A method for virtual screening of sEH inhibitors from traditional Chinese medicine ingredients and application thereof

Abstract

The application discloses a method for screening sEH inhibitors from traditional Chinese medicine components and application, and comprises the following steps: constructing a pharmacophore model composed of 5 pharmacodynamic characteristic elements, i.e., 1 aromatic ring center, 2 hydrophobic centers and 2 hydrogen bond donors, to virtually screen traditional Chinese medicine components, and then performing molecular docking screening and in-vitro sEH activity inhibition experiments, so that isorhamnetin, acacetin, hydroxysafflor yellow A, cirsiline or cosmosiin are screened for the first time and have sEH inhibition effects, and the IC 50 range is 13.63 μM-37.81 μM, K i value range is 7.612 μM-28.05 μM, and the sEH inhibitors can be applied to preparation.

Description

Technical Field

[0001] The present invention relates to a method for screening sEH inhibitors and applications thereof, in particular to a method for virtually screening sEH inhibitors from traditional Chinese medicine components and applications thereof. Background Art

[0002] Soluble epoxide hydrolase (sEH) is an enzyme in the arachidonic acid cascade. Cytochrome P450 (CYP) cyclooxygenases CYP2J and CYP2C catalyze the epoxidation of arachidonic acid to produce epoxyeicosatrienoic acids (EETs). sEH catalyzes the hydrolysis of epoxide EETs into their corresponding diols. EETs have potent vasodilatory effects, modulating inflammation and protecting the myocardium from ischemia-reperfusion injury by inhibiting NF-κB. However, sEH rapidly hydrolyzes EETs to produce the corresponding dihydroxyeicosatrienoic acids, which have significantly lower bioactivity than EETs. Preclinical studies have shown that sEH knockout increases EET production and enhances anti-inflammatory and cardiovascular protective effects. Therefore, sEH has become an important target for new drug development. sEH inhibitors can play a role in a variety of diseases, such as anti-inflammation, analgesia (inflammatory or neuropathic pain), anti-hypertension, protection against ischemic damage, improvement of myocardial hypertrophy and prevention of myocardial remodeling, anti-atherosclerosis and treatment of renal failure.

[0003] Currently, sEH inhibitors primarily fall into two categories: urea compounds and amide compounds. GSK2256294, a picoM-level sEH inhibitor, is in Phase 2 clinical trials for chronic obstructive pulmonary disease. Clinical trial results have shown that it can cause significant headaches and contact dermatitis in healthy subjects. Furthermore, obese smokers experience varying degrees of nasopharyngitis and induce kidney stones. TMS-007, used to treat acute ischemic stroke, only causes mild epistaxis, but can only be administered intravenously. This single route of administration limits its clinical application.

[0004] Traditional Chinese medicine (TCM) has been used clinically for thousands of years and boasts diverse structures, providing a rich resource of compounds for drug screening. Current TCM screening still relies primarily on traditional high-throughput screening, which involves isolating and extracting TCM components from the herbal remedies and then conducting experimental validation to determine their inhibitory effects on their respective targets. For example, the TCM components disclosed in the literature as sEH inhibitors—such as taxifolin and tanshinone IIa—were identified through high-throughput screening. This screening method requires obtaining compound standards, which is costly and time-consuming.

[0005] Virtual screening ( in SilicoVirtual screening (VFS) technology is a common strategy used in the early stages of drug discovery to rapidly identify chemical entities with potential interactions with target proteins. Prior to conducting bioactivity tests, multiple target molecules in a compound library are pre-screened using computer virtual screening techniques to obtain small molecules with high affinity for the target protein. Virtual screening techniques can be divided into ligand-based virtual screening methods, which primarily utilize pharmacophore models, and receptor-based virtual screening methods, which primarily utilize molecular docking. The former begins with reported active compounds and searches for similar compounds in the database based on similarity; the latter begins with receptor proteins and focuses on spatial and energy matching between the compound and the receptor protein, effectively avoiding activity changes caused by minor structural changes in the active compound. However, the construction of the pharmacophore model is crucial; for the same target protein, different pharmacophore models can lead to completely different screening results.

