A screening method for designing aptamer based on genetic algorithm-ant colony algorithm and aptamer
Patent Information
- Application Number
- CN202610936132.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-26
- Publication Date
- 2026-09-15
Smart Images
Figure CN122761963A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of nucleic acid aptamer engineering and computational biology, and in particular to a nucleic acid aptamer computational screening method that combines a genetic algorithm (GA) with an ant colony optimization (ACO) algorithm. The combined genetic algorithm and ant colony optimization method can be simply referred to as the GA-ACO method, as well as the nucleic acid aptamers obtained therefrom and their applications. Background Technology
[0002] Nucleic acid aptamers are single-stranded DNA or RNA molecules that can bind to specific target molecules with high affinity and high specificity. Compared with antibodies, nucleic acid aptamers have advantages such as ease of chemical synthesis and modification, better stability, small batch-to-batch variability, and a wide range of targets, and have been widely used in food safety testing, environmental monitoring, biosensing, and disease diagnosis.
[0003] Currently, nucleic acid aptamers are typically obtained from random nucleic acid libraries through the Systematic Evolution of Ligands (SELEX) technique. This method generally requires multiple rounds of target binding, unbound sequence separation, bound sequence amplification, and enrichment, which suffers from long screening cycles, cumbersome experimental procedures, high experimental costs, and insufficient automation. Furthermore, the actual capacity of random nucleic acid libraries is limited by experimental conditions, making it difficult to cover the entire sequence space, potentially leading to some high-affinity candidate sequences not being fully sampled or being lost during multiple rounds of enrichment.
[0004] With the development of computational biology, molecular simulation, and intelligent optimization algorithms, computer-aided nucleic acid aptamer screening has gradually become an important technical approach to reduce the number of wet-process experiments and improve screening efficiency. Through methods such as structure prediction, molecular docking, molecular dynamics simulation, and binding free energy calculation, the binding affinity of candidate nucleic acid aptamers to target molecules and the stability of the complex can be predicted before experimental validation. However, relying solely on molecular docking or molecular dynamics simulation usually only allows for evaluation and screening within a limited candidate library, and the ability to actively generate and optimize new nucleic acid aptamer sequences is limited.
[0005] Genetic algorithms possess strong global exploration capabilities, searching for potential high-fitness candidate sequences in the nucleic acid sequence space through operations such as selection, crossover, and mutation. However, they are prone to premature convergence in the later stages of iteration and lack the ability to fine-tune local dominant sites and their base combinations. Ant colony algorithms can strengthen dominant paths through pheromone positive feedback mechanisms, showing advantages in local search and solution refinement. However, when used alone, their search performance depends on the initial pheromone distribution, and without effective global search results as guidance, they are prone to getting stuck in local regions.
[0006] Existing methods for computational screening of nucleic acid aptamers still suffer from problems such as insufficient sequence space search, difficulty in continuously strengthening local dominant sites, and the need to improve the efficiency of candidate sequence optimization. Therefore, there is an urgent need to provide a method for computational screening of nucleic acid aptamers that combines the global exploration capability of genetic algorithms with the local refinement capability of ant colony algorithms. This method involves using elite sequences obtained by genetic algorithms to guide ant colony algorithms for local optimization at the site-base level, and improving the affinity and screening efficiency of candidate nucleic acid aptamers through multiple rounds of joint iteration.
[0007] It should be noted that the above background information is only used to help understand the technical solution of the present invention, and does not necessarily mean that the above content is common knowledge that was known to those skilled in the art before the application date of the present invention. Summary of the Invention
[0008] The purpose of this invention is to provide a computational screening method for nucleic acid aptamers based on a joint design of genetic algorithm and ant colony algorithm, in order to solve the problems of long screening cycle, high experimental cost, insufficient sequence space search, difficulty in continuously strengthening local dominant sites, and low efficiency of candidate sequence optimization in existing nucleic acid aptamer screening methods.
