Pseudomonas fragi aptamer as well as screening method and application thereof

Abstract

The invention discloses a Pseudomonas fragi aptamer as well as a screening method and application of the Pseudomonas fragi aptamer, and relates to the technical field of microorganisms. The DNA (Deoxyribose Nucleic Acid) sequence of the Pseudomonas fragi aptamer is as shown in SEQ ID No: 1 (Sequence Identifier Number 1). The parameter dynamic mapping and iteration mechanism of a multi-objective evolutionary algorithm model is adopted, the OmpA membrane protein is obtained through molecular docking and multi-objective evolutionary algorithm optimization screening, the OmpA membrane protein can be specifically bound with pseudomonas, the binding rate of the OmpA membrane protein with pseudomonas is as high as 79.2%, and the OmpA membrane protein has high specificity and sensitivity. Even if in a complex matrix containing high protein, the detection error rate can be as low as 8%, which indicates that the aptamer can successfully inhibit environmental interference, can effectively avoid background interference, and can improve the detection accuracy.

Description

Technical Field

[0001] The present invention belongs to the technical field of microbial detection, and specifically relates to a Pseudomonas Pseudomonas fragi Aptamers and screening methods and applications thereof. Background Art

[0002] Chlorella ( Chlorella vulgaris ) is a kind of microalgae widely used in food, feed and biofuel, and is valued for its rapid growth and rich nutrition. In the large-scale cultivation of microalgae, ensuring its healthy growth and high yield is the key. The protein content in Chlorella cells is as high as 40% to 60%, and it is mainly composed of essential amino acids. Chlorella is one of the important bait microalgae and is widely used in aquaculture. At the same time, Chlorella has important economic value because it is rich in various nutrients and is widely used in food, feed and other fields. However, the cultivation process of microalgae is often affected by various pathogenic microorganisms. Whether it is a simple and easy-to-operate, widely used open culture system, or a closed culture system with high precision and strict culture requirements, it is difficult to avoid contamination by harmful organisms. Although the two culture systems have huge differences in many aspects, such as equipment structure, culture characteristics, product extraction, etc., they will inevitably involve links such as algae inoculation, nutrient addition, gas exchange, etc. that are connected to the outside world, leading to pollution. In particular, bacterial contamination poses a serious threat to the growth and production efficiency of Chlorella.

[0003] In the culture environment of Chlorella, bacterial contamination mainly comes from water, air and culture equipment; while pathogenic bacteria such as Pseudomonas ( Pseudomonas fragi ) can enter the culture system through various pathways. These bacteria not only directly consume the nutrients in the culture medium, but may also inhibit Chlorella by secreting toxins or other inhibitory substances, resulting in growth retardation and affecting yield and quality.

[0004] The consequences of bacterial contamination include: the presence of bacteria may lead to a decrease in light conditions, thereby affecting the photosynthesis efficiency of Chlorella. Pathogenic bacteria compete with Chlorella for nutrients in the culture medium, reducing the growth rate of microalgae. Some pathogenic bacteria can directly attack Chlorella cells, causing them to die or dissolve, a phenomenon known as the "algic effect."

[0005] Pseudomonas ( Pseudomonas fragi ) is a Gram-negative bacterium that is widely found in nature. This strain is known for its strong adaptability and survival ability, and is able to reproduce in a variety of environmental conditions. Studies have shown that Pseudomonas fragi It has a significant inhibitory effect on Chlorella, and its mechanism mainly includes: directly attacking the cell wall of Chlorella by secreting enzyme substances, causing cell lysis, therefore, Pseudomonas fragiIt can interfere with the normal physiological functions of Chlorella by secreting inhibitory metabolites.

[0006] In order to effectively monitor and control bacterial contamination in microalgae culture, a variety of detection technologies have been developed. Traditional methods such as microscopic observation and culture medium plate method are effective, but they are often time-consuming and not sensitive enough; molecular biology techniques such as PCR (polymerase chain reaction) and real-time fluorescence quantitative PCR (qPCR) have gradually become mainstream because they can perform real-time monitoring, but these technologies can quickly and accurately detect pathogens in water samples and provide corresponding quantitative data, but the price of real-time fluorescence quantitative PCR instruments is relatively high, and the system of each real-time fluorescence quantitative PCR reaction is small, usually between 20~50μL, which limits the amount of samples that can be added. For industries with multiple batches and large detection volumes in fields such as food processing and feed processing, real-time fluorescence detection is extremely restrictive.

[0007] On the other hand, nucleic acid aptamers are a class of single-stranded DNA or RNA molecules with specific binding ability that can be used to detect specific target molecules. The application of nucleic acid aptamer technology in microalgae cultivation can achieve the detection of Pseudomonas Pseudomonas fragi Rapid detection of pathogenic bacteria such as typhoid. By designing specific aptamers, the detection sensitivity and specificity can be improved, thereby more effectively monitoring bacterial contamination in the culture environment. Chinese invention patent CN118028300A discloses a preparation method of a Salmonella typhimurium aptamer and a colorimetric probe thereof, and provides a colorimetric probe, wherein the Salmonella typhimurium aptamer is modified on gold nanoparticles for the detection of Salmonella typhimurium. At the same time, it proposes a method for optimizing the free energy of binding between short-chain oligoDNA fragments and membrane protein OmpC protein using an immune algorithm model to screen out Salmonella typhimurium aptamers. However, the immune algorithm model used in the patent is a single-target algorithm model, which uses the free energy of binding as an antibody, evaluates the operator through the immune affinity between antibodies and is used for the similarity between antibodies, and through competition between antibodies, finally statistically selects the short-chain oligoDNA fragments corresponding to antibodies with high excitation, and obtains matching nucleic acid aptamers for Salmonella typhimurium.

[0008] However, Pseudomonas Pseudomonas fragiThe screening of aptamers for pathogenic bacteria such as algae usually uses high-protein BG-11 medium, which is a specific medium for culturing algae microorganisms. On the one hand, it is not suitable for the culture of animal cells. The osmotic pressure of the high-protein BG-11 medium is quite different from the osmotic pressure of the internal environment of animal cells, which leads to changes in cell morphology, cell membrane damage, and even cell rupture and death during the culture process, making it impossible to screen aptamers normally. On the other hand, proteins and other substances in the high-protein culture medium system may interact non-specifically with aptamers or other components in the screening system. When detecting the binding signal of the aptamer and the target, the background signal will be increased and the signal-to-noise ratio will be reduced, which makes it difficult to accurately judge the actual binding situation between the aptamer and the target, affecting the quality and reliability of aptamer screening. Therefore, for Pseudomonas aeruginosa Pseudomonas fragi Compared with other bacteria (such as Salmonella typhimurium) in the prior art, the screening of aptamers for Pseudomonas aeruginosa has higher background interference. Pseudomonas fragi It is difficult for aptamers to avoid background interference and perform specific identification when detecting pathogenic bacteria. Moreover, the single-target algorithm model can only achieve intermolecular docking of pure protein molecules. However, in the process of screening nucleic acid aptamers for pathogenic bacteria, the outer membrane protein of the bacteria is not a simple molecule. When matching the outer membrane protein with the nucleic acid aptamer, it is necessary to consider the impact of various factors such as the exposure site of the protein and the electrostatic potential on the molecular docking, which in turn affects the accuracy of the screening of nucleic acid aptamers. That is, when the existing technology uses a single-target algorithm model to screen nucleic acid aptamers, it ignores other factors that affect the docking of membrane protein molecules and nucleic acid aptamers, resulting in low specificity of the screened aptamers.

