict antimicrobial peptides in the deep-sea cold seep sediment smORFs database. During training, the focus is on accuracy rather than recall. The mature peptide model of ampir software, amPEPpy and its recommended model, APIN and its recommended model, AI4AMP and AMPLify are used to evaluate the novelty of the candidate antimicrobial peptides obtained in the random forest training, and a candidate AMPs database is obtained.

[0032] In this invention, step (4) specifically includes the method of predicting the solubility of antimicrobial peptides in the candidate AMPs database using the candidate AMPs database: using the software Protein-Sol (default parameters) to predict the solubility of antimicrobial peptides in the candidate AMPs database.

[0033] In this invention, in step (4), when the predicted solubility of the antimicrobial peptide in the candidate AMPs database is specifically 0.708 to 0.939, it is the antimicrobial peptide that is expected to be screened.

[0034] The present invention also provides an antimicrobial peptide derived from a deep-sea cold seep. This antimicrobial peptide specifically comprises an amino acid fragment with the sequence shown in SEQ ID NO:8.

[0035] In this invention, the antimicrobial peptide exhibits excellent inhibitory effects on cells such as Bacillus subtilis, Escherichia coli, Vibrio parahaemolyticus, and yeast, and has great potential for application as a drug against microbial infections such as Gram-positive bacteria, Gram-negative bacteria, aquatic pathogens, and fungi.

[0036] In this invention, the amino acid sequence of the antimicrobial peptide is the core of its corresponding biological activity. Those skilled in the art can select appropriate methods from the prior art to synthesize the antimicrobial peptide according to actual needs, limited to obtaining the antimicrobial peptide with the expected amino acid sequence. This invention does not impose any special limitations on its specific preparation method.

[0037] With the aim of rapidly obtaining large quantities and high purity antimicrobial peptides, this invention also provides a method for preparing the aforementioned antimicrobial peptides from deep-sea cold seeps. Specifically, this method includes: using the Fmoc solid-phase synthesis method to couple amino acids according to the amino acid sequence shown in SEQ ID NO:8 to synthesize the antimicrobial peptides.

[0038] In this invention, the Fmoc solid-phase synthesis method refers to a technique for building peptide chains through stepwise reactions on a solid support. Compared with traditional genetic engineering techniques, it can achieve automated synthesis of antimicrobial peptides and the obtained antimicrobial peptides have high purity.

[0039] Based on the excellent broad-spectrum antibacterial potential exhibited by the above-mentioned antimicrobial peptides, the present invention also provides the application of the above-mentioned deep-sea cold seep-derived antimicrobial peptides in the preparation of drugs for antimicrobial infection.

[0040] In this invention, the antimicrobial peptide exhibits excellent inhibitory activity in one or more microorganisms, including Bacillus subtilis, Escherichia coli, Vibrio parahaemolyticus, and yeast, with a growth inhibition rate superior to that of nisin, a known antimicrobial peptide with excellent antimicrobial activity.

[0041] The embodiments of the present invention are described in detail below. These embodiments are intended to explain the present invention and should not be construed as limiting the present invention. Where specific techniques or conditions are not specified in the embodiments, they are performed according to the techniques or conditions described in the literature in the art or according to the product instructions. Reagents or instruments used, unless otherwise specified, are all commercially available conventional products.

[0042] Example 1

[0043] This embodiment illustrates a screening method for obtaining antimicrobial peptides from deep-sea cold seep sediments. The screening method specifically includes:

[0044] 1. Construction of genomic data from deep-sea cold seep sediments

[0045] Acquire data from the following locations: Eastern North Pacific (ENP, ODP site 1244), Santa Monica Mounds (SMM), Western Gulf of Mexico (WGM), Eastern Gulf of Mexico (EGM), Northwestern Gulf of Mexico (NGM), Scotia Basin (SB), Haakon Mosby mud volcano (HM), Mediterranean Sea (MS), Laptev Sea (LS), and the South China Sea [Jiaolong cold seep (JL), Shenhu area (SH), Haiyang4 cold seep (HY4), Qiongdongnan Basin (QDN), and Xisha Trough (XST).] Genome data of deep-sea cold seep sediments were constructed from the gene extraction and sequencing results of 165 sediment samples from 16 cold seep sites (Trough), seahorse cold seep (HM1, HM3, HM5, HM_SQ, S11, SY5 and SY6) and F site cold seep (RS, SF, FR and SF_SQ) from the seabed oxygenated sediment-water interface to the anoxic layer (the deepest point reaching 68.55m below the seabed).

[0046] 2. Construction of a deep-sea cold seep sediment smORFs database

[0047] (1) The deep-sea cold seep sediment genome data were quality controlled using the software fastp (v0.23.2; default parameters) and the Read_QC module (v1.3.2; -skip-bmtagger) in MetaWRAP, low-quality data were filtered out and the sequence data were trimmed and corrected to obtain a high-quality sequence set.

[0048] (2) The high-quality sequence set was assembled using the software MEGAHIT (v1.1.3) with two running parameters: “default parameters” and “parameters:--k-min 27--kmin-1pass--presets meta-large”, to obtain an overlap cluster sequence set including 59,011,641 overlap sequences.

[0049] (3) The software SmORFinder (default parameters) and GMSC-mapper (default parameters) were used to predict small protein smORFs with a length of less than or equal to 100aa from the contiguous sequence set and the smORFs database.