[0006] To date, there have been no reports on virtual screening of sEH inhibitors from traditional Chinese medicine ingredients. Summary of the Invention

[0007] The present invention aims to solve the problems existing in the prior art and provides a method and application for virtual screening of sEH inhibitors from traditional Chinese medicine components.

[0008] The technical solution of the present invention is: a method for virtual screening of sEH inhibitors from traditional Chinese medicine ingredients, which is carried out according to the following steps:

[0009] Step 1. Virtual screening based on pharmacophore model

[0010] Step 1.1 Select the best pharmacophore model

[0011] Step 1.1.1 Select training set and preprocess

[0012] Several small molecule compounds with diverse structures that inhibit sEH were selected as training sets. The 2D structures of the compounds were drawn using InDraw software and saved in mol format. The structures were then imported into DS for structural optimization and hydrogenation pretreatment.

[0013] Step 1.1.2 Build the initial pharmacophore model

[0014] Import the preprocessed training set into the Conmon Feature Pharmacophore Generation module and set the Principal and MaxOmitFeat parameters according to the compound activity: IC 50For compounds with concentrations <10 nM, the Principal parameter was set to 2, and the MaxOmitFeat parameter was set to 0. For the remaining compounds, both the Principal and MaxOmitFeat parameters were set to 1. Pharmacophore characteristic element clustering analysis was performed to query the common pharmacophore characteristic elements in the training set. Characteristic elements such as hydrogen bond acceptors, hydrogen bond donors, hydrophobic centers, negatively charged centers, and aromatic ring centers were selected, and best-fitting was used to generate up to 255 conformations for each small molecule to characterize the conformational space of the small molecule. Conformations with energy values ​​within the energy threshold of 20 kcal / mol were retained. After calculation, multiple initial pharmacophore models were generated.

[0015] Step 1.1.3 Select the best pharmacophore model

[0016] A test set consisting of multiple positive and negative compounds targeting sEH was downloaded from the Binding Database. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) of each initial pharmacophore model were calculated. The initial pharmacophore model with high sensitivity, specificity, and AUC was selected as the optimal pharmacophore model. The optimal pharmacophore model consisted of five pharmacophoric characteristic elements: one aromatic ring center, two hydrophobic centers, and two hydrogen bond donors.

[0017] Step 1.2 Virtual screening based on pharmacophore model

[0018] The Ligand Pharmacophore Mapping module was used with the Maximum Omitted Features parameter set to 1. A flexible matching approach was used to quickly match the optimal pharmacophore model to the database, and compounds with a matching value greater than 2.0 were selected.

[0019] Step 2. Molecular docking screening

[0020] Step 2.1 Receptor selection and pretreatment

[0021] The crystal structure of human sEH was downloaded from the PDB database with a resolution of 2.94 Å and imported into DS2020. The ProteinPrepare module was selected for pretreatment with hydrogenation, dehydration, and amino acid fragment completion.

[0022] Step 2.2 Define the active site

[0023] Expand the pretreated receptor, select the original ligand 2RU, expand Define and Edit BindingSite, select From Current Selection, define the active site based on the original ligand, modify the active site radius to 10.25 Å, and delete 2RU.

[0024] Step 2.3 Semi-flexible molecular docking

[0025] The compounds obtained by virtual screening of the pharmacophore model were imported into the CDOCKER module, the receptor was selected as 4OCZ, 10 conformations were randomly generated for each compound, and the RMSD threshold was set to 0.5 Å;

[0026] Step 2.4 Display and analysis of non-bonded interactions

[0027] In the tool browser, click the Ligand Interactions program to display the non-bonded interactions of amino acids. Click Show 2D Diagram to generate a two-dimensional planar diagram of the ligand-protein interaction. By observing the ligand-protein interaction and key amino acids and groups, multiple candidate sEH target compounds can be identified.