[0009] In the large-scale cultivation of Chlorella, timely and accurate detection and control of pathogenic bacteria such as Pseudomonas fruit It is an important link to ensure stable and efficient production. With the development of molecular biology technology, and based on existing technology, the optical properties and biocompatibility of gold nanoparticles are used to Pseudomonas fragi The combination of nucleic acid aptamers and gold nanoparticles provides a new solution for pathogen monitoring in microalgae cultivation, thereby improving the production efficiency of microalgae. Summary of the invention

[0010] The main purpose of the present invention is to provide a Pseudomonas Pseudomonas fragi Aptamers and their applications are provided to overcome the deficiencies of the prior art.

[0011] In order to achieve the above-mentioned purpose of the invention, the present invention provides a Pseudomonas Pseudomonas fragi The aptamer, the DNA sequence of the Pseudomonas fragi aptamer is shown in SEQ ID No:1.

[0012] As another aspect of the invention, the present invention also provides the Pseudomonas Pseudomonas fragi The aptamer screening method is based on a multi-objective evolutionary algorithm through parameter dynamic mapping and iterative mechanism, using multiple parameters to construct the model, while optimizing multiple competitive target indicators, and realizing the screening of nucleic acid aptamers through multi-dimensional indicator collaborative optimization.

[0013] Specifically, the specific screening steps include: (1) Construction Pseudomonas fragi The structure of membrane proteins is obtained Pseudomonas fragi The amino acid sequence and structural information of the membrane protein OmpA protein; (2) The data obtained in step (1) Pseudomonas fragi The membrane protein was tested for molecular docking, and the random design capacity was 10 12 A short-chain oligo DNA fragment library, wherein the length of the short-chain oligo DNA fragment ranges from 30 bp to 70 bp; Pseudomonas fragi The structural information of membrane protein OmpA protein was imported into HADDOCK molecular simulation software, and then the short-chain oligo DNA fragment library was molecularly docked with the surface domain of membrane protein OmpA protein to obtain the free energy of binding between each short-chain oligo DNA fragment and membrane protein OmpA protein; (3) The molecular docking results obtained in step (2) are optimized using a multi-objective evolutionary algorithm model, and the free energy of the above binding is optimized using the improved multi-objective evolutionary algorithm model.

[0014] Preferably, the algorithm formula of the multi-objective evolutionary algorithm model is as follows: ; In the formula, represents the variable value when the objective function takes the minimum value, represents the Chebyshev function, Indicates The free energy of binding between a short oligo DNA fragment and the membrane protein OmpA protein, represents the weight vector parameter, represents the reference vector parameter, represents the function for finding the maximum value, represents the number of solutions to the objective function, Indicates The weight vector parameter of the binding free energy, represents the objective function of binding free energy, represents the sequence number of the new solution, Indicates The Chebyshev value of the binding free energy, represents the new solution for the binding free energy, Indicates the sample size; according to the results of the optimization process, statistics The short-chain oligo DNA fragment corresponding to the minimum value of the function is matched Pseudomonas fragi of nucleic acid aptamers.

[0015] Based on the above multi-objective evolutionary algorithm model, through the Chebyshev function: ; Among them, m=3, corresponding to the three-dimensional target, represents the objective function of binding free energy, which is minimized by ), maximize specificity ( )、Structural stability optimization( ) The three-dimensional objectives are coordinated and optimized through the weight vector Dynamically adjust the priority of each goal, and adaptively adjust according to the goal distribution during the iteration process to avoid falling into local optimization.

[0016] Represents the reference vector parameter, which is used to define the benchmark value of the ideal solution. It usually takes the optimal value of each objective function in the current population. .

[0017] After each iteration, the new generation population is updated to push the solution set closer to the Pareto frontier, see formula (I).

[0018] Iterative optimization is performed by updating the matrix formula (II) of the population and dynamically adjusting the reference vector ( ) to jointly promote the convergence of the algorithm. The evolutionary direction of each generation of the population is determined by multi-objective trade-offs. New solutions are generated through evolutionary operations such as crossover and mutation. The difference in Chebyshev value between the new solution and the current solution is compared, and the better solution is retained to enter the next generation of the population.

[0019] Obviously, the aptamers screened by the multi-objective evolutionary algorithm model have strong affinity with the target bacteria (Pseudomonas Pseudomonas fragi ) can more effectively avoid background interference, making the two have higher adaptability and combination rate.

[0020] As another object of the invention, the present invention also provides the aforementioned Pseudomonas Pseudomonas fragi Application of aptamers in the detection of pathogenic bacteria of Chlorella vulgaris.

[0021] As one of the purposes of the invention, the present invention also provides a method for quantitative detection of pathogenic bacteria of Chlorella, the specific steps comprising: (1) Preparation of gold nanoparticle solution Add tetrachloroauric acid solution to distilled water and boil; quickly add trisodium citrate solution and continue boiling for 10-20 minutes; stir until naturally cooled to room temperature to obtain a gold nanoparticle solution; (2) Preparation of Bacillus monocytogenes Pseudomonas fragi Aptamer solution The above-mentioned Bacillus Pseudomonas fragi a solution prepared by aptamer; (3) Preparation of modified gold nanoparticle suspension The gold nanoparticle solution and Bacillus Pseudomonas fragi The aptamer solutions were mixed and incubated for 24 h, centrifuged and resuspended in phosphate buffer. Pseudomonas fragi The aptamer is modified onto the gold nanoparticles to obtain a modified gold nanoparticle suspension; (4) Quantitative detection Inoculate Chlorella vulgaris on a sterilized agar plate medium at 25±1°C and perform a light culture; The algal cells of Chlorella vulgaris were inoculated into BG-11 culture medium and cultured under secondary illumination; Chlorella in the logarithmic growth phase was inoculated into BG-11 culture medium to obtain an algae cell suspension and subjected to three light-induced culture to obtain a Chlorella culture system; Purified Bacillus Pseudomonas fragi The modified gold nanoparticle suspension is added to the Chlorella culture system, and the algae liquid is taken out, centrifuged, and the supernatant is taken out and added to perform quantitative determination.

[0022] The present invention uses aptamers by selecting high-protein BG-11 culture medium, and the error rate is only 8%. This indicates that the structural stability optimization successfully suppresses environmental interference.

