An online analysis platform for functional genes in bioplastic degradation

Abstract

The present invention relates to the field of gene analysis technology, and discloses an online analysis platform for functional genes of bioplastic degradation, including a data input module, a functional gene analysis module, a degradation path prediction module, a degradation path optimization module, a database and a data visualization module; the functional gene analysis module is used to identify sequences similar to known degradation functional genes according to the comparison of gene sequences; the degradation path prediction module is used to simulate the degradation path of bioplastics according to the identified functional genes; the degradation path optimization module is used to optimize the simulated bioplastic degradation path through a degradation path fusion optimization algorithm to obtain an optimal degradation path. The present invention can achieve comprehensive analysis and optimization of the bioplastic degradation path through gene sequence comparison, functional gene annotation, degradation path prediction and optimization, etc., significantly improve the degradation efficiency of bioplastics, shorten the degradation time, and ensure its environmental protection.

Description

Technical Field

[0001] The present invention relates to the field of gene analysis, and in particular to an online analysis platform for functional genes in bioplastic degradation. Background Art

[0002] Under the current initiative to reduce the use of plastics, the types and usage of bioplastics are increasing. These measures aim to reduce the negative impact of traditional plastics on the environment and promote sustainable plastic alternatives. However, although the replacement rate of bioplastics currently accounts for only about 1% of non-bioplastics, its annual usage can still reach as much as 2 million tons. This shows the significant growth in market demand for bioplastics and recognition of environmentally friendly materials. Among the many types of bioplastics, thermoplastic biodegradable plastics (PBAT) have become a material that has attracted much attention due to their excellent mechanical properties and biodegradability. PBAT is widely used in packaging materials, agricultural mulch films and other fields, demonstrating its versatility and practicality in different application scenarios.

[0003] However, with the increasing use of bioplastics, how to efficiently degrade these bioplastics has become a key issue in environmental protection and resource recycling. Although bioplastics are degradable, their degradation efficiency and speed in the natural environment still need to be improved. If they cannot be effectively degraded, these bioplastics may still cause pollution to the environment. Therefore, how to improve the degradation efficiency of bioplastics, especially PBAT, through scientific research and technical means has become a major issue that needs to be solved urgently.

[0004] Existing studies have shown that microorganisms that can degrade PBAT have potential application prospects. These microorganisms can not only accelerate the degradation of bioplastics in the environment, but also provide valuable guidance for the processing and manufacturing of degradable plastics. The research and application of these microorganisms will not only help solve the problem of bioplastic degradation, but also promote the research and development and promotion of new environmentally friendly materials. However, there are still few studies on PBAT-degrading microorganisms and their degradation mechanisms. Many studies are only in the initial exploration stage, lacking systematicity and depth, and lack of systematic analysis of existing research. In this case, it is urgent to conduct in-depth analysis and research on microorganisms that degrade PBAT and their functional genes through genetic engineering and bioinformatics.

[0005] Through genetic engineering and bioinformatics, we can gain a deep understanding of the functional genes and action mechanisms of microorganisms that degrade PBAT. These studies can not only reveal the molecular mechanism of microbial degradation of PBAT, but also optimize and enhance the degradation ability of these microorganisms through genetic modification and other means. At the same time, these research results can also be applied to the production and processing of bioplastics, guiding the modification and optimization design of bioplastics, and improving their degradation performance and environmental benefits. Constructing an online analysis platform for biodegradable film degradation genes can solve the problem of lack of systematic research technology in this field. To this end, the present invention proposes an online analysis platform for functional genes for bioplastic degradation. Summary of the invention

[0006] 1. Technical issues to be solved

[0007] In view of the shortcomings of the prior art, the present invention provides an online analysis platform for functional genes in bioplastic degradation, which has the advantages of comprehensive analysis and optimization of the degradation pathway of bioplastics, significantly improving the degradation efficiency of bioplastics, shortening the degradation time, and ensuring its environmental friendliness, thereby solving the problems in the background technology.

[0008] (II) Technical solution

[0009] In order to achieve the above-mentioned advantages of comprehensive analysis and optimization of the degradation path of bioplastics, significantly improve the degradation efficiency of bioplastics, shorten the degradation time, and ensure its environmental protection, the specific technical solutions adopted by the present invention are as follows:

[0010] A functional gene online analysis platform for bioplastic degradation, comprising a data input module, a functional gene analysis module, a degradation path prediction module, a degradation path optimization module, a database and a data visualization module;

[0011] A data input module, used to input the bioplastic sample information to be analyzed and the gene sequence data of the target microorganism;

[0012] Functional gene analysis module, used to identify sequences similar to known degradation functional genes based on gene sequence alignment and annotate functional genes;

[0013] A degradation pathway prediction module is used to simulate the degradation pathway of bioplastics based on the identified functional genes and analyze potential enzyme activities;

[0014] The degradation path optimization module is used to optimize the simulated bioplastic degradation path through the degradation path fusion optimization algorithm to obtain the optimal degradation path;

[0015] A database for storing information on known genes and enzymes related to bioplastic degradation, as well as information on known microbial species involved in bioplastic degradation and their genomes;

[0016] Data visualization module, used to graphically display sequence alignment results and degradation pathway maps;

[0017] Among them, the degradation path optimization module includes a path particle definition module, a path particle initialization module, a fitness value calculation module, a path particle iterative optimization module and an optimization result output module;

[0018] A path particle definition module, used for defining each preliminary degradation path in the preliminary degradation path set as a path particle corresponding thereto;

[0019] Path particle initialization module, used to randomly initialize the mass, acceleration, initial velocity, tension and Poisson parameters of each path particle;

[0020] A fitness value calculation module is used to calculate the fitness value of each path particle based on degradation efficiency, degradation time and environmental protection index, and record the local optimal value and global optimal value of each path particle;

[0021] The path particle iterative optimization module is used to dynamically adjust the path particle speed and position based on collision detection, disturbance judgment and Poisson distribution control, and update the path particle sequence in combination with the game theory model to continuously update the local optimal value and the global optimal value;

[0022] The optimization result output module is used to output the optimal degradation path of bioplastics according to local optimal values ​​and global optimal values.

[0023] Preferably, the functional gene analysis module includes a gene sequence comparison module and a gene annotation module;

[0024] Among them, the gene sequence comparison module is used to compare the input gene sequence with the known gene sequences in the database, identify and screen out sequences similar to the known degradation function genes;

[0025] The gene annotation module is used to map the screened gene sequences to the annotation data, obtain their functional annotation information, and integrate the classification results to perform association analysis.

