An aptamer of Pseudomonas fragi and its screening method and application

Abstract

The present invention discloses a Pseudomonas fragi Pseudomonas fragi aptamer and its application, relating to the technical field of microorganisms. The Pseudomonas fragi Pseudomonas fragi aptamer has a DNA sequence as shown in SEQ ID No: 1. The present invention is obtained by parameter dynamic mapping and iterative mechanism of a multi-objective evolutionary algorithm model, and is optimized and screened through molecular docking and multi-objective evolutionary algorithm. It can specifically bind to the OmpA membrane protein of Pseudomonas, and its binding rate to Pseudomonas fragi is as high as 79.2%. It has high specificity and sensitivity. Even the error rate in the detection in a complex matrix containing high protein can be as low as 8%, indicating that the aptamer can successfully inhibit environmental interference, effectively avoid background interference, and improve the detection accuracy.

Description

Technical Field

[0001] The present invention belongs to the technical field of microbial detection, and particularly relates to a Pseudomonas fragi Pseudomonas fragi aptamer and its screening method and application. Background Art

[0002] Chlorella ( Chlorella vulgaris ) is a microalgae widely used in the fields of food, feed, and biofuels, and has attracted attention due to 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% - 60%, and it is mainly composed of essential amino acids. Chlorella is one of the important bait microalgae and is widely used in the aquaculture process. At the same time, Chlorella has important economic value due to its rich variety of nutrients and is widely used in the fields of food, feed, etc. However, the cultivation process of microalgae is often affected by various pathogenic microorganisms. Whether it is an open cultivation system that is simple, easy to operate, and widely used, or a closed cultivation system with high precision and strict cultivation requirements, it is difficult to avoid the contamination of harmful organisms. Although the two cultivation systems have great differences in many aspects, such as equipment structure, cultivation characteristics, product extraction, etc., they will inevitably involve links connected to the outside world, such as algal species inoculation, nutrient salt addition, gas exchange, etc., resulting in the formation of contamination. Especially the contamination of bacteria poses a serious threat to the growth and production efficiency of Chlorella.

[0003] In the cultivation environment of Chlorella, the bacterial contamination mainly comes from water sources, air, and cultivation equipment; and pathogenic bacteria such as Pseudomonas ( Pseudomonas fragi ) and the like can enter the cultivation system through various channels. These bacteria will not only directly consume the nutrients in the culture medium, but may also have an inhibitory effect on Chlorella by secreting toxins or other inhibitory substances, resulting in its growth being blocked, and thus affecting the yield and quality.

[0004] The consequences of bacterial contamination include: the presence of bacteria may lead to a decrease in light conditions, thus 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 their death or lysis, and this phenomenon is called the "algae-lysing effect".

[0005] Pseudomonas ( Pseudomonas fragi ) is a Gram-negative bacterium that widely exists in nature. This strain is famous for its strong adaptability and survival ability and can reproduce under various environmental conditions. Research shows that Pseudomonas fragi 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] To effectively monitor and control bacterial contamination in microalgae culture, a variety of detection techniques 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. However, although these techniques can quickly and accurately detect pathogenic bacteria in water samples and provide corresponding quantitative data, the price of real-time fluorescence quantitative PCR instruments is relatively high, and the reaction system of each real-time fluorescence quantitative PCR is relatively small, usually between 20 and 50 μL, which limits the sample volume 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 has great limitations.

[0007] On the other hand, nucleic acid aptamers are single-stranded DNA or RNA molecules with specific binding abilities and can be used to detect specific target molecules. Applying nucleic acid aptamer technology in microalgae culture can achieve rapid detection of pathogenic bacteria such as Pseudomonas fragi Pseudomonas fragi. 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 Salmonella typhimurium aptamer and its colorimetric probe, providing a colorimetric probe that modifies the Salmonella typhimurium aptamer onto gold nanoparticles for the detection of Salmonella typhimurium. At the same time, it proposes a method for optimizing the free energy of the binding between short-chain oligonucleotide DNA fragments and membrane protein OmpC protein using an immune algorithm model to screen out the Salmonella typhimurium aptamer. However, the immune algorithm model adopted in this patent is a single-objective algorithm model, which takes the binding free energy as an antibody, evaluates the immune affinity operator between antibodies and uses it for the similarity between antibodies, and through the competition between antibodies, finally statistically selects the short-chain oligonucleotide DNA fragment corresponding to the antibody with high excitation degree to obtain the nucleic acid aptamer that matches Salmonella typhimurium.

[0008] However, Pseudomonas fragi Pseudomonas fragiThe screening of aptamers against pathogenic bacteria such as usually uses a high-protein BG-11 medium, which is a specific medium for culturing algal microorganisms. On the one hand, it is not suitable for culturing animal cells. The osmotic pressure of the high-protein BG-11 medium is quite different from that of the animal cell internal environment, resulting in problems such as cell morphological changes, cell membrane damage, and even cell rupture and death during the culturing process, making it impossible to carry out the aptamer screening normally. On the other hand, substances such as proteins in the high-protein culture medium system may have non-specific interactions with aptamers or other components in the screening system. When detecting the binding signal between the aptamer and the target, it will increase the background signal and reduce the signal-to-noise ratio, making it difficult to accurately judge the true binding situation between the aptamer and the target, and affecting the quality and reliability of aptamer screening. Therefore, for Pseudomonas fragi Pseudomonas fragi the screening of aptamers has higher background interference compared to other bacteria in the prior art (such as Salmonella typhimurium). Therefore, the Pseudomonas fragi Pseudomonas fragi aptamers screened using the currently disclosed single-target algorithm model are difficult to avoid background interference and perform specific recognition when detecting pathogenic bacteria. Moreover, the single-target algorithm model can only achieve intermolecular docking of pure protein molecules. However, during the process of screening nucleic acid aptamers against pathogenic bacteria, the outer membrane proteins of the bacteria are not simple molecules. When matching the outer membrane proteins with nucleic acid aptamers, various influencing factors such as the exposed sites and electrostatic potential of the proteins need to be considered, which affects the docking of molecules and further affects the accuracy of the screening of nucleic acid aptamers. That is, when the prior art uses the single-target algorithm model to screen nucleic acid aptamers, other influencing factors in the docking of membrane protein molecules and nucleic acid aptamers are ignored, resulting in low specificity of the screened aptamers.

