A type 1 diabetes vaccine based on self-antigen polypeptide molecules

Abstract

This invention discloses a type 1 diabetes vaccine based on autoantigen polypeptide molecules. The invention provides a series of immunogenic autoantigens as active ingredients in type 1 diabetes vaccines. This invention utilizes computer-simulated amino acid mutations of type 1 diabetes autoantigen sequences obtained from type 1 diabetes patients, supplemented by rational design based on the HLA-peptide-TCR ternary complex structure; targeted optimization enhances the binding affinity between the antigen and immune molecules, thereby achieving significant proliferation of type 1 diabetes-related CD4+ T lymphocytes. The autoantigens of this invention will serve as the basis for the development of type 1 diabetes vaccines in the form of artificially synthesized polypeptide molecules.

Description

Technical Field

[0001] This invention belongs to the field of biomedicine and relates to a scheme for modifying and optimizing polypeptide molecules by amino acid mutation to obtain novel self-antigens, and using them as the basis for the development of type I diabetes vaccines. Specifically, it relates to a type I diabetes vaccine based on self-antigen polypeptide molecules. Background Technology

[0002] Type 1 diabetes is an autoimmune disease caused by the autonomous activation of CD4+ and CD8+ lymphocytes. While the specific antigens directly activating CD4+ T cell immune responses remain unidentified, a host of potential autoantigens (autoantigens) have been widely reported, including but not limited to GAD65, HSP, ZnT8, PDX1, insulin, and some autoimmune disease-related neoantigens. Insulin and other related peptides have long been used as potential autoantigens to activate active and controlled CD4+ T cell immune responses, thereby achieving vaccine efficacy. However, insulin and several related peptides have shown complex results in activating CD4+ T cell immune responses. HLA-DQ8, an overexpressed human leukocyte antigen (HLA) gene in type 1 diabetes patient samples, has been found to have complex binding conformations and states with autoantigen peptides, making accurate determination of binding affinity impossible. Since the successful formation of the HLA-peptide-T cell receptor (TCR) ternary complex is an important prerequisite for activating the CD4+ T cell immune response, the complex HLA-peptide binding conformation will seriously affect the evaluation of the efficacy of the antigen peptide molecule in activating the CD4+ T cell immune response.

[0003] The etiology of type 1 diabetes is complex and not yet fully understood. Furthermore, the lack of a clear understanding of the relevant immune molecular activation mechanisms has resulted in limited success in the development of type 1 diabetes vaccines. In addition, the diverse patient populations with type 1 diabetes and varying disease progression and trajectories make it difficult to standardize vaccine development and predict vaccine efficacy. Most existing type 1 diabetes vaccines are based on insulin or other pancreatic substances, requiring additional consideration of the vaccine's impact on insulin secretion. Summary of the Invention

[0004] To address the shortcomings of the aforementioned background technology, this invention provides a type 1 diabetes vaccine based on autoantigen polypeptide molecules. This invention can accurately describe and measure the overall binding conformational differences caused by single-point or multi-point mutations during the optimization design of autoantigen polypeptide molecules; it provides a computer-simulated method for calculating the binding affinity of the HLA-peptide-TCR ternary complex, which is more time-saving and labor-saving than experimental methods. Based on existing autoantigens, it conducts numerous amino acid mutation simulation experiments, representing a new approach to obtaining autoantigen polypeptide molecules that effectively induce autoimmune responses.

[0005] The present invention adopts the following technical solution:

[0006] This invention is based on a type 1 diabetes autoantigen sequence (CARQEDTAMVYYFDYW) obtained from a type 1 diabetes patient. Through computer simulation of numerous single-point, double-point, and exchangeable amino acid mutations, the binding affinity of the autoantigen to related immune molecules is rapidly and accurately measured. Autoantigen peptide molecules with higher HLA-DQ8 and TCR binding affinity compared to known type 1 diabetes autoantigens are screened. Several leading autoantigen sequences are selected for T cell proliferation tracing experiments using carboxyfluorescein acetoacetate (CFSE). Autoantigen peptide molecules that experimentally validate effectively induce type 1 diabetes-related CD4+ T lymphocyte proliferation are selected; these are the immunogenic autoantigens. The obtained autoantigens will serve as the basis for the development of a type 1 diabetes vaccine in the form of synthetically produced peptide molecules.