[0009] To achieve the above objectives, the present invention provides a method for nucleic acid aptamer calculation and screening, comprising the following steps:
[0010] S1. Determine the seed nucleic acid aptamer sequence of the target molecule, specifically including: collecting reported target nucleic acid aptamer sequences, performing conserved region analysis and structural prediction on candidate sequences, constructing three-dimensional conformations of candidate sequences, performing molecular docking of candidate sequences with target molecules, performing molecular dynamics simulations on candidate sequences with high docking scores, and determining the seed nucleic acid aptamer sequence based on the conserved recognition regions of candidate sequences, the conformational stability of the complex, and the binding mode.
[0011] S2. Using the seed nucleic acid aptamer sequence as a template, a genetic algorithm is used to globally explore the nucleic acid aptamer sequence space to obtain several genetic algorithm elite sequences with high fitness.
[0012] S3. Initialize the site-base pheromone matrix according to the elite sequence of the genetic algorithm, and construct candidate nucleic acid aptamer sequences site by site using the ant colony algorithm according to the probability transition rules jointly determined by the pheromone intensity and the heuristic function to obtain the local optimized sequence;
[0013] S4. The global exploration of the genetic algorithm and the local optimization of the ant colony algorithm are jointly iterated. After each round of joint iteration, the candidate nucleic acid aptamer sequence with the highest fitness is used as the seed nucleic acid aptamer sequence for the next round of joint iteration.
[0014] S5. The candidate nucleic acid aptamer sequences obtained from the joint iteration are included in the candidate sequence pool. After deduplication and fitness sorting, high-fitness candidate nucleic acid aptamer sequences are selected for post-screening. The post-screening includes performing molecular dynamics simulations on the complexes formed by the candidate nucleic acid aptamer sequences and target molecules, analyzing the root mean square deviation (RMSD), root mean square fluctuation (RMSF), and binding conformation stability of the complexes, and evaluating the binding ability of the candidate nucleic acid aptamer sequences to target molecules through MM-PBSA binding free energy, thereby screening out the target nucleic acid aptamers. The fitness is determined by the molecular docking binding free energy between the candidate nucleic acid aptamer sequences and target molecules.
[0015] In one embodiment, the target molecule is any one of small molecule toxins, food safety contaminants, environmental contaminants, drug molecules, or biomarkers.
[0016] Furthermore, the global exploration of the genetic algorithm includes: using the seed nucleic acid aptamer sequence as a template, constructing an initial population through site-by-site random mutation; generating offspring candidate sequences using selection, crossover, mutation, and elite retention strategies during the iteration process, and sorting the candidate sequences according to fitness.
[0017] Furthermore, after each round of global exploration by the genetic algorithm, the top-ranked candidate nucleic acid aptamer sequences are selected as the elite sequences of the genetic algorithm according to their fitness. The statistical results of the base types of each base site in the elite sequences of the genetic algorithm are used to construct or update the site-base pheromone matrix in the local optimization of the ant colony algorithm.
[0018] Furthermore, the rows of the site-base pheromone matrix correspond to the base sites of the nucleic acid aptamer sequence, and the columns correspond to the four base states of A, T, G, and C. Based on the frequency of occurrence of A, T, G, and C at each base site in the elite sequence of the genetic algorithm, an initial pheromone intensity is assigned to the corresponding site-base combination to guide the ant colony algorithm to perform local search within the advantageous sequence region obtained by the genetic algorithm.
[0019] Furthermore, after each iteration, the ant colony algorithm updates the site-base pheromone matrix by combining pheromone evaporation and pheromone increment reinforcement based on candidate sequence fitness, and limits the pheromone intensity within a preset upper and lower limit range to reduce the risk of premature convergence.
[0020] Furthermore, the molecular dynamics simulations include system construction, energy minimization, equilibrium simulation, and production simulation, and the candidate nucleic acid aptamer sequences are comprehensively evaluated using RMSD, RMSF, binding conformation, and MM-PBSA binding free energy.
[0021] The present invention also provides a novel nucleic acid aptamer, which is obtained by screening using the above-described nucleic acid aptamer calculation and screening method.
[0022] In one specific embodiment, the novel nucleic acid aptamer is AF26I, a nucleic acid aptamer targeting aflatoxin B1, with the nucleotide sequence: 5'-CACGTGTTGTCTCTCTGTGTGTAGTG-3'.
