A method of screening for polypeptides having small intestinal antioxidant stress

Abstract

The application discloses a kind of polypeptides with small intestine antioxidant stress screening method, belong to biological information technology field.Step is: in protein database, by comparing crystal structure, obtain active screening target keap1 crystal structure, determine target receptor;With common 20 kinds of amino acids as foundation, establish the polypeptide ligand library with the binding force of target receptor is 2-6 amino acid number;Binding between macromolecular receptor and small molecule ligand is scored and predicted using Autodock Vina in software PyRx.The application can quickly, efficiently screen polypeptide with small intestine antioxidant activity, with the advantages of wide target range, strong operability, low investment.

Description

Technical Field

[0001] This invention belongs to the field of bioinformatics technology and relates to a method for screening peptides, particularly a method for screening peptides with small intestinal antioxidant stress. Background Technology

[0002] The intestinal barrier, formed by small intestinal epithelial cells, prevents bacteria, toxins, and other harmful substances in the intestinal lumen from entering the blood system and invading other organs. Oxidative stress-induced oxidative damage to intestinal epithelial cells is one of the main causes of intestinal barrier function loss.

[0003] Oxidative stress is a physiological and pathological response of cells and tissues caused by the production of reactive oxygen species (ROS) and reactive nitrogen species (RNS) in the body under harmful internal and external environmental stimuli. Because these free radicals can directly or indirectly oxidize or damage DNA, proteins, and lipids, inducing gene mutations, protein denaturation, and lipid peroxidation, they are considered major risk factors for aging and various important diseases in humans and animals. Studies have shown that the Keap1-Nrf2-ARE signaling pathway is one of the most important mechanisms for cellular defense against oxidative stress damage. The transcriptional regulation of many proteins with detoxification and antioxidant defense functions depends on the activation of the Nrf2 signaling pathway. The Nrf2 signaling pathway has become a target for the prevention and treatment of oxidative stress-related diseases. It can activate cellular adaptive responses to cope with various oxidative stress injuries. Keap1 is a negative regulator of Nrf2, and Nrf2 is the main effector of the ARE system. Negative regulation of Keap1 protein to activate the ARE system can induce the expression of a series of antioxidant genes. Therefore, the Nrf2 signaling pathway plays an important role in the peptides that combat oxidative stress in the human small intestine. Numerous studies have reported that some antioxidant peptides, after acting on the body, can occupy the binding sites of Nrf2 and Keap1, promoting the uncoupling of Nrf2 and Keap1.

[0004] Bioactive peptides are a class of bioactive peptide molecules that possess various physiological functions in humans and animals. However, for large polypeptide molecules, the different types and sequences of amino acids, coupled with the influence of various forces, result in highly variable and diverse structures, making research on large polypeptide molecules difficult and complex, requiring significant investment of funds and manpower. Currently, research on antioxidant stress peptides is still in its early stages both domestically and internationally, mainly focusing on the antioxidant and free radical scavenging abilities of peptides. Research on peptides inhibiting Keap1 activity has not yet been reported. The purpose of this invention is to predict the interaction between peptides and Keap1. Summary of the Invention

[0005] The purpose of this invention is to provide a method for screening peptides with small intestinal antioxidant stress, using computer science and molecular docking technology to develop a high-throughput screening method for peptides with small intestinal antioxidant stress in humans and animals.

[0006] To achieve the objectives of this invention, the following technical solution is adopted: A method for screening peptides with small intestinal antioxidant stress resistance includes the following steps: (1) Identification of target receptor: Based on the different capture methods or small molecules bound, the crystal structure of the activity screening target keap1 is obtained by comparing crystal structures in the protein database to identify the target receptor; (2) Establishment of a polypeptide ligand library: A polypeptide ligand library with 2-6 amino acids that have binding affinity to the target receptor was established based on 20 common amino acids. (3) Molecular docking prediction of peptides in the peptide ligand library and target receptors. The docking prediction uses Autodock Vina in PyRx software to score and predict the binding between macromolecular receptors and small molecule ligands. Specifically, the spatial fit of different conformations of each ligand with the keap1 structure is calculated and scored by Autodock Vina in turn. The free energy of binding between the receptor and ligand is represented. The smaller the binding free energy, the more stable the binding between the two.