[0026] Preferably, the gene sequence comparison module compares the input gene sequence with the known gene sequences in the database, identifies and screens out sequences similar to the known degradation function genes, including:

[0027] Perform quality control on the input gene sequence, shear the adapter sequence, and remove poor-quality reads;

[0028] Based on the sequence alignment algorithm, the preprocessed gene sequence is compared with the known gene sequences in the database to identify sequences similar to the known degradation function genes;

[0029] It is determined whether the similarity score in the comparison result is higher than the preset similarity score threshold. If so, the gene sequence is retained; if not, the gene sequence is removed.

[0030] Preferably, the degradation pathway prediction module includes a degradation pathway simulation module and an enzyme activity prediction module;

[0031] Among them, the degradation pathway simulation module is used to simulate the degradation pathway of bioplastics based on the identified functional genes, and predict the intermediate and final products in the degradation process through dynamic programming and environmental condition simulation;

[0032] The enzyme activity prediction module is used to analyze and predict the activity of enzymes involved in the degradation process.

[0033] Preferably, the degradation pathway simulation module simulates the degradation pathway of bioplastics according to the identified functional genes, and predicts the intermediate products and final products in the degradation process through dynamic programming and environmental condition simulation, including:

[0034] Obtain the identified functional genes and their annotation information, and perform data format conversion and preprocessing;

[0035] Based on bioinformatics tools and databases, the degradation pathways of several bioplastics were constructed using known biodegradation pathways to obtain a set of degradation pathways;

[0036] Dynamic programming was used to simulate the degradation pathway of bioplastics under different conditions and to predict the intermediate and final products produced during the degradation process.

[0037] The experimental data or literature data are combined to simulate the impact of different environmental conditions on each degradation pathway, and the degradation pathway is adjusted based on the simulation results to obtain a preliminary degradation pathway set.

[0038] Preferably, the enzyme activity prediction module includes the following steps when analyzing and predicting the activity of enzymes involved in the degradation process:

[0039] Obtain and analyze the identified enzyme gene sequence, determine the enzyme family and functional domain, and use homology modeling to predict the enzyme's three-dimensional structure;

[0040] Simulate the interaction between enzymes and intermediates and final products of bioplastic degradation, and use molecular docking to predict the active site of the enzyme and its binding mode with the substrate;

[0041] Based on machine learning technology, combined with enzyme structure and sequence characteristics, the activity and stability of the enzyme are predicted, and the key amino acid residues and their mechanism of action are identified.

[0042] Among them, the calculation formula of the fitness value of the path particle is:

[0043]

[0044] In the formula, Z i represents the fitness value of the particle in the ith path; w1, w2 and w3 represent the weights of degradation efficiency, degradation time and environmental protection index value respectively; E i represents the degradation efficiency of the particles in the ith path; E min represents the minimum degradation efficiency of the particle in the ith path; E max represents the maximum degradation efficiency of the particles in the ith path; T i represents the degradation time of the particle in the ith path; T min represents the minimum degradation time of the particle in the ith path; T max represents the maximum degradation time of the particle in the ith path; S i represents the environmental index value of the particle in the ith path; S min represents the minimum environmental index value of the particle in the ith path; S max Represents the maximum environmental index value of the particle in the i-th path.

[0045] Preferably, the path particle iterative optimization module dynamically adjusts the path particle speed and position based on collision detection, disturbance judgment and Poisson distribution control, and updates the path particle sequence in combination with the game theory model, and continuously updates the local optimal value and the global optimal value, including:

[0046] Check whether each path particle collides. If so, update the speed and position of the path particle in reverse. If not, keep the current speed and position unchanged.

[0047] Evaluate whether each path particle meets the perturbation condition. If so, change the speed and position of the path particle through random perturbation. If not, keep the current speed and position unchanged.

[0048] Determine whether the path particle has the ability to update. If so, update the speed and position of the path particle. If not, skip the update process of the path particle.

[0049] Calculate the Poisson distribution function value and determine whether the Poisson distribution function value is less than the preset threshold. If so, enter the threshold stage of the third-level deceleration, update the path particle speed according to the Newton mechanics update formula, randomly select some path particles to obtain similarity information, and combine the intelligent single particle and game theory model to update the path particle sequence, and calculate the fitness value of the path particle. If not, enter the Poisson distribution control acceleration stage, update the path particle speed according to the acceleration update formula, randomly select some path particles to obtain similarity information, and combine the intelligent single particle and game theory model to update the path particle sequence, and calculate the fitness value of the path particle;

[0050] Determine whether the fitness value of the particle in the current path is higher than the historical local optimal value. If so, use the fitness value of the particle in the current path to replace the historical local optimal value. If not, retain the historical local optimal value.

[0051] Determine whether the fitness value of the particle in the current path is higher than the global optimal value. If so, replace the global optimal value with the fitness value of the particle in the current path. If not, retain the global optimal value.

[0052] Among them, the calculation formula of the Poisson distribution function value is:

[0053]

[0054] The expression of Newtonian mechanics update formula is:

[0055]

[0056] F t =Ma

[0057] The acceleration update formula is expressed as:

[0058]

[0059] Where P represents the value of the Poisson distribution function; d represents the number of iterations; λ represents the Poisson parameter; e represents the base of the natural logarithm; ! represents the factorial; V t represents the speed of the path particle at time t based on the three-level deceleration threshold; V0 represents the initial speed of the path particle; M represents the mass of the path particle; F t represents the pulling force of the path particle at time t; Level 1Particle, Level 2Particle and Level 3Particle represent the first-level path particle, the second-level path particle and the third-level path particle respectively; v t ′ represents the velocity of the path particle at time t based on Poisson distribution control; V max represents the maximum velocity of the path particles; n represents the total number of path particles; a represents the acceleration of the path particles.