[0007] The immunogenic autoantigen, as the active ingredient in a type 1 diabetes vaccine, is presented in the form of one or more immunogenic autoantigen polypeptide molecules, or one or more polypeptide chains containing immunogenic autoantigen peptide segments, or one or more polynucleotides containing the amino acid sequence of immunogenic autoantigen peptide segments. A type 1 diabetes vaccine can be prepared using the immunogenic autoantigen as the active ingredient, and this type 1 diabetes vaccine can be used in combination with other type 1 diabetes medications.

[0008] The amino acid mutations involve sites 4, 5, 6, and 7 of the autoantigen. The target amino acid mutations are valine (Val, V), tyrosine (Tyr, Y), isoleucine (Ile, I), methionine (Met, M), cysteine ​​(Cys, C), and glutamine (Gln, Q).

[0009] The self-antigens designed in this invention include the following amino acid sequences: CARQEDTAMYVYFDYW, CARQEDTAMVVVFDYW, CARQEDTAMVCYFDYW, and CARQEDTAMVRRFDYW.

[0010] The selection and source of the type 1 diabetes autoantigen sequence are unrelated to whether the type 1 diabetes patient is currently receiving type 1 diabetes-related treatment. The specific method for obtaining it is as follows: first, at least a portion of the genes of the type 1 diabetes patient are sequenced; then, the genes of the type 1 diabetes patient and healthy individuals are compared to obtain the type 1 diabetes autoantigen sequence.

[0011] The above screening method identifies autoantigen peptide molecules with higher HLA-DQ8 and TCR binding affinity compared to autoantigen sequences in type 1 diabetes. The specific method is as follows:

[0012] 1. Construct the HLA-peptide-TCR ternary complex, the HLA-peptide binary complex, and the all-atom three-dimensional structure of the peptide molecule.

[0013] 2. The dynamic states of the HLA-peptide-TCR ternary complex, the HLA-peptide binary complex, and the peptide molecule were simulated using molecular dynamics.

[0014] 3. Structural characterization of the HLA-peptide-TCR ternary complex, the HLA-peptide binary complex, and the dynamic conformational changes of peptide molecules.

[0015] 4. Define the “bound state” and “unbound state” of an immune molecular complex system.

[0016] 5. Using the free energy perturbation method, the original amino acid at a specified site on the polypeptide molecule is mutated to the target amino acid based on the "bound state", and the system free energy gained or consumed in the process is calculated.

[0017] 6. Using the free energy perturbation method, the original amino acid at a specified site on the polypeptide molecule is mutated to the target amino acid based on the "unbound state", and the system free energy gained or consumed in the process is calculated.

[0018] 7. Subtract the free energy difference obtained from the "unbound state" from the free energy difference obtained from the "bound state" to obtain the binding affinity of the polypeptide molecule to HLA, and the binding affinity of the HLA-polypeptide binary complex formed by the polypeptide molecule and the HLA molecule from the antigen sequence to TCR.

[0019] 8. Screen for candidate peptide molecules with high affinity for HLA and TCR molecules.

[0020] The beneficial effects of this invention are as follows:

[0021] This invention successfully screened out autoantigen peptide molecules with higher binding affinity to HLA-DQ8 and type 1 diabetes-related TCRs, and confirmed through CFSE-based T cell proliferation experiments that they can more effectively activate CD4+ T cells to induce an immune response, making them suitable as a basis for the development of type 1 diabetes vaccines in the form of artificially synthesized autoantigen peptide molecules.

[0022] This invention can accurately describe and measure the overall binding conformational differences caused by single or multiple mutations during the optimization design of autoantigen polypeptide molecules; it provides a computer-simulated method for calculating the binding affinity of HLA-peptide-TCR ternary complexes, which is more time-saving and labor-saving than experiments. Based on existing autoantigens, it conducts a large number of amino acid mutation simulation experiments, which is a new way to obtain autoantigen polypeptide molecules that can effectively induce autoimmune responses. Attached Figure Description

[0023] The present invention will be further described below with reference to the accompanying drawings;

[0024] Figure 1 It is the full-atom three-dimensional structure of the HLA-type I diabetes autoantigen polypeptide molecule (sequence: CARQEDTAMVYYFDYW) binary complex. The core antigenic region of the polypeptide molecule is shown as light gray, the non-antigenic region of the polypeptide molecule is shown as dark gray, and the HLA-DQ8 molecule is shown as transparent gray.

