Immunogenicity-reducing polypeptide, fusion protein and design method thereof
By screening and simulating natural amino acids to construct a peptide library, peptide sequences with immunomodulatory effects were screened out and fused with antifreeze proteins. This solved the problems of long cycle, high cost and functional loss in the optimization of protein immunogenicity in existing technologies, and achieved the goal of reducing immunogenicity while maintaining functional stability.
Patent Information
- Application Number
- CN202510636196.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-16
- Publication Date
- 2025-11-21
AI Technical Summary
Existing technologies for reducing the immunogenicity of exogenous proteins suffer from drawbacks such as long development cycles, high costs, limited accuracy, and damage to functional domains. In particular, the modification of antifreeze proteins leads to a decrease in ice crystal binding capacity and difficulty in ensuring conformational stability.
Immunosensitive sequences were screened by analyzing the properties of natural amino acids, a peptide library was constructed and molecular dynamics simulations were performed to screen out peptide sequences with good immunosensitive effects, and then fused with functional fragments to form fusion proteins.
It significantly reduces the immunogenicity of proteins while maintaining or improving their functional properties, such as antifreeze ability, and ensures conformational stability.
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Figure CN120998288A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of biomaterials, specifically relating to an immunogenicity-reducing polypeptide, a fusion protein, and its design method. Background Technology
[0002] With the rapid development of biomedical technology, recombinant protein drugs have shown great application value in areas such as tumor treatment, immune regulation, and anticoagulation. However, exogenous protein molecules may be recognized as antigens by the host immune system, triggering antibody-mediated immune responses (i.e., immunogenicity), leading to reduced drug efficacy, shortened half-life, or even severe allergic reactions.
[0003] Traditional immunogenicity optimization methods primarily rely on experimental epitope screening and amino acid substitution strategies. For example, after identifying potential epitopes through in vitro T cell proliferation assays or MHC class II molecule binding assays, structural modification is performed using alanine scanning or homologous sequence substitution. However, these methods have significant drawbacks: 1) the experimental cycle is lengthy, lasting several months, and is costly; 2) the accuracy of epitope prediction is limited by static structural analysis and cannot reflect the impact of dynamic conformational changes in proteins on epitope exposure; 3) sequence modification may disrupt functional domains of proteins, leading to loss of activity.
[0004] Antifreeze proteins (AFPs) have significant potential applications in biomedical fields such as cryopreservation and tissue engineering. However, natural AFPs readily trigger immune responses in mammals, especially fish-derived AFPs, whose rigid β-helical structure exposes multiple linear epitopes. Existing techniques attempt to reduce immunogenicity through random mutations or cross-species homologous sequence transplantation, but these often result in a decrease of more than 50% in their ice crystal binding capacity, and the conformational stability of the modified protein is difficult to guarantee. This highlights the limitations of traditional methods in the synergistic optimization of function and immunogenicity. Summary of the Invention
[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide an immunogenicity-reducing polypeptide, a fusion protein, and a design method thereof.
[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:
[0007] A method for designing immunogenic de-immunogenic peptides includes the following steps: S1) determining the length of the immunogenic de-immunogenic sequence fragment; S2) selecting amino acids based on the properties of twenty natural amino acids to obtain natural amino acids suitable for designing immunogenic de-immunogenic sequences; S3) randomly arranging and combining the selected amino acids, and repeating each arrangement n times to generate an immunogenic de-immunogenic peptide library, using prediction software to predict the tertiary structure; S4) performing molecular dynamics simulations on all sequences in the immunogenic de-immunogenic peptide library, and after the simulations are completed, exporting the corresponding data based on the characteristics of the target immunogenic de-immunogenic sequence and screening them; S5) increasing the number of repetitions of the screened peptide sequences to the protein length, performing molecular dynamics simulations again, and screening them again based on the corresponding performance to obtain the target immunogenic de-immunogenic peptide sequence.
[0008] In step S1), the length of the deimmunization sequence fragment is 5 amino acid sequences.