[0023] The present invention also provides the application of the above-mentioned nucleic acid aptamer in the detection of aflatoxin B1.
[0024] In one embodiment, the detection is a fluorescence detection based on a graphene oxide fluorescence quenching-recovery system, wherein the 5' end of the nucleic acid aptamer is labeled with a fluorescent group, graphene oxide is used as a fluorescence quenching material, and aflatoxin B1 binds to the nucleic acid aptamer, causing the fluorescence signal to recover, thereby realizing the detection of aflatoxin B1.
[0025] Compared with nucleic acid aptamer optimization methods that use genetic algorithms or ant colony algorithms alone, this invention can simultaneously take into account the global search capability of the sequence space and the fine optimization capability of local dominant sites, reducing the inefficiency caused by insufficient search range or random search direction during candidate sequence optimization.
[0026] This invention can directly link the optimization direction of candidate sequences with target binding ability, so that the sequence features with better binding ability are preferentially retained and enhanced in subsequent iterations, thereby increasing the proportion of high-affinity candidate nucleic acid aptamers and reducing the number of low-binding-ability sequences entering subsequent validation.
[0027] This invention can perform targeted optimization of local base sites that affect target binding while preserving the core recognition backbone of the seed nucleic acid aptamer, thereby reducing the risk of the core recognition structure being destroyed by completely random mutations and improving the stability and controllability of candidate sequence optimization.
[0028] This invention employs a multi-index post-screening mechanism to comprehensively evaluate candidate nucleic acid aptamers. It not only examines the initial binding conformation and molecular docking binding free energy of the candidate sequence and target molecule, but also combines molecular dynamics simulation trajectory analysis and MM-PBSA binding free energy calculations to verify the root mean square deviation (RMSD), root mean square fluctuation (RMSF), binding conformational stability, and thermodynamic binding stability of the complex. This improves the reliability of candidate nucleic acid aptamer screening results and reduces the risk of false positives due to relying solely on a single molecular docking score.
[0029] In one specific embodiment, using aflatoxin B1 as a target, the nucleic acid aptamer AF26I is screened and obtained using the method described in this invention. This aptamer can bind to aflatoxin B1 and can be used for aflatoxin B1 detection based on a graphene oxide fluorescence quenching-recovery system. Attached Figure Description
[0030] To more clearly illustrate the technical solution of the present invention, the accompanying drawings are briefly described below. It should be understood that the following drawings are only used to illustrate some embodiments of the present invention and do not constitute a limitation on the scope of protection of the present invention.
[0031] Figure 1 is a schematic diagram of the overall process of the nucleic acid aptamer calculation and screening method based on the joint optimization of genetic algorithm and ant colony algorithm of the present invention.
[0032] Figure 2 shows the convergence and sequence diversity analysis of the GA-ACO joint optimization process in the embodiment of the present invention. Figure 2(a) shows the candidate sequence fitness change curve, Figure 2(b) shows the population fitness variance change curve, and Figure 2(c) shows the Hamming distance analysis between high fitness sequences.
[0033] Figure 3 is a mutation convergence analysis diagram of the GA-ACO joint optimization process in the embodiment of the present invention. Figure 3(a) is a correlation analysis diagram of the combination score and 3' end mutation, Figure 3(b) is a heatmap of site mutation frequency at different optimization stages, and Figure 3(c) is a mutation distribution diagram of high fitness candidate sequences.
[0034] Figure 4 shows the evaluation results of MM-PBSA binding free energy after the parental aptamer AF26 and the optimized aptamer AF26I formed complexes with aflatoxin B1 in the embodiments of the present invention.
[0035] Figure 5 shows the results of the binding ability of AF26I to aflatoxin B1 and its detection application verification in the embodiments of the present invention. Figure 5(a) shows the ultraviolet absorption spectroscopy characterization results, and Figure 5(b) shows the colorimetric characterization results of the gold nanoparticles. Figure 5 (c) shows the results of circular dichroism characterization, and Figure 5(d) shows the standard curve for detecting aflatoxin B1 based on the graphene oxide fluorescence quenching-recovery system. Detailed Implementation
[0036] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. Equivalent substitutions or conventional modifications made based on the technical concept of the present invention should all be included within the scope of protection of the present invention.