[0028] Step 3. In vitro sEH enzyme activity inhibition assay

[0029] The obtained multiple candidate sEH target compounds were subjected to in vitro sEH activity inhibition experiments, and the compounds with an inhibition rate greater than 50% at a concentration of 100 μmol were selected as sEH target compounds.

[0030] The invention relates to the use of isoliquiritigenin, acacetin, hydroxysafflor yellow A, cynarin or quercetin obtained by the above method in the preparation of sEH inhibitors.

[0031] The present invention constructs a pharmacophore model consisting of five pharmacophoric characteristic elements, namely, an aromatic ring center, two hydrophobic centers, and two hydrogen bond donors, to conduct virtual screening of traditional Chinese medicine ingredients. Then, through molecular docking screening and in vitro sEH activity inhibition experiments, isoliquiritigenin, acacetin, hydroxysafflor yellow A, cynarin, or quercetin are screened for the first time to have sEH inhibitory effects. 50 The range is 13.63μM~37.81μM, K i The values ​​range from 7.612 μM to 28.05 μM, which can be used to prepare sEH inhibitors. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Schematic diagram of the optimal pharmacophore model structure and its matching with the training set molecules according to an embodiment of the present invention.

[0033] Figure 2 Schematic diagram of the interaction between isoliquiritigenin and 4OCZ amino acids.

[0034] Figure 3 Schematic diagram of the interaction between acacetin and 4OCZ amino acids.

[0035] Figure 4Schematic diagram of the interaction between taxifolin and 4OCZ amino acids.

[0036] Figure 5 Schematic diagram of the interaction between hydroxysafflor yellow A and 4OCZ amino acid.

[0037] Figure 6 Schematic diagram of the interaction between cynarin and 4OCZ amino acids.

[0038] Figure 7 Schematic diagram of the interaction between quercetin and 4OCZ amino acids.

[0039] Figure 8 Isoliquiritigenin, acacetin and taxifolin IC 50 and K i Schematic diagram of the value results.

[0040] Figure 9 It is the IC of hydroxysafflor yellow A, cynarin and quercetin 50 and K i Schematic diagram of the value results.

[0041] Figure 10 Schematic diagram of the effects of different compounds on Hcy-stimulated VSMC cell viability.

[0042] Figure 11 Schematic diagram of the effects of different compounds on the secretion of inflammatory factors by VSMC cells stimulated by Hcy.

[0043] Figure 12 Schematic diagram of the effects of different compounds on sEH protein expression. DETAILED DESCRIPTION

[0044] The present invention provides a method for screening sEH inhibitors from traditional Chinese medicine ingredients, which is carried out according to the following steps:

[0045] Step 1. Virtual screening based on pharmacophore model

[0046] Step 1.1 Select the best pharmacophore model

[0047] Step 1.1.1 Select training set and preprocess

[0048] Nine small molecule compounds with diverse structures that inhibit sEH were selected from published literature as a training set. The 2D structures of the compounds were drawn using InDraw software and saved in mol format. The structures were then imported into DS for structural optimization and hydrogenation pretreatment.

[0049] Step 1.1.2 Build the initial pharmacophore model

[0050] Import the preprocessed training set into the Conmon Feature Pharmacophore Generation module and set the Principal and MaxOmitFeat parameters according to the compound activity: IC 50 For compounds with concentrations <10 nM, the Principal parameter was set to 2, and the MaxOmitFeat parameter was set to 0. For the remaining compounds, both the Principal and MaxOmitFeat parameters were set to 1. Pharmacophore characteristic element clustering analysis was performed to query the common pharmacophore characteristic elements in the training set. Hydrogen bond acceptor, hydrogen bond donor, hydrophobic center, negatively charged center, and aromatic ring center were selected. Best mode superposition was used to generate up to 255 conformations for each small molecule to characterize the conformational space of the small molecule. Conformations with energy values ​​within the energy threshold of 20 kcal / mol were retained. After calculation, the 10 initial pharmacophore models shown in Table 1 were generated.