[0009] During the large-scale cultivation of Chlorella, timely and accurate detection and control of pathogenic bacteria such as Pseudomonas fragi are important links to ensure stable and efficient production. With the development of molecular biology technology and on the basis of the prior art, by using the optical properties and biocompatibility of gold nanoparticles, Pseudomonas fragi nucleic acid aptamers are combined with gold nanoparticles, providing a new solution for pathogen monitoring in microalgae cultivation, thereby improving the production efficiency of microalgae. Summary of the Invention

[0010] The main object of the present invention is to provide an aptamer against Pseudomonas fragi Pseudomonas fragi and its application to overcome the deficiencies of the prior art.

[0011] To achieve the above-mentioned invention object, the present invention provides an aptamer against Pseudomonas fragi Pseudomonas fragiAn aptamer, and the DNA sequence of the Pseudomonas fragi aptamer is as shown in SEQ ID No: 1.

[0012] As another aspect of the invention, the present invention also provides the Pseudomonas fragi Pseudomonas fragi aptamer screening method, which constructs a model using multiple parameters based on a multi-objective evolutionary algorithm through parameter dynamic mapping and iterative mechanism, and optimizes multiple competitive objective indicators at the same time. The screening of nucleic acid aptamers is achieved through the collaborative optimization of multi-dimensional indicators.

[0013] Specifically, the specific screening steps include:

[0014] (1) Construct the Pseudomonas fragi membrane protein structure to obtain the Pseudomonas fragi amino acid sequence and its structural information of the membrane protein OmpA protein;

[0015] (2) Conduct a molecular docking test on the membrane protein obtained in step (1), and randomly design a short-chain oligonucleotide DNA fragment library with a capacity of 10 Pseudomonas fragi , where the length range of the short-chain oligonucleotide DNA fragments is 30bp - 70bp; import the structural information of the membrane protein OmpA protein in step (1) into the HADDOCK molecular simulation software, and then conduct molecular docking between the short-chain oligonucleotide DNA fragment library and the surface domain of the membrane protein OmpA protein to obtain the binding free energy between each short-chain oligonucleotide DNA fragment and the membrane protein OmpA protein; 12 Pseudomonas fragi

[0016] (3) Optimize the results of the molecular docking obtained in step (2) using a multi-objective evolutionary algorithm model, and optimize the above binding free energy in combination with the improved multi-objective evolutionary algorithm model.

[0017] Preferably, the algorithm formula of the multi-objective evolutionary algorithm model is as follows:

[0018] ;

[0019] In the formula, represents the variable value when the objective function takes the minimum value, represents the Chebyshev function, represents the th binding free energy between the short-chain oligonucleotide 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 of the objective function, represents the ​​The weight vector parameters of the binding free energy, The objective function representing the binding free energy, Indicates the serial number of the new solution, Indicates the Chebyshev value of the binding free energy for the Indicates the new solution value of the binding free energy, Indicates the sample size; according to the results of the optimization process, count The short-chain oligonucleotide fragment corresponding to the minimum value of the function, and obtain the matching Pseudomonas fragi aptamer.

[0020] Based on the above multi-objective evolutionary algorithm model, through the Chebyshev function:

[0021] ;

[0022] Among them, m = 3, corresponding to the objectives of three dimensions, The objective function representing the binding free energy, through the minimization of the binding free energy ( ), the maximization of specificity ( ), and the optimization of structural stability ( ), the objectives of three dimensions are co-optimized, and the priority of each objective is dynamically adjusted through the weight vector and adaptively adjusted according to the objective distribution during the iteration process to avoid falling into local optimization.

[0023] Indicates the reference vector parameter, which is used to define the benchmark value of the ideal solution, usually taking the optimal value of each objective function in the current population, .

[0024] After each round of iteration, it is updated according to the new generation of population, promoting the solution set to approach the Pareto front, see formula (I).

[0025] The iterative optimization jointly promotes the convergence of the algorithm through the population update matrix formula (II) and the dynamic adjustment of the reference vector ( ). The evolutionary direction of each generation of population is determined by multi-objective trade-off. For the new solutions generated through evolutionary operations such as crossover and mutation, compare the difference in the Chebyshev values between the new solutions and the current solutions, and retain the better solutions to enter the next generation of population.

[0026] Obviously, the aptamers screened through the multi-objective evolutionary algorithm model can more effectively avoid background interference with the target bacteria (Pseudomonas fragi Pseudomonas fragi ), enabling the two to have a higher fitness and binding rate.

[0027] As another object of the invention, the present invention also provides the aforementioned Pseudomonas fragi Pseudomonas fragiApplication of aptamer in detection of pathogenic bacteria of Chlorella

[0028] As one of the purposes of the invention, the present invention also provides a quantitative detection method for pathogenic bacteria of Chlorella, and the specific steps include:

[0029] (1) Preparation of gold nanoparticle solution

[0030] Add chloroauric acid solution to distilled water and boil; quickly add trisodium citrate solution and continue boiling for 10 - 20 min; stir until cooled to room temperature naturally, then the gold nanoparticle solution is prepared.

[0031] (2) Preparation of Bacillus Pseudomonas fragi Aptamer solution

[0032] Use distilled water to prepare the solution of the aforementioned Bacillus Pseudomonas fragi Aptamer formulated solution;

[0033] (3) Preparation of modified gold nanoparticle suspension

[0034] Mix the gold nanoparticle solution and the Bacillus Pseudomonas fragi Aptamer solution and incubate for 24 h, centrifuge and then resuspend with phosphate buffer solution. The Bacillus Pseudomonas fragi Aptamer is modified on the gold nanoparticles to obtain a modified gold nanoparticle suspension;

[0035] (4) Quantitative detection

[0036] Inoculate Chlorella on a sterilized agar plate medium at 25 ± 1 °C and conduct a primary light culture;

[0037] Inoculate the algal cells of Chlorella into BG - 11 medium and conduct a secondary light culture;

[0038] Take Chlorella in the logarithmic growth phase and inoculate it into BG - 11 medium to obtain an algal cell suspension and conduct a tertiary light culture to obtain a Chlorella culture system;

[0039] Add the purified Bacillus Pseudomonas fragi To the above - mentioned Chlorella culture system, take the algal liquid, centrifuge, take the supernatant and add the modified gold nanoparticle suspension for quantitative determination.

