Mycobacterium tuberculosis epitope mRNA vaccine predicted and screened through computer simulation and application of mycobacterium tuberculosis epitope mRNA vaccine

Abstract

The invention discloses a Mycobacterium tuberculosis epitope mRNA (messenger Ribonucleic Acid) vaccine simulated, predicted and screened by a computer, which is prepared by the following steps: bacterial protein sequence acquisition and signal peptide analysis: acquiring amino acid sequences of proteins ESAT6, CFP10, Ag85A, Ag85B, TB10.4, PPE68, PPE18 and Rv1813c through a database; the method comprises the following steps: analyzing physicochemical properties of proteins, including the number of amino acids, the molecular weight, the instability index and the overall average value of water solubility, analyzing and predicting the allergy of the epitopes by using a server, and generating all potential mutants by using the server to predict and measure the toxicity of the epitopes; the multi-epitope mRNA vaccine candidate is successfully constructed by adopting computer simulation screening and optimization of the mycobacterium tuberculosis multi-epitope antigen, the vaccine design scheme is efficient, controllable and broad-spectrum, a theoretical basis and a candidate construct are provided for early research and development of tuberculosis vaccines, and the vaccine is expected to be used for prevention and control of tuberculosis and has positive influence on public health.

Description

Computer simulation for predicting and screening Mycobacterium tuberculosis epitope mRNA vaccines and their application Technical Field

[0001] This invention relates to the field of biotechnology, and in particular to a computer simulation for predicting and screening Mycobacterium tuberculosis epitope mRNA vaccines and their applications. Background Technology

[0002] Tuberculosis (TB) is a serious infectious disease caused by Mycobacterium tuberculosis (MTB). It remains one of the leading causes of death worldwide, with particularly high morbidity and mortality rates in developing countries. Currently, the only approved vaccine for TB prevention is BCG. While it offers some protection against disseminated TB in children, its efficacy in preventing pulmonary TB in adolescents and adults is limited, varies significantly across regions, and cannot effectively prevent the reactivation of latent infections. Therefore, developing safer and more effective novel TB vaccines has become a key focus of current research.

[0003] Traditional epitope screening methods mainly rely on in vitro screening in laboratories and animal model validation, which are time-consuming, costly, and difficult to rapidly obtain broad-spectrum protective antigenic peptides. With the development of computational biology, epitope screening methods based on immunogenicity prediction and molecular simulation are gradually becoming important tools for vaccine design.

[0004] While some studies have attempted to use multi-epitope antigenic peptides in vaccine design, systematic methods are still lacking for splicing strategies, optimal combinations, and optimized design suitable for mRNA vaccines. Furthermore, there is a lack of systematic integration of computer prediction, multi-epitope design, and mRNA expression technologies in tuberculosis vaccine development.

[0005] Therefore, there is an urgent need for a method that can rapidly and systematically screen and optimize Mycobacterium tuberculosis multi-epitope antigenic peptides and construct compositions for mRNA vaccines to improve the immunogenicity and protective efficacy of vaccines. Summary of the Invention

[0006] To address the aforementioned problems, this invention proposes a computer simulation method for predicting and screening Mycobacterium tuberculosis epitope mRNA vaccines and their applications.