[0051] Table 1

[0052]

[0053] Step 1.1.3 Select the best pharmacophore model

[0054] A test set consisting of 70 positive compounds and 225 negative compounds targeting sEH was downloaded from the Binding Database (https: / / www.bindingdb.org / ). The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) of each initial pharmacophore model were calculated. The initial pharmacophore model with the highest sensitivity, specificity, and AUC was selected as the optimal pharmacophore model. A comparison shows that No. 4 in Table 1 has the highest sensitivity, specificity, and AUC, making it the optimal pharmacophore model.

[0055] The optimal pharmacophore model structure is as follows Figure 1 As shown in A, it consists of 5 pharmacodynamic characteristic elements, including 1 aromatic ring center, 2 hydrophobic centers and 2 hydrogen bond donors; the best match with the training set molecules is as follows Figure 1 B (Fitvalue=4.99962); the worst match with the training set molecules is as follows Figure 1 As shown in C (Fitvalue=1.55905).

[0056] Step 1.2 Virtual screening based on pharmacophore model

[0057] The Ligand Pharmacophore Mapping module was used with the Maximum Omitted Features parameter set to 1. A flexible matching approach was used to quickly match the optimal pharmacophore model to the database, and compounds with matching values ​​greater than 2.0 were selected, resulting in 5942 compounds.

[0058] Step 2. Molecular docking screening

[0059] Step 2.1 Receptor selection and pretreatment

[0060] The human sEH crystal structure (PDB ID: 4OCZ) was downloaded from the PDB database (https: / / www.rcsb.org / ) with a resolution of 2.94 Å. The structure was imported into DS2020 and the Protein Prepare module was selected for pretreatment, including hydrogenation, dehydration, and amino acid fragment completion.

[0061] Step 2.2 Define the active site

[0062] Expand the pretreated receptor, select the original ligand 2RU, expand Define and Edit BindingSite, select From Current Selection, define the active site based on the original ligand, modify the active site radius to 10.25 Å, and delete 2RU.

[0063] Step 2.3 Semi-flexible molecular docking

[0064] 5942 compounds obtained through virtual screening of the pharmacophore model were imported into the CDOCKER module. The receptor was selected as 4OCZ. Ten conformations were randomly generated for each compound. The RMSD threshold was set to 0.5 Å to ensure that the docked conformations were as diverse as possible.

[0065] Step 2.4 Display and analysis of non-bonded interactions

[0066] In the tool browser, click the Ligand Interactions program to display the non-bonded amino acid interactions. Click Show 2D Diagram to generate a two-dimensional planar diagram of the ligand-protein interaction. By observing the ligand-protein interaction and key amino acids and groups, 25 candidate sEH target compounds were identified as shown in Table 2.

[0067] Table 2

[0068]

[0069] Step 3. In vitro sEH enzyme activity inhibition assay

[0070] The 25 candidate sEH target compounds obtained were subjected to in vitro sEH activity inhibition experiments, and compounds with an inhibition rate greater than 50 at a concentration of 100 μmol were selected as sEH target compounds. Finally, isoliquiritigenin, acacetin, taxifolin, hydroxysafflor yellow A, cynarin and quercetin were screened as sEH target compounds, namely sEH inhibitors.

[0071] The virtual screening results of the 6 screened compounds are shown in Table 2:

[0072]

[0073] In Table 2, the Fitvalue is the pharmacophore matching value, and CdockerEnergy and CdockerInteractionEnergy are the molecular docking scoring values.

[0074] The schematic diagrams of the interaction between the 6 screened compounds and 4OCZ amino acids are shown as follows: Figure 2-Figure 7 As shown, they are isoliquiritigenin, acacetin, taxifolin, hydroxysafflor yellow A, cynarin and quercetin.