[0007] Further, the number of amino acids in the peptide ligand library described in step (2) is 5, of which the middle 3 amino acids are the same, and the types of amino acids at the N-terminus and C-terminus are different; the second to last amino acid of the peptide RAP-Y is selected as the key amino acid, and the following amino acids are selected: basic amino acids (lysine (K), arginine (R), histidine (H)), acidic amino acids (aspartic acid (D), glutamic acid (E)), and nonpolar amino acids (alanine (A), isoleucine (I), leucine (L)). The highest score is determined by molecular docking energy value, and the docking energy is the lowest (see Table 1). The peptide sequence is then determined to be *API*. The number of amino acids in the peptide is 5, of which the middle 3 amino acids are the same. By changing the types of amino acids at the N-terminus and C-terminus, the number of peptides in the pentapeptide ligand library is greatly reduced, which brings convenience to the research. The peptide ligand library is a 400-kinase pentapeptide ligand library based on 20 common amino acids that constitute proteins or peptides.

[0008] The steps for using PyRx software for scoring and prediction are as follows: 1) Draw the three-dimensional structure of the target receptor based on the determined target receptor and create a receptor file with structural parameters; 2) Draw three-dimensional structures based on the established peptide ligand library and create ligand files with structural parameters; the three-dimensional structures of pentapeptide ligands are generated by drawing pentapeptide ligands using Chem3D software, and the force field is optimized. At the same time, the optimized amino acids and peptide molecules are saved in SDF format to prepare for molecular docking. 3) Based on the receptor and ligand files, pre-calculate the grid diagram for each atom type in the docking ligand; 4) Establish docking parameter files based on receptor and ligand files; 5) Perform docking calculations based on the files generated in steps 1) to 4), save the docking results in the results file, and perform scoring prediction.

[0009] Furthermore, step (3) involves predicting the molecular docking of peptides in the peptide ligand library with the target receptor. Specifically, Autodock Vina in PyRx software is used to score the binding between macromolecular receptors and small molecule ligands. The spatial fit of different conformations of each ligand with the keap1 structure is calculated and scored sequentially, representing the free energy of binding between the receptor and the ligand. The smaller the binding free energy, the more stable the binding. The preferred docking center coordinates are (5.1, 9.1, 1.1), and the docking box size is 60 x 60 x 60. The inhibitory activity of the target receptor is analyzed by the amino acid composition and position of the peptide, and then visualized.

[0010] This invention first uses molecular docking to predict the interaction between P1 and Keap1. Since the peptide has a structure mimicking Nrf2, docking the peptide with Keap1 allows free Nrf2 to translocate to the cell nucleus and bind to the antioxidant response element ARE, initiating the transcription and expression of downstream antioxidant-related genes (such as CAT, SOD, GPXI, etc.), thereby protecting cells from oxidative damage. The peptide's Nrf2-like structure allows the pentapeptide to form a small bend, facilitating better binding with Keap1. Specifically, the process includes identifying the target receptor; establishing a peptide library with binding affinity to the target receptor; and predicting molecular docking between peptides in the peptide ligand library and the target receptor.

[0011] In summary, this invention focuses on the significant impact of peptides on the key oxidation target keap1, broadening the research direction of antioxidant stress peptides. At the same time, by using computer software and molecular docking technology, high-throughput screening of peptides was carried out, successfully establishing a scoring mechanism for the functionality of active peptides, and analyzing the influence of amino acid composition and structure on the functionality of active peptides.