[0040] The present invention uses a high - protein BG - 11 medium for the use of aptamer, and the error rate is only 8%,

[0041] indicating that the optimization of structural stability successfully inhibits environmental interference.

[0042] Preferably, in step (1), the mass fraction of the chloroauric acid solution is 1%; the volume ratio of the chloroauric acid solution to distilled water is 1:100; the mass fraction of the trisodium citrate solution is 1%; the volume ratio of the chloroauric acid solution to the trisodium citrate solution is 1:2.5.

[0043] Preferably, in step (2), the single-celled bacterium Pseudomonas fragi The concentration of the aptamer solution is 5 - 20 μM.

[0044] Preferably, in step (3), the method for determining whether the single-celled bacterium Pseudomonas fragi is successfully modified with the aptamer on the gold nanoparticles includes: the ultraviolet absorption wavelength of the gold nanoparticles shifts from 520 nm to 523 nm.

[0045] Preferably, in step (4), the conditions for the first light incubation include: the light-dark cycle is 12 h / 12 h, and the incubation is carried out at a light intensity of 80 μmol·m -2 ·s -1 .

[0046] Preferably, the conditions for the second light incubation include: the light-dark cycle is 12 h / 12 h, the temperature is 25 ± 1 °C, the light intensity is 100 μmol·m -2 ·s -1 , and the pH value of the algal solution is 7.

[0047] Preferably, the conditions for the third light incubation include: sterile air rich in CO2 is bubbled into the algal cell suspension, where the volume percentage of CO2 is 1%, and the incubation lasts for 10 d; the volume ratio of Chlorella vulgaris to BG-11 medium is 1:5.

[0048] As an aspect of the invention, the invention also provides a colorimetric probe for detecting pathogenic bacteria of Chlorella vulgaris. After mixing the gold nanoparticle solution and the solution prepared with the aforementioned single-celled bacterium Pseudomonas fragi aptamer and incubating for 24 h, centrifuging and then resuspending with phosphate buffer, the single-celled bacterium Pseudomonas fragi aptamer is modified on the gold nanoparticles, thus obtaining the colorimetric probe.

[0049] Based on the above technical solutions, the invention also provides the method for killing the above-mentioned extremely heat-resistant spores when applied to the fields of food processing, feed processing and biofuel technology. By adopting the above technical solutions, pathogenic bacteria in microalgae can be detected.

[0050] The invention has significant technical advantages and can be widely applied to pathogen monitoring in microalgae culture systems and related fields, providing important support for the development of bio-agricultural technologies.

[0051] Compared with the prior art, the beneficial effects of the invention are as follows:

[0052] (1) The nucleic acid aptamer obtained by screening in the present invention can specifically bind to the OmpA membrane protein of Pseudomonas ( Pseudomonas fragi ), and its binding rate to the target bacterium (Pseudomonas fragi Pseudomonas fragi ) is as high as 79.2%, and the binding rates to other common environmental pathogenic bacteria, such as Staphylococcus aureus, Escherichia coli, Salmonella, and Pseudomonas aeruginosa, are all lower than 15%. This indicates that the aptamer has high specificity and sensitivity, can effectively avoid background interference, and improve the detection accuracy. Pseudomonas fragi

[0053] (2) Based on the multi-objective evolutionary algorithm model, through the multi-objective collaborative optimization of minimizing free energy, maximizing specificity, and optimizing structural stability, the present invention reduces background interference and improves the specificity of aptamer screening, and screens nucleic acid aptamers with better adaptability, stability, and specificity. Even in the complex matrix of BG-11 medium containing high protein, the error rate is as low as 8%, indicating that the optimization of structural stability successfully inhibits environmental interference; obviously, the multi-objective evolutionary algorithm of the present invention realizes the comprehensive improvement of aptamer performance through parameter dynamic mapping and iterative mechanism, and solves the limitations of single-objective algorithms.

[0054] (3) By covalently coupling the aptamer of the present invention with gold nanoparticles, a colorimetric probe is constructed and 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 gold nanoparticles, thereby realizing visual detection, shortening the detection time, improving the detection efficiency, and simplifying the operation steps. Pseudomonas fragi

[0055] (4) The detection method of the present invention is not only low in cost, but also has good versatility in complex matrices (such as BG-11 medium). Even under the interference of nutrients such as high protein, its detection error rate is only 8%, proving its reliability in the actual application environment.

[0056] (5) The detection method of the present invention is not only low in cost, but also has good versatility in complex matrices (such as BG-11 medium). Even under the interference of nutrients such as high protein, its detection error rate is only 8%, proving its reliability in the actual application environment.

[0057] (6) The present invention uses molecular docking technology combined with an improved multi-objective evolutionary algorithm to ensure the minimization of the binding free energy between the nucleic acid aptamer and the target protein, 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

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0059] Figure 1 For the present invention Pseudomonas fragi Secondary structure diagram of the aptamer.

[0060] Figure 2 For the present invention Pseudomonas fragi Flow cytometry binding rate diagram of the aptamer.

[0061] Figure 3 For the present invention Pseudomonas fragi Three-dimensional structure diagram of the OmpA membrane protein.

[0062] Figure 4 For the present invention, standard curve diagrams of different concentrations for colorimetric detection Pseudomonas fragi of. Detailed implementation manners

[0063] The technical solutions of the present invention will be further explained and described in more detail below in conjunction with several embodiments.

[0064] The present invention provides an Pseudomonas fragi aptamer, and the Pseudomonas fragi DNA sequence of the aptamer is shown in SEQ ID No: 1.

[0065] As a preferred embodiment, the Pseudomonas fragi screening method of the aptamer includes: using a multi-objective evolutionary algorithm model to optimize the binding free energy generated by molecular docking between short-chain oligonucleotide DNA fragments and the membrane protein OmpA protein through parameter dynamic mapping and iterative mechanisms, and screening to obtain a matching Pseudomonas fragi nucleic acid aptamer.