[0075] The six sEH target compounds obtained were subjected to the following in vitro sEH activity inhibition experiment:

[0076] 1. Prepare the working solution

[0077] 1) Preparation of sEH target compound standard solution

[0078] Six sEH target compound standards (isoliquiritigenin, acacetin, taxifolin, hydroxysafflor yellow A, cynarin, and quercetin, hereinafter referred to as compounds) were prepared as 40 mM stock solutions and subsequently diluted with buffer to appropriate concentrations for experiments.

[0079] 2) Preparation of sEH enzyme, probe substrate, probe product and positive drug

[0080] The human recombinant sEH mother solution with a concentration of 128 μg / mL was prepared with buffer; 40 mM positive drug TPPU, probe substrate PHOME mother solution and probe product mother solution were prepared with DMSO, and the solutions were aliquoted and stored at -80°C for later use.

[0081] 2. Establishment of the sEH in vitro enzyme activity assay

[0082] PHOME was used as the probe substrate, and human recombinant sEH stock solution was added. The hydrolysis reaction was performed entirely in buffer. The specific reaction system was as follows: a total reaction volume of 200 μL, including: 20 μL human recombinant sEH stock solution, 1 μL probe substrate stock solution, 1 μL compound, and 178 μL buffer. The buffer, compound, and human recombinant sEH stock solution were added on ice and mixed thoroughly. The mixture was then pre-incubated at 37°C for 1 minute. The probe substrate stock solution was then added to initiate the reaction and timed. After incubation in a 37°C thermostatic metal bath with shaking for the appropriate reaction time, 100 μL of acetonitrile was added to terminate the reaction. The product fluorescence was monitored using a microplate reader with excitation / emission wavelengths set to 330 nm / 456 nm in 2 nm increments.

[0083] 3. Inhibitor half inhibitory concentration (IC 50 ) detection

[0084] For each compound, 6 to 7 concentration points were selected to test the IC of the compound for inhibiting sEH. 50 The inhibitory rate of the compound against human sEH was calculated using the following formula: Inhibition rate = [1 - (experimental group - background group) ÷ (blank control group - background group)] × 100%. All samples were performed in triplicate, and the calculated results are mean ± standard deviation. The IC values ​​were calculated using nonlinear fitting with GraphPad Prism 8.0 software. 50 value.

[0085] 4. Inhibition Kinetics Analysis

[0086] IC 50 and K i Both characterize the affinity of the compound for the enzyme, but K i With IC 50 In comparison, it is not affected by variables such as enzyme concentration and substrate concentration, and can more accurately reflect the inhibitory strength of the inhibitor on the target. The smaller the value, the stronger the inhibitory ability. K i The specific detection method is as follows: According to IC 50 As a result, 4 different substrate concentrations and 5 different inhibitor concentrations were selected, and the substrate concentrations covered the K m , the concentration of the inhibitor covers the IC 50, where the substrate concentrations were: 2μM, 4μM, 6μM, 10μM, the isoliquiritigenin concentrations were 0μM, 5μM, 10μM, 20μM, 40μM, the taxifolin and hydroxysafflor yellow A concentrations were 0μM, 5μM, 10μM, 25μM, 50μM, and the acacetin, cynarin, and quercetin concentrations were 0μM, 10μM, 25μM, 50μM, 100μM. The substrate concentration was fixed and different concentrations of inhibitors were added. The results were analyzed by Lineweaver-Burk analysis using GraphPad Prism 8.0 software to determine the inhibition type, and the inhibition kinetic constants were calculated by secondary plotting. Then, according to the inhibitor type, the corresponding formula was selected to draw the Michaelis-Menten plot. The formula is as follows:

[0087] 1) Competitive inhibitor: V = (V max S) / [K m (+I / K i ) + S];

[0088] 2) Non-competitive inhibitor: V = (V max S) / [(K m +S) (1+I / K i )];

[0089] 3) Mixed inhibitor: V=(V max S) / [(K m +S) (1+I / α K i )] .