[0060] Preferably, combining the intelligent single particle with the game theory model to update the path particle sequence, and calculating the fitness value of the path particle includes:

[0061] Select the path particle with the highest fitness from the current path particle group as the intelligent single path particle, and record the position and speed of the intelligent single path particle;

[0062] The position and speed of the smart single-path particle are propagated to other path particles, and each path particle obtains the position and speed information of the smart single-path particle; the position of the current path particle is adjusted according to the position information of the smart single-path particle;

[0063] The path particle group is divided into several subgroups, and the game is carried out based on the path particles within each subgroup; each path particle selects its own game strategy and adjusts its position and speed according to the strategy;

[0064] The path particles select a cooperation or competition strategy according to their current fitness, wherein the path particles with a fitness higher than a preset fitness threshold select a cooperation strategy, and the path particles with a fitness lower than the preset fitness threshold select a competition strategy;

[0065] According to the position and speed of the path particles adjusted by the intelligent single path particles and the game theory model, the path particle sequence is updated, and the fitness value of each path particle after the update is calculated.

[0066] (III) Beneficial effects

[0067] Compared with the prior art, the present invention provides a configuration method with the following beneficial effects:

[0068] 1) The present invention can systematically study and identify microorganisms and their functional genes that can degrade PBAT through efficient gene sequence alignment, functional gene annotation, degradation pathway prediction and optimization, etc., which not only helps to understand the degradation mechanism of PBAT, but also provides technical support for the removal of biodegradable plastics in the environment and the processing and manufacturing of biodegradable plastics, and promotes the sustainable development of the bioplastics industry. Through comprehensive analysis and optimization of the bioplastics degradation pathway, the degradation efficiency of bioplastics can be significantly improved, the degradation time can be shortened, and its environmental friendliness can be ensured, providing a practical technical approach to solving the current bioplastics degradation problem.

[0069] 2) The present invention can not only realize comprehensive analysis and annotation of input gene sequences through gene sequence alignment and functional gene annotation, identify sequences similar to known degradation functional genes, and provide basic data support for subsequent degradation path prediction, but also can realize detailed simulation and analysis of bioplastic degradation paths based on degradation path simulation and enzyme activity prediction, predict potential enzyme activity, and help users understand the biochemical process of bioplastic degradation; at the same time, the sequence alignment results and degradation path maps can be graphically displayed, so that users can intuitively understand the results of gene analysis and degradation path prediction, thereby improving user experience.

[0070] 3) The present invention optimizes the simulated bioplastic degradation path by utilizing a degradation path fusion optimization algorithm, thereby dynamically adjusting the speed and position of the particles based on collision detection, disturbance condition judgment, and Poisson distribution control, combined with intelligent single-path particles and game theory models, thereby significantly improving the optimization effect of the bioplastic degradation path, thereby not only improving the degradation efficiency and shortening the degradation time, but also effectively enhancing environmental protection, and thus can play an important role in the online analysis platform of functional genes for bioplastic degradation, bringing significant technical effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0072] Figure 1 It is a structural block diagram of a functional gene online analysis platform for bioplastic degradation according to an embodiment of the present invention.

[0073] In the figure:

[0074] 1. Data input module; 2. Functional gene analysis module; 21. Gene sequence alignment module; 22. Gene annotation module; 3. Degradation path prediction module; 31. Degradation path simulation module; 32. Enzyme activity prediction module; 4. Degradation path optimization module; 41. Path particle definition module; 42. Path particle initialization module; 43. Fitness value calculation module; 44. Path particle iterative optimization module; 45. Optimization result output module; 5. Database; 6. Data visualization module. DETAILED DESCRIPTION

[0075] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0076] According to an embodiment of the present invention, a functional gene online analysis platform for bioplastic degradation is provided.

[0077] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1As shown, a functional gene online analysis platform for bioplastic degradation according to an embodiment of the present invention includes a data input module 1, a functional gene analysis module 2, a degradation path prediction module 3, a degradation path optimization module 4, a database 5 and a data visualization module 6;

[0078] Data input module 1, used to input the bioplastic sample information to be analyzed and the gene sequence data of the target microorganism;

[0079] Functional gene analysis module 2, used to identify sequences similar to known degradation functional genes based on gene sequence comparison and annotate the functional genes;

[0080] Specifically, the functional gene analysis module 2 includes a gene sequence comparison module 21 and a gene annotation module 22;

[0081] The gene sequence comparison module 21 is used to compare the input gene sequence with the known gene sequences in the database, identify and screen out sequences similar to the known degradation function genes, specifically including:

[0082] Perform quality control on the input gene sequences, cut adapter sequences, and remove low-quality reads to ensure the accuracy of the alignment results;

[0083] Select and configure appropriate sequence alignment algorithms, such as BLAST (Basic Local Alignment Search Tool), HMMER (Hidden Markov Model), Bowtie, etc., and optimize parameters according to requirements;

[0084] Based on the sequence alignment algorithm, the preprocessed gene sequence is compared with the known gene sequences in the database (reading the alignment result file and parsing the alignment information of each sequence, including alignment score, E-value, similarity, etc.), and sequences similar to known degradation function genes are identified;

[0085] It is determined whether the similarity score in the comparison result is higher than the preset similarity score threshold. If so, the gene sequence is retained; if not, the gene sequence is removed and the matching sequence with high confidence is retained.

[0086] The gene annotation module 22 is used to map the screened gene sequences to the annotation data, obtain their functional annotation information, integrate the classification results, and perform association analysis, specifically including:

[0087] Select a suitable annotation database to obtain gene function annotation information, and map the matching sequences screened by the gene sequence alignment module to the annotation database to obtain its functional annotation information;

[0088] Integrate annotation information from different databases to classify and hierarchically display gene functions, such as molecular functions, cellular processes, and biological processes;

[0089] Based on the annotation information, the relationships between functional genes are analyzed, possible functional gene networks and pathways are identified, and the synergistic effects of gene functions are further understood.

[0090] Degradation pathway prediction module 3, used to simulate the degradation pathway of bioplastics based on the identified functional genes and analyze the potential enzyme activity;

[0091] Specifically, the degradation pathway prediction module 3 includes a degradation pathway simulation module 31 and an enzyme activity prediction module 32;

[0092] The degradation pathway simulation module 31 is used to simulate the degradation pathway of bioplastics according to the identified functional genes, and predict the intermediate products and final products in the degradation process through dynamic programming and environmental condition simulation, specifically including:

[0093] Obtain the identified functional genes and their annotation information, and perform data format conversion and preprocessing to ensure data integrity and consistency;

[0094] Based on bioinformatics tools and databases, the degradation pathways of several bioplastics were constructed using known biodegradation pathways to obtain a set of degradation pathways, including:

[0095] 1) Data collection and preparation

[0096] Collect known biodegradation pathway data: Obtain metabolic pathway data related to biodegradation from the database; download detailed information including degradation enzymes, metabolites and their relationships.