[0025] Figure 2 It is the solution-accessible area of ​​the peptide molecule that binds to HLA. The core antigenic sites of the peptide molecule are numbered 1-9, and the complete sites of the peptide molecule are numbered 1'-16'.

[0026] Figure 3 The mutations are based on a single amino acid mutation of the type 1 diabetes autoantigen polypeptide molecule (sequence: CARQEDTAMVYYFDYW). The mutations that produce higher HLA binding affinity are shown in light gray, while the others are shown in dark gray.

[0027] Figure 4 The mutations are based on amino acid double mutations or exchange mutations of the type 1 diabetes autoantigen polypeptide molecule (sequence: CARQEDTAMVYYFDYW). Mutations that produce higher HLA binding affinity are shown in light gray, while the rest are shown in dark gray.

[0028] Figure 5 This is a free energy dissociation analysis of amino acid double mutations or crossover mutations that have high HLA binding affinity for type 1 diabetes autoantigen polypeptide molecules (sequence: CARQEDTAMVYYFDYW);

[0029] Figure 6The study involved single mutations of 13 amino acids at anchor site 6 of the type 1 diabetes autoantigen polypeptide molecule (sequence: CARQEDTAMVYYFDYW) to analyze the relationship between changes in HLA binding affinity and amino acid side chain size or hydrophobic properties.

[0030] Figure 7 This is the all-atom three-dimensional structure of the HLA-type 1 diabetes autoantigen polypeptide molecule (sequence: CARQEDTAMVYYFDYW)-TCR ternary complex. Anchor sites 6-7 of the polypeptide molecule are shown as light gray, the rest of the polypeptide molecule is shown as gray, the HLA-DQ8 molecule is shown as transparent gray, the dark part on the lower left is the α chain of the TCR molecule, and the light part on the lower right is the β chain of the TCR molecule.

[0031] Figure 8 It is based on the type 1 diabetes autoantigen polypeptide molecule (sequence: CARQEDTAMVYYFDYW) through amino acid double mutation or exchange mutation. The change in HLA binding affinity is shown in gray, and the change in TCR binding affinity is shown in dark gray.

[0032] Figure 9 This study summarizes single mutations, double mutations, or exchange mutations (left) that produce high HLA binding affinity for the autoantigen polypeptide molecule (sequence: CARQEDTAMVYYFDYW) in type 1 diabetes, and selects 7 autoantigen sequences for experimental verification.

[0033] Figure 10 It is a carboxyfluorescein acetoacetate (CFSE) tracer targeting optimized self-antigen peptide molecules, which, combined with a standard gating strategy, effectively distinguishes between proliferating (lower CFSE) and non-proliferating (higher CFSE) CD4+ T cells;

[0034] Figure 11 The comparison shows the percentage of CD4+ T cells before and after activation of the optimized self-antigen peptide molecule using carboxyfluorescein acetoacetate (CFSE) (left) and fold comparison (right).

[0035] Figure 12 The image shows a comparison of the percentage of CD69 before and after activation of the optimized self-antigen peptide molecule using carboxyfluorescein acetoacetate (CFSE) (left) and the fold increase (right). Detailed Implementation

[0036] The present invention will be further described below with reference to the embodiments.

[0037] Example 1: Structural characterization of existing type 1 diabetes autoantigen polypeptide molecules (i.e., type 1 diabetes autoantigen sequences obtained from type 1 diabetes patients) and their HLA molecular complexes.

[0038] Existing HLA-DQ8-binding autoantigen peptides are typically 12-20 amino acids long due to their open binding sites, which is 2-11 amino acids longer than peptides binding type I HLA. However, the core region that directly interacts with HLA generally contains only 9-10 amino acids, while the N-terminus and C-terminus of the peptide molecule are mostly exposed in solution and do not directly interact with HLA. Therefore, accurately determining the solution-accessible area of ​​each site on the peptide molecule helps in the preliminary assessment of whether the site is suitable for amino acid mutation and effectively screens sites with a higher success rate for optimization. The specific process is as follows:

[0039] 1. Identify the existing type 1 diabetes autoantigen polypeptide molecule, whose sequence is CARQEDTAMVYYFDYW.