[0066] In some specific embodiments, the Pseudomonas fragi operation steps of the screening method of the aptamer are as follows:

[0067] (1) Construct the Pseudomonas fragi structure of the membrane protein, and obtain the Pseudomonas fragi amino acid sequence and structural information of the membrane protein OmpA protein;

[0068] (2) Perform molecular docking tests on the Pseudomonas fragi membrane protein obtained in step (1), and randomly design a capacity of 10 12A library of short-chain oligonucleotide DNA fragments, where the length range of the short-chain oligonucleotide DNA fragments is 30bp - 70bp; in step (1), Pseudomonas fragi The structural information of the membrane protein OmpA is imported into the HADDOCK molecular simulation software, and then molecular docking is performed between the short-chain oligonucleotide DNA fragment library and the surface domain of the membrane protein OmpA to obtain the free energy of binding between each short-chain oligonucleotide DNA fragment and the membrane protein OmpA.

[0069] (3) Optimize the results of the molecular docking obtained in step (2), and optimize the above-mentioned binding free energy by combining an improved multi-objective evolutionary algorithm model, where the algorithm formula of the model is as follows:

[0070] ;

[0071] In the formula, represents the variable value when the objective function takes the minimum value, represents the Chebyshev function, represents the th free energy of binding between a short-chain oligonucleotide DNA fragment and the membrane protein OmpA, represents the weight vector parameter, represents the reference vector parameter, represents the function for finding the maximum value, represents the number of solutions of the objective function, represents the th weight vector parameter of the binding free energy, represents the objective function of the binding free energy, represents the serial number of the new solution, represents the th Chebyshev value of the binding free energy, represents the new solution value of the binding free energy, represents the sample size; according to the results of the optimization process, count the short-chain oligonucleotide DNA fragments corresponding to the minimum value of the function to obtain the Pseudomonas fragi matching

[0072] 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.

[0073] 1. Code function decomposition

[0074] - Generate random DNA sequences:

[0075] - The function `generate_random_dna(length)` uses Python's `random.choices` to randomly select characters of the 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`.

[0076] - Creating a DNA library:

[0077] - 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 within the typical length range of aptamers.

[0078] - In the code, `nμM_sequences = 1012`, meaning that 10¹² sequences can be generated.

[0079] - Output verification:

[0080] - Finally, the code prints the first 10 sequences in the library to verify whether the generated sequences meet the expectations.

[0081] 2. Basic principles of aptamer screening

[0082] Aptamers are single-stranded DNA or RNA molecules that can bind to specific targets (such as proteins) with high affinity and high specificity. Their screening is usually achieved through the following steps:

[0083] - Initial library generation: Generate a library containing a large number (usually 10 12 to 10 15 ) of random nucleic acid sequences as the starting point for screening.

[0084] - Selection: Incubate the library with the target molecule (such as a protein) to screen out the sequences that can bind.

[0085] - Amplification and iteration: Amplify the binding sequences by PCR and repeat the selection and amplification processes to gradually enrich the high-affinity sequences and finally obtain the aptamers.

[0086] The most commonly used method in experimental methods is SELEX (Systematic Evolution of Ligands by Exponential Enrichment), while computational methods simulate this process on a computer through molecular simulation and optimization algorithms (such as molecular docking and evolutionary algorithms).

[0087] 3. Association between the code and the aptamer screening

[0088] The association between this Python code and the aptamer screening is mainly reflected in the following aspects:

[0089] (1) Simulating the generation of the initial library

[0090] - Function: The code generates a library containing 10¹² random DNA sequences through the `create_dna_library` function, which directly corresponds to the first step in SELEX or computational screening - creating an initial random library.

[0091] - Parameter design:

[0092] - Sequence length (30 - 70bp): The length of aptamers usually ranges from 20 to 80 bases. The 30 - 70bp range set in the code is consistent with the commonly used length in experiments, which is sufficient to form complex secondary structures for target binding.

[0093] - Library capacity (10¹²): SELEX experiments usually use 10 12 to 10 15 sequence numbers to ensure diversity. The `nμM_sequences = 1012` in the code conforms to this order of magnitude, simulating the real diversity requirements.

[0094] - Significance: In experiments, this library is generated through chemical synthesis, while the code simulates this process programmatically on the computer, providing basic data for subsequent computational screening.

[0095] (2) The starting point of computational screening

[0096] - 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, and then the binding free energy of each sequence to the target protein is calculated through molecular docking (such as HADDOCK), and the best aptamer is screened out through the optimization algorithm.

[0097] - Relevance: The library generated by this code is the starting point of the computational screening process, similar to the initially chemically synthesized library in SELEX, but it is digital and suitable for in silico screening.

[0098] (3) Simplifying the differences from reality

[0099] - No fixed primer region: In experimental SELEX, the sequences of the initial library usually contain fixed primer regions (for PCR amplification) sandwiched between random regions in the middle. However, the sequences generated by the code are completely random without fixed regions. This simplification might be because computational screening doesn't need to simulate the amplification step and only focuses on sequence diversity and binding ability.

[0100] - Computational limitations: Although the code sets the library capacity to `10^12`, in actual operation, generating and storing such a large number of sequences is infeasible due to memory limitations. Therefore, this code is more likely a conceptual demonstration rather than actual execution.

[0101] 4. The specific location of the code in the screening process

[0102] In the complete nucleic acid aptamer screening process, the function of this code is limited to the initial library generation stage:

[0103] - Subsequent steps (not reflected in the code):

[0104] - Binding evaluation: Simulate the binding of each sequence to the target protein through molecular docking and calculate the binding free energy.

[0105] - Optimization screening: Use methods such as multi-objective evolutionary algorithms to screen out the sequences with the strongest binding ability from the library.

[0106] - Role of the code: It provides input data for these subsequent steps, that is, a diverse random DNA library.

[0107] In the context of computational screening, this library might be the initial population of a multi-objective evolutionary algorithm, and through iterative optimization, finally output high-affinity aptamer candidates.