[0007] The technical solution adopted in this invention is as follows: A computer simulation prediction and screening of Mycobacterium tuberculosis epitope mRNA vaccine, the preparation of which includes the following steps: S1 (bacterial protein sequence acquisition and signal peptide analysis step), obtaining the amino acid sequences of proteins ESAT6, CFP10, Ag85A, Ag85B, TB10.4, PPE68, PPE18, and Rv1813c from a database; using software to analyze the physicochemical properties of the proteins, including the overall average of amino acid number, molecular weight, instability index, and water solubility; using a server to analyze and predict the allergenicity of the epitopes; using a server to generate all potential mutants to predict and measure the toxicity of the epitopes; identifying the N-terminal signal peptide sequence and transmembrane helical region of the protein; S2 (T cell epitope prediction and molecular docking analysis with HLA alleles step), prediction of helper T lymphocyte epitopes, selecting HLA-DRB1*15:01, DRB1*09:01, DRB1*07:01, and DQB1*05:01 for MHC analysis. For Class II epitope prediction, high-affinity epitopes were initially screened using a percentile rank ≤ 1%. Subsequently, alleles were ranked from highest to lowest score, and the top 10 candidate epitopes for each allele were selected for further analysis. Strongly binding peptides were also screened to assist in verifying epitope binding ability. For cytotoxic T lymphocyte epitope prediction, HLA-A*11:01 and HLA-A*24:02 were selected for epitope prediction, with a percentile rank ≤ 1% used for initial screening of high-affinity epitopes. Subsequently, alleles were ranked from highest to lowest score, and the top 10 candidate epitopes for each allele were selected for further analysis. Strongly binding peptides were also screened to assist in verifying epitope binding ability. For molecular docking of T cell dominant epitopes with HLA alleles in three-dimensional interactions, HLA-DRB1*07:01 and HLA-DRB1*15:01 were selected as MHC alleles. Representatives of Class II, HLA-A*11:01, HLA-A*02:01, and HLA-A*24:02, were used as representatives of Class I MHC. The amino acid sequences were input into the server in single-letter format to generate 100 candidate models. The system automatically clustered the models based on energy scores and output the Top 5 structural models. The three models with the lowest energy were downloaded for subsequent molecular docking analysis. The corresponding HLA molecular structures were downloaded from the database and processed by software to remove unnecessary ligands. Subsequently, semi-flexible docking analysis was performed using an online molecular docking platform, and the binding sites and conformational stability were further visualized and analyzed.S3 (Population Coverage Analysis): Based on CTL and HTL epitopes and their corresponding HLA class I and II allele combinations obtained through bioinformatics prediction, population coverage analysis was conducted on the global population and some regional populations to evaluate the potential applicability of vaccine candidate epitopes in different populations. S4 (mRNA Vaccine Sequence Design and Prediction and Validation of Secondary and Three-Dimensional Structures): mRNA vaccine sequence design and optimization were performed. CTL epitopes were linked via "AAY," and HTL epitopes via "GPGPG," forming structurally ordered multi-epitope tandem segments. A tPA signal peptide was introduced at the N-terminus of the construct and linked to the adjuvant hBD3 via the rigid linker peptide EAAAK. A MITD signal peptide was introduced at the C-terminus. Codon optimization was performed on the coding region. Using humans as the target host, the algorithm reverse-transcribed the amino acid sequence according to codon usage preferences and optimized GC content, CAI value, and sequence stability. The optimized sequence had a CAI range of 0–1, close to 1.0, and a GC content controlled between 50% and 60%.

[0008] In the above construction method, a multi-epitope mRNA vaccine candidate was successfully constructed by using computer simulation to screen and optimize Mycobacterium tuberculosis multi-epitope antigens. This vaccine design scheme is efficient, controllable, and broad-spectrum, providing a theoretical basis and candidate construct for the early development of tuberculosis vaccines. It is expected to be used for tuberculosis prevention and control and have a positive impact on public health.

[0009] Optionally, the steps of designing the mRNA vaccine sequence and predicting and validating its secondary and three-dimensional structures also include predicting the secondary structure of the mRNA vaccine, using online tools to predict the secondary structure of the mRNA vaccine construct, and evaluating its minimum free energy and stability.

[0010] Optionally, the mRNA vaccine sequence design and its secondary and 3D structure prediction and verification steps also include the prediction and verification of the secondary and 3D structures of the vaccine peptide. The secondary structure of the vaccine peptide is evaluated using prediction tools to obtain the distribution ratio of its conformations such as α-helix, β-sheet, and random coil. The 3D structure is predicted using a server to evaluate the Ramachandran conformation distribution and statistically analyze non-bonded interactions.

[0011] Optionally, it may also include an immune simulation step, which uses an immune simulation platform to simulate in vitro immune responses and analyzes the antibody titers, cytokine levels and memory cell counts induced by the vaccine.

[0012] Optionally, it also includes a molecular docking step for vaccine design, using a molecular docking server to perform molecular docking analysis on the construct protein structure and pattern recognition receptors TLR-4 and TLR-3.

[0013] Optionally, a molecular dynamics simulation step is also included, which uses an online server to perform modal analysis on the protein to predict flexible regions and potential conformational changes.

[0014] Optionally, the amino acid sequence of the protein ESAT6 is shown in SEQ ID NO.1, the amino acid sequence of the protein CFP10 is shown in SEQ ID NO.2, the amino acid sequence of the protein Ag85A is shown in SEQ ID NO.3, the amino acid sequence of the protein Ag85B is shown in SEQ ID NO.4, the amino acid sequence of the protein TB10.4 is shown in SEQ ID NO.5, the amino acid sequence of the protein PPE68 is shown in SEQ ID NO.6, the amino acid sequence of the protein PPE18 is shown in SEQ ID NO.7, the amino acid sequence of the protein Rv1813c is shown in SEQ ID NO.8, the nucleotide sequence of the mRNA vaccine is shown in SEQ ID NO:10, and the amino acid sequence of the antigenic epitope peptide transcribed from the mRNA vaccine is shown in SEQ ID NO:9.