[0040] 2. Constructing the all-atom three-dimensional structure of the HLA-peptide binary complex using protein structure prediction methods. Figure 1 ).

[0041] 3. Place the HLA-peptide binary complex in water molecules and add ions with a concentration equivalent to that of physiological ions.

[0042] 4. Perform molecular dynamics simulations on the system. The simulation temperature is 310 K, the pressure is 1 bar, the simulation step size is 2 fs, and the preset total number of simulation steps is 50,000,000.

[0043] 5. Determine whether the structure has reached a steady state based on the root mean square deviation (RMSD) of the overall system structure.

[0044] 6. Determine whether the structure has reached a steady state based on the RMSD of the system (especially for the peptide molecule binding region).

[0045] 7. Analyze the solution-accessible area ratio of each site on the polypeptide molecule ( Figure 2 ).

[0046] Combination Figure 1 and Figure 2 It is evident that the core region of the selected autoantigen polypeptide molecule (sequence: CARQEDTAMVYYFDYW) directly interacts with HLA, while possessing either a low overall solution-accessible area ratio or a high embedded area ratio (100% - solution-accessible area ratio). Furthermore, anchor sites (sites 1, 4, 6, and 9) obtained from structural analysis of other type II HLA-binding polypeptide molecules all exhibit high embedded area ratios. Analysis of solution-accessible area can further identify non-anchor sites with high embedded area ratios, such as site 7, as potential amino acid mutation targets. Figure 2 ).

[0047] Example 2: Single amino acid mutation based on existing type 1 diabetes autoantigen polypeptide molecules

[0048] Constructing the all-atom three-dimensional structure of the HLA-peptide binary complex ( Figure 1 Using the free energy perturbation method, the original amino acid at a specified site on an existing type 1 diabetes self-antigen polypeptide molecule is mutated to the target amino acid. The difference in system free energy gained or consumed during this process is calculated, and then the binding affinity of the mutated self-antigen polypeptide molecule to this HLA is calculated. Specific computational experiments include:

[0049] 1. The dynamic binding state of the HLA-peptide binary complex was simulated using molecular dynamics, and it was defined as the "binding state".

[0050] 2. The dynamic state of existing type 1 diabetes autoantigen polypeptide molecules was simulated using molecular dynamics, and defined as the "unbound state".

[0051] 3. Using the free energy perturbation method, the original amino acid at a specified site on the existing type I diabetes self-antigen polypeptide molecule is mutated to the target amino acid based on the "bound state", and the system free energy gained or consumed in the process is calculated.

[0052] 4. Using the free energy perturbation method, the original amino acid at a specified site on the existing type I diabetes self-antigen polypeptide molecule is mutated to the target amino acid based on the "unbound state", and the system free energy gained or consumed in the process is calculated.

[0053] 5. Subtract the system free energy difference obtained from the "non-bonded state" from the system free energy difference obtained from the "bonded state" to obtain the relative free energy difference.

[0054] 6. Calculate the binding affinity of the self-antigen polypeptide molecule obtained after mutation to this HLA based on the relative free energy difference.

[0055] Multiple single-point amino acid mutation calculations were performed on the selected existing type 1 diabetes autoantigen peptide molecule (sequence: CARQEDTAMVYYFDYW) to rapidly and effectively screen candidate peptide molecules with high binding affinity for HLA-DQ8. Specific amino acid mutations performed included, but were not limited to:

[0056] 1. Single mutations were performed on amino acids with similar side chains targeting the anchoring sites of existing type 1 diabetes autoantigen polypeptide molecules.

[0057] 2. To target the non-anchoring sites of existing type 1 diabetes autoantigen peptide molecules, single mutations were performed on amino acids that enhance the structural freedom of the peptide molecules.

[0058] 3. Based on the anchoring or non-anchoring sites of existing type 1 diabetes antigen peptide molecules, and with the help of prior knowledge obtained from amino acid mutation experiments of other peptide molecules, select amino acids that may enhance binding affinity for single mutation.

