Screening method of polypeptide inhibitor targeting IL-1beta / IL-1R1 interaction interface

Abstract

The invention discloses a screening method of a polypeptide inhibitor targeting an IL-1beta / IL-1R1 interaction interface. According to the screening method, computer simulation screening is carried out, a molecular dynamics driven peptide fragment screening system and a polypeptide affinity verification system are included, and activity identification or verification is carried out on the screened polypeptide inhibitor. The screening method is simple, efficient, high in accuracy and low in cost, and can be used for screening and developing the polypeptide inhibitor targeting the IL-1beta / IL-1R1 interaction interface.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and more specifically to a method for screening peptide inhibitors that target the IL-1β / IL-1R1 interaction interface. Background Technology

[0002] Interleukin-1 (IL-1) is a key cytokine in the immune system, primarily existing in two forms: IL-1α and IL-1β. IL-1β, one of the earliest studied interleukins, is a potent inflammatory cytokine that mediates non-specific roles in inflammatory responses, activating immune cells and inducing the expression and secretion of inflammatory cytokines and chemokines. It exerts its active function by binding to its corresponding IL-1 receptor 1 (IL-1R1) on the surface of target cells. Therefore, blocking the interaction between IL-1β and IL-1R1 can effectively inhibit excessive inflammatory responses, reduce tissue damage, and improve metabolic dysfunction. Current strategies for blocking IL-1β / IL-1R1 interaction mainly include neutralizing monoclonal antibodies targeting IL-1β and IL-1R1 antagonists and small molecule inhibitors, but these methods suffer from drawbacks such as high cost, short half-life, and off-target effects. Blocking the interaction between IL-1β and IL-1R1 through protein-protein interaction (PPI) interface inhibitors may be a better option. The main challenge in designing PPI inhibitors is the large and relatively flat interaction interface, the lack of obvious binding pockets or grooves, and the discontinuous binding sites, making it difficult to find matching small molecule compounds. In contrast, peptides have a much larger interaction area with proteins compared to small molecules, making peptide-based inhibitors a relatively simple and feasible approach. Summary of the Invention

[0003] The purpose of this invention is to provide a screening method for peptide inhibitors targeting the IL-1β / IL-1R1 interaction interface. The screening method of this invention obtains the hot spot amino acid residues of the IL-1β and IL-1R1 complex interaction through computer simulation, and then uses computer-aided design of peptide molecules to study their effect on IL-1β activity by inhibiting the binding of IL-1β and IL-1R1 complex, thereby providing candidate drug molecules for inflammation treatment.

[0004] To achieve the objectives of this invention, the following implementation scheme is provided.

[0005] In one embodiment, the present invention provides a method for screening peptide inhibitors targeting the IL-1β / IL-1R1 interaction interface. The screening method is carried out by computer simulation and includes a molecular dynamics-driven peptide screening system, a peptide affinity verification system, and optionally, activity identification or verification of the screened peptide inhibitors.

[0006] In some embodiments, the molecular dynamics-driven peptide screening system of the above-mentioned screening method of the present invention refers to resolving the stable conformation of the IL-1β / IL-1R1 complex through 80ns molecular dynamics simulation, combining the MM / GBSA free energy decomposition to quantify the residue contribution, and screening out high-contribution peptides with a contribution ratio of over 80%.

[0007] In some embodiments, the peptide affinity verification system in the screening method of the present invention refers to the quantitative assessment of the affinity between candidate peptides and IL-1R1 by calculating the binding free energy of peptide-protein molecular docking.

[0008] In one specific embodiment, the present invention provides a screening method for peptide inhibitors targeting the IL-1β / IL-1R1 interaction interface. The screening method employs computer simulation screening and specifically includes the following steps: 1) obtaining the crystal structure of the IL-1β / IL-1R1 complex from the RCSB database (PDB:1ITB), removing water molecules and ligands using PyMol, and separating the single-chain structures of IL-1β and IL-1R1;

[0009] 2) Molecular dynamics simulation and stability verification: Molecular dynamics simulation and stability verification were performed in AMBER2022 using the ff19SB force field and TIP3P water model. RMSD analysis confirmed that the complex was stable after 10 ns. Fluctuation range;

[0010] 3) Key residue identification and peptide screening: The binding free energy of the complex is calculated using MM / GBSA, the contribution value of residues is decomposed, high contribution residues with |ΔG|≥2.0kcal / mol are screened, and key peptides are determined based on the spatial proximity of residues;

[0011] 4) Peptide-receptor docking and affinity assessment: Key peptides were docked with IL-1R1 using the MDockPeP server. The optimal conformation of each peptide was selected, and the binding free energy was calculated using AMBER to identify the strongest binding peptide, thus screening out peptide inhibitors targeting the IL-1β / IL-1R1 interaction interface; and

[0012] 5) Optionally, the selected peptide inhibitors are subjected to cell activity verification.