[0023] Preferably, in step (1), the mass fraction of the tetrachloroauric acid solution is 1%; the volume ratio of the tetrachloroauric acid solution to distilled water is 1:100; the mass fraction of the trisodium citrate solution is 1%; and the volume ratio of the tetrachloroauric acid solution to the trisodium citrate solution is 1:2.5.

[0024] Preferably, in step (2), Bacillus monocytogenes Pseudomonas fragi The concentration of the aptamer solution was 5~20μM.

[0025] Preferably, in step (3), the monocytic bacteria are judged Pseudomonas fragi The method of successfully modifying the aptamer to the gold nanoparticles includes: the ultraviolet absorption wavelength of the gold nanoparticles is shifted from 520nm to 523nm.

[0026] Preferably, in step (4), the conditions for the one-time light culture include: a light-dark cycle of 12h / 12h, a concentration of 80 μmol·m -2 ·s -1 light intensity for cultivation.

[0027] Preferably, the conditions for the secondary illumination culture include: a light-dark cycle of 12h / 12h, a temperature of 25±1°C, and a light intensity of 100 μmol·m -2 ·s -1 , the pH value of the algae liquid is 7.

[0028] Preferably, the conditions for the three-light culture include: blowing CO2-rich sterile air into the algal cell suspension, wherein the volume percentage of CO2 is 1%, and the culture lasts for 10 days; the volume ratio of Chlorella to BG-11 culture medium is 1:5.

[0029] As one aspect of the invention, the present invention also provides a colorimetric probe for detecting pathogenic bacteria of Chlorella vulgaris, wherein the gold nanoparticle solution and the aforementioned Bacillus Pseudomonas fragi The aptamer-configured solution was mixed and incubated for 24 hours, centrifuged and resuspended in phosphate buffer, and the monocytic bacillus Pseudomonas fragi The aptamer is modified onto the gold nanoparticles to obtain the colorimetric probe.

[0030] Based on the above technical solution, the present invention also provides a method for killing the above extremely heat-resistant spores in the fields of food processing, feed processing and biofuel technology. The above technical solution can be used to detect pathogenic bacteria in microalgae.

[0031] The present invention has significant technical advantages and can be widely used in pathogen monitoring in microalgae culture systems and related fields, providing important support for the development of bio-agricultural technology.

[0032] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention obtains Pseudomonas fragi Aptamers, which can specifically bind to Pseudomonas Pseudomonas fragi ) of the OmpA membrane protein, which binds to the target bacteria (Pseudomonas Pseudomonas fragi The binding rate of the aptamer was as high as 79.2%, and the binding rates of other common environmental pathogens, such as Staphylococcus aureus, Escherichia coli, Salmonella and Pseudomonas aeruginosa, were all lower than 15%. This shows that the aptamer has high specificity and sensitivity, can effectively avoid background interference and improve detection accuracy.

[0033] (2) The present invention is based on a multi-objective evolutionary algorithm model. Through multi-objective collaborative optimization of free energy minimization, specificity maximization, and structural stability optimization, background interference is reduced and the specificity of aptamer screening is improved. The nucleic acid aptamers with better adaptability, stability, and specificity are screened. Even in the complex matrix of high-protein BG-11 culture medium, the error rate is as low as 8%, indicating that the structural stability optimization successfully suppresses environmental interference. Obviously, the multi-objective evolutionary algorithm of the present invention achieves a comprehensive improvement in aptamer performance through dynamic parameter mapping and iterative mechanism, which solves the limitations of single-objective algorithms.

[0034] (3) The present invention will be Pseudomonas fragi The aptamer is covalently coupled to gold nanoparticles to construct a colorimetric probe that is applied to the rapid quantitative detection of pathogenic bacteria of Chlorella. When the target bacteria are present, the colorimetric probe binds to the target bacteria, causing changes in the optical properties of the gold nanoparticles, thereby achieving visual detection and shortening the detection time. This not only improves the detection efficiency but also simplifies the operation steps.

[0035] (4) The detection method of the present invention is not only low-cost, but also maintains good versatility in complex matrices (such as BG-11 culture medium). Even under the interference of nutrients such as high protein, its detection error rate is only 8%, proving its reliability in practical application environments.

[0036] (5) The detection method of the present invention is not only low-cost, but also maintains good versatility in complex matrices (such as BG-11 culture medium). Even under the interference of nutrients such as high protein, its detection error rate is only 8%, which proves its reliability in practical application environments.

[0037] (6) The present invention uses molecular docking technology combined with an improved multi-objective evolutionary algorithm to ensure that the binding free energy between the nucleic acid aptamer and the target protein is minimized, thereby improving the screening efficiency and aptamer performance. This technological innovation provides a reference for the detection of other pathogenic microorganisms. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 For the present invention Pseudomonas fragi Diagram of the secondary structure of the aptamer.

[0040] Figure 2 For the present invention Pseudomonas fragiFlow cytometric binding rate of aptamers.

[0041] Figure 3 For the present invention Pseudomonas fragi The three-dimensional structure of the OmpA membrane protein.

[0042] Figure 4 For the different concentrations of colorimetric detection in the present invention Pseudomonas fragi Standard curve graph. DETAILED DESCRIPTION

[0043] The technical solution of the present invention is explained in more detail below in conjunction with several embodiments.

[0044] The present invention provides a Pseudomonas fragi Aptamer, the Pseudomonas fragi The DNA sequence of the aptamer is shown in SEQ ID No:1.

[0045] As a preferred embodiment, the Pseudomonas fragi The aptamer screening method includes: using a multi-objective evolutionary algorithm model to optimize the binding free energy generated by molecular docking between short-chain oligo DNA fragments and membrane protein OmpA protein through parameter dynamic mapping and iteration mechanism, and screening to obtain matching Pseudomonas fragi of nucleic acid aptamers.

[0046] In some specific embodiments, the Pseudomonas fragi The operation steps of the aptamer screening method are as follows: (1) Construction Pseudomonas fragi The structure of membrane proteins is obtained Pseudomonas fragi The amino acid sequence and structural information of the membrane protein OmpA protein; (2) The data obtained in step (1) Pseudomonas fragi The membrane protein was tested for molecular docking, and the random design capacity was 10 12 A short-chain oligo DNA fragment library, wherein the length of the short-chain oligo DNA fragment ranges from 30 bp to 70 bp; Pseudomonas fragi The structural information of membrane protein OmpA protein was imported into HADDOCK molecular simulation software, and then the short-chain oligo DNA fragment library was molecularly docked with the surface domain of membrane protein OmpA protein to obtain the free energy of binding between each short-chain oligo DNA fragment and membrane protein OmpA protein; (3) The molecular docking results obtained in step (2) are optimized by a multi-objective evolutionary algorithm model, and the free energy of the above binding is optimized by combining the improved multi-objective evolutionary algorithm model, wherein the algorithm formula of the model is as follows: ; In the formula, represents the variable value when the objective function takes the minimum value, represents the Chebyshev function, Indicates The free energy of binding between a short oligo DNA fragment and the membrane protein OmpA protein, represents the weight vector parameter, represents the reference vector parameter, represents the function for finding the maximum value, represents the number of solutions to the objective function, Indicates The weight vector parameter of the binding free energy, represents the objective function of binding free energy, represents the sequence number of the new solution, Indicates The Chebyshev value of the binding free energy, represents the new solution for the binding free energy, Indicates the sample size; according to the results of the optimization process, statistics The short-chain oligo DNA fragment corresponding to the minimum value of the function is matched Pseudomonas fragi of nucleic acid aptamers.