[0059] Single-mutation calculations yielded two groups of amino acid mutations with enhanced binding affinity, namely M4L and M4I. Figure 3 Further structural analysis revealed that mutations in leucine (Leu, L) or isoleucine (Ile, I) significantly enhanced hydrophobic interactions near site 4, successfully achieving the rational design of the self-antigen peptide molecule. The mutation strategy employed in this invention differs from traditional bioinformatics methods. By using structural biology and molecular dynamics simulations to target the binding patterns of the core region of the peptide molecule, it efficiently and rationally proposes amino acid mutations that may enhance HLA binding.

[0060] Example 3: Amino acid double mutation or exchange mutation based on existing type 1 diabetes autoantigen polypeptide molecules

[0061] Constructing the all-atom three-dimensional structure of the HLA-peptide binary complex ( Figure 1 Using the free energy perturbation method, the original amino acid at a specified site on an existing type 1 diabetes self-antigen polypeptide molecule is mutated to the target amino acid. The difference in system free energy gained or consumed during this process is calculated, and then the binding affinity of the self-antigen polypeptide molecule obtained after mutation to this HLA is calculated.

[0062] The specific computational experiments implemented included:

[0063] 1. The dynamic state of existing type 1 diabetes autoantigen polypeptide molecules is simulated using molecular dynamics, and defined as the "bound state".

[0064] 2. The dynamic state of existing type 1 diabetes autoantigen polypeptide molecules was simulated using molecular dynamics, and defined as the "unbound state".

[0065] 3. Using the free energy perturbation method, the original amino acid at a specified site on the existing type I diabetes self-antigen polypeptide molecule is mutated to the target amino acid based on the "bound state", and the system free energy gained or consumed in the process is calculated.

[0066] 4. Using the free energy perturbation method, the original amino acid at a specified site on the existing type I diabetes self-antigen polypeptide molecule is mutated to the target amino acid based on the "unbound state", and the system free energy gained or consumed in the process is calculated.

[0067] 5. Subtract the system free energy difference obtained from the "non-bonded state" from the system free energy difference obtained from the "bonded state" to obtain the relative free energy difference.

[0068] 6. Calculate the binding affinity of the self-antigen polypeptide molecule obtained after mutation to this HLA based on the relative free energy difference.

[0069] Multi-site amino acid mutation calculations were performed on the selected existing type 1 diabetes autoantigen peptide molecule (sequence: CARQEDTAMVYYFDYW) to rapidly and effectively screen candidate autoantigen peptide molecules with high binding affinity to HLA-DQ8. Specific calculation experiments included, but were not limited to:

[0070] 1. Perform amino acid double mutations or exchange mutations at sites 3, 4, 5, 6, and 7 of the existing type 1 diabetes autoantigen polypeptide molecule.

[0071] 2. Free energy decomposition analysis was performed on the experimental results of multi-site amino acid mutation calculations of the autoantigen in type 1 diabetes.

[0072] 3. Rational high-throughput amino acid mutation targeting site 6 of the existing type 1 diabetes autoantigen polypeptide molecular anchoring site ( Figure 4 ),

[0073] This allows for the screening of more immunogenic peptide molecules.

[0074] Through multi-site amino acid mutation calculation experiments, five groups of amino acid mutations with enhanced binding affinity were obtained, including V5Y_Y6V, Y6F_Y7F, Y6I_Y7I, Y6Q_Y7Q, and Y6V_Y7V. Figure 4 Free energy dissociation analysis showed that site 6, the molecular anchoring site of the existing type 1 diabetes autoantigen peptide, was the major contributing site in all five data sets. Figure 5 Further, 13 amino acid mutations were performed on site 6 of the existing type 1 diabetes autoantigen peptide molecular anchor. The results showed that the enhanced binding affinity caused by amino acid mutations at this site was highly correlated with the size and hydrophobicity of the mutated amino acid. Figure 6 Structural analysis showed that the binding pocket at site 6 was mainly composed of hydrophobic amino acids with cyclic side chains.