[0013] Among them, the amino acid sequence of IL-1β protein in the 1LTB crystal structure is APVRSLNCTLRDSQQKSLVMSGPYELKALHLQGQDMEQQVVFSMSFVQGEESNDKIPVALGLKEKNLY LSCVLKDDKPTLQLESVDPKNYPKKKMEKRFVFNKIEINNKLEFESAQFPNWYISTSQAENMPVFLGG TKGGQDITDFTMQFVSS.

[0014] In some specific implementations, the screening method of the present invention described above, the molecular dynamics simulation and stability verification process in step 2) includes (a) energy minimization, (b) NPT equilibration (300K, 1atm), and (c) 80ns dynamics simulation; and the determination of key peptides in step 4) where the total binding energy contribution of high-contributing residues exceeds 80%.

[0015] Using the screening method of the present invention described above, the key peptides screened are residues 2-10 (peptide1), 12-15 (peptide2), 29-37 (peptide3), 50-57 (peptide4), or 92-97 (peptide5), and the strongest peptide screened is Peptide3.

[0016] In some embodiments, the present invention also provides a peptide inhibitor for treating inflammation, which is a peptide 1 or peptide 3 that targets the IL-1β / IL-1R1 interaction interface.

[0017] The core technology of the screening method of this invention lies in:

[0018] 1. Molecular dynamics-driven peptide screening: The stable conformation of the IL-1β / IL-1R1 complex was resolved by 80ns molecular dynamics simulation. Combined with MM / GBSA free energy decomposition to quantify residue contributions (with a threshold of |ΔG|≥2.0kcal / mol), five high-contribution peptides of IL-1β (residues 2-10, 12-15, 29-37, 50-57, and 92-97) were screened out, with their total binding energy contribution accounting for more than 80%.

[0019] 2. Peptide affinity verification system: The binding free energy of peptide-protein molecular docking was calculated to quantitatively evaluate the affinity of candidate peptides with IL-1R1 (Peptide3 binding free energy as low as -41.3 kcal / mol).

[0020] 3. Identification of key inhibitory peptides: Cellular experiments confirmed that the screened peptides significantly inhibited the expression of inflammatory factors. Among them, Peptide3 (residues 29-37) exhibited high cellular activity, reducing IL-1β mRNA expression by 68% at a concentration of 100 μM. This indicates that the screening method of the present invention is effective, accurate, low-cost, and efficient, making it suitable for screening and developing peptide inhibitors targeting the IL-1β / IL-1R1 interaction interface.

[0021] 4. The screening method of the present invention solves the main difficulties currently faced in the design of PPI inhibitors, such as the large and relatively flat interaction interface, the lack of obvious binding pockets or grooves, and the discontinuous binding sites, making it difficult to find matching small molecule compounds. Attached Figure Description

[0022] Figure 1 This is a diagram of the IL-1β / IL-1R1 protein complex model (PDB:1LTB) from Example 1;

[0023] Figure 2 Here is the RMSD image of the IL-1β / IL-1R1 protein complex from Example 1;

[0024] Figure 3 This is a diagram showing the binding free energy of each amino acid in the interaction between IL-1β and IL-1R1 proteins in Example 1.

[0025] Figure 4 This is a bar chart showing the free energy of amino acid sites (binding free energy < -2 Kcal / mol) that contribute significantly to the binding free energy in IL-1β in Example 2.

[0026] Figure 5 This is a docking model diagram of Peptide1-5 and IL-1R1 in Example 2;

[0027] Figure 6 The bar chart shows the changes in IL-1β expression levels in U2OS cells after co-incubation with peptide 1-5 in Example 2. Detailed Implementation

[0028] The following embodiments are provided to describe the present invention in more detail. However, these embodiments are provided only to help further understand the present invention and are not intended to limit the present invention. Those skilled in the art should understand that equivalent substitutions or corresponding improvements made to the content of the present invention still fall within the protection scope of the present invention.

[0029] Example 1: Screening Method for Peptide Inhibitors

[0030] The screening method of the present invention can be implemented through the following steps:

[0031] 1. Download the crystal structure (PDB: 1LTB) of the IL-1β / IL-1R1 protein complex from the RCSB protein database (https: / / www.rcsb.org / structure / 1ITB). Use PyMOL software to remove water and other organic molecules from the protein structure, and extract the IL-1β and IL-1R1 protein structure PDB files separately.