[0009] The screening method in step S2) is to remove hydrophobic amino acids, positively charged amino acids and amino acids containing disulfide bonds; the natural amino acids that obtain the immunomodulatory sequence are asparagine (N), glutamine (Q), proline (P), serine (S) and glycine (G).
[0010] In step S3), n is 2-50 times; preferably 3 times.
[0011] The dynamic simulation process in steps S4) and S5) is independent as follows: molecular dynamics simulation is performed using the GROMOS96 54a7 force field and SPC water molecule model, the force field parameters are unified, and the corresponding data is exported, analyzed, calculated and filtered using the gmx command in the GROMACS software package.
[0012] The screening method was based on the radius of gyration of the peptide, the number of hydrogen bonds around the peptide, and the number of water molecules around the peptide after the simulation. The larger the radius of gyration, the more hydrogen bonds around the peptide, and the more water molecules around the peptide, the better the immune-lowering effect was considered.
[0013] The present invention also includes an immunogenicity-reducing polypeptide obtained using the aforementioned design method; preferably, the immunogenicity-reducing polypeptide is QNGPS, SGNQP, SQPGN, QGPNS, or GQPNS; more preferably, it is QNGPS and SGNQP.
[0014] The present invention also includes a method for designing a fusion protein, comprising the following steps: 1) obtaining a deimmunogenic polypeptide using the design method for deimmunogenic polypeptides according to any one of claims 1-6; 2) fusing the deimmunogenic polypeptide obtained in step 1) with a functional fragment.
[0015] The present invention also includes a fusion protein, characterized in that it is obtained using the design method described above.
[0016] The fusion protein includes the immunomodulatory polypeptide sequence and a functional fragment; preferably, the functional fragment is an antifreeze protein fragment; preferably, it is the antifreeze protein of the small turtle shell of Junggar; preferably, the sequence length ratio of the immunomodulatory polypeptide sequence to the functional fragment is (1-10):(1-10); more preferably, it is 1:1.
[0017] Compared with the prior art, the beneficial effects of the present invention are:
[0018] This invention constructs an immunomodulatory sequence peptide library through the analysis and screening of the properties of natural amino acids. Using molecular dynamics simulations, sequences with better immunomodulatory effects are screened out. Verification has shown that the screened immunomodulatory sequences exhibit significant immunomodulatory effects. This invention opens up a new theoretical path for protein immunogenicity optimization research based on synthetic biology methods. Attached Figure Description
[0019] Figure 1 This is a schematic diagram of the process for designing immunomodulatory sequences based on molecular dynamics simulations, as described in this invention.
[0020] Figure 2 This is an immunomodulatory peptide library constructed by arranging and combining five selected amino acids.
[0021] Figure 3 This involves simulating sequences from a peptide library for 20 ns to obtain the peptide radius of rotation data.
[0022] Figure 4 This is to obtain the number of hydrogen bonds around a peptide after simulating a sequence in a peptide library for 20 ns.
[0023] Figure 5 This is to obtain the number of water molecules surrounding a peptide after simulating a sequence in a peptide library for 20 ns.
[0024] Figure 6 The protein radius of gyration data was derived by increasing the number of repetitions of sequences in a peptide library to the protein length after simulating 20 ns.
[0025] Figure 7 To simulate the protein length by increasing the number of repeats of sequences in a peptide library to 20 ns, the hydrogen bond count data around the protein was derived.
[0026] Figure 8 To simulate the protein length by increasing the number of repeats of sequences in a peptide library to 20 ns, the number of water molecules around the protein was derived.
[0027] Figure 9 This is a sequence map of the synthesized fusion protein plasmid.
[0028] Figure 10 The effect of PBS with 0.01% (w / v) concentration of antifreeze protein on inhibiting ice recrystallization.
[0029] Figure 11 The average ice crystal area relative to PBS for 0.01% (w / v) concentration of antifreeze protein.
[0030] Figure 12 The morphology of ice crystals in a 0.01% (w / v) concentration antifreeze protein solution and their size after 10 seconds of growth are compared.
[0031] Figure 13 The growth rate of ice crystals in a 0.01% (w / v) concentration of antifreeze protein solution.
