Method for high-throughput screening of toxic substances targeting OAT1 protein
By constructing a three-dimensional structural model of the hOAT1 protein and conducting virtual screening of a compound library, the problem of high-throughput screening for toxic substances targeting the OAT1 protein was solved, achieving efficient and low-cost pollutant identification and identifying potential environmental factors that induce hyperuricemia.
Patent Information
- Application Number
- CN202511674104.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-14
- Publication Date
- 2026-02-13
AI Technical Summary
There is currently no efficient and low-cost method for high-throughput screening of toxic substances targeting the OAT1 protein, making it difficult to identify environmental factors that induce hyperuricemia.
By constructing a three-dimensional structural model of the hOAT1 protein, molecular docking and molecular dynamics simulations were performed. Combined with virtual screening of the compound library, key residues and binding pockets that bind to hOAT1 were screened out. Batch docking was performed using Autodock Vina, and small molecule compounds with binding energies below -10.5 kcal/mol were screened out as toxic substances.
This technology enables efficient, low-cost, and highly specific screening of toxic substances targeting the OAT1 protein, rapidly identifying potential high-risk pollutants and providing evidence for the environmental causes of hyperuricemia.
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Figure CN121528294A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application belongs to the technical field of bioinformatics and environmental toxicology, ecological toxicology, and specifically relates to a method for high-throughput screening of toxic substances targeting OAT1 protein. BACKGROUND
[0002] In recent years, the prevalence of hyperuricemia (HUA) in China has been increasing year by year, and is showing a trend of younger generation. It has become the second metabolic disease after diabetes. In addition to causing gout, elevated blood uric acid is also closely related to the occurrence and development of chronic kidney disease, endocrine metabolism, cardiovascular and cerebrovascular diseases and other systemic diseases. In 2010, a study entitled "Environment and Disease Risks" published in Science suggested that 70-90% of disease risks may be closely related to environmental pollution.
[0003] Under normal circumstances, uric acid in the blood of the human body is filtered by the glomerulus into the renal tubule, 90% of which is transported to the blood after being absorbed by the reabsorption transporter protein of the proximal renal tubule, and part of it flows through the distal renal tubule and is transported to the renal tubule by the secretion transporter protein, and is finally excreted with urine. Studies have shown that 90% of hyperuricemia is caused by reduced uric acid excretion. Human organic anion transporter 1 (hOAT1) promotes the entry of uric acid into the renal tubule, which is a key transporter protein for uric acid excretion in the human body. Studies have shown that the concentration of blood uric acid in the human body is positively correlated with new pollutants such as perfluorooctanoic acid (PFOA), perfluorooctanesulfonyl compound (PFOS), bisphenol A (BPA) and the like. These pollutants bind to OAT1, leading to reduced uric acid excretion and posing a risk of disease.
[0004] Although existing studies have found that many chemicals can cause HUA by binding to hOAT1, most of them are based on experimental methods and only study the association between some new pollutants and HUA. At present, there are more than 350,000 chemicals and their mixtures used in the global market, and it is not clear whether other chemicals can cause HUA. If experiments are carried out one by one, blood sample analysis, chemical standard purchase and other issues are involved, which is time-consuming and costly. Therefore, high-throughput screening of toxic substances targeting OAT1 protein has become an important problem to be solved. Virtual screening technology based on receptor structure is an important method for drug development and environmental pollutant screening, which can quickly identify potential molecules that bind to receptor proteins. However, so far, there is no virtual screening method for toxic substances targeting OAT1 protein. SUMMARY
[0005] In view of this, the purpose of the present application is to provide a method for high-throughput screening of toxic substances targeting OAT1 protein.
[0006] In order to achieve the above object, the present application provides the following technical solutions: In the first aspect, the present application provides a method for high-throughput screening of toxic substances targeting OAT1 protein, comprising the following steps: (1) obtaining the amino acid sequence of hOAT1 protein, and constructing a three-dimensional structure model of hOAT1 protein by homology; (2) performing molecular docking and molecular dynamics simulation on PFOA and the three-dimensional structure model of hOAT1 protein constructed in step (1), to determine the key residues and binding pocket range of the pollutants combined with hOAT1; (3) based on the existing chemical list in China, removing the repeated substances, mixtures and inorganic substances, constructing a compound library, and obtaining their SMILES code; (4) using the receptor-based virtual screening method to dock the small molecules in the compound library in step (3) with hOAT1 protein in batches; (5) ranking according to the docking binding energy score, and screening the toxic substances targeting hOAT1 protein.