[0032] 2. Use the molecular dynamics software AMBER2022 to check the integrity of the IL-1β and IL-1R1 protein structure pdb files, and generate the IL-1β / IL-1R1 protein complex pdb file (see...). Figure 1 (As shown).

[0033] 3. Molecular dynamics simulations were performed on the crystal structure of the IL-1β / IL-1R1 complex obtained from the RCSB protein database PDB (1LTB) using the ff19SB force field and TIP3P water model in the molecular dynamics software AMBER2022. First, 80 ns of dynamics simulations were conducted under energy minimization, isothermal-barometric equilibrium (NPT, 300 K, 1 atm), and ambient temperature and pressure conditions to obtain stable conformational trajectories. The root mean square deviation (RMSD) of atoms showed that the system underwent significant relaxation and conformational adjustment in the early stages of the simulation, with a significant conformational fluctuation event occurring at approximately 10 ns. Subsequently, the system entered a relatively stable kinetic equilibrium period, and the RMSD was... The fluctuation within a certain range indicates that the IL-1β / IL-1R1 protein complex structure has good stability (e.g., Figure 2 (As shown).

[0034] 4. Subsequently, the overall binding free energy of the complex was calculated using the MM / GBSA method in AMBER2022 software, and residue free energy decomposition was performed to quantify the contribution of each amino acid residue to the binding free energy (e.g., Figure 3 (As shown).

[0035] 5. The identification criterion for key residues is |ΔG| ≥ 2.0 kcal / mol (e.g., Figure 4 (As shown).

[0036] 6. Five high-contribution peptides of IL-1β (residues 2-10, 12-15, 29-37, 50-57, 92-97) were screened out, with a total energy contribution of over 80%. The amino acid sequences of the screened high-contribution peptides (also known as key peptides) are shown in Table 1.

[0037] Table 1. Amino acid sequences of the extracted peptides

[0038] Serial Number Extract peptide sequence number amino acid sequence peptide1 Pro2-Leu10 PVRSLNCTL peptide2 Asp12-Gln15 DSQQ peptide3 Leu29-Glu37 LHLQGQDME peptide4 Glu50-Pro57 EESNDKIP peptide5 Lys92-Phe99 KKKMEKRF

[0039] 7. Using the MDockPeP peptide-protein docking server (https: / / zougrouptoolkit.missouri.edu / mdockpep / ), molecular docking was performed on IL-1R1 on the five key peptides of IL-1β (peptide1: residues 2-10; peptide2: residues 12-15; peptide3: residues 29-37; peptide4: residues 50-57; peptide5: residues 92-97) previously screened by free energy analysis.

[0040] 8. After docking is completed, the complex models with the highest scores in each peptide segment are selected based on the docking scores to obtain the binding models of the key peptides (5 key peptide segments) and IL-1R1 (e.g., Figure 5 (As shown)

[0041] 9. To assess the affinity of the peptides for IL-1R1, the binding free energies of peptides 1-5 and IL-1R1 protein-protein interaction models were calculated using AMBER software (peptide1: -39.2358 kcal / mol; peptide2: -7.4 kcal / mol; peptide3: -41.3 kcal / mol; peptide4: -10.6 kcal / mol; peptide5: -9.1 kcal / mol). The results are shown in Table 2. The results indicate that peptide3 (29-37) had the lowest binding free energy (-41.3 kcal / mol), indicating that it had the strongest affinity for IL-1R1, thus identifying it as a target peptide inhibitor.

[0042] Table 2. AMBER simulation calculations of the binding free energy of Peptide 1-5 and IL-1R1

[0043] Peptide serial number amino acid sequence Combination free energy (Kcal / mol) 1 PVRSLNCTL -39.2 2 DSQQ -7.4 3 LHLQGQDME -41.3 4 EESNDKIP -10.6 5 KKKMEKRF -9.1

[0044] Example 2: Activity Identification and Verification of Peptide Inhibitors

[0045] 1. Five candidate polypeptide sequences (peptide1-5) of the key peptides (peptide1, peptide2, peptide3, peptide4, peptide5) screened in Example 1 were entrusted to Jier Biochemical (Shanghai) Co., Ltd. for solid-phase synthesis (purity >85%).