[0090] Where V is the reaction rate; V max is the maximum reaction rate; S is the substrate concentration, I is the inhibitor concentration; K m is the Michaelis constant; K i Inhibition kinetic constants.

[0091] IC values ​​of the 6 compounds screened by the present invention are 50 and K i The results are shown in Table 3 and Figure 8 、 Figure 9 shown.

[0092]

[0093] The results showed that isoliquiritigenin, acacetin, taxifolin, hydroxysafflor yellow A, cynarin or quercetin had sEH inhibitory effects, and their IC 50 The range is 13.63μM~37.81μM,K i The values ​​range from 7.612 μM to 28.05 μM, which can be used to prepare sEH inhibitors.

[0094] 5. Cell-based drug efficacy evaluation experiments

[0095] The present invention adopts a homocysteine ​​(Hcy)-stimulated VSMC cell proliferation model to evaluate the efficacy of the screened compounds (traditional Chinese medicine ingredients).

[0096] 5.1 Cell culture

[0097] Rat VSMCs were cultured in high-glucose medium supplemented with 10% fetal bovine serum in a cell culture incubator at 37°C with 5% CO2. The medium was changed every 24 hours and passaged every 2 days. Cells in the logarithmic growth phase were cryopreserved or used for subsequent experiments.

[0098] 5.2 Evaluation of the inhibitory effect of traditional Chinese medicine components on VSMC proliferation

[0099] VSMC cells in the logarithmic growth phase were taken in a volume of 100 μL and 5×10 4 Cells were seeded uniformly at a density of 100 μL / well in a 96-well plate. After cell attachment, the original culture medium was discarded and 100 μL of blank culture medium was added for 5–6 hours to synchronize cell growth. The cells were divided into control, model, TPPU, tanshinone IIa, and experimental groups to test the inhibitory effects of the TCM components on VSMC proliferation. A pure TCM component group was also established to eliminate the effects of the TCM components themselves on cell viability. Five replicates were set up in each group. The final drug concentrations in each group were 100 μM Hcy, 40 nM TPPU, and 20 μM of each TCM component, respectively. After 24 hours, the original culture medium was discarded, and 100 μL of CCK8 solution was added to each well for a 1-hour reaction. The absorbance was measured at 450 nm. Cell viability was calculated according to the formula: cell viability = (model group - blank group) / (control group - blank group) x 100%.

[0100] The results are as follows Figure 10 The results showed that different compounds had an effect on VSMC cell viability (n=5, ****P <0.0001; nsv.s. control; ##### P<0.0001; ### P<0.001; # P<0.05 vs model). Compared with the control group, the cell viability of the model group was significantly increased. Compared with the model group, all the Chinese herbal ingredient groups reduced cell viability to varying degrees, with the isoliquiritigenin group and the hydroxysafflor yellow A group showing more significant reductions.

[0101] 5.3 Detection of inflammatory factors

[0102] To compare the effects of compounds at the same concentration on inflammation, the final concentration of each Chinese herbal ingredient was 20 μM. The cell supernatant of each group was collected and the levels of interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and carbon monoxide (NO) were measured according to the kit procedures.

[0103] The results are as follows Figure 11 The results showed that the effects of Chinese herbal medicine components on the secretion of inflammatory factors by VSMCs stimulated by Hcy (n=3, ***P <0.0001vs control; ##### P<0.0001; ### P<0.001; ## P<0.01; # P<0.05; ns vs model) were significant. Compared with the control group, the levels of IL-6, TNF-α, and NO secreted by the model group were significantly increased. Compared with the model group, isoliquiritigenin, acacetin, taxifolin, and hydroxysafflor yellow A all reduced the levels of IL-6, TNF-α, and NO to varying degrees. Isoliquiritigenin had a comparable effect to the positive control drug tanshinone IIa. Cynarin and quercetin inhibited the expression of TNF-α but had no significant effect on IL-6 and NO.