[0108] 5. Feasibility of actual operation

[0109] - Memory issues: Generating 10 12 sequences and storing them in memory (`dna_library` list) exceeds the capabilities of most computers. Assuming each sequence is on average 50 characters, storing 10^12 sequences requires approximately 50 TB of memory, which is infeasible on conventional hardware.

[0110] - Solutions: In practical applications, the library size can be reduced (such as 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.

[0111] - Intention of the code: Printing the first 10 sequences indicates that this code is more likely to illustrate the principle of library generation rather than actually running the entire 1012 library.

[0112] The association between the Python code adopted in the present invention and the aptamer screening lies in that it simulates the initial library generation stage of the screening process, including:

[0113] - Direct association: The code generates a library containing 10 12 random DNA sequences (with a length of 30 - 70 bp), which is consistent with the requirements of the initial random library in SELEX and computational screening.

[0114] - Application scenario: In computational screening, this library can be used as the input for molecular docking and optimization algorithms to screen for high - affinity aptamers.

[0115] - Limitation: The code ignores the primer region, and the library scale is computationally infeasible, indicating that it is more of a conceptual simulation rather than a practical implementation.

[0116] Therefore, this code is the first step in the aptamer screening process, providing a basis for subsequent binding evaluation and optimization, reflecting the common ground between experimental and computational methods, and at the same time reflecting the flexibility and limitations of computational simulation.

[0117] On the other hand, the multi - objective evolutionary algorithm model adopted in the present invention has significant differences from the immune algorithm model in the prior art. The immune algorithm model is based on a single objective, simulating the biological immune system, and screening aptamers through the calculation of antibody affinity and excitation degree (formula such as sim(abi) = a×aff(abi) - b×aff(abj)). This method emphasizes the competition and selection mechanism among antibodies, which is completely different from the iterative optimization strategy of the multi - objective evolutionary algorithm of the present invention.

[0118] The present invention is based on the Chebyshev function, screening out the best aptamers by minimizing the free energy and possibly taking into account multiple objectives (such as binding specificity or other parameters). The iterative optimization strategy of the present invention is based on the multi - objective evolutionary algorithm that can optimize multiple conflicting objectives simultaneously, finding a balance among binding affinity, specificity, and stability. The aptamers screened out not only perform excellently in a single index, but also have more comprehensive overall performance and are more suitable for actual needs.

[0119] The multi - objective evolutionary algorithm (MOEA) can optimize multiple conflicting objectives simultaneously by finding a set of Pareto optimal solutions (i.e., the Pareto front), representing the best trade - off among different objectives. And due to Pseudomonas fragi the particularity of the culture conditions of the membrane protein and microalgae based on the present invention, as well as the influence of various factors such as the exposed sites and electrostatic potential of the membrane protein in aptamer screening on molecular docking, the conventional single - objective algorithm model is not applicable toPseudomonas fragi Screening of aptamers. Therefore, the present invention applies a multi-objective evolutionary algorithm in aptamer selection. During the nucleic acid aptamer screening process, MOEA can optimize multiple performance indicators simultaneously, such as binding affinity, specificity, stability, etc., ensuring that the selected aptamers perform excellently in multiple aspects. The multi-objective evolutionary algorithm optimizes the single-objective immune algorithm. For example, the immune algorithm in CN118028300A only screens out the aptamer with the strongest binding to the target protein by minimizing the binding free energy. Since its target is the Salmonella typhimurium detection, although it has the advantages of rapid screening and low detection limit (4.56 CFU / mL), its detection error rate cannot be guaranteed. Especially in the complex matrix background of high-protein medium required for microalgae culture systems, reducing background interference is more important than in other media. Therefore, the present invention applies the multi-objective evolutionary algorithm model to microalgae culture systems (such as the culture of Chlorella vulgaris containing BG-11 medium), which can maintain high sensitivity and low error rate (the detection error rate is only 8%) in complex matrices.

[0120] The multi-objective evolutionary algorithm adopted by the present invention is based on the Chebyshev function. By minimizing the free energy and possibly taking into account multiple objectives (such as binding specificity or other parameters), the best aptamer is screened out. The immune algorithm model in the prior art is based on a single objective, simulating the biological immune system, and screening aptamers through the calculation of antibody affinity and excitation degree (formula such as sim(abi)= a×aff(abi) - b×aff(abj)). This method emphasizes the competition and selection mechanism among antibodies, which is different from the iterative optimization strategy of the evolutionary algorithm.

[0121] In some specific embodiments, there is also provided a Pseudomonas fragi Application of aptamer in the detection of Chlorella pathogenic bacteria.

[0122] Specifically, the operation steps of the detection are as follows: (1) Prepare a gold nanoparticle solution. Add 1 mL of 1% mass concentration of chloroauric acid solution to 100 mL of distilled water and boil; quickly add 2.5 mL of 1% mass concentration of trisodium citrate solution and continue boiling for 15 min; continue stirring until it cools naturally to room temperature; obtain a stable gold nanoparticle solution; (2) Prepare Pseudomonas fragi aptamer solution. Use distilled water to Pseudomonas fragi aptamer to prepare an aptamer solution with a concentration of 10 μM Pseudomonas fragi aptamer solution; (3) Covalently couple to prepare a colorimetric probe. According to a volume ratio of 5:2, mix the gold nanoparticle solution and Pseudomonas fragiMix the aptamer solution and incubate at 37 °C for 24 h; centrifuge at 12,000 rpm for 20 min at 4 °C to remove the excess nucleic acid aptamer; resuspend with 600 μL of phosphate buffer to obtain the colorimetric probe; when the ultraviolet absorption wavelength of the gold nanoparticles shifts from 520 nm to 523 nm, it indicates that the nucleic acid aptamer in the colorimetric probe is successfully modified onto the gold nanoparticles; (4) The colorimetric probe is used for the Pseudomonas fragi quantitative detection during the culture of Chlorella. First, inoculate Chlorella into a sterilized agar plate medium at 25 ± 1 °C and culture it with a light-dark cycle of 12 h / 12 h at a light intensity of 80 μmolm -2 s -1 ; then inoculate the algal cells of Chlorella into a sterile Erlenmeyer flask containing BG-11 medium and culture it at a temperature of 25 ± 1 °C and a light intensity of 100 μmol•m -2 •s -1 , maintain the pH value of the algal solution at 7, and the light-dark cycle is 12 h / 12 h; Take the Chlorella in the logarithmic growth phase and inoculate it into the sterilized BG-11 medium for culture in a columnar photobioreactor. The volume ratio of Chlorella to the medium is 1:5. Bubble sterile air rich in CO2 into the algal cell suspension, where the volume percentage of CO2 is 1%, and the culture lasts for 10 d. Add the purified pathogenic bacteria Pseudomonas fragi to the above Chlorella culture system, take the algal solution, centrifuge, take the supernatant, and add the colorimetric probe for quantitative determination.