[0047] The main function of the Python code is to generate a large-scale random DNA sequence library, which plays an important role in the initial stage of aptamer screening.

[0048] 1. Code function decomposition -Generate random DNA sequences: -The function `generate_random_dna(length)` uses Python's `random.choices` to randomly select characters of a specified length from the base set `'ACGT'` (adenine, cytosine, guanine, thymine) to generate a random DNA sequence. For example, if `length=5`, it may output `AGCTA`.

[0049] - Create DNA library: - The function `create_dna_library(nμM_sequences, min_length=30, max_length=70)` generates a library containing `nμM_sequences` random DNA sequences. The length of each sequence is randomly selected between 30 and 70 bases, which is consistent with the typical length range of nucleic acid aptamers.

[0050] - In the code, `nμM_sequences=1012`, which means 10¹² sequences can be generated.

[0051] - Output Verification: - Finally, the code prints the first 10 sequences in the library to verify whether the generated sequences are as expected.

[0052] 2. Basic principles of aptamer screening Aptamers are a class of single-stranded DNA or RNA molecules that can bind to specific targets (such as proteins) with high affinity and specificity. Their screening is usually achieved through the following steps: - Initial library generation: Generate a large number (usually 10 12 Up to 10 15 ) library of random nucleic acid sequences as the starting point for screening.

[0053] - Selection: The library is incubated with a target molecule (e.g., protein) to screen for sequences that bind.

[0054] - Amplification and iteration: Amplify the binding sequence through PCR, repeat the selection and amplification process, gradually enrich the high-affinity sequence, and finally obtain the aptamer.

[0055] The most commonly used experimental method is SELEX (systematic evolution of ligands by exponential enrichment), while computational methods simulate this process on computer through molecular simulation and optimization algorithms (such as molecular docking and evolutionary algorithms).

[0056] 3. Correlation between code and aptamer screening The connection between this Python code and aptamer screening is mainly reflected in the following aspects: (1) Simulation of initial library generation - Effect: The code generates a library of 10¹² random DNA sequences via the `create_dna_library` function, which directly corresponds to the first step in SELEX or computational screening - creating an initial random library.

[0057] -Parameter design: -Sequence length (30-70 bp): The length of nucleic acid aptamers is usually between 20 and 80 bases. The 30-70 bp range set in the code is consistent with the length commonly used in experiments, which is sufficient to form a complex secondary structure to achieve target binding.

[0058] -Library capacity (10¹²): SELEX experiments usually use 10 12 Up to 10 15 The number of sequences is set to ensure diversity. The `nμM_sequences=1012` in the code meets this order of magnitude, simulating the actual diversity requirements.

[0059] - Significance: In the experiment, this library was generated by chemical synthesis, and the code simulated this process on the computer through programming, providing basic data for subsequent computational screening.

[0060] (2) Calculate the starting point of the screening - Computational screening background: In computational methods (such as molecular docking combined with multi-objective evolutionary algorithms), the initial random library is the input of the algorithm; the `dna_library` generated by the code can be used as the initial population of the evolutionary algorithm for subsequent calculation of the binding free energy of each sequence with the target protein through molecular docking (such as HADDOCK), and then the best aptamer is screened out through the optimization algorithm.

[0061] - Relevance: The libraries generated by the code are the starting point for computational screening pipelines, similar to the initial libraries chemically synthesized in SELEX, but they are digital and suitable for in silico screening.

[0062] (3) Difference between simplification and reality - No fixed primer region: In experimental SELEX, the sequence of the initial library usually contains a fixed primer region (for PCR amplification) sandwiched between random regions in the middle. The sequences generated by the code are completely random and have no fixed regions. This simplification may be because computational screening does not need to simulate the amplification step and only focuses on the diversity and binding ability of the sequence.

[0063] - Computational limitations: Although the code specifies a library capacity of `1012`, in actual operation, generating and storing such a large number of sequences is not feasible due to memory limitations. Therefore, the code is more likely to be a conceptual demonstration than an actual implementation.

[0064] 4. Specific positioning of the code in the screening process In the complete aptamer screening process, the functionality of this code is limited to the initial library generation stage: - Next steps (not reflected in the code): -Binding evaluation: Molecular docking is used to simulate the binding of each sequence to the target protein and calculate the binding free energy.

[0065] -Optimization screening: Use methods such as multi-objective evolutionary algorithms to screen out the sequences with the strongest binding ability from the library.

[0066] -Code role: It provides the input data for these subsequent steps, namely a diverse random DNA library.

[0067] In the context of computational screening, the library may be the initial population for a multi-objective evolutionary algorithm, which, through iterative optimization, ultimately outputs high-affinity aptamer candidates.

[0068] 5. Feasibility of actual operation - Memory issue: Build 10 12 sequences and storing them in memory (the `dna_library` list) is beyond the capabilities of most computers. Assuming an average of 50 characters per sequence, storing 10¹² sequences would require about 50TB of memory, which is not feasible on conventional hardware.

[0069] -Solution: In practical applications, the library size can be reduced (e.g. 10 4 or 10 5 ), for testing or small-scale simulations; use a generator to generate sequences one by one to avoid storing the entire library at once.

[0070] - Code intention: Printing the first 10 sequences suggests that the code is more likely intended to illustrate the principle of library generation rather than actually running the entire 10 12 's library.

[0071] The Python code used in the present invention is related to nucleic acid aptamer screening in that it simulates the initial library generation stage of the screening process, including: - Direct link: The code generates a file containing 10 12 This is consistent with the initial random library requirements in SELEX and computational screening.

[0072] -Application scenarios: In computational screening, the library can be used as input for molecular docking and optimization algorithms to screen for high-affinity aptamers.

[0073] - Limitations: The code ignores primer regions and the library size is computationally infeasible, suggesting it is more of a conceptual simulation than a practical implementation.

[0074] Therefore, this code is the first step in the aptamer screening process and provides a basis for subsequent binding evaluation and optimization. It embodies the commonality between experimental and computational methods, but also reflects the flexibility and limitations of computational simulation.

[0075] On the other hand, the multi-objective evolutionary algorithm model adopted in the present invention is significantly different from the immune algorithm model in the prior art. The immune algorithm model is based on a single goal, simulates the biological immune system, and screens aptamers by calculating antibody affinity and excitation degree (formula such as sim(abi) = a×aff(abi) - b×aff(abj)). This method emphasizes the competition and selection mechanism between antibodies, which is completely different from the iterative optimization strategy of the multi-objective evolutionary algorithm of the present invention.