[0097] Obtaining sequence information of genes and enzymes: Using databases to collect sequence information and annotations of genes and enzymes related to biodegradation.

[0098] 2) Initial degradation pathway construction

[0099] Initial pathway construction: Based on the known biodegradation pathway information, the initial bioplastic degradation pathway is constructed using tools such as KEGG Pathway Mapper.

[0100] Pathway diagram drawing: Use bioinformatics software such as Cytoscape to draw metabolic network diagrams to show the relationships between metabolites and enzymes.

[0101] 3) Annotation of gene and enzyme functions

[0102] InterProScan, Blast2GO and other tools were used to perform functional annotation of genes and enzymes to confirm their roles in the degradation pathway.

[0103] 4) Constructing a set of degradation pathways

[0104] Generate diverse degradation pathways: Based on different bioplastic types, construct multiple degradation pathways that adapt to different degradation conditions (such as temperature, pH, and oxygen concentration).

[0105] Integrated pathway set: All generated degradation pathways were integrated into a complete degradation pathway set covering different bioplastic types and environmental conditions.

[0106] Dynamic programming is used to simulate the degradation path of bioplastics under different conditions and predict the intermediate and final products produced during the degradation process, including:

[0107] 1) Define degradation pathways and states

[0108] Degradation pathway representation: The degradation pathway of bioplastics is represented as a directed graph, where nodes represent intermediates and final products, and edges represent degradation reactions.

[0109] State definition: Each state is defined as the intermediate product in the current degradation pathway and the current environmental conditions (such as temperature, pH value, oxygen concentration, etc.).

[0110] 2) Construct the state transfer matrix

[0111] State transition: Based on the chemical reactions of the degradation pathway, a state transition matrix is ​​constructed to represent the probability or rate of transition from one state to another. The rate constant and conditional dependency of each reaction are determined.

[0112] Initialization conditions: Set the initial state according to the initial conditions (such as the initial amount of bioplastics, environmental conditions).

[0113] 3) Dynamic programming algorithm design

[0114] Recursive formula: Use the recursive formula of dynamic programming to calculate the state at different time steps. Let p[t][s] represent the probability of being in state s at time t: p[t][s] = max s′ {p[t-1][s′]+T[s′][s]·f(s′,s)}, where s′ represents the state of the previous time step, T[s′][s] represents the rate of transition from state s′ to state s, and f(s′,s) represents the amount of product generated from state s′ to state s;

[0115] Boundary conditions: The initial condition p[0][s0] corresponds to the probability of the initial state s0.

[0116] 4) Path simulation and prediction

[0117] Path simulation: Starting from the initial state, the state distribution at different time steps is calculated step by step using the recursive formula. The dynamic changes of environmental conditions, such as temperature fluctuations and pH changes, are considered during the simulation process.

[0118] Product prediction: Based on the calculation results, the amount of intermediate and final products at each time step is predicted. A dynamic path diagram is generated during the degradation process to show the generation and consumption process of the products.

[0119] The experimental data or literature data are combined to simulate the effects of different environmental conditions (such as temperature, pH value, microbial species, etc.) on each degradation pathway. The degradation pathway is adjusted based on the simulation results to obtain a preliminary degradation pathway set.

[0120] The enzyme activity prediction module 32 is used to analyze and predict the activity of enzymes involved in the degradation process, specifically including:

[0121] Obtain the identified enzyme gene sequence, use tools (such as BLAST, Pfam, InterPro, etc.) to perform detailed sequence analysis, determine the enzyme family and functional domain, and use homology modeling to predict the enzyme's three-dimensional structure to provide a basis for subsequent enzyme activity analysis;

[0122] Simulate the interaction between enzymes and intermediates and final products of bioplastic degradation, and use molecular docking to predict the active site of the enzyme and its binding mode with the substrate;

[0123] Based on machine learning technology, combined with enzyme structure and sequence characteristics, the activity and stability of the enzyme are predicted, and key amino acid residues and their mechanism of action are identified, including:

[0124] 1) Data collection and preparation

[0125] Collect enzyme sequence and structural data: Obtain enzyme amino acid sequence and 3D structure data from the database. Download data on known enzyme activity and stability for model training and validation.

[0126] Feature extraction: Extract amino acid composition, physicochemical properties (such as hydrophobicity, charge, etc.), sequence conservation, etc. Extract secondary structure, tertiary structure information, spatial location of active sites, etc. Use software tools (such as BioPython, PyMOL) to extract these features;

[0127] 2) Feature Engineering

[0128] Feature standardization and normalization: Standardize the extracted features to make them have the same scale for easier processing by machine learning models.

[0129] Feature selection: Use feature selection methods (such as principal component analysis (PCA) and recursive feature elimination (RFE)) to screen out the most important features, reduce data dimensions, and improve model performance.

[0130] 3) Model construction and training

[0131] Select machine learning algorithm: Commonly used algorithms include Random Forest, Support Vector Machine (SVM), Neural Network, etc.

[0132] Model training: Divide the dataset into a training set and a test set (e.g. 80% training, 20% test). Use the training set to train the model and adjust the parameters to optimize the model performance.

[0133] 4) Prediction and verification

[0134] Model validation: Use the test set to evaluate the prediction performance of the model and calculate indicators such as accuracy, precision, and recall. Use cross-validation to further verify the stability and generalization ability of the model.

[0135] Identify key amino acid residues: Identify the amino acid residues that have the greatest impact on enzyme activity and stability through feature importance analysis.

[0136] 5) Analysis of the mechanism of action of key residues

[0137] Structural biology analysis: locate the identified key amino acid residues in the three-dimensional structure of the enzyme, analyze their spatial position and relationship with the active site, and use molecular dynamics simulation (MD simulation) to study the effects of key residues on enzyme structure and function.

[0138] Functional validation: The role of key residues is verified through mutation experiments to observe the effect of mutations on enzyme activity and stability. For example, site-directed mutagenesis is used to change key residues and measure the activity and stability of the mutant enzyme.