[0015] The application of an mRNA vaccine as described above in the prevention of tuberculosis.

[0016] The beneficial effects of this invention are: by using computer simulation to screen and optimize Mycobacterium tuberculosis multi-epitope antigens, a multi-epitope mRNA vaccine candidate was successfully constructed. This vaccine design scheme is efficient, controllable, and broad-spectrum, providing a theoretical basis and candidate constructs for the early development of tuberculosis vaccines. It is expected to be used for tuberculosis prevention and control, and will have a positive impact on public health. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0018] Figure 1 shows the prediction results of DeepTMHMM 1.0; Figures 2A-2H show the signal peptide predictions of 8 proteins using SignalP 6.0; Figure 3 shows the construction of the mRNA vaccine (A) and a schematic diagram of the final vaccine construct components (B); Figure 4 shows the optimal secondary structure (A) and centroid secondary structure (B) of the mRNA predicted using the RNAfold website; Figure 5A shows the secondary structure of the vaccine obtained using the PSIPRED server; Figure 5B shows the secondary structure of the vaccine obtained using the SOPMA server; Figure 5C shows the tertiary structure of the peptide obtained using the Robbetta server; Figure 5D shows the Z-score analysis using the Pro-SA web server; Figure 5E shows the Laplace plot analysis using the PROCHECK server; Figure 5F shows the structural quality analysis using the ERRAT server; Figure 6 shows the in vitro immune response simulation of the mRNA vaccine construct using the C-ImmSim immune simulation platform; Figure 7... Molecular dynamics simulations, normal mode analysis, and receptor-ligand interaction plots are presented. Figure 7A shows the vaccine-TLR3 docking complex using the Cluspro server; Figure 7B is the covariance matrix; Figure 7C is the elastic network model using the iMODS server; Figure 7D is the variance plot; Figure 7E is the deformability plot; Figure 7F is the B-factor plot; and Figure 7G shows the eigenvalues ​​of the vaccine-TLR3 complex. Figures 8-11 show molecular dynamics simulations, normal mode analysis, and receptor-ligand interaction plots. In Figure 8, A shows the vaccine-TLR4 docking complex using the Cluspro server; B is the covariance matrix; and C is the elastic network model using the iMODS server. The figures in Figure 9 are labeled as follows: D is the variance plot; E is the deformability plot; Figure 10 is the B-factor plot; and Figure 11 shows the eigenvalues ​​of the vaccine-TLR4 complex. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] This invention integrates advanced computational immunology methods and population-specific HLA polymorphisms to precisely screen dominant immune epitopes among key antigens of Mycobacterium tuberculosis, and systematically designs and optimizes multi-epitope mRNA vaccine constructs. By predicting epitopes and analyzing population coverage of eight important immune-related antigens of Mycobacterium tuberculosis, and performing molecular structure prediction, immune receptor docking, and immune simulation on the optimized multi-epitope mRNA vaccine construct, this invention significantly improves the expression stability and immune activation efficiency of the vaccine. This method breaks through the bottleneck of separating epitope screening and functional verification in traditional vaccine design, achieving efficient closed-loop verification from theoretical design to in vivo immune efficacy evaluation, and providing a reliable technical solution for the systematic development of tuberculosis mRNA vaccines. Example 1

[0021] Based on the attached figures and the data in the table, a computer simulation prediction and screening of Mycobacterium tuberculosis epitope mRNA vaccine is prepared by the following steps: Step 1 (1) Search for bacterial protein sequences: The following amino acid sequences of the proteins are obtained using the UniProt database (http: / / www.Uniprot.org): (1) ESAT6 (P9WNK7, SEQ ID NO.1), (2) CFP10 (P9WNK5, SEQ ID NO.2), (3) Ag85A (P9WQP3, SEQ ID NO.3), (4) Ag85B (P9WQP1, SEQ ID NO.4), (5) TB10.4 (P9WNK3, SEQ ID NO.5), (6) PPE68 (P9WHW9, SEQ ID NO.6), (7) PPE18 (L7N675, SEQ ID NO.7), (8) Rv1813c (P9WLS1, SEQ ID NO.8).