[0012] Compared with existing technologies, this invention can rapidly and efficiently screen peptides with small intestinal antioxidant activity, and has the advantages of a wide target range, strong operability, and low investment. Attached Figure Description

[0013] Figure 1 This is the three-dimensional structure of L166 combined with Keap1 in Example 1; Figure 2 This is a two-dimensional structural diagram of the combination of L166 and Keap1 in Example 1; Figure 3 This is the three-dimensional structure of L129 combined with Keap1 in Example 1; Figure 4 This is a two-dimensional structural diagram of the combination of L129 and Keap1 in Example 1; Figure 5 This is the three-dimensional structure of L168 combined with Keap1 in Example 1; Figure 6 This is a two-dimensional structural diagram of the combination of L168 and Keap1 in Example 1; Figure 7 To implement the Vina score where n is 1-25 in step 1; Figure 8 To implement the Vina score where n is 26-50 in step 1; Figure 9 To implement the Vina score where n is 51-75 in step 1; Figure 10 To implement the Vina score where n is 76-100 in step 1; Figure 11 To implement the Vina score where n is 101-125 in step 1; Figure 12 To implement the Vina score where n is 126-150 in step 1; Figure 13 To implement the Vina score where n is 151-175 in step 1; Figure 14 To implement the Vina score where n is 176-200 in step 1; Figure 15 To implement the Vina score where n is 201-225 in step 1; Figure 16 To implement the Vina score where n is 226-250 in step 1; Figure 17 To implement the Vina score where n is 251-275 in step 1; Figure 18 To implement the Vina score where n is 276-300 in step 1; Figure 19 To implement the Vina score where n is 301-325 in step 1; Figure 20 To implement the Vina score where n is 326-350 in step 1; Figure 21To implement the Vina score where n is 351-375 in step 1; Figure 22 To implement the Vina score where n is 376-400 in step 1. Implementation

[0014] The present invention will be further described below with reference to the accompanying drawings and embodiments. Those skilled in the art can refer to the content of this document and appropriately improve the process parameters to achieve the desired result. It should be particularly noted that all similar substitutions and modifications are obvious to those skilled in the art and are considered to be included in this invention. The methods and products of the present invention have been described through preferred embodiments. Those skilled in the art can clearly modify or appropriately change and combine the methods described herein without departing from the content, spirit, and scope of the present invention to implement and apply the technology of the present invention. Example

[0015] All reagents involved in the embodiments of this invention are commercially available products and can be purchased through commercial channels.

[0016] (1) Target receptor structure determination: The structure varies depending on the capture method or the small molecules it binds to. By comparing crystal structures in the protein database PDB, the binding of peptide molecules to keap1 is simulated. The structure of keap1 and peptide crystals is selected for molecular docking. The three-dimensional crystal structure of the receptor keap1 is shown in the figure below. Figure 1 As shown; (2) Establishment of peptide ligand library: Based on the 20 common amino acids (L-type) that constitute proteins or peptides, a library of 400 pentapeptide ligands was established. The three-dimensional structure of the ligand library was drawn using Chem3D software. At the same time, the energy of the ligand structure was optimized using force field. The optimized peptide molecules were saved in sdf format for later use. (3) Molecular docking: Autodock Vina software was used to perform high-throughput screening of keap1 peptide inhibitors.

[0017] Specifically, the Chem3D software was used to draw the two-dimensional structure of the ligand library, and the energy of the ligand structure was optimized using a force field. Finally, the sdf format file was converted into a pbdqt format file; the crystal structure 2flu of keap1 was downloaded from the PDB database to obtain the PDB format file. Energy grid calculations were performed on chain A to locate the center position of the original ligand NMN. The box size was 60 x 60 x 60 grid points, which represents the ligand-receptor binding site. The original ligand NMN was deleted, the original water molecules were removed, polar hydrogen atoms were added, and the gas teiger charge was calculated before redistributing AD4 type atoms. Finally, the file was saved in pdbqt format for later use. The binding between the macromolecular receptor and the small molecule ligand was scored using Autodock Vina software in PyRx. The pdbqt format file of keap1 was set as the macromolecular receptor file, and the qpbdqt format files of all ligands were imported as small molecule ligand files. Based on the original position of the ligand peptide in keap1, the docking center coordinates were set to (5.1, 9.1, 1.1), and the docking box size was set to 60 x 60 x 60. Other parameters were left at the software default values. Then, the active peptide ligands were docked one by one with the keap1 crystal structure using Autodock. Vina calculated the spatial fit between the different conformations of each ligand and the keap1 crystal structure and scored them accordingly.