[0032] Figure 14 The images show flow cytometry analysis of DC cells after co-incubation with LPS and antifreeze protein, respectively. Detailed Implementation
[0033] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and preferred embodiments.
[0034] Example 1
[0035] Design methods for immunogenic de-immunogenic peptides ( Figure 1 The flowchart shown includes the following steps:
[0036] S1: Determine the length of the immunoreducing sequence fragment. In one specific embodiment, the immunoreducing fragment length is selected to be 5 amino acids.
[0037] S2: Based on the properties of twenty natural amino acids, amino acids were selected to obtain natural amino acids suitable for designing immunomodulatory sequences. In a specific embodiment, based on the analysis of the hydrophilicity, charge, and other characteristics of natural amino acids, the specific screening method was to remove hydrophobic amino acids, positively charged amino acids, and amino acids containing disulfide bonds, and select glycine (G), proline (P), serine (S), glutamine (Q), and asparagine (N) for immunomodulatory sequence design.
[0038] S3: Selected amino acids are randomly arranged and combined, and this process is repeated a limited number of times to generate an immunomodulatory peptide library. Tertiary structures are then predicted using predictive software.
[0039] In one specific embodiment, step S3 specifically includes the following sub-steps:
[0040] S31: Five natural amino acids are arranged to obtain 120 pentapeptide sequences.
[0041] S32: An immunomodulatory peptide library was generated by repeating 120 pentapeptide sequences n=3 times. The resulting peptide library is shown below. Figure 2 As shown.
[0042] S33: Use PEP-FOLD4 to predict the tertiary structure of sequences in the peptide library to obtain PDB files.
[0043] S4: Construct a simulated water box and set force field parameters to perform molecular dynamics simulations on all sequences in the immunomodulatory peptide library. After the simulation is completed, export the corresponding data according to the characteristics of the target immunomodulatory sequence and perform screening.
[0044] In one specific embodiment, step S4 specifically includes the following sub-steps:
[0045] S41: Use the pdb2gmx tool built into GROMACS to convert the previously obtained PDB file into a GRO format file and select the SPC water model;
[0046] S42: Use the editconf tool to create a cubic water cell with a side length of 0.6 nm, and place the peptide molecule at the center of the water cell. Then, use the solvate command to place the peptide molecule into the constructed water cell.
[0047] S43: Set the force field parameters, use the V-rescale algorithm for temperature control, and the Parrinel lo-Rahman algorithm for pressure control. Set the cutoff distance to 1 nm, the step size to 2 fs, and the total simulation time to 20 ns.
[0048] S44: Perform molecular dynamics simulations under the set conditions. After the simulation is complete, export the radius of gyration data to observe the looseness of the peptide structure. The simulation results are as follows: Figure 3 As shown; the number of hydrogen bonds around the peptide during the simulation and the number of water molecules around the peptide after the simulation are derived to observe the peptide's hydration capacity. The simulation results are as follows. Figure 4-5 As shown.
[0049] Based on comprehensive analysis, a larger radius of gyration, more hydrogen bonds around the peptide, and more water molecules around the peptide are considered to indicate a better immunomodulatory effect. The proportions of these three factors are equal. Five sequences, QNGPS, SGNQP, SQPGN, QGPNS, and GQPNS, were selected for further screening.
[0050] S5: For a subset of peptide sequences with good performance, the number of repetitions is increased to the protein length, and molecular dynamics simulations are performed again. Based on the corresponding performance, the sequences are screened again to obtain the target immunomodulatory peptide sequences.
[0051] In one specific embodiment, step S5 specifically includes the following sub-steps:
[0052] S51: After increasing the number of repetitions to 12 for the five peptides that need further screening, the structure was predicted using Robbetta to obtain the PDB file.
[0053] S52: Use the pdb2gmx tool built into GROMACS to convert the previously obtained PDB file into a GRO format file and select the SPC water model.
[0054] S53: Use the editconf tool to create a cubic water cell with a side length of 0.6 nm, and place the peptide molecule at the center of the water cell. Then, use the solvate command to place the peptide molecule into the constructed water cell.