[0022] (2) Physicochemical property analysis of bacterial proteins: In mRNA vaccine antigen design, a comprehensive analysis of the physicochemical properties of candidate bacterial proteins is an important prerequisite for ensuring vaccine safety and efficacy. The VaxiJen Web server (https: / / ddg-pharmfac.net / vaxijen / VaxiJen / VaxiJen.html) predicts antigenicity in an alignment-independent manner based on the physicochemical properties of epitopes. Antigenicity analysis helps assess their potential to elicit an immune response and guides the screening of fragments with strong immunogenicity; bacteria are the primary focus, with a threshold of 0.4. The online software ProtParam (http: / / web.expasy.org / protparam / ) is used to analyze the physicochemical properties of proteins, including the overall average of amino acid number, molecular weight, instability index, and water solubility (GRAVY). The AllerTop V.2.0 Web server (http: / / www.ddg-pharmfac.net / AllerTOP) is used to predict the allergenicity of epitopes using default settings. Allergenicity assessment helps avoid potential allergic risks and improves the human applicability and safety of vaccines. Finally, the ToxinPred server (https: / / webs.iiitd.edu.in / raghava / toxinpred / multi_submit.php) generates all potential mutants using default settings to predict and measure epitope toxicity. Toxicity prediction effectively excludes harmful peptides that may cause cell damage or adverse reactions. Systematic analysis combining antigenicity, toxicity, and allergenicity, among other immunophysicochemical characteristics, can significantly optimize mRNA vaccine construction strategies, reduce experimental risks, and accelerate the vaccine development process.

[0023] (3) Prediction of signal peptides: Signal peptides can guide protein expression through the secretory pathway, restricting their processing and presentation via the MHC I pathway, which is detrimental to the activation of CD8+ T cell responses. To enhance the immunoprotective effect mediated by cytotoxic T cells, it is necessary to ensure that the antigen is expressed in the cytoplasm. Therefore, it is necessary to remove the signal peptide of Mycobacterium tuberculosis antigen in the design of mRNA vaccines.

[0024] SignalP-6.0 (https: / / services.healthtech.dtu.dk / service.php?SignalP-6.0) is specifically designed to identify N-terminal signal peptide sequences of proteins, effectively predicting their presence and cleavage sites; while DeepTMHMM-1.0 (https: / / dtu.biolib.com / DeepTMHMM) is used to predict transmembrane helical regions. Using both together helps distinguish signal peptides from potential N-terminal transmembrane regions, improving the accuracy of signal peptide identification, and is particularly valuable in the prediction of secreted proteins and the design of mRNA vaccine antigens.

[0025] Step 2 (1) Prediction of helper T lymphocyte epitopes: Based on the distribution of HLA alleles in the population and their immunological relevance in tuberculosis infection and vaccine response, this study selected HLA-DRB1*15:01, DRB1*09:01, DRB1*07:01, and DQB1*05:01, which are widely expressed in the tuberculosis vaccination population and have representative and predictive value, for MHC class II epitope prediction. Helper T lymphocyte (HTL) epitopes play a key role in inducing and regulating immune responses mainly by binding to MHC class II molecules. To screen potential HTL epitopes, this study used two commonly used prediction tools: IEDB (http: / / tools.immuneepitope.org / ) and NetMHC-IIpan-4.0 (https: / / services.healthtech.dtu.dk / service.php?NetMHCIIpan-4.0). The peptide length was set to 12–18 mer in IEDB predictions, while NetMHC-IIpan-4.0 used the default 15-mer peptide. All other prediction parameters for NetMHC-IIpan-4.0 remained at their default settings. In the IEDB prediction results, high-affinity epitopes were initially screened using a percentile rank ≤ 1% as the threshold; then, they were sorted from highest to lowest score for each allele, and the top 10 candidate epitopes for each allele were selected for subsequent analysis. The NetMHC-IIpan-4.0 prediction results were used to screen for strongly binding peptides according to default criteria to help verify the binding ability of the epitopes.