[0050] (4) The smORFs database was processed using the software Mmseq2 with parameters set to -c 1.0, --min-seq-id 1.0 (i.e., 100% amino acid similarity and 100% coverage), resulting in a single-case sequence set (86%) containing 99,840,370 single-case sequences and a non-single-case sequence set (14%) containing 15,870,581 non-single-case sequences. The parameters of the software Mmseq2 were set to -c 0.9, --min-seq-id 0.9 (i.e., 90% amino acid similarity and 90% coverage), and the non-single-case sequence set was processed to obtain a representative sequence set. The single-case sequence set was aligned to the representative sequence set to obtain single-column sequences homologous to the representative sequence set. The homologous single-column sequences were merged with the non-single-case sequence set to obtain the deep-sea cold seep sediment smORFs database containing 23,496,022 small protein sequences.

[0051] 3. Construction of the candidate AMPs database

[0052] The Macrel software was used to process the deep-sea cold seep sediment smORFs database. The random forest algorithm was used to predict antimicrobial peptides in the database, with a focus on accuracy rather than recall during training, resulting in 34,045 antimicrobial peptide sequences. The novelty of the antimicrobial peptide sequences obtained from the random forest training was evaluated using the mature peptide model of ampir, amPEPpy and its recommendation model, APIN and its recommendation model, AI4AMP, and AMPLify software, resulting in a candidate AMPs database.

[0053] 4. Screening and acquisition of antimicrobial peptides

[0054] The solubility of antimicrobial peptides in the candidate AMPs database was predicted using the software Protein-Sol (default parameters). Antimicrobial peptides with a predicted solubility > 0.5 and originating from dominant species in deep-sea cold seeps were identified as the expected antimicrobial peptides for screening. Their specific amino acid sequences are shown in Table 1.

[0055] Table 1.

[0056]

[0057]

[0058] 5. The antimicrobial peptide molecules shown in Table 1 were synthesized by an external synthesis company using the Fmoc solid-phase synthesis method.

[0059] Example 2

[0060] This embodiment illustrates the antimicrobial activity of the antimicrobial peptides obtained in the above embodiments. The specific tests include:

[0061] (1) Escherichia coli DH5α, Bacillus subtilis, Vibrio parahaemolyticus VP.1997 and Saccharomyces cerevisiae BY4741 were inoculated into LB liquid medium at an inoculation rate of 1% and cultured in a constant temperature shaker under the corresponding culture conditions for 12 h to obtain the bacterial solution to be tested.

[0062] The culture conditions for Escherichia coli DH5α, Bacillus subtilis, and Vibrio parahaemolyticus VP.1997 were 37℃, 220rpm, and 16h; the culture conditions for Saccharomyces cerevisiae BY4741 were 30℃, 220rpm, and 54h.

[0063] (2) The antimicrobial peptide provided in Example 1 was mixed with ddH2O to prepare an antimicrobial peptide solution with a final concentration of 400 μmol / L; and a 400 μmol / L nisin solution (Sangon Biotech (Shanghai) Co., Ltd., catalog number A410681) was used as a positive control.

[0064] (3) Add 100 μL of LB liquid medium to each well of a 96-well cell culture plate. Then, add 50 μL of antimicrobial peptide solution and nisin solution to each well of the 96-well cell culture plate. Mix well by pipetting and aspirating. Use an equal volume of LB liquid medium as a blank control. Measure the OD of the bacterial culture solution using LB liquid medium. 600 The value was adjusted to 0.2, and then the adjusted bacterial solution was added to a 96-well cell plate at a rate of 50 μL / well. The bacterial solutions were cultured according to the culture conditions in (1), and the OD of the solution was measured after the culture was completed. 600The value was calculated, and the growth inhibition rate was obtained according to the following formula. The results are shown in Table 2.

[0065] Growth inhibition rate = (A0 - A) X ) / A0×100%

[0066] Where A0 is the OD of the blank control group at the end of the culture. 600 Value; A X The OD values ​​of the antimicrobial peptide solution and nisin solution at the end of culture were measured. 600 value.

[0067] Table 2.

[0068]

[0069]

[0070] As shown in Table 2, the antimicrobial peptides 1-10 obtained by the method provided in Example 1 have a certain inhibitory effect on the growth of one or more cells of Bacillus subtilis, Escherichia coli, Vibrio parahaemolyticus and yeast. That is, the antimicrobial peptides 1-10 obtained by screening all have a certain antimicrobial activity, and the screening accuracy rate can reach 100%.

[0071] Among them, compared with the existing known antimicrobial peptide nisin and the screened antimicrobial peptides, antimicrobial peptide 8 with an amino acid sequence as shown in SEQ ID NO:8 can effectively inhibit the growth of Bacillus subtilis, Escherichia coli, Vibrio parahaemolyticus and yeast. It also has excellent inhibitory effects on Gram-positive bacteria, Gram-negative bacteria, aquatic pathogens and fungi, and has broad-spectrum antimicrobial potential and good application prospects.

[0072] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention without departing from the principles and spirit of the present invention.

Claims

1. A deep-sea cold-spring derived antibacterial peptide, characterized in that, The antibacterial peptide is an amino acid fragment with a sequence as shown in SEQ ID NO:

8.

2. The process for the preparation of antibacterial peptide of deep-sea cold-spring origin as claimed in claim 1, wherein, The preparation method comprises the following steps: coupling amino acids according to the amino acid sequence shown in SEQ ID NO: 8 by using an Fmoc solid-phase synthesis method to synthesize the antibacterial peptide.

3. Use of the deep-sea cold-spring derived antibacterial peptide according to claim 1 for the preparation of a medicament for the treatment of microbial infections, characterized in that, The microorganism is one or more of Bacillus subtilis, Escherichia coli, Vibrio parahaemolyticus and yeast.

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