[0037] This specific embodiment uses aflatoxin B1 as an example target to illustrate the nucleic acid aptamer computation and screening method of the present invention based on joint optimization of genetic algorithm and ant colony algorithm. It should be noted that the present invention is not limited to aflatoxin B1. For other small molecule toxins, food safety pollutants, environmental pollutants, drug molecules or biomarkers for which seed nucleic acid aptamer or candidate nucleic acid aptamer sequences have been obtained, the nucleic acid aptamer sequence optimization method described in the present invention can also be used. Figure 1 shows the overall flow of the method of the present invention. The following describes each step in conjunction with specific embodiments. Specific Implementation Method 1: Determination of Seed Nucleic Acid Aptamer Sequence
[0038] In one specific implementation, 10 reported aflatoxin B1 aptamer sequences were collected as candidate sequences. These candidate sequences were then imported into the MUSCLE online tool in FASTA format for multiple sequence alignment and homology analysis to identify conserved recognition regions and eliminate redundant sequences. Subsequently, the secondary structure of the candidate sequences was predicted using the Mfold online server, and the conformation with the lowest fold free energy ΔG was selected as the dominant secondary structure.
[0039] The superior secondary structure was used for three-dimensional structural modeling, and the resulting three-dimensional structure underwent single-stranded DNA conformation correction. Subsequently, the three-dimensional structures of the candidate nucleic acid aptamers were molecularly docked with aflatoxin B1 to obtain the binding conformation of the candidate nucleic acid aptamers with aflatoxin B1 and the molecular docking binding free energy. For candidate complexes with high docking scores, further molecular dynamics simulations were performed to evaluate the conformational stability, RMSD, RMSF, and binding mode of the complexes.
[0040] Based on the combined sequence length, conserved recognition region, molecular docking results, and stability in molecular dynamics simulations, AF26 was determined to be the seed nucleic acid aptamer sequence for subsequent GA-ACO co-optimization. AF26 is 26 nt in length, fully retains the core recognition motif, has a molecular docking binding free energy of -9.0 kcal / mol with aflatoxin B1, and maintains stable binding in 100 ns molecular dynamics simulations. The nucleotide sequence of AF26 is: 5'-CACGTGTTGTCTCTCTGTGTCTCGTG-3'.
[0041] The MUSCLE, Mfold, 3D structure modeling, molecular docking, and molecular dynamics simulation software and parameters described above are only specific implementation methods used in this embodiment. Those skilled in the art can also use other software, algorithms, or parameter combinations with the same functions to complete the corresponding operations. Specific Implementation Method Two: Global Exploration Using Genetic Algorithms
[0042] The initial population for the genetic algorithm was constructed using AF26 as a template. Specifically, candidate nucleic acid aptamer sequences were generated through site-by-site random mutation, and AF26 was retained as one of the individuals in the initial population.
[0043] For each candidate nucleic acid aptamer sequence in the initial population, structural construction and molecular docking were performed. Fitness was calculated based on the binding free energy of the candidate nucleic acid aptamer sequence with aflatoxin B1. The lower the binding free energy, the stronger the binding tendency of the candidate nucleic acid aptamer sequence with aflatoxin B1, and the higher the fitness of the corresponding candidate sequence.
[0044] During the iteration process of the genetic algorithm, selection, crossover, mutation, and elitism retention strategies are used to generate candidate sequences for offspring. The selection operation is used to retain candidate sequences with high fitness as parents; the crossover operation is used to exchange partial base fragments between different parent sequences; the mutation operation is used to introduce base changes at one or more sites in the candidate sequence; the elitism retention strategy is used to directly retain candidate sequences with high fitness rankings to the next generation to avoid losing advantageous candidate sequences during the iteration process.