[0123] Unless otherwise specified, the raw materials used in the following examples are all conventional biochemical reagents; the experimental methods, unless otherwise specified, are all conventional methods; the quantitative tests in the following examples, unless otherwise specified, are all set with three repeated experiments, and the results are averaged; the %, in the following examples, unless otherwise specified, are all mass percentages. In the following examples, the synthesized aptamer was purchased from Sangon Biotech (Shanghai) Co., Ltd.; unless otherwise specified, other raw materials used were all purchased from Sinopharm Chemical Reagent Co., Ltd. It should be reminded that the phosphate buffer used in the present invention is a 0.1 M sterile PBS buffer with a pH value of 7.4. It should be reminded that all the bacterial strains used in the present invention were purchased from the American Type Culture Collection (ATCC), among which Pseudomonas fragi the number 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.

[0124] It should be noted that all the instrument equipment, raw material reagents or method steps used in this application are ensured to be processed under sterile conditions.

[0125] Instrumentation, raw materials, reagents, or method steps not mentioned in the present invention are conventional or well-known technical methods to those skilled in the art and will not be elaborated in the present invention.

[0126] The technical solution of the present invention will be described in detail below through specific examples.

[0127] Example 1

[0128] This example provides an Pseudomonas fragi aptamer, whose sequence is shown in SEQ ID No: 1. The Pseudomonas fragi aptamer is a nucleic acid aptamer, and its characterization information is as Figure 1 shown, and its secondary structure is as shown in the RNAStructure analysis diagram.

[0129] Refer to Figure 2 , where Figure 2 The results measured by the method for A in it show that Pseudomonas fragi the binding rate of the aptamer to the pathogenic bacterium Pseudomonas fragi by flow cytometry is 79.2%, indicating that the nucleic acid aptamer obtained in the present invention is feasible and meets the usage requirements. And Figure 2 B in Pseudomonas fragi is the binding rate of the aptamer to Staphylococcus aureus, Figure 2 C in Pseudomonas fragi is the binding rate of the aptamer to Escherichia coli, Figure 2 D in Pseudomonas fragi is the binding rate of the aptamer to Salmonella, Figure 2 E in Pseudomonas fragi is the binding rate of the aptamer to Pseudomonas aeruginosa. It can be seen that the highest binding rate does not exceed 15%, so common environmental pathogenic bacteria do not constitute a specific and effective interference.

[0130] Specifically, Pseudomonas fragi the screening method of the aptamer includes:

[0131] (1) Construct the structure of the membrane protein of Pseudomonas fragi , and obtain the amino acid sequence and its structural information of the membrane protein OmpA protein of Pseudomonas fragi ;

[0132] OmpA protein (https: / / www.uniprot.org / uniprotkb / A0A266LSE3 / entry), and its three-dimensional structure diagram is as Figure 3 shown.

[0133] (2) Perform molecular docking tests on the membrane protein obtained in step (1), and randomly design a capacity of 10 Pseudomonas fragi 12 ​A library of short-chain oligonucleotide DNA fragments, where the length range of the short-chain oligonucleotide DNA fragments is 30 bp - 70 bp;

[0134] The core objective of this algorithm is to screen out the nucleic acid aptamer with the strongest binding ability to the target protein ( Pseudomonas fragi OmpA membrane protein) through molecular docking and optimization. The logical relationship between its steps and free energy screening can be decomposed as follows:

[0135] Step 1: Molecular docking to generate initial free energy data

[0136] The algorithm first uses the molecular docking technology to simulate the binding of a randomly generated short-chain oligonucleotide DNA library (length range 30 - 70 bp, capacity 10 12 ) to the OmpA protein by using the HADDOCK molecular simulation software. The output of molecular docking is the binding free energy between each DNA fragment and the OmpA protein. The binding free energy is a key indicator to measure the binding stability between the DNA fragment and the protein, and the lower the value, the stronger the binding. This process provides the basic data for subsequent screening, that is, the initial free energy distribution.

[0137] Step 2: Multi-objective evolutionary algorithm to optimize the free energy

[0138] Next, the free energy data obtained from molecular docking is input into a multi-objective evolutionary algorithm for optimization. This 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 (combining with the argmin function). The multi-objective evolutionary algorithm not only focuses on a single objective (minimizing the free energy), but also may balance other potential objectives (such as sequence diversity), and finally outputs the DNA fragment with the lowest binding free energy as the aptamer candidate.

[0139] There is a clear progressive relationship between molecular docking and the multi-objective evolutionary algorithm: first, molecular docking provides the original free energy data, which is equivalent to "coarse screening", generating the binding ability information of a large number of candidate DNA fragments, and then the multi-objective evolutionary algorithm conducts "fine screening" based on these data, and screens out the sequence with the lowest free energy through iterative optimization.

[0140] These two steps combine to form a complete free energy screening process: from extensive initial data to gradually focusing on the optimal solution, ensuring that the screened aptamer has the highest binding affinity. This logical relationship enables the algorithm to efficiently screen out the nucleic acid aptamer with the strongest binding to the target protein from a large number of candidate sequences.