[0139] Among them, BLAST, Pfam and InterPro are widely used tools and databases in bioinformatics for sequence analysis and functional annotation of genes and proteins. The following is a detailed introduction to them:

[0140] 1. BLAST (Basic Local Alignment Search Tool)

[0141] BLAST is a tool for comparing nucleic acid or protein sequences, which can quickly find sequences similar to a given query sequence in a sequence database. It identifies similar fragments through a local alignment algorithm and provides alignment scores and statistical significance.

[0142] Purpose: Search for sequences similar to the query sequence, identify homologous genes or proteins, and perform function prediction.

[0143] How it works: By constructing a local alignment of the query sequence and the database sequence, high-scoring fragments are found and their significance is evaluated based on a statistical model.

[0144] Common applications: gene annotation, homology search, sequence alignment, evolutionary analysis.

[0145] 2. Pfam (Protein Families Database)

[0146] Pfam is a database containing protein families, where each family consists of multiple protein sequences with similar functions or domains. Pfam provides Hidden Markov Models (HMMs) and multiple sequence alignments for each family.

[0147] Purpose: Identify functional domains in protein sequences, annotate protein functions, and perform structure and function prediction.

[0148] How it works: Use the HMMER tool to align sequences through the hidden Markov model and identify the domains in the protein.

[0149] Common applications: protein function prediction, domain identification, evolutionary analysis.

[0150] 3. InterPro

[0151] InterPro is a resource that integrates multiple protein family and domain databases to provide comprehensive protein functional annotation information. It combines information from multiple member databases (such as Pfam, PRINTS, PROSITE, etc.) to provide unified functional annotations.

[0152] Purpose: Provide comprehensive protein function annotation, integrate domain and family information from different sources, and predict protein function.

[0153] How it works: Integrates sequence and structure information from multiple member databases to provide unified functional annotations through consistent annotation standards.

[0154] Common applications: protein function annotation, domain identification, and integrated analysis.

[0155] The degradation path optimization module 4 is used to optimize the simulated bioplastic degradation path through a degradation path fusion optimization algorithm to obtain an optimal degradation path;

[0156] Specifically, the degradation path optimization module 4 includes a path particle definition module 41, a path particle initialization module 42, a fitness value calculation module 43, a path particle iterative optimization module 44 and an optimization result output module 45;

[0157] A path particle definition module 41 is used to define each preliminary degradation path in the preliminary degradation path set as a path particle corresponding thereto, wherein each path particle represents a bioplastic degradation path, including variables such as chemical reaction steps, reaction conditions (temperature, humidity, microbial species, etc.), and reaction time;

[0158] A path particle initialization module 42 is used to randomly initialize the mass, acceleration, initial velocity, tension and Poisson parameters of each path particle;

[0159] The fitness value calculation module 43 is used to calculate the fitness value of each path particle based on the degradation efficiency, degradation time and environmental protection index (the higher the fitness value, the better the path), and record the local optimal value and global optimal value of each path particle, specifically including:

[0160] The importance of setting degradation efficiency, degradation time and environmental index value is represented by weights w1, w2 and w3 respectively, and the sum of the weights should be 1;

[0161] Since the dimensions of each index are different, they need to be standardized. The degradation efficiency and degradation time of the i-th path particle are set to be E and i 、T i and S i , the standardized formula is as follows:

[0162] Among them, the standardized degradation efficiency E i ′:

[0163]

[0164] Standardized degradation time T i ′:

[0165]

[0166] Standardized environmental index value S i ′:

[0167]

[0168] The calculation formula of the fitness value of the path particle is:

[0169] Zi =ω1·E i ′+ω2·T i ′+ω3·S i '

[0170] In the formula, Z i represents the fitness value of the particle in the ith path; w1, w2 and w3 represent the weights of degradation efficiency, degradation time and environmental protection index value respectively; E i represents the degradation efficiency of the particles in the ith path; E min represents the minimum degradation efficiency of the particle in the ith path; E max represents the maximum degradation efficiency of the particles in the ith path; T i represents the degradation time of the particle in the ith path; T min represents the minimum degradation time of the particle in the ith path; T max represents the maximum degradation time of the particle in the ith path; S i represents the environmental index value of the particle in the ith path; S min represents the minimum environmental index value of the particle in the ith path; S max represents the maximum environmental index value of the particle in the i-th path;

[0171] In each iteration, the local optimal value and global optimal value of each path particle are updated. If the fitness value of the current path particle is higher than its historical local optimal value, the fitness value of the current path particle is used to replace its historical local optimal value; if the fitness value of the current path particle is higher than the global optimal value, the fitness value of the current path particle is used to replace the global optimal value.

[0172] The path particle iterative optimization module 44 is used to dynamically adjust the path particle speed and position based on collision detection, disturbance judgment and Poisson distribution control, and update the path particle sequence in combination with the game theory model, and continuously update the local optimal value and the global optimal value, specifically including:

[0173] Check whether each path particle collides. If so, update the velocity and position of the path particle in reverse. For example, if the path particle velocity v i The strain after the collision is -v i , then the update formula is: i * =-v i ; Update the position of the path particle: x i * =f·(o+p)-x i , where f represents the shrinkage factor, x i ∈[o, p], so that it can escape from the collision state; if not, the current speed and position are kept unchanged. The particle collision detection can prevent the particle from falling into the local optimum;

[0174] Evaluate whether each path particle meets the perturbation condition. If so, change the speed and position of the path particle by random perturbation (for example, at the current position x i Add a random position perturbation coefficient δ x :x i * =x i +δ x ; The speed can also be adjusted accordingly: v i * =v i +δ v , δ v represents the random velocity perturbation coefficient) to increase the diversity of the search space. If not, keep the current velocity and position unchanged;

[0175] Determine whether the path particle has the ability to update (such as the speed has not dropped to zero). If so, update the speed and position of the path particle (update the speed of the path particle: v i * =v i +a i ·Δt, Δt represents the time step; update the position of the path particle: x i * =x i +v i ·Δt), if not, skip the update process of the path particle;

[0176] Calculate the Poisson distribution function value and determine whether the Poisson distribution function value is less than the preset threshold. If so, enter the threshold stage of the third-level deceleration, update the path particle speed according to the Newton mechanics update formula, randomly select some path particles to obtain similarity information, and combine the intelligent single particle and game theory model to update the path particle sequence, and calculate the fitness value of the path particle. If not, enter the Poisson distribution control acceleration stage, update the path particle speed according to the acceleration update formula, randomly select some path particles to obtain similarity information, and combine the intelligent single particle and game theory model to update the path particle sequence, and calculate the fitness value of the path particle;

[0177] Determine whether the fitness value of the particle in the current path is higher than the historical local optimal value. If so, use the fitness value of the particle in the current path to replace the historical local optimal value. If not, retain the historical local optimal value.