[0046] 2. Cell experiments were performed using the human osteosarcoma cell line U2OS, cultured in DMEM high-glucose medium (containing 10% FBS and 1% penicillin-streptomycin), and passaged to the 3rd-5th generation at 37℃ and 5% CO2. Cells were then cultured at 1×10⁶ cells / year. 4Cells were seeded at a density of / well in 96-well plates. After 24 hours of adhesion, the medium was replaced with culture medium containing different concentrations of peptides (0, 10, 50, 100 μM), with three replicates per group. After a total incubation of 24 hours, cells were collected for RNA extraction.

[0047] 3. To systematically evaluate the regulatory effects of five candidate peptide compounds (peptides 1-5) on the expression of inflammatory factors, RT-qPCR was used to detect the changes in IL-1β mRNA expression levels after co-incubation with human osteosarcoma cells U2OS. The results are as follows: Figure 6 As shown in the figure. The results showed that peptide3 had the strongest inhibitory effect (68% ↓), significantly better than other peptides. This indicates that the screening method used in this study has high accuracy.

[0048] in conclusion:

[0049] The screening method of this invention, through computational screening and experimental verification, has developed a novel peptide inhibitor targeting the IL-1β / IL-1R1 protein-protein interaction (PPI). It establishes for the first time a screening pathway for IL-1β functional peptides, consisting of "kinetic simulation - free energy decomposition - peptide docking - cell verification"; and discovers that the IL-1β 29-37 peptide (Peptide 3) possesses ultra-high affinity (-41.3 kcal / mol) and potent anti-inflammatory activity.

Claims

1. A screening method for polypeptide inhibitors targeting the interface of IL-1β / IL-1R1 interaction, comprising a computer simulation screening, a molecular dynamics driven peptide segment screening system, a polypeptide affinity verification system, and optionally, an activity identification or verification of the screened polypeptide inhibitors.

2. The screening method of claim 1, wherein the molecular dynamics driven peptide segment screening system is to analyze the stable conformation of IL-1β / IL-1R1 complex by 80 ns molecular dynamics simulation, and to screen high contribution peptide segments by combining MM / GBSA free energy decomposition to quantify residue contribution.

3. The screening method of claim 1, wherein the polypeptide affinity verification system is to quantitatively evaluate the affinity of candidate peptide segments to IL-1R1 by using polypeptide-protein molecular docking binding free energy calculation.

4. The screening method of claim 1, comprising the following steps: 1) obtaining the crystal structure of IL-1β / IL-1R1 complex from RCSB database (PDB: 1ITB), removing water molecules and ligands using PyMol, and separating the single chain structure of IL-1β and IL-1R1; 2) molecular dynamics simulation and stability verification: (a) energy minimization; (b) NPT equilibration (300 K, 1 atm); (c) 80 ns dynamics simulation; 3) key residue identification and peptide segment screening: calculating the binding free energy of the complex by MM / GBSA, decomposing the residue contribution value, screening high contribution residues with |ΔG|≥2.0 kcal / mol, and determining key peptide segments based on the spatial proximity of residues; 4) polypeptide-receptor docking and affinity evaluation: docking the key peptide segments to IL-1R1 by MDockPeP server, selecting the optimal conformation of each peptide segment, calculating the binding free energy by AMBER, and confirming the strongest binding peptide segment, i.e., the polypeptide inhibitor targeting the interface of IL-1β / IL-1R1 interaction; and 5) optionally, verifying the cell activity of the screened polypeptide inhibitor.

5. The screening method of claim 4, wherein the molecular dynamics simulation and stability verification of step 2) comprises (a) energy minimization; (b) NPT equilibration (300 K, 1 atm); and (c) 80 ns dynamics simulation. 2) Molecular dynamics simulation and stability verification: In AMBER2022, ff19SB force field and TIP3P water model were used, and RMSD analysis confirmed that the complex was stable after 10 ns fluctuation range; 6. The screening method of claim 4, wherein the key peptide segments of step 3) are residues 2-10 (peptide 1), 12-15 (peptide 2), 29-37 (peptide 3), 50-57 (peptide 4), or 92-97 (peptide 5), and the high contribution residues have a total binding energy contribution of more than 80%.

7. The screening method of claim 6, wherein the key peptide segments are residues 2-10 (peptide 1), 12-15 (peptide 2), 29-37 (peptide 3), 50-57 (peptide 4), or 92-97 (peptide 5).

8. The screening method of claim 4, wherein the strongest binding peptide segment of step 4) is peptide 3.

9. A polypeptide inhibitor for treating inflammation, which is peptide 1 or peptide 3 targeting the interface of IL-1β / IL-1R1 interaction. ​ ​ ​ ​

Images