[0055] S54: Set the force field parameters, use the V-rescale algorithm for temperature control, and the Parrinello-Rahman algorithm for pressure control. Set the cutoff distance to 1 nm, the step size to 2 fs, and the total simulation time to 20 ns.
[0056] S55: Perform molecular dynamics simulations under the set conditions. After the simulation is complete, export the radius of gyration data from the simulation to observe the looseness of the peptide structure. The simulation results are as follows: Figure 6 As shown; the number of hydrogen bonds around the peptide during the simulation and the number of water molecules around the peptide after the simulation are derived to observe the peptide's hydration capacity. The simulation results are as follows. Figure 7-8 As shown.
[0057] Based on comprehensive analysis, the larger the radius of gyration, the more hydrogen bonds around the peptide, and the more water molecules around the peptide, the better the immune-lowering effect. The proportions of the three factors are equal. Finally, two sequences with loose structure and strong hydration capacity were selected for protein design: QNGPS and SGNQP.
[0058] Example 2
[0059] A method for designing a fusion protein includes the following steps:
[0060] After repeating the designed immunomodulatory sequences to a certain length, they are fused with functional fragments to prepare low-immunogenic antifreeze proteins.
[0061] In one specific embodiment, the following sub-steps are included:
[0062] S61: Select antifreeze protein from the small-breasted turtle shell of Junggar, which has certain immunogenicity, for protein fusion design.
[0063] S62: Protein design was performed using a 1:1 ratio of the length of the immunomodulatory sequence fragment to the length of the antifreeze protein fragment, such as... Figure 9 The image shows plasmid sequences of two low-immunogenic antifreeze proteins. SEQ ID NO. 1-5 are the nucleotide sequences of the natural antifreeze protein MpAFP5 and the fused low-immunogenic antifreeze protein, respectively. SEQ ID NO. 6-10 are the amino acid sequences of the natural antifreeze protein MpAFP5 and the fused low-immunogenic antifreeze protein, respectively. MpAFP5-GES and MpAFP5-KE are control sequences, and GES and EK are known low-immunogenic fragments.
[0064] S63: Perform antifreeze and immunogenicity tests on the fusion protein.
[0065] The purified, lyophilized, low-immunogenic antifreeze protein was prepared into a 1 mg / mL solution using PBS. Then, 12 μL of the solution was dropped from a height of 1 meter onto a thin aluminum block placed in liquid nitrogen, forming a thin ice sheet. The sample was then placed in a -60°C cooling phase for 1 min. Afterward, the sample was heated to -6°C at a heating rate of 20°C / min and annealed at -6°C for 30 min. Images were taken every 5 min during annealing. PBS solution was used as a negative control. The mean crystal area (MGAs) of the ice crystals was measured using NIS-Elements D imaging software. Five regions were randomly selected, and the area of all ice crystals in these regions was measured. Each sample was tested three times, and the mean area was calculated using Prism 5.0 software. Figure 10 and Figure 11 As shown, the results indicate that, compared to natural antifreeze proteins, fusion proteins also have a good ability to inhibit ice recrystallization and can significantly reduce the average grain area of ice crystals.
[0066] Ice crystal morphology was characterized using a custom-designed temperature controller (Model 3040, Newport, Irvine, CA, USA) placed on a microscope. Approximately 0.5 μL of a 1 mg / mL protein solution was injected into the oil droplet. A sliding glass cover was placed on the temperature controller to prevent water evaporation from the sample, with water used as a negative control. The solution was then nucleated at -20°C. The temperature was increased to melt large ice blocks, forming crystals of the desired size. The temperature was then decreased by 0.06°C, and the morphology of the ice crystals was recorded, along with the measurement of their growth rate. Figure 12 and Figure 13 As shown, the results indicate that, compared to natural antifreeze proteins, fusion proteins also have a good ability to modify ice crystal morphology and can significantly inhibit the growth rate of ice crystals.