[0178] Determine whether the fitness value of the particle in the current path is higher than the global optimal value. If so, replace the global optimal value with the fitness value of the particle in the current path. If not, retain the global optimal value.

[0179] When all particles have no update capability, the algorithm ends; otherwise, it returns to the update capability judgment step to continue iterating.

[0180] Specifically, the calculation formula for the Poisson distribution function value is:

[0181]

[0182] The expression of Newtonian mechanics update formula is:

[0183]

[0184] F t =Ma

[0185] The acceleration update formula is expressed as:

[0186]

[0187] Where P represents the value of the Poisson distribution function; d represents the number of iterations; λ represents the Poisson parameter; e represents the base of the natural logarithm; ! represents the factorial; V t represents the speed of the path particle at time t based on the three-level deceleration threshold; V0 represents the initial speed of the path particle; M represents the mass of the path particle; F t represents the pulling force of the path particle at time t; Level 1Particle, Level 2Particle and Level 3Particle represent the first-level path particle, the second-level path particle and the third-level path particle respectively; v t ′ represents the velocity of the path particle at time t based on Poisson distribution control; V max represents the maximum velocity of the path particles; n represents the total number of path particles; a represents the acceleration of the path particles.

[0188] Among them, in the three-level deceleration model, Level 1Particle, Level 2Particle and Level 3Particle represent path particles with different fitness value levels. These path particles are classified according to the quality of their fitness values ​​and equipped with different deceleration models to control their acceleration. Specifically:

[0189] Level 1 Particle

[0190] Meaning: The group of particles with the highest fitness value are usually the particles with the strongest global search ability.

[0191] Acceleration model: exponential growth with time t, the formula is F t =e t .

[0192] This model rapidly increases the acceleration of particles, thereby enhancing their exploration capabilities and is suitable for quickly searching for the global optimal solution.

[0193] Level 2Particle

[0194] Indicates: A group of particles with medium fitness values ​​have a relatively balanced search ability.

[0195] Acceleration model: linear growth with time t, the formula is F t =20t.

[0196] This model makes the acceleration of particles increase steadily, which is suitable for balancing the search between global and local, and preventing falling into the local optimum too early.

[0197] Level 3Particle

[0198] Indicates: The group of particles with the lowest fitness value are usually particles with stronger local search ability.

[0199] Acceleration model: logarithmically grows with time t, the formula is F t =100lnt.

[0200] This model makes the acceleration of particles increase slowly, which is suitable for fine searching of local optimal solutions and preventing excessive acceleration from causing unstable search.

[0201] Specifically, combining the intelligent single particle with the game theory model to update the path particle sequence and calculate the fitness value of the path particle includes:

[0202] Select the path particle with the highest fitness from the current path particle group as the intelligent single path particle P best , and record the position x of the smart single-path particle best and speed v best ;

[0203] The position and velocity of the smart single-path particle are propagated to other path particles. Each path particle obtains the position and velocity information of the smart single-path particle as its local reference. According to the position information of the smart single-path particle, the position of the current path particle is adjusted. The update formula is:

[0204] v i * =v i +c1·r1·(x best -x i )

[0205] x i * =x i +v i ·Δt

[0206] In the formula, c1 represents the learning factor, r1 is a random number between [0,1];

[0207] The path particle group is divided into several subgroups, and the game is played based on the path particles within each subgroup (the game strategy includes cooperation and competition to improve the overall fitness of the group); each path particle selects its own game strategy and adjusts its position and speed according to the strategy;

[0208] The path particles select a cooperation or competition strategy according to their current fitness, wherein the path particles with a fitness higher than a preset fitness threshold select a cooperation strategy, and the path particles with a fitness lower than the preset fitness threshold select a competition strategy;

[0209] Cooperation strategy: x i * =x i +β·(x j -x i );

[0210] Competitive strategy: x i * =x i +γ·(x i -x k );

[0211] In the formula, x j represents the location information of the partner, β represents the cooperation coefficient, and x k represents the competitor's position information, and γ represents the competition coefficient.

[0212] According to the position and speed of the path particles adjusted by the intelligent single path particles and the game theory model, the path particle sequence is updated, and the fitness value of each path particle after the update is calculated. The sequence update formula is: i * =x i +v i ·Δt.

[0213] The optimization result output module 45 is used to output the optimal degradation path of the bioplastic according to the local optimal value and the global optimal value, that is, when the iteration is completed, the path corresponding to the global optimal value is output, which is the optimal degradation path.

[0214] Database 5, used to store information on known genes and enzymes related to bioplastic degradation, as well as information on known microbial species involved in bioplastic degradation and their genomes;

[0215] Specifically, the steps for building the database are as follows:

[0216] 1) Information integration of bacterial species degradation pathways for PBAT

[0217] Data sources: consult journal literature, journal articles, Chinese and foreign books, KEGG database and BRENDA database.

[0218] Integration content: record and summarize PBAT degradation pathway information, and classify and summarize it into a complete degradation pathway and gene table.

[0219] Output: Complete degradation pathway map of PBAT.

[0220] 2) Screening of specific functional gene database

[0221] Keyword screening: The enzymes that degrade PBAT are mainly esterase, lipase, cutinase, and arylesterase, so esterase, hydrolase, lipase, and arylesterase are used to screen in Database Commons.

[0222] Database selection: The ESTHER database was selected for preliminary sequence expansion.

[0223] 3) Access to seed banks

[0224] Data acquisition: Python scripts were used to download gene data involved in degradation from the KEGG database.

[0225] Screening content: Only retain the gene sequences of microbial enzymes to obtain seed library v1.

[0226] 4) HMM model construction

[0227] Sequence classification: Classify the sequences in the seed library.

[0228] Multiple sequence alignment: Multiple sequence alignment was performed using mafft and poorly aligned sequences were trimmed using trimAI.

[0229] Model tree building: Use IQ-tree to build the tree, analyze the credibility and retain the core sequence.

[0230] Model generation: The core sequence was further aligned to obtain the .aln file, and finally the HMM model of PBAT degradation gene was obtained.