[0026] (2) Epitope prediction for cytotoxic T lymphocytes: HLA-A*02:01 is one of the most common HLA-A alleles in humans, while HLA-A*11:01 and HLA-A*24:02 are frequently distributed in Asian populations. CTL epitope prediction was performed using the IEDB MHC I binding prediction tool (http: / / tools.immuneepitope.org / ) and the NetCTLpan4.1 server (https: / / services.healthtech.dtu.dk / service.php?NetCTLpan-4.1), with the peptide length set to 10 mer and parameters kept at default. In the IEDB prediction results, high-affinity epitopes were initially screened using a percentile rank ≤ 1% as the threshold; then, they were sorted from highest to lowest score for each allele, and the top 10 candidate epitopes for each allele were selected for subsequent analysis. The NetMHC-IIpan-4.1 prediction results were used to screen for strongly binding peptides according to default criteria to assist in verifying the binding ability of the epitopes.

[0027] (3) Molecular docking of T cell dominant epitopes and HLA alleles in three-dimensional interaction: Molecular docking analysis of T cell dominant epitopes and HLA alleles plays an important role in the design of Mycobacterium tuberculosis mRNA vaccines, which can significantly improve the accuracy, coverage and immune effect of candidate vaccines. It is an indispensable key link in the development of mRNA vaccines.

[0028] To assess the three-dimensional interactions between T-cell dominant epitopes and prevalent HLA alleles, this study used MHC molecules with resolved three-dimensional structures for molecular docking analysis. HLA-DRB1*09:01 and HLA-DQB1*05:01 were not included in the analysis due to the lack of experimentally resolved structures in the Protein Database (PDB). Accordingly, HLA-DRB1*07:01 and HLA-DRB1*15:01 were selected as representatives of MHC class II, and HLA-A*11:01, HLA-A*02:01, and HLA-A*24:02 were selected as representatives of MHC class I for subsequent studies. The three-dimensional structures of peptides were predicted using the PEP-FOLD3 online server (http: / / bioserv.rpbs.univ-paris-diderot.fr / services / PEP-FOLD3 / ). The amino acid sequences were input into the server interface in single-letter format, keeping the default parameters, generating 100 candidate models. The system automatically clusters and outputs Top 5 structural models based on energy scores (sOPEP). The three models with the lowest energy (PDB format) are downloaded for subsequent molecular docking analysis. The corresponding HLA molecular structures are downloaded from the RCSB PDB database (https: / / www.rcsb.org / ) and processed using PyMOL software to remove unnecessary ligands. Subsequently, semi-flexible docking analysis is performed using the HDOCK (http: / / hdock.phys.hust.edu.cn / ) online molecular docking platform. All dockings are performed using default parameter settings. The results are sorted according to the Docking Score, and the binding sites and conformational stability are further visualized and analyzed. The interaction between epitopes and various residues of MHC alleles is evaluated using the LIGPLOT website. Step 3 Population coverage analysis: To evaluate the potential coverage of the screened T cell epitopes to different populations, the population coverage analysis tool provided by the IEDB database (http: / / tools.iedb.org / population / ) is used for analysis

[43] . Based on CTL and HTL epitopes and their corresponding HLA class I and II allele combinations obtained through bioinformatics prediction, population coverage analyses were conducted in global populations and specific regional populations (including China, East Asia, South Asia, and Europe) to assess the potential applicability of vaccine candidate epitopes in different populations. All HLA allele names followed the IEDB recommended format.The analysis uses default parameters and outputs include projected population coverage, average hits / HLA combinations recognized, and the number of hits / HLA combinations recognized to cover 90% of the population (PC90), which are used to evaluate the potential applicability of vaccine design in different populations.

[0029] Step 4 (1) mRNA vaccine sequence design and optimization: In previous studies, we screened and obtained dominant CTL and HTL epitopes from Mycobacterium tuberculosis and used them for the subsequent construction of mRNA vaccines. The selected CTL epitopes were linked by “AAY” and the HTL epitopes were linked by “GPGPG” to form a structurally ordered multi-epitope tandem segment. In order to enhance vaccine expression and antigen presentation efficiency, a tPA signal peptide was introduced at the N-terminus of the construct and linked to the adjuvant hBD3 by the rigid linker peptide EAAAK; a MITD signal peptide was introduced at the C-terminus to promote antigen processing in the endoplasmic reticulum and transmembrane transport.

[0030] To improve the expression efficiency and stability of vaccine constructs in mammalian cells, this study used the IDTCodon Optimization Tool (https: / / www.idtdna.com / CodonOpt) to optimize the coding region with codons. Using humans (Homo sapiens) as the target host, the algorithm reverse-transcribed the amino acid sequence based on codon usage preferences and optimized GC content, CAI value, and sequence stability. The optimized sequence had a CAI close to 1.0 and a GC content controlled between 50–60%. Rare codons, potential aberrant splicing sites, and internal stop codons were removed during optimization. The final sequence, used for subsequent mRNA construction, includes a 5′UTR, Kozak sequence, signal peptide, adjuvant, epitope region, MITD, 3′UTR, and Poly(A) tail, providing a foundation for subsequent in vitro transcription and expression.