[0104] 5.4 Detection of sEH protein expression by Western immunoblotting

[0105] 1) Protein sample preparation

[0106] Discard the supernatant and wash each cell group three times with an appropriate amount of cold PBS. Collect the cells using a cell scraper and transfer them to a centrifuge tube. Centrifuge at 800-1000 rpm for 10 minutes. Add an appropriate amount of PBS to each tube and centrifuge at 800-1000 rpm for 5 minutes, washing twice. While retaining the cell pellet, thoroughly remove the supernatant and add 500 μL of cell lysis buffer to each tube. After low-temperature shaking and lysis for approximately 30 minutes, centrifuge at 20,000 rpm for 20 minutes at 4°C to collect the supernatant and store at -80°C until needed.

[0107] 2) Detection of protein concentration by BCA method

[0108] Prepare a standard protein sample according to the kit instructions. Dilute the sample stock solution 10-fold with ultrapure water and evenly plate 20 μL per well in a 96-well plate, with triplicate wells per group. Quickly add 200 μL of BCA working solution, mix thoroughly, and incubate at 37°C with shaking for 30 minutes. Measure absorbance at 562 nm using a microplate reader. Plot a standard curve and calculate sample concentrations using the standard curve equation. Load 50 μg of protein per well. Add 5× loading buffer and pure water, mix thoroughly, and then boil at 100°C for 10 minutes to denature the protein. Store at -80°C until needed.

[0109] 3) Electrophoresis

[0110] (1) Glue preparation: Prepare 10% stacking gel and separation gel according to the instructions of the gel preparation kit.

[0111] (2) Sample loading: Use a pipette to absorb the protein marker and the sample to be tested. After the pipette tip is inserted into the bottom of the sample loading well, slowly and evenly add the sample.

[0112] (3) Electrophoresis: After the sample is flattened at 80V, switch the voltage to 120V and stop electrophoresis when bromophenol blue reaches the edge of the gel.

[0113] (4) Transfer: Activate a PVDF membrane of the same size as the gel with methanol, and soak the filter paper and fiber pad with an appropriate amount of transfer solution for later use. Open the transfer chuck, with the black side facing upwards, and lay the fiber pad and three filter papers flat. Gently place the gel in the center of the filter paper, and lay the PVDF membrane on top. Cover with three layers of filter paper and fiber pad. Use a glass rod to continuously remove bubbles. Avoid contact between the filter papers on both sides of the membrane to prevent short circuits. Place the transfer tank in an ice box and transfer the membrane at a constant current of 200 mA for about 75 minutes.

[0114] (5) Blocking: Place the PVDF membrane in rapid blocking solution and block on a shaker at room temperature for 10 minutes.

[0115] (6) Immunoreaction: The primary antibody was diluted to an appropriate concentration with TBST and placed in a hybridization bag with the membrane. Incubate at 4°C overnight. Wash the membrane three times with TBST for 15 minutes each. Dilute the secondary antibody to an appropriate concentration with TBST. Incubate at room temperature for 2 hours. Wash the membrane three times with TBST for 15 minutes each.

[0116] (7) Development: Prepare the luminescent developer according to the ECL high-sensitivity kit, mix thoroughly, and evenly spread the developer on the PVDF membrane. Automatically expose and develop the image. Use Image J professional image analysis software to analyze the grayscale value of the bands.

[0117] (8) Graphpad Prism 8.0.2 statistical software was used for data analysis. One-way analysis of variance was used for comparison of means among multiple groups, and P < 0.05 was considered statistically significant.

[0118] The results are as follows Figure 12 The results showed that different compounds affected sEH protein expression (n=3, ****P <0.0001 vs control; ##### P<0.0001; ### P<0.001; ## P<0.01 vs model). Compared with the control group, sEH expression was significantly increased in VSMC cells after Hcy modeling. Compared with the model group, the compounds isoliquiritigenin, acacetin, taxifolin, and hydroxysafflor yellow A all reduced sEH protein expression, while cynarin and quercetin did not significantly reduce sEH expression.