[0045] In one specific implementation, the initial population size of the genetic algorithm is 20, including one AF26 seed sequence and 19 candidate sequences generated based on random mutations of AF26; the selection method is tournament selection, where three candidate sequences are randomly selected each time and the sequence with the highest fitness is retained as the parent; the crossover method is single-point crossover with a crossover probability of 0.8; the mutation rate per site during the genetic iteration process is 0.1; each round of the genetic algorithm globally explores and iterates continuously for 6 generations, and after the iteration, the top 5 candidate sequences are selected according to fitness as the elite sequences of the genetic algorithm, and used to initialize or update the site-base pheromone matrix of the ant colony algorithm. Specific Implementation Method 3: Local Optimization of Ant Colony Algorithm
[0046] After each round of global exploration by the genetic algorithm, several candidate nucleic acid aptamer sequences with high fitness are selected as elite sequences for the genetic algorithm. The frequency of occurrence of the four bases (A, T, G, and C) at each base site in these elite sequences is statistically analyzed, and a site-base pheromone matrix is constructed based on this frequency. The rows of the site-base pheromone matrix correspond to the base sites of the nucleic acid aptamer sequences, and the columns correspond to the four base states (A, T, G, and C). For an AF26 seed sequence of length 26 nt, the site-base pheromone matrix is a 26×4 matrix.
[0047] During the ant colony algorithm iteration, each ant selects bases site by site according to the probability transition rule, starting from the first base site, until a complete candidate nucleic acid aptamer sequence is constructed. The probability of selecting the j-th base at the i-th site is calculated using the following formula: in, The pheromone intensity of base j at site i is used to characterize the degree to which this site-base combination is adopted and reinforced by high-fitness candidate sequences in each iteration; The value of the heuristic function for base j at site i is used to characterize the degree to which the site-base combination is likely to form a high-fitness nucleic acid aptamer sequence based on the evaluation results of existing candidate sequences; α and β represent the weights of the pheromone term and the heuristic term, respectively.
[0048] After each iteration of the ant colony algorithm, molecular docking is performed on the newly generated candidate nucleic acid aptamer sequences, and their fitness is calculated. The site-base pheromone matrix is updated based on the candidate sequence fitness, and the pheromone update is performed as follows: in, Indicates the pheromone evaporation coefficient. This represents the pheromone increment, which is positively correlated with the fitness of the candidate nucleic acid aptamer sequence.
[0049] In one specific implementation, the pheromone weight α is 1.0, the heuristic function weight β is 2.0, the pheromone evaporation coefficient ρ is 0.3, the pheromone increment coefficient Q is 100, and the pheromone intensity is limited to the range of 0.1–10.0. Each round of ant colony optimization uses 15 ants to construct candidate nucleic acid aptamer sequences, and the ant colony algorithm executes 5 iterations. By limiting the pheromone intensity range, the risk of premature convergence caused by excessive accumulation of pheromone from a few site-base combinations can be reduced. The generated candidate nucleic acid aptamer sequences are included in the candidate sequence pool for subsequent deduplication, sorting, and screening. Specific Implementation Method 4: GA-ACO Joint Iteration and Determination of AF26I
[0050] In this embodiment, the GA-ACO joint iteration is performed for 3 rounds. Each round of joint iteration includes global exploration by the genetic algorithm and local optimization by the ant colony algorithm, and the candidate nucleic acid aptamer sequence with the highest fitness in that round is fed back as the seed sequence for the next round, so that the screening process forms a closed loop of "global exploration by genetic algorithm - local optimization by ant colony algorithm - optimal sequence feedback update".
[0051] After three rounds of joint iterations, the generated candidate nucleic acid aptamer sequences were included in the candidate sequence pool and sorted by deduplication and fitness, resulting in 531 deduplicated candidate nucleic acid aptamer sequences. Among them, 119 candidate nucleic acid aptamer sequences had molecular docking binding free energies lower than the parental aptamer AF26 (-9.0 kcal / mol), accounting for approximately 22.4% of the total candidate sequences. These results indicate that the GA-ACO joint optimization method can effectively generate multiple candidate sequences with higher affinity than the original seed aptamer AF26. Theoretically, these optimized nucleic acid aptamers are expected to exhibit higher detection sensitivity in AFB1 detection. As shown in Figures 2 and 3, the optimal fitness increased from -9.0 kcal / mol of the parental AF26 to -9.9 kcal / mol, an optimization margin of approximately 10%; the mutations were mainly concentrated at positions 18-23 of the 3' end, indicating that the method of this invention can perform targeted optimization of local binding interfaces while preserving the core recognition framework.