[0031] (2) Secondary Structure Prediction of mRNA Vaccines: In mRNA vaccine design, secondary structure prediction helps assess its stability, translation efficiency, and immunogenicity. RNAfold predicts the most stable folded structure of RNA based on the principle of minimum free energy (MFE) and provides bracket notation, free energy value (ΔG), and visualization, which can intuitively display typical structures such as stem-loop and hairpin, facilitating the optimization and stability analysis of vaccine constructs. In this study, the RNAfold online tool (http: / / rna.tbi.univie.ac.at / cgi-bin / RNAWebSuite / RNAfold.cgi) was used to predict the secondary structure of mRNA vaccine constructs and assess their minimum free energy and stability. The input sequence was in FASTA format, and all parameters were set to default.

[0032] (3) Prediction and validation of secondary and 3D structures of vaccine peptides: The secondary structures of vaccine peptides were evaluated using PSIPRED and SOPMA prediction tools to obtain the distribution ratio of conformations such as α-helices, β-sheets, and random coils. PSIPRED (http: / / bioinf.cs.ucl.ac.uk / psipred / ) has high prediction accuracy by combining sequence conservation information and is suitable for fine structure analysis; while SOPMA (https: / / npsa.lyon.inserm.fr / cgi-bin / npsa_automat.pl?page= / NPSA / npsa_sopma.html) does not require database support, has a fast prediction speed, and is suitable for rapid structure screening of peptide fragments. The combination of the two helps to improve the reliability and comprehensiveness of the prediction. The 3D structure of vaccine peptides was predicted using the Robbetta server (https: / / robetta.bakerlab.org / ). The model structure was analyzed using ProSA-web to obtain Z-score values, which reflect the similarity between the predicted structure and the known structure, thereby checking the overall quality of the structure. The Ramachandran conformational distribution was evaluated using the PROCHECK tool in the SAVES v6.0 platform, and the non-bonded interactions of the structure were statistically analyzed using the ERRAT tool.

[0033] In this embodiment, the nucleotide sequence of the mRNA vaccine is shown in SEQ ID NO:10, and the amino acid sequence of the multi-epitope peptide transcribed by the mRNA vaccine is shown in SEQ ID NO:9. The multi-epitope peptide is composed of 20 Mycobacterium tuberculosis epitope peptides predicted and screened by computer in a specific order, and adjacent epitopes are connected by linking peptides.

[0034] Step 5: Immunosimulation: To predict the in vivo immune response potential of the designed mRNA vaccine construct, this study used the C-ImmSim immunosimulation platform (http: / / 150.146.2.1 / C-IMMSIM / index.php) to simulate the in vitro immune response. The Simulation Steps parameter was set to 350, corresponding to a simulated immunization cycle of approximately 350 days. Three vaccine injections were added by clicking the "AddInjection" button twice, with the following settings: the first injection at step 1 (day 0), the second at step 84 (day 28), and the third at step 168 (day 56); the type of each injection was set to "vaccine (no LPS)," the antigen quantity was 1000, and the adjuvant concentration was the default value of 100. All other simulation parameters were set to default. The codon-optimized vaccine amino acid sequence was pasted into the "Paste FASTA sequence" area, and then the "Submit Job" button was clicked to submit the task. After the simulation is complete, download the results PDF file from the “OUTPUT” area and analyze key immunological parameters such as vaccine-induced antibody titers, cytokine levels, and the number of memory cells.

[0035] Step 6: Design of Molecular Docking for the Vaccine: To predict whether the mRNA vaccine protein can bind to the host's innate immune receptors and activate the innate immune pathway, this study used the ClusPro 2.0 molecular docking server (https: / / cluspro.bu.edu / ) to perform molecular docking analysis on the construct protein structure and pattern recognition receptors TLR-4 (PDB ID: 3FXI) and TLR-3 (PDB ID: 1ZIW). RoseTTAFold predicted the three-dimensional structure of the mRNA vaccine protein, saved it in PDB format, and uploaded it to the ClusPro platform as the docking ligand; the crystal structure of TLR-4 or TLR-3 was used as the receptor input. The docking program was run with default parameters, and the system automatically generated multiple docking conformations and clustered them according to energy scores. The complex with the lowest binding energy was selected for further structural visualization and interface analysis to evaluate the feasibility of the vaccine protein binding to TLRs and provide a theoretical basis for its immune activation potential.