[0231] 5) Database construction

[0232] Similarity comparison: Use Diamond to compare the seed library v1 with the ESTHER database and extract the aligned sequences.

[0233] HMM model alignment: Use the HMM model to align with the ESTHER database and extract the aligned sequences.

[0234] Data integration: Merge and remove duplicates to get database v2.

[0235] 6) Construction of comprehensive library

[0236] Similarity comparison: Use Diamond to compare database v2 with the NR library and extract the aligned sequences.

[0237] HMM model comparison: Use the HMM model to compare with the NR library and extract the corresponding sequence.

[0238] Data integration: Merge and remove duplicates to obtain comprehensive library v3.

[0239] 7) Additional steps: mining of undiscovered genes and construction of database v4

[0240] Literature search: Environmental literature on PBAT degradation was searched in the Web of Science Core Collection database, and sequencing project numbers and treatment conditions were summarized.

[0241] Public data download: Use SRA Toolkit to download sequencing data and convert them into fastq files.

[0242] Quality control: Use fastp to control data quality and retain clean reads.

[0243] Data binning: Use Megahit to assemble and bin the data, retaining high-quality MAGs.

[0244] Functional gene and pathway mining: Use Diamond to compare with the comprehensive library v3, identify and quantify the degradation functional genes of the metagenome, explore its degradation pathways, and screen typical degradation functional bacteria.

[0245] Construction of database v4: extract the aligned sequences in the comprehensive library v3, merge and remove duplicates to form the PBAT degradation gene database v4.

[0246] 8) Results

[0247] Core database: v3 and v4 serve as the core database of the PBAT degradation functional gene database, which is used for subsequent data comparison and the basis of the online analysis platform.

[0248] These steps describe in detail the construction process of the PBAT degradation functional gene database, including data acquisition, screening, integration, comparison and quality control, and ultimately form a comprehensive functional gene database.

[0249] The data visualization module 6 is used to graphically display the sequence alignment results and degradation pathway maps.

[0250] In summary, with the help of the above technical solutions of the present invention, the present invention can systematically study and identify microorganisms and their functional genes that can degrade PBAT through efficient gene sequence alignment, functional gene annotation, degradation pathway prediction and optimization, etc., which not only helps to understand the degradation mechanism of PBAT, but also provides technical support for the removal of biodegradable plastics in the environment and the processing and manufacturing of biodegradable plastics, and promotes the sustainable development of the bioplastics industry. Through comprehensive analysis and optimization of the bioplastics degradation pathway, the degradation efficiency of bioplastics can be significantly improved, the degradation time can be shortened, and its environmental protection can be ensured, providing a practical technical approach to solving the current bioplastics degradation problem.

[0251] In addition, the present invention can not only realize comprehensive analysis and annotation of input gene sequences through gene sequence alignment and functional gene annotation, identify sequences similar to known degradation functional genes, and provide basic data support for subsequent degradation path prediction, but also can realize detailed simulation and analysis of bioplastic degradation paths based on degradation path simulation and enzyme activity prediction, predict potential enzyme activity, and help users understand the biochemical process of bioplastic degradation; at the same time, it can also graphically display sequence alignment results and degradation path maps, so that users can intuitively understand the results of gene analysis and degradation path prediction, thereby improving user experience.

[0252] In addition, the present invention optimizes the simulated bioplastic degradation path by utilizing a degradation path fusion optimization algorithm, thereby dynamically adjusting the speed and position of the particles based on collision detection, disturbance condition judgment, and Poisson distribution control, combined with intelligent single-path particles and game theory models, thereby significantly improving the optimization effect of the bioplastic degradation path, thereby not only improving the degradation efficiency and shortening the degradation time, but also effectively enhancing environmental protection, and thus can play an important role in the online analysis platform of functional genes for bioplastic degradation, bringing significant technical effects.