[0067] Protein immunogenicity was tested using human dendritic cells (DC cells). 2 × 10⁶ cells were collected. 5DC cells were transferred to 48-well plates, and 0.25 mg / mL of low-immunogenic antifreeze protein was added to each well. LPS solution was used as a positive control. The plates were incubated at 37°C and 5% CO2 for 72 h, during which cell state and morphological changes were observed. After incubation, the cell suspension was transferred to 5 mL flow cytometry tubes, centrifuged at 400 g for 10 min, and the supernatant was discarded. The cells were washed twice with sterile PBS, centrifuged at 400 g for 10 min each time, and the supernatant was discarded. Then, appropriate amounts of FITC anti-human HLA-DR antibody and PE anti-human CD11 c antibody were added to the flow cytometry tubes, and the plates were incubated at room temperature in the dark for 30 min. After incubation, the cells were washed twice with 1 mL of flow cytometry loading buffer, centrifuged at 400 g for 10 min, and the supernatant was discarded to remove unbound antibodies. Finally, the cell pellet was resuspended in 500 μL of flow cytometry loading buffer, transferred to flow cytometry tubes, and analyzed by flow cytometry. Figure 14 As shown, compared to LPS and natural antifreeze proteins, the immunogenicity of the fusion protein after linking to the deimmunization sequence was significantly reduced.
[0068] SEQ ID NO.1
[0069] MpAFP5 nucleotide sequence
[0070] CCATGGGCCAGTGCACCGGCGGCAGCGATTGCACGAGCTGCACCGTTGCCTGCACCAATTGTGAAAACTGCCCAAACGCCGTTACCTGTACCGATAGCACCAACTGTATTAATGCGCAAACGTGCACCGGTAGCACCAATTGTAACAACGCGGTGACGTGTACCGGCAGTTATAACTGCAACAAGGCGGTTACCTGCACCAATAGCTTTGATTGCTTCGAAGCCGTGACGTGCACCGATAGTACGAACTGCTATAAGGCGACCGCCTGCACCCGCAGTACGGGCTGCCCAAACAAAGGCGGTGGCGGTAGCTTAGTTCCGCGCGGCAGCGGCGGTGGCAGTCAGTGTACCGGTGGTAGTGATTGCACGAGCTGCACGGTGGCGTGCACCAACTGCGAAAACTGCCCGAACGCCGTGACCTGCACGGATAGTACGAACTGCATTAACGCGCAGACGTGCACGGGCAGTACGAACTGTAACAATGCCGTGACGTGCACCGGCAGCTACAACTGTAACAAGGCGGTGACGTGCACCAACAGTTTCGATTGCTTTGAGGCGGTGACCTGCACCGACAGTACCAACTGTTATAAAGCGACGGCGTGCACCCGCAGCACCGGCTGCCCGAACAAGGGCCATCACCATCACCATCATTAAAAGCTT
[0071] SEQ ID NO.2
[0072] Nucleotide sequence of MpAFP5-EK