[0036] Step 7: Molecular Dynamics Simulation: To assess the structural stability of the vaccine construct or its complex with the TLR receptor, Normal Mode Analysis (NMA) was performed using the iMODS online server (http: / / imods.iqfr.csic.es / ) to predict the protein's flexible regions and potential conformational changes. The vaccine protein structure or docking complex was uploaded to the iMODS platform in PDB format, with all parameters set to default. This analysis helps identify flexible hinge regions, functionally related cooperative motion modes, and structural rearrangements caused by ligand binding. All animation results and related files were downloaded and further visualized and analyzed using PyMOL (version 2.5.2). Example 2

[0037] The vaccine prepared as described in Example 1 was used for the prevention of tuberculosis.

[0038] The encoded sequences of SEQ ID NO.1 to SEQ ID NO.10 are shown below.

[0039]

[0040]

[0041] Table 2

[0042] In Table 2, 1 represents non-antigenic, non-sensitizing, and non-toxic peptides; 2 represents antigenic, sensitizing, and toxic peptides. Table 3...

[0043] In Table 3, 1 represents non-antigenic, non-sensitizing, and non-toxic peptides; 2 represents antigenic, sensitizing, and toxic peptides. Table 4...

[0044] Table 1 above shows the physicochemical properties of eight candidate proteins.

[0045] Table 2 shows the predicted antigenicity, allergenicity, and toxicity of eight candidate proteins based on cytotoxic T-cell epitopes (CTLs).

[0046] Table 3 shows the predicted antigenicity, allergenicity, and toxicity of helper T cell epitopes (HTLs) for eight candidate proteins.

[0047] Table 4 shows the docking results of CTL epitopes and HTL epitopes with alleles.

[0048] The Chinese meanings of the various English labels in Figure 1 are as follows: DeepTMHMM - Most likely Topology | Type: Globular + SP: DeepTMHMM most likely topology | Type: Soluble protein + signal peptide Outside: Extracellular; Signal: Signal; Inside: Intracellular; DeepTMHMM - Posterior Probabilities: DeepTMHMM posterior probability; Pronbability: Probability; Sequence: Sequence.

[0049] The Chinese meanings of the English labels in Figures 2A to 2H are as follows: SignalP 6.0 prediction: Sequence: SignalP 6.0 prediction result: sequence; Rrobability: probability; Protein sequence: protein sequence.

[0050] The above-described embodiments only illustrate some aspects of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A computer simulation method for predicting and screening Mycobacterium tuberculosis epitope mRNA vaccines, characterized in that, The preparation process includes the following steps: bacterial protein sequence acquisition and signal peptide analysis, obtaining the amino acid sequences of proteins ESAT6, CFP10, Ag85A, Ag85B, TB10.4, PPE68, PPE18, and Rv1813c from a database; analyzing the physicochemical properties of the proteins using software, including the overall average of amino acid number, molecular weight, instability index, and water solubility; using a server to analyze and predict the allergenicity of epitopes; generating all potential mutants using a server to predict and measure the toxicity of epitopes; identifying the N-terminal signal peptide sequence and transmembrane helical region of the protein; T cell epitope prediction and molecular docking analysis with HLA alleles; prediction of helper T lymphocyte epitopes, selecting HLA-DRB1*15:01, DRB1*09:01, DRB1*07:01, and DQB1*05:01 for MHC class II epitope prediction, using percentile... High-affinity epitopes were initially screened using a percentile rank ≤ 1%. Subsequently, alleles were ranked from highest to lowest score, and the top 10 candidate epitopes for each allele were selected for further analysis. Strongly binding peptides were also screened to assist in verifying epitope binding ability. For cytotoxic T lymphocyte epitope prediction, HLA-A*11:01 and HLA-A*24:02 were selected for epitope prediction, with a percentile rank ≤ 1% used for initial screening of high-affinity epitopes. Alleles were then ranked from highest to lowest score, and the top 10 candidate epitopes for each allele were selected for further analysis. Strongly binding peptides were also screened to assist in verifying epitope binding ability. For molecular docking of T cell dominant epitopes with HLA alleles in three-dimensional interactions, HLA-DRB1*07:01 and HLA-DRB1*15:01 were selected as MHC alleles. Representatives of Class II, HLA-A*11:01, HLA-A*02:01, and HLA-A*24:02, were used as representatives of Class I MHC. The amino acid sequences were input into the server in single-letter format to generate 100 candidate models. The system automatically clustered these models based on energy scores and output the Top 5 structural models. The three models with the lowest energy were downloaded for subsequent molecular docking analysis. The corresponding HLA molecular structures were downloaded from the database and processed using software to remove unnecessary ligands. Semi-flexible docking analysis was then performed using an online molecular docking platform, and the binding sites and conformational stability were further visualized and analyzed. Population coverage analysis was conducted based on CTL and HTL epitopes obtained through bioinformatics prediction and their corresponding HLA I and II allele combinations. Population coverage analysis was performed on the global population and populations in selected regions to assess the potential applicability of vaccine candidate epitopes in different populations.The steps involved in mRNA vaccine sequence design and prediction and validation of its secondary and 3D structures. This included mRNA vaccine sequence design and optimization, linking CTL epitopes via "AAY" and HTL epitopes via "GPGPG" to form structurally ordered multi-epitope tandem regions. A tPA signal peptide was introduced at the N-terminus of the construct and linked to the adjuvant hBD3 via the rigid linker peptide EAAAK. A MITD signal peptide was introduced at the C-terminus. Codon optimization was performed on the coding region. Using humans as the target host, the algorithm reverse-transcribed the amino acid sequence based on codon usage preferences and optimized GC content, CAI value, and sequence stability. The optimized sequence achieved a CAI of 1.0 and a GC content controlled between 50% and 60%.