[0075] Example 4: Characterizing the effect of amino acid mutations on T cell immune recognition

[0076] Constructing the all-atom three-dimensional structure of the HLA-peptide-TCR ternary complex ( Figure 7Using the free energy perturbation method, the original amino acid at a specified site on an existing type 1 diabetes autoantigen polypeptide molecule is mutated to the target amino acid. The difference in system free energy gained or consumed during this process is calculated, and then the binding affinity of TCR to the HLA-peptide binary complex is calculated. Multi-site amino acid mutation calculation experiments were performed on the selected existing type 1 diabetes autoantigen polypeptide molecule (sequence: CARQEDTAMVYYFDYW). The specific calculation experiments included:

[0077] 1. The dynamic binding state of the HLA-peptide-TCR ternary complex was simulated by molecular dynamics, and it was defined as the "binding state".

[0078] 2. The dynamic binding state of the HLA-peptide binary complex was simulated using molecular dynamics, and it was defined as the "unbound state".

[0079] 3. Using the free energy perturbation method, the original amino acid at a specified site on the existing type I diabetes self-antigen polypeptide molecule is mutated to the target amino acid based on the "bound state", and the system free energy gained or consumed in the process is calculated.

[0080] 4. Using the free energy perturbation method, the original amino acid at a specified site on the existing type I diabetes self-antigen polypeptide molecule is mutated to the target amino acid based on the "unbound state", and the system free energy gained or consumed in the process is calculated.

[0081] 5. Subtract the system free energy difference obtained from the "non-bonded state" from the system free energy difference obtained from the "bonded state" to obtain the relative free energy difference.

[0082] 6. The binding affinity of TCR to the HLA-peptide binary complex was calculated based on the relative free energy difference.

[0083] Based on previously obtained amino acid mutation combinations that enhance HLA binding affinity, three single mutations (Y6C, Y6I, Y6V), two double mutation combinations (Y6V_Y7V, Y6R_Y7R), and one exchange mutation (V5Y_Y6V) were selected for multi-site amino acid mutation calculation experiments to measure TCR binding affinity. Two groups of amino acid mutations with enhanced TCR binding affinity, two groups with no significant change, and two groups with weakened TCR binding affinity were obtained. Figure 8 Structural analysis revealed that the α chain of the TCR is the main domain that contacts the polypeptide molecule, and different amino acid mutations can cause significant differences in the TCR binding mode.

[0084] Example 5: Validation of optimized self-antigen peptide molecules using CFSE tracing and flow cytometry.

[0085] Validation of optimized autoantigen peptide molecules (such as fluorescein acetoacetate (CFSE) data using methods for analyzing CFSE data) Figure 9 This can induce high lymphocyte proliferation, and when combined with standard gating strategies, it can effectively distinguish between proliferating (lower CFSE) and non-proliferating (higher CFSE) CD4+ T cells. Figure 10 The experiment simultaneously added the original autoantigen polypeptide molecule, the existing type 1 diabetes autoantigen polypeptide molecule (x-ld, sequence: CARQEDTAMVYYFDYW), an insulin analog (Ins-mim), an anti-CD3-CD28 antibody (anti-CD3-28), and a control group without any antigen (UNS); pep-stim represents the addition of different antigen polypeptide molecules. One single mutation (Y6C), two double mutations (Y6V_Y7V, Y6R_Y7R), and one exchange mutation (V5Y_Y6V) showed significantly increased CD4+ T cell proliferation, 2-7 times higher than the proliferation rate without the antigen. Figure 11 Meanwhile, all these optimized autoantigen peptide molecules showed activation of CD69 protein on the surface of CD4+ T cells, and the effect was superior to existing autoantigen peptide molecules for type 1 diabetes. Figure 12 ).

Claims

1. A type 1 diabetes vaccine based on self-antigen polypeptide molecules, the effective components of the type 1 diabetes vaccine are a series of immunogenic self-antigens, characterized in that: The self-antigen is obtained by amino acid mutation of a type I diabetes self-antigen sequence CARQEDTAMVYYFDYW, and the amino acid mutation involves positions 4, 5, 6 and 7 of the self-antigen, and the mutation targets valine V, tyrosine Y, isoleucine I, methionine M, cysteine C and glutamine Q; and the self-antigen sequence is specifically one or any combination of CARQEDTAMYVYFDYW, CARQEDTAMVVVFDYW, CARQEDTAMVCYFDYW and CARQEDTAMVRRFDYW.

2. A type 1 diabetes vaccine based on a self-antigen polypeptide molecule according to claim 1, characterized in that: The vaccine can be used in combination with other type I diabetes drugs.

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