[0141] The corresponding python code is as follows:

[0142] “import random

[0143] def generate_random_dna(length):

[0144] """Generate a random DNA sequence"""

[0145] return ''.join(random.choices('ACGT', k=length))

[0146] def create_dna_library(nμM_sequences, min_length=30, max_length=70):

[0147] """Create a library of short oligonucleotide DNA fragments"""

[0148] dna_library = []

[0149] for _ in range(nμM_sequences):

[0150] length = random.randint(min_length, max_length)

[0151] dna_sequence = generate_random_dna(length)

[0152] dna_library.append(dna_sequence)

[0153] return dna_library

[0154] # Generate a library of short oligonucleotide DNA fragments with a capacity of 10^12

[0155] nμM_sequences =

[0156] dna_library = create_dna_library(nμM_sequences)

[0157] # Output some generated DNA sequences for verification

[0158] print("Sample DNA sequences from the library:")

[0159] for seq in dna_library[:10]: # Only output the first 10 sequences

[0160] print(seq)”。

[0161] Import the structural information of the membrane protein OmpA protein in step (1) Pseudomonas fragi into the HADDOCK molecular simulation software, and then perform molecular docking between the short-chain oligonucleotide DNA fragment library and the surface domain of the membrane protein OmpA protein to obtain the binding free energy between each short-chain oligonucleotide DNA fragment and the membrane protein OmpA protein;

[0162] The above operations are as follows: Obtain the OmpA protein structure: Obtain the three-dimensional structure file of the OmpA protein (such as in PDB format) from a relevant database (such as PDB).

[0163] Import into HADDOCK: Import the downloaded OmpA structure file into the HADDOCK software.

[0164] Prepare for docking:

[0165] Set the docking parameters in HADDOCK and select the short-chain oligonucleotide DNA fragment as the ligand.

[0166] Define the surface domain of the OmpA protein.

[0167] Run docking: Start HADDOCK to perform molecular docking calculations and obtain the binding free energy between each short-chain oligonucleotide DNA fragment and the OmpA protein.

[0168] Analyze the results: After docking is completed, extract the binding free energy data between each short-chain oligonucleotide DNA and OmpA for subsequent analysis and optimization.

[0169] (3) Optimize the results of the molecular docking obtained in step (2) using a multi-objective evolutionary algorithm model

[0170] Optimize the above binding free energy using an improved multi-objective evolutionary algorithm model, and the algorithm formula of the model is as follows:

[0171] ;

[0172] In the formula, represents the variable value when the objective function takes the minimum value, represents the Chebyshev function, represents the th binding free energy between the short-chain oligonucleotide 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 of the objective function, Represents the weight vector parameter of the th binding free energy, Represents the serial number of the new solution, Represents the th Chebyshev value of the binding free energy, Represents the value of the new solution of the binding free energy, Represents the sample size;

[0173] Among them, the running code of the algorithm formula is as follows:

[0174] "import numpy as np

[0175] def objective_function(x):

[0176] """Objective function: Calculate the binding free energy"""

[0177] # Here it is assumed that x is the free energy of the short-chain DNA fragment binding to OmpA

[0178] return x # In actual applications, the free energy should be calculated according to specific situations

[0179] def chebyshev_function(f_values, z_star):

[0180] """Calculate the Chebyshev function value"""

[0181] return np.max([f - z_star for f in f_values])

[0182] def multi_objective_evolutionary_algorithm(dna_energy_values, lambda_weights, z_star):

[0183] """Improved multi-objective evolutionary algorithm model"""

[0184] m = len(dna_energy_values) # Number of objective functions

[0185] g_te_values = [chebyshev_function(dna_energy_values, z_star) for _ in range(m)]

[0186] # Find the short DNA fragment corresponding to the minimum value

[0187] min_index = np.argmin(g_te_values)

[0188] # Return the optimization result

[0189] return min_index, g_te_values[min_index]

[0190] # Example data

[0191] nμM_sequences = 100 # Assume there are 100 short DNA fragments

[0192] dna_energy_values = np.random.rand(nμM_sequences) 10 # Randomly generate the binding free energy

[0193] lambda_weights = np.random.rand(nμM_sequences) # Randomly generate the weight vector

[0194] z_star = np.mean(dna_energy_values) # Assume the reference vector is the average of the free energies

[0195] # Perform the optimization process

[0196] optimal_index, optimal_value = mμLti_objective_evolutionary_algorithm

[0197] (dna_energy_values, lambda_weights, z_star)

[0198] print(f"Optimal short DNA fragment index: {optimal_index}, corresponding binding free energy: {optimal_value}")

[0199] According to the results of the optimization process, count the short oligonucleotide DNA fragments corresponding to the minimum value of the function to obtain the matchingPseudomonas fragi nucleic acid aptamer

[0200] Example 2

[0201] The operation steps of using the aptamer in Example 1 to prepare a colorimetric probe are as follows: Pseudomonas fragi

[0202] (1) Preparation of gold nanoparticle solution

[0203] Preparation of gold nanoparticles: Add 1 mL of 1% chloroauric acid solution by mass to 100 mL of distilled water and boil; quickly add 2.5 mL of 1% trisodium citrate solution by mass and continue boiling for 15 min; continue stirring until it cools naturally to room temperature; obtain a stable gold nanoparticle solution and store it at 4 °C for further use.

[0204] (2) Preparation of Pseudomonas fragi aptamer solution

[0205] Dissolve the Pseudomonas fragi aptamer in distilled water to prepare an aptamer solution with a concentration of 10 μM; Pseudomonas fragi

[0206] Selection of nucleic acid aptamer: The working concentration of the nucleic acid aptamer is 10 μM; the tested nucleic acid aptamer is shown as SEQ ID NO.1.

[0207] (3) Covalent coupling to prepare a colorimetric probe

[0208] Mix the gold nanoparticle solution and Pseudomonas fragi the aptamer solution according to a volume ratio of 5:2, incubate at 37 °C for 24 h; centrifuge at 12000 rpm at 4 °C for 20 min to remove the excess nucleic acid aptamer; add 650 μL of phosphate buffer to resuspend and obtain a colorimetric probe; store at 4 °C for further use.