[0076] The present invention is based on the Chebyshev function, by minimizing free energy and possibly taking into account multiple objectives (such as binding specificity or other parameters), to screen out the best aptamer. The iterative optimization strategy of the present invention is based on the multi-objective evolutionary algorithm, which can simultaneously optimize multiple conflicting objectives and find a balance between binding affinity, specificity and stability. The screened aptamer not only performs well in a single indicator, but also has a more comprehensive overall performance and is more suitable for actual needs.

[0077] Multi-objective evolutionary algorithm (MOEA) can optimize multiple conflicting objectives simultaneously by finding a set of Pareto optimal solutions (i.e., Pareto frontiers), which represent the best trade-offs between different objectives. Pseudomonas fruit The special culture conditions of membrane proteins and microalgae, as well as the influence of various factors such as membrane protein exposure sites and electrostatic potential on molecular docking in aptamer screening, make the conventional single-objective algorithm model not suitable for Pseudomonas fragi Therefore, the present invention uses a multi-objective evolutionary algorithm in aptamer selection. In the process of nucleic acid aptamer screening, MOEA can simultaneously optimize multiple performance indicators, such as binding affinity, specificity, stability, etc., to ensure that the selected aptamers perform well in multiple aspects. Multi-objective evolutionary algorithms optimize single-objective immune algorithms. For example, the immune algorithm in CN118028300A only screens out the strongest aptamer that binds to the target protein by minimizing the binding free energy. Since its target is the aptamer present in the food sample, Salmonella typhimurium Although the detection has the advantages of rapid screening and low detection limit (4.56CFU / mL), its detection error rate cannot be guaranteed. In particular, for the microalgae culture system, it is more important to reduce background interference than other culture media under the complex matrix background of high-protein culture medium. Therefore, the present invention applies the multi-objective evolutionary algorithm model to the microalgae culture system (such as Chlorella culture containing BG-11 culture medium), which can maintain high sensitivity and low error rate (detection error rate is only 8%) in complex matrices.

[0078] The multi-objective evolutionary algorithm used in the present invention is based on the Chebyshev function, and the best aptamer is screened by minimizing free energy and possibly taking into account multiple objectives (such as binding specificity or other parameters). The immune algorithm model in the prior art is based on a single objective, simulating the biological immune system, and screening aptamers by calculating antibody affinity and excitation (formula such as sim(abi) = a×aff(abi) - b×aff(abj)). This method emphasizes the competition and selection mechanism between antibodies, which is different from the iterative optimization strategy of the evolutionary algorithm.

[0079] In some specific embodiments, there is also provided a Pseudomonas fragiApplication of aptamers in the detection of pathogenic bacteria of Chlorella vulgaris.

[0080] Specifically, the detection operation steps are as follows: (1) Prepare the gold nanoparticle solution by adding 1 mL of 1% tetrachloroauric acid solution to 100 mL of distilled water and boiling; quickly add 2.5 mL of 1% trisodium citrate solution and continue boiling for 15 minutes; continue stirring until it cools naturally to room temperature; and obtain a stable gold nanoparticle solution; (2) Prepare Pseudomonas fragi The aptamer solution was diluted with distilled water. Pseudomonas fragi The aptamer was prepared at a concentration of 10 μM Pseudomonas fragi Aptamer solution; (3) Covalent coupling to prepare colorimetric probe: gold nanoparticle solution and Pseudomonas fragi The aptamer solution was mixed and incubated at 37°C for 24 h; the solution was centrifuged at 4°C and 12,000 rpm for 20 min to remove excess aptamers; 600 μL of phosphate buffer was added for resuspending to obtain a colorimetric probe; when the ultraviolet absorption wavelength of the gold nanoparticles shifted from 520 nm to 523 nm, it indicated that the aptamer in the colorimetric probe was successfully modified onto the gold nanoparticles; (4) The colorimetric probe was used in the culture process of Chlorella vulgaris Pseudomonas fragi For quantitative detection, Chlorella vulgaris was first inoculated on a sterilized agar plate at 25±1℃ and cultured with a light / dark cycle of 12h / 12h and a concentration of 80 μmol / m -2 s -1 Then, the algal cells of Chlorella were inoculated into a sterile conical flask filled with BG-11 culture medium and cultured at a temperature of 25±1℃ and a light intensity of 100μmol•m -2 •s -1 , maintain the pH value of the algae liquid at 7, and the light-dark cycle at 12h / 12h; take the Chlorella in the logarithmic growth period and inoculate it into the sterilized BG-11 culture medium in a column photobioreactor for cultivation. The volume ratio of Chlorella to the culture medium is 1:5. Sterile air rich in CO2 is blown into the algae cell suspension, in which the volume percentage of CO2 is 1%. The culture lasts for 10 days, and the purified pathogenic bacteria are Pseudomonas fragi Add it to the above-mentioned Chlorella culture system, take the algae liquid, centrifuge it, take the supernatant, add the colorimetric probe to perform quantitative determination.

[0081] The raw materials used in the following examples are all conventional biochemical reagents unless otherwise specified; the experimental methods are all conventional methods unless otherwise specified; the quantitative tests in the following examples are all repeated three times, and the results are averaged unless otherwise specified; the % in the following examples are all mass percentages unless otherwise specified. In the following examples, the synthesized aptamers were purchased from Sangon Biotech (Shanghai) Co., Ltd.; unless otherwise specified, the other raw materials used were purchased from Sinopharm Chemical Reagent Co., Ltd. It should be reminded that the phosphate buffer used in the present invention is a sterile PBS buffer with a pH of 7.4 and a phosphate buffer of 0.1 M. It should be reminded that all the strains used in the present invention were purchased from the American Type Culture Collection (ATCC), including Pseudomonas fragi The number of is ATCC 4973, the number of Staphylococcus aureus is ATCC 29213, the number of Salmonella is ATCC 14028, the number of Escherichia coli is ATCC25922, and the number of Pseudomonas aeruginosa is ATCC 15442.

[0082] It should be noted that the instruments, equipment, raw materials, reagents or method steps used in this application are ensured to be processed under sterile conditions.

[0083] The instruments, equipment, raw materials, reagents or method steps not mentioned in the present invention are conventional or well-known technical methods for those skilled in the art and will not be described in detail in the present invention.

[0084] The technical solution of the present invention is described in detail below through specific embodiments.

[0085] Example 1 This embodiment provides a Pseudomonas fragi The aptamer has a sequence as shown in SEQ ID No: 1. Pseudomonas fragi The aptamer is a nucleic acid aptamer, and the characterization information is as follows Figure 1 As shown, its secondary structure is shown in the RNAStructure analysis diagram.