[0052] Molecular dynamics simulations were performed on the candidate nucleic acid aptamer sequences with the best overall evaluation from the candidate sequence pool for 100 ns. The optimized candidate sequence with the best overall evaluation was designated AF26I, with the nucleotide sequence: 5'-CACGTGTTGTCTCTCTGTGTGTAGTG-3'. Specifically, the nucleic acid aptamer was subjected to an AMBER-OL15 force field, and the topological parameters of aflatoxin B1 were generated based on GAFF. The system was placed in an aqueous model, and trajectory analysis was performed after energy minimization, NVT equilibrium, NPT equilibrium, and production period simulation. The binding stability of the candidate nucleic acid aptamer sequences to aflatoxin B1 was evaluated by analyzing the RMSD, RMSF, and binding conformational stability of the complex.
[0053] As shown in Figure 4, based on molecular dynamics simulations, the MM-PBSA method was further used to calculate the binding free energies of the parental aptamer AF26 and the optimized aptamer AF26I after forming complexes with aflatoxin B1. The calculation results show that the binding free energy of the AF26I-aflatoxin B1 complex stabilized at approximately -30 kcal / mol in the later stages of the simulation, while the binding free energy of the AF26-aflatoxin B1 complex was approximately -13 to -16 kcal / mol, indicating that AF26I has stronger thermodynamic binding stability than the parental aptamer AF26. Combining the molecular docking binding free energy, RMSD, RMSF, binding conformational stability, and MM-PBSA binding free energy, AF26I was determined to be the preferred nucleic acid aptamer for targeting aflatoxin B1. Specific Implementation Method 5: AF26I Binding Verification and Detection Application
[0054] like Figure 5 As shown, the binding ability of AF26I to aflatoxin B1 was experimentally verified. The results showed that ultraviolet absorption spectroscopy, gold nanoparticle colorimetry, and circular dichroism spectroscopy all exhibited the characteristic response of AF26I after binding to aflatoxin B1, indicating that AF26I can recognize and bind to aflatoxin B1.
[0055] In one specific embodiment, a method for detecting aflatoxin B1 based on a graphene oxide fluorescence quenching-recovery system is constructed using AF26I as the recognition element. The 5' end of AF26I is labeled with a fluorescent group, and graphene oxide serves as the fluorescence quenching material. When aflatoxin B1 is absent, the fluorescently labeled AF26I is adsorbed by graphene oxide, quenching the fluorescence signal. When aflatoxin B1 is present, AF26I specifically binds to aflatoxin B1, leading to the recovery of the fluorescence signal, thereby achieving the detection of aflatoxin B1. This detection system exhibits a good linear response to aflatoxin B1 in the range of 0–20 ppb, with a detection limit of 3.97 ppb, lower than the detection limit of 5.70 ppb of the parental aptamer AF26 system, indicating that AF26I has better detection sensitivity.
[0056] The above results demonstrate that this invention, through global exploration using a genetic algorithm, local refinement using an ant colony algorithm, optimal sequence feedback iteration, molecular dynamics simulation, and post-screening using MM-PBSA, can achieve targeted optimization of nucleic acid aptamer sequences and obtain nucleic acid aptamer AF26I that can be used for the detection of aflatoxin B1.
[0057] The above specific embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention. Those skilled in the art can adaptively adjust the specific parameters or implementation methods in the global exploration of the genetic algorithm, the local optimization of the ant colony algorithm, molecular dynamics simulation, the evaluation of binding free energy, and the construction of the detection system according to different target molecules, different seed nucleic acid aptamer sequences, and different detection application scenarios without departing from the technical concept of the present invention. All such adjustments, equivalent substitutions, or conventional modifications should be covered within the scope of protection of the present invention.