[0209] Detection of the mixture with Pseudomonas fragi and salt solution: Select PBS buffer as the blank control test solution. At the same time, centrifuge the Pseudomonas fragi bacterial solution, wash it three times with PBS buffer, then resuspend and perform serial dilution with PBS buffer to prepare bacterial solutions with 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 bacterial solution to be tested. First, take 100 μL of the prepared colorimetric probe and incubate it with 100 μL of the blank control solution to be tested and 100 μL of the gradient-diluted bacterial solution to be tested at 37 °C for 60 min to obtain the incubation solution. Then, add 50 μL of a salt 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 logarithm of the bacterial concentration of multiple bacterial solutions to be tested as the abscissa to plot a standard curve, obtain the linear regression equation, and obtain the correlation coefficient and detection limit.

[0210] Plot the ultraviolet absorption spectral curves of bacterial solutions to be tested with 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, and take the relative ultraviolet absorption intensity value at 523 nm as the ordinate and the logarithm of the bacterial concentration corresponding to each of the above ultraviolet absorption spectral curves as the abscissa to plot a standard regression curve. For details, refer to Figure 4 , whose linear regression equation is y = -0.12773x + 0.96913, and whose correlation coefficient ( r 2 ) is 0.98623, and whose detection limit is 6.29 CFU / mL. It can be seen that the aptamer designed in the present invention Pseudomonas fragi has a good effect and meets the normal use requirements.

[0211] Next, perform a specificity test: Replace Pseudomonas fragi with Staphylococcus aureus, Salmonella, Escherichia coli, and Pseudomonas aeruginosa respectively; and prepare bacterial solutions to be tested with a concentration of 10 6 CFU / mL respectively.

[0212] The results show that the relative ultraviolet absorption intensity values of the bacterial solutions to be tested after adding Staphylococcus aureus, Salmonella, Escherichia coli, and Pseudomonas aeruginosa are 0.92, 0.87, 0.94, and 0.81 respectively. At the same time, under the same conditions, the relative ultraviolet absorption intensity value of the bacterial solution to be tested after adding Pseudomonas fragi is 0.22. In addition, under the same conditions, the relative ultraviolet absorption intensity value of the blank control solution to be tested is 1.00. It can be explained that the Pseudomonas fragi nucleic acid aptamer of the present invention Pseudomonas fragi has good specificity.

[0213] Example 3

[0214] On the basis of Example 2, the detection of real samples was added. After centrifuging the Chlorella vulgaris liquid, the BG-11 culture medium sample and the nucleic acid aptamer designed by the present invention were selected for testing: Pseudomonas fragi for testing:

[0215] It should be reminded that in this example, in order to verify whether the above standard curve can be effectively applied to the determination under interference conditions, the differences between artificial quantitative inoculation with bacteria and the number of bacteria detected by the probe in the BG-11 culture medium sample were respectively carried out, so as to know that even in real samples, the colorimetric probe established by the present invention can be effectively detected under the influence of nutrients such as high protein.

[0216] It should be noted that the BG-11 culture medium involved in this example has the following components (g / L): 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, ZnSO4·4H2O 0.220 g / L, Co(NO3)2·6H2O 0.049 g / L, MnCl2·4H2O 1.860 g / L, H3BO3 2.860 g / L.

[0217] During the experiment, Pseudomonas fragi was artificially inoculated into 1 mL of BG-11 culture medium. After inoculation, Pseudomonas fragi the final concentration was 10 6 CFU / mL. It was centrifuged at 5000×g for 5 min, the supernatant was discarded, and the precipitate was resuspended with 1 mL of PBS buffer to obtain a bacteria-containing solution to be measured. First, 100 μL of the prepared colorimetric probe was taken and incubated with 100 μL of the bacteria-containing solution to be measured at 37 °C for 60 min to obtain an incubation solution. Then, 50 μL of a salt solution (magnesium chloride solution, with a molar concentration of 0.1 M) was added, incubated for 10 min, and the relative ultraviolet absorption intensity value was measured at 523 nm.

[0218] The test results showed that after calculation, in the environment of artificially inoculating Pseudomonas fragi in the BG-11 culture medium sample, the Pseudomonas fragi concentration measured by the colorimetric probe was 0.92 10 6 CFU / mL, almost the same as its true concentration (10 6CFU / mL), it can be seen that even without establishing the corresponding standard curve and linear regression equation in the BG-11 medium sample environment, the standard curve and linear regression equation established in the pure PBS sample environment still have good versatility. Thus, 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.

[0219] In addition, the inventors of this case also referred to the foregoing embodiments and conducted tests with other raw materials, process operations, and process conditions described in this specification, and all obtained relatively ideal results.

[0220] It should be understood that the technical solutions of the present invention are not limited to the limitations of the above specific embodiments. Any technical deformation made according to the technical solutions of the present invention without departing from the purpose of the present invention and the scope protected by the claims falls within the protection scope of the present invention.

Claims

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

1.

2. A Pseudomonas fragariae as claimed in claim 1 Pseudomonas fragi Application of aptamers in detection of pathogenic bacteria of Chlorella vulgaris; the pathogenic bacteria of Chlorella vulgaris is Pseudomonas fragariae Pseudomonas fragi .

3. A quantitative detection method for Chlorella pathogenic bacteria, 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 Pseudomonas fragrans Pseudomonas fragi Aptamer solution The Pseudomonas fragariae described in claim 1 was added with distilled water Pseudomonas fragi a solution prepared by aptamer; (3) Preparation of modified gold nanoparticle suspension Pseudomonas fragariae Pseudomonas fragi The aptamer solutions were mixed and incubated for 24 hours, 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 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; Pseudomonas fragariae 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.

4. The method for quantitative detection of pathogenic bacteria of Chlorella according to claim 3, 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), Pseudomonas fragariae Pseudomonas fragi The concentration of the aptamer solution was 5~20 μM.

5. The method for quantitative detection of pathogenic bacteria of Chlorella according to claim 3, characterized in that: In step (3), the Pseudomonas fragariae 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.

6. The method for quantitative detection of Chlorella pathogenic bacteria according to claim 3, 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.

7. A colorimetric probe for detecting pathogenic bacteria of Chlorella, characterized in that: The gold nanoparticle solution and the Pseudomonas fragariae 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 Pseudomonas fragariae Pseudomonas fragi The aptamer is modified onto the gold nanoparticles to obtain the colorimetric probe.

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