[0073] CCATGGGCCATCATCACCATCACCACGGAGGCGGATCTCAATGCACTGGAGGATCAGACTGTACATCATGCACCGTTGCCTGTACTAATTGCGAAAACTGTCCAAATGCCGTTACATGTACCGACTCAACTAATTGCATTAATGCCCAGACGTGCACAGGTTCGACCAACTGTAACAACGCTGTGACCTGTACGGGTTCTTACAACTGCAATAAAGCGGTTACTTGTACTAACAGCTTCGATTGCTTTGAAGCGGTCACCTGCACCGACTCCACGAATTGCTATAAAGCAACCGCCTGTACCCGTAGCACTGGCTGCCCGAACAAAGGCGGCGGGGGAAGTAAAGAGAAGGAGAAAGAGAAAGAAAAAGAAAAGGAAAAAGAAAAAGAGAAAGAAAAAGAAAAGGAAAAAGAAAAAGAGAAAGAAAAAGAAAAGGAAAAAGAAAAAGAGAAGGAAAAAGAAAAAGAGAAAGAAAAAGAAAAAGAAAAGGAGAAAGAGAAGGAAAAAGAAAAAGAAAAAGAAAAAGAGAAAGAAAAAGAAAAGGAAAAGGAAAAAGAGAAAGAAAAAGAAAAAGAGAAAGAGAAGGAAAAAGAAAAGGAAAAAGAAAAAGAAAAAGAAGGGGGAGGCAGTCATCATCATCATCATCACTAAAAGCTT
[0074] SEQ ID NO.3
[0075] Nucleotide sequence of MpAFP5-GES
[0076] TAATACGACTCACTATAGGGGAATTGTGAGCGGATAACAATTCCCCTCTAGAAATAATTTTGTTTAACTTTAAGAAGGAGATATACCATGGGCAGCAGCCATCATCATCATCATCACAGCAGCGGCCTGGTGCCGCGCGGCAGCcatatgCACCATCATCACCATCACGGTGGTGGTAGCCAGTGCACCGGTGGTAGCGACTGCACTTCTTGCACCGTTGCGTGCACTAACTGCGAGAACTGTCCGAACGCAGTGACTTGCACCGATTCTACCAACTGCATCAACGCGCAGACTTGTACCGGCTCTACTAACTGCAACAACGCGGTTACCTGCACTGGTTCTTACAACTGCAACAAAGCGGTTACTTGCACCAACTCTTTCGACTGCTTCGAAGCTGTTACCTGCACCGACTCCACCAACTGCTACAAAGCAACCGCGTGCACTCGTTCTACTGGTTGCCCGAACAAAGGTGGCGGTGGTTCTGGCGAAGGTTCTGGTGAAGGCTCCGAAGGTGAAGGTTCTGAGGGCTCTGGTGAGGGTGAAGGCAGCGAAGGTAGCGGCGAAGGTGAAGGCGGTTCTGAGGGTTCTGAAGGCGAAGGCGGTAGTGAGGGTAGCGAAGGCGAAGGTGGTTCCGAAGGCTCTGAAGGTGAAGGTTCCGGTGAAGGTAGCGAGGGCGAAGGTTCCGAAGGTAGTGGTGAAGGTGAGGGATCTGAAGGTAGCGGTGAAGGTGAGGGTGGTTCTGAAGGTTCCGAAGGCGAGGGTGGTAGTGAAGGTGGTGGTGGTTCTCACCACCACCATCACCACaagcttGCGGCCGCACTCGAGCACCACCACCACCACCACTGA
[0077] SEQ ID NO.4
[0078] Nucleotide sequence of MpAFP5 - QNGPS
[0079] CATATGCATCATCACCATCACCACGGCGGCGGTGGTTCCCAGTGCACCGGCGGTTCTGACTGCACCTCTTGCACCGTTGCGTGCACTAACTGCGAAAACTGCCCGAACGCAGTTACCTGTACCGACTCCACCAACTGCATCAACGCGCAGACCTGCACCGGCTCTACTAACTGCAACAACGCGGTTACTTGCACTGGTTCTTACAACTGCAATAAAGCTGTTACCTGCACCAACTCTTTCGACTGCTTTGAAGCTGTGACCTGCACTGACTCTACCAACTGCTACAAAGCGACCGCGTGCACCCGTTCTACCGGCTGTCCGAACAAAGGTGGCGGTGGTGGTTCTCAGAACGGCCCAAGCCAAAATGGCCCTTCTCAGAATGGTCCATCTCAGAACGGACCGTCCCAGAACGGCCCGAGCCAGAATGGACCTTCTCAAAATGGACCAAGTCAAAACGGTCCATCCCAGAATGGTCCTAGTCAGAATGGCCCGAGTCAGAACGGTCCTTCCCAGAACGGTCCGTCCCAAAATGGTCCGAGTCAAAATGGTCCAAGCCAGAACGGACCTTCCCAAAACGGTCCGAGCCAAAACGGACCATCTCAAAACGGCCCATCCCAAAACGGCCCGTCTGGTGGTGGCGGTTCTCATCACCACCACCATCACTAAAAGCTT
[0080] SEQ ID NO.5
[0081] Nucleotide sequence of MpAFP5 - SGNQP