[0119] Experiments show that the isoliquiritigenin, acacetin, hydroxysafflor yellow A, cynarin and quercetin screened out by the present invention can be used to prepare sEH inhibitors.

Claims

1. A method for virtual screening of sEH inhibitors from traditional Chinese medicine ingredients, characterized in that Follow these steps: Step 1. Virtual screening based on pharmacophore model Step 1.1 Select the best pharmacophore model Step 1.1.1 Select training set and preprocess Several small molecule compounds with diverse structures that inhibit sEH were selected as the training set. The 2D structures of the compounds were drawn using InDraw software and saved in mol format. The structures were then imported into DS2020 for structural optimization and hydrogenation pretreatment. Step 1.1.2 Build the initial pharmacophore model Import the preprocessed training set into the Conmon Feature Pharmacophore Generation module and set the Principal and MaxOmitFeat parameters according to the compound activity: IC 50 For compounds with concentrations <10 nM, the Principal parameter was set to 2, and the MaxOmitFeat parameter was set to 0. For the remaining compounds, both the Principal and MaxOmitFeat parameters were set to 1. Pharmacophore characteristic element clustering analysis was performed to query the common pharmacophore characteristic elements in the training set. Characteristic elements such as hydrogen bond acceptors, hydrogen bond donors, hydrophobic centers, negatively charged centers, and aromatic ring centers were selected, and best-fitting was used to generate up to 255 conformations for each small molecule to characterize the conformational space of the small molecule. Conformations with energy values ​​within the energy threshold of 20 kcal / mol were retained. After calculation, multiple initial pharmacophore models were generated. Step 1.1.3 Select the best pharmacophore model A test set consisting of multiple positive and negative compounds targeting sEH was downloaded from the Binding Database. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) of each initial pharmacophore model were calculated. The initial pharmacophore model with high sensitivity, specificity, and AUC was selected as the optimal pharmacophore model. The optimal pharmacophore model consisted of five pharmacophoric characteristic elements: one aromatic ring center, two hydrophobic centers, and two hydrogen bond donors. Step 1.2 Virtual screening based on pharmacophore model The Ligand Pharmacophore Mapping module was used with the Maximum Omitted Features parameter set to 1. A flexible matching approach was used to quickly match the optimal pharmacophore model to the database, and compounds with a matching value greater than 2.0 were selected. Step 2. Molecular docking screening Step 2.1 Receptor selection and pretreatment The crystal structure of human sEH was downloaded from the PDB database with a resolution of 2.94 Å. It was imported into DS2020 and the Protein Prepare module was selected for pretreatment with hydrogenation, dehydration, and amino acid fragment completion. Step 2.2 Define the active site Expand the pretreated receptor, select the original ligand 2RU, expand Define and Edit Binding Site, select From Current Selection, define the active site based on the original ligand, modify the active site radius to 10.25 Å, and delete 2RU. Step 2.3 Semi-flexible molecular docking The compounds obtained by virtual screening of the pharmacophore model were imported into the CDOCKER module, the receptor was selected as 4OCZ, 10 conformations were randomly generated for each compound, and the RMSD threshold was set to 0.5 Å; Step 2.4 Display and analysis of non-bonded interactions In the tool browser, click the Ligand Interactions program to display the non-bonded interactions of amino acids. Click Show2D Diagram to generate a two-dimensional planar diagram of the ligand-protein interaction. By observing the ligand-protein interaction and key amino acids and groups, multiple candidate sEH target compounds can be identified. Step 3. In vitro sEH enzyme activity inhibition assay The obtained multiple candidate sEH target compounds were subjected to in vitro sEH activity inhibition experiments, and the compounds with an inhibition rate greater than 50 at a concentration of 100 μmol were selected as sEH target compounds.

2. Use of isoliquiritigenin, acacetin, hydroxysafflor yellow A, cynarin and quercetin obtained by virtual screening according to the method of claim 1 in the preparation of sEH inhibitors.

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