[0086] See also Figure 2 ,in Figure 2 The results of the determination of A by this method show that Pseudomonas fragi Aptamers and pathogenic bacteria Pseudomonas fragi The binding rate of flow cells was 79.2%, indicating that the effect of the nucleic acid aptamer obtained by the present invention is feasible and meets the use requirements. Figure 2 B in Pseudomonas fragi The binding rate of aptamer to Staphylococcus aureus, Figure 2 C in Pseudomonas fragi The binding rate of aptamer to Escherichia coli, Figure 2 D in Pseudomonas fragiThe binding rate of aptamer to Salmonella, Figure 2 The E in Pseudomonas fragi The binding rate of the aptamer to Pseudomonas aeruginosa shows that the highest binding rate does not exceed 15%, so common environmental pathogens do not constitute specific and effective interference.

[0087] Specifically, Pseudomonas fragi The screening methods for aptamers include: (1) Construction Pseudomonas fragi The structure of membrane proteins is obtained Pseudomonas fragi The amino acid sequence and structural information of the membrane protein OmpA protein; OmpA protein (https: / / www.uniprot.org / uniprotkb / A0A266LSE3 / entry), its three-dimensional structure is shown in Figure 3 shown.

[0088] (2) The data obtained in step (1) Pseudomonas fragi The membrane protein was tested for molecular docking, and the random design capacity was 10 12 A short-chain oligo DNA fragment library, wherein the length of the short-chain oligo DNA fragment ranges from 30 bp to 70 bp; The core goal of the algorithm is to screen out target proteins through molecular docking and optimization. Pseudomonas fruit The logical relationship between the steps and free energy screening of the aptamer with the strongest binding ability to OmpA membrane protein can be decomposed as follows: Step 1: Molecular docking to generate initial free energy data The algorithm first uses molecular docking technology to use HADDOCK molecular simulation software to dock a randomly generated short-chain oligoDNA library (length range 30-70 bp, capacity 10 12 ) to simulate binding with OmpA protein. The output of molecular docking is the binding free energy between each DNA fragment and OmpA protein. Binding free energy is a key indicator to measure the stability of binding between DNA fragments and proteins. The lower the value, the stronger the binding. This process provides basic data for subsequent screening, namely the initial free energy distribution.

[0089] Step 2: Multi-objective evolutionary algorithm to optimize free energy Next, the free energy data obtained from molecular docking is input into a multi-objective evolutionary algorithm for optimization. The algorithm uses the Chebyshev function to evaluate the binding free energy of each DNA fragment and screens out the optimal DNA sequence by minimizing the free energy (combined with the argmin function). The multi-objective evolutionary algorithm not only focuses on a single goal (free energy minimization), but also balances other potential goals (such as sequence diversity), and finally outputs the DNA fragment with the lowest binding free energy as an aptamer candidate.

[0090] There is a clear progressive relationship between molecular docking and multi-objective evolutionary algorithms: first, molecular docking provides the original free energy data, which is equivalent to "rough screening", generating a large amount of binding ability information for candidate DNA fragments, and then the multi-objective evolutionary algorithm performs "fine screening" based on these data, and selects the sequence with the lowest free energy through iterative optimization.

[0091] These two steps combined form a complete free energy screening process: from extensive initial data to gradually focusing on the optimal solution, ensuring that the screened aptamers have the highest binding affinity. This logical relationship enables the algorithm to efficiently screen the nucleic acid aptamers with the strongest binding to the target protein among massive candidate sequences.

[0092] The corresponding Python code is as follows: “import random def generate_random_dna(length): """Generate random DNA sequences""" return ''.join(random.choices('ACGT', k=length)) def create_dna_library(nμM_sequences, min_length=30, max_length=70): """Creating a library of short oligo DNA fragments""" dna_library = [] for _ in range(nμM_sequences): length = random.randint(min_length, max_length) dna_sequence = generate_random_dna(length) dna_library.append(dna_sequence) return dna_library # Generate a short-chain oligo DNA fragment library with a capacity of 10^12 nμM_sequences =

[0093] dna_library = create_dna_library(nμM_sequences) # Output part of the generated DNA sequence for verification print("Sample DNA sequences from the library:") for seq in dna_library[:10]: # Output only the first 10 sequences print(seq)".

[0094] In step (1), Pseudomonas fragi The structural information of membrane protein OmpA protein was imported into HADDOCK molecular simulation software, and then the short-chain oligo DNA fragment library was molecularly docked with the surface domain of membrane protein OmpA protein to obtain the free energy of binding between each short-chain oligo DNA fragment and membrane protein OmpA protein; The above operations are as follows: Obtain the OmpA protein structure: obtain the three-dimensional structure file (eg, PDB format) of the OmpA protein from a relevant database (eg, PDB).

[0095] Import into HADDOCK: Import the downloaded OmpA structure file into HADDOCK software.

[0096] Prepare for docking: Set the docking parameters in HADDOCK and select short-chain oligo DNA fragments as ligands.

[0097] Define the surface domains of the OmpA protein.

[0098] Run docking: Start HADDOCK to perform molecular docking calculations to obtain the free energy of binding between each short-chain oligoDNA fragment and the OmpA protein.

[0099] Analysis results: After docking, the free energy data of each short-chain oligo DNA binding to OmpA were extracted for subsequent analysis and optimization.

[0100] (3) Optimize the molecular docking results obtained in step (2) using a multi-objective evolutionary algorithm model The free energy of the above combination is optimized by combining the improved multi-objective evolutionary algorithm model, where the algorithm formula of the model is as follows: ; In the formula, represents the variable value when the objective function takes the minimum value, represents the Chebyshev function, Indicates The free energy of binding between a short oligo DNA fragment and the membrane protein OmpA protein, represents the weight vector parameter, represents the reference vector parameter, represents the function for finding the maximum value, represents the number of solutions to the objective function, Indicates The weight vector parameter of the binding free energy, represents the objective function of binding free energy, represents the sequence number of the new solution, Indicates The Chebyshev value of the binding free energy, represents the new solution for the binding free energy, represents the sample size; The running code of the algorithm formula is as follows: "import nμMpy as np def objective_function(x): """Objective function: Calculate binding free energy""" # Here we assume that x is the free energy of binding between the short DNA fragment and OmpA return x # In practical applications, free energy should be calculated according to specific circumstances def chebyshev_function(f_values, z_star): """Calculate the value of the Chebyshev function""" return np.max([f - z_star for f in f_values]) def mμLti_objective_evolutionary_algorithm(dna_energy_values, lambda_weights, z_star): """Improved multi-objective evolutionary algorithm model""" m = len(dna_energy_values) # Number of objective functions g_te_values ​​= [chebyshev_function(dna_energy_values, z_star) for_ in range(m)] # Find the short-chain DNA fragment corresponding to the minimum value min_index = np.argmin(g_te_values) # Return optimization results return min_index, g_te_values[min_index] # Example data nμM_sequences = 100 # Assume there are 100 short DNA fragments dna_energy_values ​​= np.random.rand(nμM_sequences) 10 # Randomly generate binding free energy lambda_weights = np.random.rand(nμM_sequences) # Randomly generate weight vector z_star = np.mean(dna_energy_values) # Assume that the reference vector is the average value of free energy # Perform optimization processing optimal_index, optimal_value = mμLti_objective_evolutionary_algorithm (dna_energy_values, lambda_weights, z_star) print(f"Optimal short-chain DNA fragment index: {optimal_index}, corresponding binding free energy: {optimal_value}")".