[0082] CATCACCATCATCACCACGGTGGCGGCGGTTCCCAGTGCACCGGCGGTTCTGACTGCACCTCTTGCACCGTTGCGTGCACTAACTGCGAAAACTGCCCGAACGCGGTTACTTGCACCGACTCCACCAACTGTATCAACGCGCAGACCTGCACTGGTAGCACCAACTGCAACAACGCAGTTACCTGCACCGGCTCTTACAACTGTAACAAAGCAGTGACCTGCACCAACTCTTTCGACTGCTTTGAAGCTGTTACCTGTACCGACTCTACCAACTGCTACAAAGCGACCGCGTGCACCCGTTCTACTGGTTGCCCGAACAAAGGTGGCGGTGGCGGTTCTAGCGGCAACCAACCGAGTGGCAATCAACCAAGTGGCAACCAACCAAGCGGAAATCAACCGAGCGGCAATCAGCCATCTGGTAATCAACCGTCCGGAAATCAGCCGTCTGGCAATCAGCCGAGCGGTAATCAACCTTCTGGCAACCAGCCATCCGGAAACCAACCATCCGGCAATCAACCTAGCGGAAACCAGCCAAGTGGAAACCAACCTTCCGGCAACCAGCCGTCCGGTAATCAACCATCTGGAAACCAGCCGAGTGGTAATCAGCCTTCTGGAAATCAGCCAGGTGGTGGTGGTTCTCACCATCACCACCATCACTAA
[0083] SEQ ID NO.6
[0084] Amino acid sequence of MpAFP5
[0085] MGQCTGGSDCTSCTVACTNCENCPNAVTCTDSTNCINAQTCTGSTNCNNAVTCTGSYNCNKAVTCTNSFDCFEAVTCTDSTNCYKATACTRSTGCPNKGGGGSLVPRGSGGGSQCTGGSDCTSCTVACTNCENCPNAVTCTDSTNCINAQTCTGSTNCNNAVTCTGSYNCNKAVTCTNSFDCFEAVTCTDSTNCYKATACTRSTGCPNKGHHHHHH*
[0086] SEQ ID NO.7
[0087] Amino acid sequence of MpAFP5-KE
[0088] MGHHHHHHGGGSQCTGGSDCTSCTVACTNCENCPNAVTCTDSTNCINAQTCTGSTNCNNAVTCTGSYNCNKAVTCTNSFDCFEAVTCTDSTNCYKATACTRSTGCPNKGGGGSKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEKEGGGSHHHHHH*
[0089] SEQ ID NO.8
[0090] Amino acid sequence of MpAFP5-GES
[0091] MGSSHHHHHHSSGLVPRGSHMHHHHHHGGGSQCTGGSDCTSCTVACTNCENCPNAVTCTDSTNCINAQTCTGSTNCNNAVTCTGSYNCNKAVTCTNSFDCFEAVTCTDSTNCYKATACTRSTGCPNKGGGGSGEGSGEGSEGEGSEGSGEGEGSEGSGEGEGGSEGSEGEGGSEGSEGEGGSEGSEGEGSGEGSEGEGSEGSGEGEGSEGSGEGEGGSEGSEGEGGSEGGGGSHHHHHH*
[0092] SEQ ID NO.9
[0093] Amino acid sequence of MpAFP5-QNGPS
[0094] HHHHHHGGGGSQCTGGSDCTSCTVACTNCENCPNAVTCTDSTNCINAQTCTGSTNCNNAVTCTGSYNCNKAVTCTNSFDCFEAVTCTDSTNCYKATACTRSTGCPNKGGGGGSQNGPSQNGPSQNGPSQNGPSQNGPSQNGPSQNGPSQNGPSQNGPSQNGPSQNGPSQNGPSQNGPSQNGPSQNGPSQNGPSQNGPSQNGPSQNGPSGGGGSHHHHHH*
[0095] SEQ ID NO.10
[0096] MpAFP5-SGNQP amino acid sequence
[0097] HHHHHHGGGGSQCTGGSDCTSCTVACTNCENCPNAVTCTDSTNCINAQTCTGSTNCNNAVTCTGSYNCNKAVTCTNSFDCFEAVTCTDSTNCYKATACTRSTGCPNKGGG GGSSGNQPSGNQPSGNQPSGNQPSGNQPSGNQPSGNQPSGNQPSGNQPSGNQPSGNQPSGNQPSGNQPSGNQPSGNQPSGNQPSGNQPSGNQPSGNQPGGGGSHHHHHH*
[0098] In summary, this invention constructs an immunomodulatory sequence peptide library through the analysis and screening of the properties of natural amino acids. Furthermore, by employing molecular dynamics simulations, sequences with superior immunomodulatory effects can be screened. Verification has shown that the screened immunomodulatory sequences exhibit significant immunomodulatory activity. This invention opens a new theoretical pathway for optimizing protein immunogenicity based on synthetic biology methods.