2. The computer simulation prediction and screening of Mycobacterium tuberculosis epitope mRNA vaccine according to claim 1, characterized in that, The steps for designing and predicting and validating the secondary and three-dimensional structures of the mRNA vaccine sequence also include predicting the secondary structure of the mRNA vaccine, using online tools to predict the secondary structure of the mRNA vaccine construct, and evaluating its minimum free energy and stability.

3. The computer simulation prediction and screening of Mycobacterium tuberculosis epitope mRNA vaccine according to claim 1, characterized in that, The mRNA vaccine sequence design and its secondary and 3D structure prediction and verification steps also include the prediction and verification of the secondary and 3D structures of vaccine peptides. The secondary structure of the vaccine peptides is evaluated using prediction tools to obtain the distribution ratio of its conformations such as α-helix, β-sheet, and random coil. The 3D structure is predicted using a server to evaluate the Ramachandran conformation distribution and statistically analyze non-bonded interactions.

4. The computer simulation prediction and screening of Mycobacterium tuberculosis epitope mRNA vaccine according to claim 1, characterized in that, It also includes an immune simulation step, which uses an immune simulation platform to simulate in vitro immune responses and analyzes the antibody titers, cytokine levels and memory cell counts induced by the vaccine.

5. The computer simulation prediction and screening of Mycobacterium tuberculosis epitope mRNA vaccine according to claim 1, characterized in that, It also includes the molecular docking step for vaccine design, using a molecular docking server to perform molecular docking analysis on the construct protein structure and pattern recognition receptors TLR-4 and TLR-3.

6. The computer simulation prediction and screening of Mycobacterium tuberculosis epitope mRNA vaccine according to claim 1, characterized in that, It also includes a molecular dynamics simulation step, which uses an online server to perform modal analysis to predict the flexible regions and potential conformational changes of the protein.

7. The computer simulation prediction and screening of Mycobacterium tuberculosis epitope mRNA vaccine according to claim 1, characterized in that, The amino acid sequence of the protein ESAT6 is shown in SEQ ID NO.1, the amino acid sequence of the protein CFP10 is shown in SEQ ID NO.2, the amino acid sequence of the protein Ag85A is shown in SEQ ID NO.3, the amino acid sequence of the protein Ag85B is shown in SEQ ID NO.4, the amino acid sequence of the protein TB10.4 is shown in SEQ ID NO.5, the amino acid sequence of the protein PPE68 is shown in SEQ ID NO.6, the amino acid sequence of the protein PPE18 is shown in SEQ ID NO.7, the amino acid sequence of the protein Rv1813c is shown in SEQ ID NO.8, the nucleotide sequence of the mRNA vaccine is shown in SEQ ID NO:10, and the amino acid sequence of the antigenic epitope peptide transcribed from the mRNA vaccine is shown in SEQ ID NO:

9.

8. The use of an mRNA vaccine as described in any one of claims 1 to 7 in the prevention of tuberculosis.

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