Claims
1. A method for computational screening of nucleic acid aptamers, characterized in that, Includes the following steps: S1. Determine the seed nucleic acid aptamer sequence of the target molecule; S2. Using the seed nucleic acid aptamer sequence as a template, the nucleic acid aptamer sequence space is globally explored through genetic algorithm operations including mutation, selection and crossover to obtain the genetic algorithm elite sequence. S3. Local optimization of the advantageous sequence regions selected by the genetic algorithm is performed using the ant colony algorithm; wherein, the site-base pheromone matrix is initialized according to the elite sequence of the genetic algorithm, and candidate nucleic acid aptamer sequences are constructed site by site according to the probability transition rule by the ant colony algorithm to obtain the locally optimized sequence. S4. The global exploration of the genetic algorithm and the local optimization of the ant colony algorithm are jointly iterated. After each round of joint iteration, the candidate nucleic acid aptamer sequence with the highest fitness is used as the seed nucleic acid aptamer sequence for the next round. S5. After deduplication and fitness sorting of the candidate nucleic acid aptamer sequences obtained by joint iteration, select high-fitness candidate sequences for molecular dynamics simulation and evaluate the binding free energy using the MM-PBSA method to screen and obtain the target nucleic acid aptamer; wherein, the fitness is determined by the molecular docking binding free energy between the candidate nucleic acid aptamer sequence and the target molecule.
2. The nucleic acid aptamer calculation and screening method according to claim 1, characterized in that, The target molecule is a small molecule fungal toxin.
3. The nucleic acid aptamer calculation and screening method according to claim 1, characterized in that, In S1, the seed nucleic acid aptamer sequence is determined by the following method: collecting reported target nucleic acid aptamer sequences, performing conserved region analysis, structure prediction, molecular docking, and molecular dynamics simulation on the target nucleic acid aptamer sequences, and determining the seed nucleic acid aptamer sequence based on the stability of the complex and the binding conformation.
4. The nucleic acid aptamer calculation and screening method according to claim 1, characterized in that, In S2, the global exploration of the genetic algorithm includes: constructing an initial population by random mutation using the seed nucleic acid aptamer sequence as a template; generating offspring candidate sequences by using selection, crossover, mutation, and elite retention strategies during the iteration process, and sorting them according to fitness; the joint iteration is performed in n rounds, where n is a positive integer greater than or equal to 2.
5. The nucleic acid aptamer calculation and screening method according to claim 1, characterized in that, In step S3, a site-base pheromone matrix τ is constructed, where each row of the matrix corresponds to a base site i in the nucleic acid aptamer sequence, and each column corresponds to one of the four base states j: A, T, G, and C. Based on the frequency of A, T, G, and C occurrences of the elite sequences from the genetic algorithm at each base site i, an initial value is assigned to the pheromone intensity τᵢⱼ of the corresponding site-base combination. The ant colony algorithm selects a base state j at each base site i according to a probability transition rule determined by a weighted average of the pheromone intensity τᵢⱼ and the heuristic function value ηᵢⱼ. After each iteration, the site-base pheromone matrix τ is updated according to the pheromone evaporation coefficient ρ and the pheromone increment Δτᵢⱼ.
6. The nucleic acid aptamer calculation and screening method according to claim 1, characterized in that, In S4, the joint iteration is performed at least twice; in each round of joint iteration, the elite sequence output by the genetic algorithm is used to initialize or update the site-base pheromone matrix of the ant colony algorithm, and the candidate nucleic acid aptamer sequence with the highest fitness in that round is fed back as the seed sequence for the next round of the genetic algorithm.
7. The nucleic acid aptamer calculation and screening method according to claim 1, characterized in that, In step S5, the molecular dynamics simulation and binding free energy evaluation include: performing molecular dynamics simulation on the complex formed by the candidate nucleic acid aptamer sequence and the target molecule, evaluating the stability and binding ability of the complex based on the simulation results, and screening the target nucleic acid aptamer.
8. A nucleic acid aptamer, characterized in that, The nucleic acid aptamers were obtained by screening using the nucleic acid aptamer calculation and screening method described in any one of claims 1 to 7.
9. The nucleic acid aptamer according to claim 8, characterized in that, The optimal nucleic acid aptamer is AF26I, which targets aflatoxin B1, and its nucleotide sequence is: 5'-CACGTGTTGTCTCTCTGTGTGTAGTG-3'.
10. The application of the nucleic acid aptamer according to claim 8 or 9 in the detection of aflatoxin B1, wherein the detection is any one of fluorescence detection, colorimetric detection, circular dichroism detection or sensor detection.