[0099] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.
Claims
1. A method for designing immunogenic de-immunogenic peptides, characterized in that, The process includes the following steps: S1) Determine the length of the immunoreducing sequence fragment; S2) Select amino acids based on the properties of twenty natural amino acids to obtain natural amino acids suitable for designing immunoreducing sequences; S3) Randomly arrange and combine the selected amino acids, and repeat each arrangement n times to generate an immunoreducing peptide library, using prediction software to predict the tertiary structure; S4) Perform molecular dynamics simulations on all sequences in the immunoreducing peptide library, and after the simulations are completed, export the corresponding data based on the characteristics of the target immunoreducing sequence and perform screening; S5) Increase the number of repetitions of the screened peptide sequences to the protein length, perform molecular dynamics simulations again, and screen again based on the corresponding performance to obtain the target immunoreducing peptide sequence.
2. The method for designing immunogenic desensitizing peptides according to claim 1, characterized in that, In step S1), the length of the deimmunization sequence fragment is 5 amino acid sequences.
3. The method for designing immunogenic de-immunogenic peptides according to claim 1, characterized in that, The screening method in step S2) is to remove hydrophobic amino acids, positively charged amino acids and amino acids containing disulfide bonds; the natural amino acids that obtain the immunomodulatory sequence are asparagine (N), glutamine (Q), proline (P), serine (S) and glycine (G).
4. The method for designing immunogenic de-immunogenic peptides according to claim 1, characterized in that, In step S3), n is 2-50 times; preferably 3 times.
5. The method for designing immunogenic desensitizing peptides according to claim 1, characterized in that... The dynamic simulation process in steps S4) and S5) is independent as follows: molecular dynamics simulation is performed using the GROMOS96 54a7 force field and SPC water molecule model, the force field parameters are unified, and the corresponding data is exported, analyzed, calculated and filtered using the gmx command in the GROMACS software package.
6. The method for designing immunogenic desensitizing peptides according to claim 5, characterized in that... The screening method is based on the radius of gyration of the peptide, the number of hydrogen bonds around the peptide, and the number of water molecules around the peptide after the simulation. The larger the radius of gyration, the more hydrogen bonds around the peptide, and the more water molecules around the peptide, the better the immune-lowering effect is considered.
7. An immunogenicity-reducing polypeptide, characterized in that, The design method described in any one of claims 1-6 is used to obtain the peptide; preferably, the immunogenic desensitizing peptide is QNGPS, SGNQP, SQPGN, QGPNS, or GQPNS; more preferably, QNGPS and SGNQP.
8. A method for designing a fusion protein, characterized in that... The process includes the following steps: 1) obtaining a deimmunogenic polypeptide using the design method of any one of claims 1-6; 2) fusing the deimmunogenic polypeptide obtained in step 1) with a functional fragment.
9. A fusion protein, characterized in that, It is obtained using the design method described in claim 8.
10. The fusion protein according to claim 9, characterized in that, It includes the immunomodulatory polypeptide sequence and functional fragment as described in claim 7; preferably, the functional fragment is an antifreeze protein fragment; more preferably, it is an antifreeze protein fragment of Junggar small-breasted turtle shell; preferably, the sequence length ratio of the immunomodulatory polypeptide sequence to the functional fragment is (1-10):(1-10); more preferably, it is 1:1.