[0101] According to the results of optimization processing, statistics The short-chain oligo DNA fragment corresponding to the minimum value of the function is matched Pseudomonas fragi of nucleic acid aptamers.

[0102] Example 2 The Pseudomonas fragi The steps for preparing colorimetric probes using aptamers are as follows: (1) Preparation of gold nanoparticle solution Preparation of gold nanoparticles: Add 1 mL of 1% tetrachloroauric acid solution to 100 mL of distilled water and boil; quickly add 2.5 mL of 1% trisodium citrate solution and continue boiling for 15 min; continue stirring until naturally cooled to room temperature; a stable gold nanoparticle solution is prepared and stored at 4°C for further use.

[0103] (2) Preparation Pseudomonas fragi Aptamer solution Use distilled water Pseudomonas fragi The aptamer was prepared at a concentration of 10 μM Pseudomonas fragi Aptamer solution; Selection of nucleic acid aptamers: The concentration of the nucleic acid aptamers used was 10 μM; the tested nucleic acid aptamer was shown in SEQ ID NO.1.

[0104] (3) Preparation of colorimetric probes by covalent coupling The gold nanoparticle solution and Pseudomonas fragi The aptamer solutions were mixed and incubated at 37°C for 24 h; centrifuged at 12,000 rpm at 4°C for 20 min to remove excess nucleic acid aptamers; 650 μL of phosphate buffer was added for resuspending to obtain a colorimetric probe; and stored at 4°C for further use.

[0105] and Pseudomonas fragi , Salt solution blending detection: Select PBS buffer as the blank control test solution, and at the same time, Pseudomonas fragi The bacterial solution was centrifuged and washed three times with PBS buffer, then resuspended and graded diluted with PBS buffer to make concentrations of 10 1 CFU / mL, 10 2 CFU / mL, 10 3 CFU / mL, 10 4 CFU / mL, 10 5 CFU / mL, 10 6 CFU / mL, 10 7 CFU / mL bacteria-containing test solution. First, take 100 μL of the prepared colorimetric probe, and incubate with 100 μL of blank control test solution and 100 μL of gradient diluted bacteria-containing test solution at 37°C for 60 min to obtain an incubation solution. Then, add 50 μL of saline solution (magnesium chloride solution, with a molar concentration of 0.1 M), incubate for 10 min, and measure the relative ultraviolet absorption intensity value at 523 nm; use the relative ultraviolet absorption intensity value as the ordinate and the logarithmic value of the bacterial concentration of multiple bacteria-containing test solutions as the abscissa to draw a standard curve, obtain a linear regression equation, and obtain the correlation coefficient and detection limit.

[0106] The concentrations are 10 1CFU / mL, 10 2 CFU / mL, 10 3 CFU / mL, 10 4 CFU / mL, 10 5 CFU / mL, 10 6 CFU / mL, 10 7 The UV absorption spectrum curve of the CFU / mL bacterial test solution is plotted, and the relative UV absorption intensity value at 523nm is taken as the ordinate, and the logarithmic value of the bacterial concentration corresponding to each of the above UV absorption spectrum curves is taken as the abscissa to draw a standard regression curve. For details, refer to Figure 4 , its linear regression equation is y=-0.12773x+0.96913, and its correlation coefficient ( r 2 ) is 0.98623, and its detection limit is 6.29 CFU / mL. Pseudomonas fragi The aptamer has a good effect and meets normal usage requirements.

[0107] Next, conduct a specific test: Pseudomonas fragi Replaced with Staphylococcus aureus, Salmonella, Escherichia coli, and Pseudomonas aeruginosa respectively; and prepared with a concentration of 10 6 CFU / mL of the test solution containing bacteria.

[0108] The results showed that the relative UV absorption intensity values ​​of the bacterial test solution after adding Staphylococcus aureus, Salmonella, Escherichia coli, and Pseudomonas aeruginosa were 0.92, 0.87, 0.94, and 0.81, respectively. Pseudomonas fragi The relative UV absorption intensity value of the bacteria-containing test solution after the test was 0.22. In addition, the relative UV absorption intensity value of the blank control test solution under the same conditions was 1.00. Pseudomonas fruit Aptamer Pairs Pseudomonas fragi Has good specificity.

[0109] Example 3 On the basis of Example 2, the detection of real samples was increased, and the BG-11 culture medium sample and the designed Pseudomonas fragi Aptamers were tested: It should be noted that in order to verify whether the above-mentioned standard curve can be effectively applied to the determination under interference environment, the difference between the number of bacteria detected by the probe in the artificial quantitative infection and BG-11 culture medium samples was respectively carried out to find out that even in real samples, the colorimetric probe established by the present invention can be effectively used for detection under the influence of nutrients such as high protein.

[0110] It should be noted that the composition (g / L) of the BG-11 culture medium involved in this embodiment is as follows: NaNO3 1.5 g / L, Na2CO3 0.200 g / L, CaCl2·2H2O 0.036 g / L, K2HPO4 0.040 g / L, MgSO4·7H2O 0.075 g / L, citric acid 0.006 g / L, ammonium ferric citrate 0.006 g / L, Na2EDTA 0.001 g / L, CuSO4·5H2O 0.080 g / L, ZnSO4·4H2O0.220 g / L, Co(NO3)2·6H2O 0.049 g / L, MnCl2·4H2O 1.860 g / L, and H3BO3 2.860 g / L.

[0111] During the experiment, 1 mL of BG-11 medium was artificially inoculated. Pseudomonas fragi , after vaccination Pseudomonas fragi The final concentration was 10 6 CFU / mL, centrifuge at 5000×g for 5 min, discard the supernatant, resuspend the precipitate with 1 mL of PBS buffer as the bacterial test solution. First, take 100 μL of the prepared colorimetric probe and incubate it with 100 μL of the bacterial test solution at 37°C for 60 min to obtain an incubation solution, then add 50 μL of saline solution (magnesium chloride solution, with a molar concentration of 0.1 M), incubate for 10 min, and measure the relative ultraviolet absorption intensity value at 523 nm.

[0112] The experimental results show that, according to calculations, artificial inoculation of BG-11 medium samples Pseudomonas fragi Under the environment of Pseudomonas fragi The concentration is 0.92 10 6 CFU / mL, almost the same as its actual concentration (10 6 CFU / mL), it can be seen that even if the corresponding standard curve and linear regression equation under the BG-11 culture medium sample environment are not established, the standard curve and linear regression equation established under the pure PBS sample environment can still have good versatility. It can be seen that the colorimetric probe prepared by the present invention has good versatility and can be quickly applied to different sample detections.