[0018] AutoDock Vina Analysis of 400 Pentapeptides: A library of 400 pentapeptide ligands was formed by combining 20 common amino acids. These 400 pentapeptides were then molecularly docked with keap1 to obtain Vina prediction scores, thereby predicting their binding affinity to keap1. The Vina scores of pentapeptides composed of 20 amino acids are shown below. Figure 7-22 As shown.

[0019] Among 400 pentapeptide structures, three peptides, L166 (WAPIP), L129 (FAPIW), and L168 (WAPIY), achieved the highest molecular docking scores with keap1. Their two-dimensional and three-dimensional structural diagrams are shown below. Figure 1-6 As shown, WAPIP forms two hydrogen bonds with ARG380 and ASN382 of Keap1; FAPIW forms six hydrogen bonds with GLY574, GLN530, TYR572, SER363, ARG380, and ASN414 of Keap1; (3) WAPIY forms six hydrogen bonds with GLN530, ASP529, TYR525, ARG483, GLY574, and ARG415 of Keap1. This indicates that the pentapeptide has a better binding affinity to Keap1 compared to other ligands.

[0020] Table 1 shows the constructed library of 400 polypeptide sequences. Table 1. 400 polypeptide sequences , .

Claims

1. A method for screening peptides that exhibit antioxidant stress response in the small intestine of humans and animals, characterized in that, Includes the following steps: (1) Identification of target receptor: Based on the capture method or the small molecule substances bound, the crystal structure was compared in the protein database to identify Keap1 as the activity screening target. (2) Establishment of a polypeptide ligand library that binds to the target receptor: A polypeptide ligand library is established based on 20 common amino acids. The polypeptide is a pentapeptide. The polypeptide ligand library is a library of 400 pentapeptide ligands established based on 20 common amino acids. The penultimate amino acid at the C-terminus of the pentapeptide is isoleucine (I). (3) Prediction of molecular docking between peptides and target receptors in peptide ligand library: AutodockVina software in PyRx was used to score the binding between macromolecular receptors and small molecule ligands, with the docking center coordinates being (5.1, 9.1, 1.1) and the docking box size being 60×60×60.

2. The method according to claim 1, characterized in that, The specific steps for molecular docking prediction in step (3) include: 1) Draw the three-dimensional structure of the target receptor based on the determined target receptor and create a receptor file with structural parameters; 2) Draw three-dimensional structures based on the established polypeptide ligand library and create ligand files with structural parameters; 3) Pre-calculate the grid diagram for each atom type in the docking ligand based on the acceptor and ligand files; 4) Establish docking parameter files based on receptor and ligand files; 5) Perform docking calculations based on the files generated in steps 1) to 4), and save the docking results in the results file.

3. The method according to claim 1, characterized in that, In step (2), the three-dimensional structure of the pentapeptide ligand is generated by drawing the Chem3D software, the force field is optimized, and the optimized polypeptide molecule is saved as sdf format.

4. The method according to claim 1, characterized in that, Step (3) is followed by: (4) Analysis of the effect of amino acid composition and position of peptides on the inhibitory activity of target receptors; (5) Visual analysis of the inhibitory activity of peptides on target receptors.

5. The method according to claim 1, characterized in that, The peptides with the lowest binding free energy to Keap1 were screened by molecular docking. The peptides were WAPIP, FAPIW, or WAPIY, and the binding free energies of WAPIP, FAPIW, and WAPIY to Keap1 were -10.84 kcal / mol, -10.74 kcal / mol, and -10.27 kcal / mol, respectively.

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