[0253] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A functional gene online analysis platform for bioplastic degradation, characterized in that: The functional gene online analysis platform for bioplastic degradation comprises a data input module (1), a functional gene analysis module (2), a degradation path prediction module (3), a degradation path optimization module (4), a database (5) and a data visualization module (6); The data input module (1) is used to input the bioplastic sample information to be analyzed and the gene sequence data of the target microorganism; The functional gene analysis module (2) is used to identify sequences similar to known degradation functional genes based on gene sequence comparison, and to annotate the functional genes; The degradation pathway prediction module (3) is used to simulate the degradation pathway of bioplastics based on the identified functional genes and analyze potential enzyme activities; The degradation path optimization module (4) is used to optimize the simulated bioplastic degradation path through a degradation path fusion optimization algorithm to obtain an optimal degradation path; The database (5) is used to store known gene and enzyme information related to bioplastic degradation, as well as to store known microbial species involved in bioplastic degradation and their genome information; The data visualization module (6) is used to graphically display the sequence alignment results and degradation pathway maps; The degradation path optimization module (4) includes a path particle definition module (41), a path particle initialization module (42), a fitness value calculation module (43), a path particle iterative optimization module (44) and an optimization result output module (45); The path particle definition module (41) is used to define each preliminary degradation path in the preliminary degradation path set as a path particle corresponding thereto; The path particle initialization module (42) is used to randomly initialize the mass, acceleration, initial velocity, tension and Poisson parameters of each path particle; The fitness value calculation module (43) is used to calculate the fitness value of each path particle based on the degradation efficiency, degradation time and environmental protection index, and record the local optimal value and global optimal value of each path particle; The path particle iterative optimization module (44) is used to dynamically adjust the path particle speed and position based on collision detection, disturbance judgment and Poisson distribution control, and update the path particle sequence in combination with the game theory model to continuously update the local optimal value and the global optimal value; The optimization result output module (45) is used to output the optimal degradation path of the bioplastic according to the local optimal value and the global optimal value; The functional gene analysis module (2) comprises a gene sequence comparison module (21) and a gene annotation module (22); The gene sequence comparison module (21) is used to compare the input gene sequence with the known gene sequences in the database, identify and screen out sequences similar to the known degradation function genes, and the gene annotation module (22) is used to map the screened gene sequences to the annotation data, obtain their functional annotation information, and integrate the classification results to perform association analysis; The gene sequence comparison module (21) compares the input gene sequence with the known gene sequences in the database, identifies and screens out sequences similar to the known degradation function genes, including: Perform quality control on the input gene sequence, cut the adapter sequence, and remove poor-quality reads. Based on the sequence alignment algorithm, compare the pre-processed gene sequence with the known gene sequences in the database, identify sequences similar to known degradation function genes, and determine whether the similarity score in the alignment result is higher than the preset similarity score threshold. If so, retain the gene sequence, otherwise, remove the gene sequence; The degradation pathway prediction module (3) comprises a degradation pathway simulation module (31) and an enzyme activity prediction module (32); The degradation pathway simulation module (31) is used to simulate the degradation pathway of bioplastics according to the identified functional genes, and predict the intermediate products and final products in the degradation process through dynamic programming and environmental condition simulation, and the enzyme activity prediction module (32) is used to analyze and predict the activity of enzymes involved in the degradation process; The degradation pathway simulation module (31) simulates the degradation pathway of bioplastics according to the identified functional genes and predicts the intermediate products and final products in the degradation process through dynamic programming and environmental condition simulation, including: Obtain the identified functional genes and their annotation information, and perform data format conversion and preprocessing. Based on bioinformatics tools and databases, use known biodegradation pathways to construct the degradation pathways of several bioplastics to obtain a set of degradation pathways. Use dynamic programming to simulate the degradation pathways of bioplastics under different conditions, predict the intermediates and final products produced during the degradation process, combine experimental data or literature data to simulate the effects of different environmental conditions on each degradation pathway, adjust the degradation pathway based on the simulation results, and obtain a preliminary set of degradation pathways. The enzyme activity prediction module (32) includes the following steps when analyzing and predicting the activity of the enzyme involved in the degradation process: Obtain the identified enzyme gene sequence and analyze it, determine the enzyme family and functional domain, use homology modeling to predict the enzyme's three-dimensional structure, simulate the interaction between the enzyme and the intermediates and final products of bioplastic degradation, use molecular docking to predict the enzyme's active site and its binding mode with the substrate, and use machine learning technology to combine enzyme structure and sequence characteristics to predict the enzyme's activity and stability, and identify key amino acid residues and their mechanism of action; The path particle iterative optimization module (44) dynamically adjusts the path particle speed and position based on collision detection, disturbance judgment and Poisson distribution control, and updates the path particle sequence in combination with the game theory model, and continuously updates the local optimal value and the global optimal value, including: Check whether each path particle collides. If so, update the speed and position of the path particle in reverse. If not, keep the current speed and position unchanged. Evaluate whether each path particle meets the perturbation condition. If so, change the speed and position of the path particle through random perturbation. If not, keep the current speed and position unchanged. Determine whether the path particle has the ability to update. If so, update the speed and position of the path particle. If not, skip the update process of the path particle. Calculate the Poisson distribution function value and determine whether the Poisson distribution function value is less than the preset threshold. If so, enter the threshold stage of the third-level deceleration, update the path particle speed according to the Newton mechanics update formula, randomly select some path particles to obtain similarity information, and combine the intelligent single particle and game theory model to update the path particle sequence, and calculate the fitness value of the path particle. If not, enter the Poisson distribution control acceleration stage, update the path particle speed according to the acceleration update formula, randomly select some path particles to obtain similarity information, and combine the intelligent single particle and game theory model to update the path particle sequence, and calculate the fitness value of the path particle; Determine whether the fitness value of the particle in the current path is higher than the historical local optimal value. If so, use the fitness value of the particle in the current path to replace the historical local optimal value. If not, retain the historical local optimal value. Determine whether the fitness value of the particle in the current path is higher than the global optimal value. If so, replace the global optimal value with the fitness value of the particle in the current path. If not, retain the global optimal value.

2. The functional gene online analysis platform for bioplastic degradation according to claim 1, characterized in that: The calculation formula of the fitness value of the path particle is: ， In the formula, Z i Indicates i The fitness value of the path particles; w 1 , w 2 and w 3 They represent the weights of degradation efficiency, degradation time and environmental index value respectively; E i Indicates i The degradation efficiency of particles in each path; E min Indicates i Minimum degradation efficiency of particles in each path; E max Indicates i The maximum degradation efficiency of particles in each path; T i Indicates i Degradation time of particles in each path; T min Indicates i Minimum degradation time of particles in each path; T max Indicates i The maximum degradation time of particles in each path; S i Indicates i Environmental protection index value of particles in each path; S min Indicates i The minimum environmental index value of the path particles; S max Indicates i The maximum environmental index value of the particles in each path.

3. The functional gene online analysis platform for bioplastic degradation according to claim 1, characterized in that: The calculation formula of the Poisson distribution function value is: ， The expression of the Newtonian mechanics update formula is: ， The acceleration update formula is expressed as: ， In the formula, P represents the value of Poisson distribution function; d Indicates the number of iterations; λ represents the Poisson parameter; e represents the base of natural logarithms; ! represents factorial; V t Indicates that the path particle is t The speed at each moment is based on the three-level speed reduction threshold; V 0 represents the initial velocity of the path particle; M represents the mass of the path particle; F t Indicates that the path particle is t The pull of the moment; Level 1 Particle, Level 2 Particle and Level 3 Particle represents the primary path particle, the secondary path particle and the tertiary path particle respectively; represents the speed of the path particle at time t based on Poisson distribution control; V max represents the maximum velocity of the path particles; n represents the total number of path particles; a Represents the acceleration of the path particles.

4. The functional gene online analysis platform for bioplastic degradation according to claim 1, characterized in that: The updating of the path particle sequence by combining the intelligent single particle with the game theory model and calculating the fitness value of the path particle includes: Select the path particle with the highest fitness from the current path particle group as the intelligent single path particle, and record the position and speed of the intelligent single path particle; The position and speed of the smart single-path particle are propagated to other path particles, and each path particle obtains the position and speed information of the smart single-path particle; the position of the current path particle is adjusted according to the position information of the smart single-path particle; The path particle group is divided into several subgroups, and the game is carried out based on the path particles within each subgroup; each path particle selects its own game strategy and adjusts its position and speed according to the strategy; The path particles select a cooperation or competition strategy according to their current fitness, wherein the path particles with a fitness higher than a preset fitness threshold select a cooperation strategy, and the path particles with a fitness lower than the preset fitness threshold select a competition strategy; According to the position and speed of the path particles adjusted by the intelligent single path particles and the game theory model, the path particle sequence is updated, and the fitness value of each path particle after the update is calculated.

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