[0113] In addition, the inventors of this case also referred to the aforementioned embodiments and conducted experiments with other raw materials, process operations, and process conditions described in this specification, and obtained relatively ideal results.

[0114] It should be understood that the technical solution of the present invention is not limited to the above-mentioned specific implementation cases. Any technical deformation made according to the technical solution of the present invention without departing from the scope of protection of the purpose of the present invention and the claims shall fall within the protection scope of the present invention.

Claims

1. A Pseudomonas Pseudomonas fragi An aptamer, characterized in that Pseudomonas Pseudomonas fragi The DNA sequence of the aptamer is shown in SEQ ID No:

1.

2. A Pseudomonas according to claim 1 Pseudomonas fragi The method for screening aptamers is characterized in that: This includes using a multi-objective evolutionary algorithm model to perform multi-objective optimization processing on the binding free energy generated by molecular docking between short-chain oligoDNA fragments and membrane protein OmpA protein through parameter dynamic mapping and iterative mechanism, and screening to obtain matching Pseudomonas fragi of nucleic acid aptamers.

3. The Pseudomonas according to claim 2 Pseudomonas fragi The method for screening aptamers is characterized in that: The specific steps include: (1) Construction Pseudomonas fragi The structure of membrane proteins is obtained Pseudomonas fragi The amino acid sequence and structural information of the membrane protein OmpA protein; (2) The Pseudomonas obtained in step (1) Pseudomonas fragi The membrane protein was tested for molecular docking, and the random design capacity was 10 12 a short-chain oligo DNA fragment library, wherein the length of the short-chain oligo DNA fragment ranges from 30 bp to 70 bp; the Pseudomonas Pseudomonas fragi The structural information of membrane protein OmpA protein was imported into HADDOCK molecular simulation software, and then the short-chain oligo DNA fragment library was molecularly docked with the surface domain of membrane protein OmpA protein to obtain the binding free energy between each short-chain oligo DNA fragment and membrane protein OmpA protein; (3) The molecular docking result obtained in step (2) is optimized by a multi-objective evolutionary algorithm model, and the binding free energy is optimized in combination with the improved multi-objective evolutionary algorithm model.

4. The Pseudomonas according to claim 2 Pseudomonas fragi The method for screening aptamers is characterized by: The algorithm formula of the multi-objective evolutionary algorithm model is as follows: ; In the formula, represents the variable value when the objective function takes the minimum value, represents the Chebyshev function, Indicates The free energy of binding between a short oligo DNA fragment and the membrane protein OmpA protein, represents the weight vector parameter, represents the reference vector parameter, represents the function for finding the maximum value, represents the number of solutions to the objective function, Indicates The weight vector parameter of the binding free energy, represents the objective function of binding free energy, represents the sequence number of the new solution, Indicates The Chebyshev value of the binding free energy represents the new solution value of the binding free energy, Indicates the sample size; according to the results of the optimization process, statistics The short-chain oligo DNA fragment corresponding to the minimum value of the function is matched Pseudomonas fragi of nucleic acid aptamers.

5. A Pseudomonas according to claim 1 Pseudomonas fragi Application of aptamers in the detection of pathogenic bacteria of Chlorella vulgaris.

6. A method for quantitative detection of pathogenic bacteria of Chlorella, characterized in that: The specific steps include: (1) Preparation of gold nanoparticle solution Add tetrachloroauric acid solution to distilled water and boil; quickly add trisodium citrate solution and continue boiling for 10-20 minutes; stir until naturally cooled to room temperature to obtain a gold nanoparticle solution; (2) Preparation of Bacillus monocytogenes Pseudomonas fragi Aptamer solution The Pseudomonas sp. according to claim 1 was added with distilled water. Pseudomonas fragi a solution prepared by aptamer; (3) Preparation of modified gold nanoparticle suspension The gold nanoparticle solution and Pseudomonas Pseudomonas fragi The aptamer solution was mixed and incubated for 24 hours, centrifuged and resuspended in phosphate buffer, and the Pseudomonas Pseudomonas fragi The aptamer is modified onto the gold nanoparticles to obtain a modified gold nanoparticle suspension; (4) Quantitative detection Inoculate Chlorella onto sterile agar plate medium and perform light culture once; The algal cells of Chlorella vulgaris were inoculated into BG-11 medium for secondary light culture; Chlorella in the logarithmic growth phase was inoculated into BG-11 culture medium to obtain an algae cell suspension and subjected to three light-induced culture to obtain a Chlorella culture system; Purified Bacillus Pseudomonas fragi The modified gold nanoparticle suspension is added to the Chlorella culture system, and the algae liquid is taken out, centrifuged, and the supernatant is taken out and added to perform quantitative determination.

7. The method for quantitative detection of Chlorella pathogenic bacteria according to claim 6, characterized in that: In step (1), the mass fraction of tetrachloroauric acid solution is 1%; the volume ratio of tetrachloroauric acid solution to distilled water is 1:100; the mass fraction of trisodium citrate solution is 1%; the volume ratio of tetrachloroauric acid solution to trisodium citrate solution is 1:2.5; and / or, in step (2), Bacillus Pseudomonas fragi The concentration of the aptamer solution was 5~20 μM.

8. The method for quantitative detection of Chlorella pathogenic bacteria according to claim 6, characterized in that: In step (3), the monocytic bacteria are judged Pseudomonas fragi The method of successfully modifying the aptamer to the gold nanoparticles includes: the ultraviolet absorption wavelength of the gold nanoparticles is shifted from 520nm to 523nm.

9. The method for quantitative detection of pathogenic bacteria of Chlorella according to claim 6, characterized in that: In step (4), the conditions of the one-time light culture include: a light-dark cycle of 12h / 12h, a concentration of 80 μmol·m -2 ·s -1 The light intensity was used for cultivation; The conditions of the secondary illumination culture include: a light-dark cycle of 12h / 12h, a temperature of 25±1°C, and a light intensity of 100 μmol·m -2 ·s -1 , the pH value of the algae solution is 7; The conditions of the three-time light culture include: blowing CO2-rich sterile air into the algae cell suspension, wherein the volume percentage of CO2 is 1%, and the culture lasts for 10 days; the volume ratio of Chlorella and BG-11 culture medium is 1:5; The temperatures of the primary illumination culture, the secondary illumination culture, and the tertiary illumination culture are all 25±1°C.

10. A colorimetric probe for detecting pathogenic bacteria of Chlorella, characterized in that: The gold nanoparticle solution and the Bacillus monocytogenes as claimed in claim 1 Pseudomonas fragi The aptamer-configured solution was mixed and incubated for 24 hours, centrifuged and resuspended in phosphate buffer, and the monocytic bacillus Pseudomonas fragi The aptamer is modified onto the gold nanoparticles to obtain the colorimetric probe.

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