[0007] Further, the amino acid sequence of hOAT1 protein in step (1) is obtained from GenBank database, GenBank: AF097490, and homology modeling is performed with rOAT1 as a template, and the amino acid sequence of rOAT1 is obtained from GenBank database, GenBank: AB004559.
[0008] Further, step (1) uses SWISS-MODEL platform to construct the three-dimensional structure of hOAT1 by homology with rOAT1 as a template.
[0009] Further, the compound library constructed in step (3) contains 18905 small molecules, most of which are organic substances.
[0010] Further, the receptor-based virtual screening method in step (4) comprises the following steps: 1) extracting hOAT1 in the stable frame number of molecular dynamics simulation in step (2) to obtain a reliable receptor structure, and using Autodock Tools to perform water removal and hydrogenation operation, and converting it to pdbqt file format; 2) according to the SMILES code of the small molecule ligand in the compound library in step (3), running python code to batch convert it to pdb file format and store it in a folder, then opening the folder storing the small molecules, using Open Babel to batch convert the small molecules to pdbqt file and performing water removal and hydrogenation operation; 3) Put the receptor converted to pdbqt format in step 1) and all ligands converted to pdbqt format in step 2) in the same folder, and also put vina.exe, vina_split.exe and vina_license.trf after the installation of Autodock or Autodock Vina in the folder; create a new conf.txt text file and fill in the docking related information: the name of the receptor pdbqt file, the docking box parameters; then create a dock.txt file and fill in the code to automatically dock the small molecule ligand to the binding pocket of the receptor in the conf.txt file; change the file suffix from.txt to.bat, and double-click the bat file to start batch docking.
[0011] Further, the small molecule compound in the compound library with a binding energy lower than -10.5 kcal / mol to the hOAT1 protein is defined as a toxic substance targeting the OAT1 protein.
[0012] Compared with the prior art, the present application has the following beneficial effects: The method for high-throughput screening of toxic substances targeting the OAT1 protein provided by the present application is a batch screening method for the hOAT1 protein, and compared with the traditional experimental method, the method of the present application has the advantages of high efficiency, low cost, high specificity, and can avoid analyzing actual blood samples with complex components, and can be used for high-throughput screening of toxic substances targeting the OAT1 protein, and provides a basis for environmental inducement of hyperuricemia, and quickly identifies potential high-risk pollutants. BRIEF DESCRIPTION OF DRAWINGS
[0013] In order to more clearly illustrate the embodiments of the present application, the drawings involved in the embodiments will be briefly introduced below.
[0014] Figure 1 The experimental flowchart of the method for high-throughput screening of toxic substances targeting the OAT1 protein of the present application.
[0015] Figure 2 The OAT1 protein structure diagram constructed by homology modeling in Example 1.
[0016] Figure 3 The Ramachandran plot result of OAT1 constructed by homology modeling in Example 1.
[0017] Figure 4 The docking result diagram of OAT1 and PFOA constructed by homology modeling in Example 1, wherein the yellow part is halogen bond interaction; the pink part is salt bridge interaction. DETAILED DESCRIPTION
[0018] The application will be described in detail below with reference to examples, but the embodiments of the application are not limited thereto. It is obvious that the examples described below are only some of the embodiments of the application, and for those skilled in the art, other similar embodiments can be obtained without creative labor, which fall within the protection scope of the application.
[0019] Software and environment configuration: SWISS-MODEL, Autodock Vina (v.1.2.1), AutoDock Tools (v.1.5.6), PYMOL, CHARMM-GUI, AMBER20, Python, Anaconda3, Open Babel.
[0020] Example 1 (1) Homology modeling of hOAT1 protein At present, the three-dimensional structure of hOAT1 protein has not been resolved by experimental techniques, but its amino acid sequence is known and highly conserved among species. Therefore, the amino acid sequences of hOAT1 (GenBank: AF097490) and rOAT1 (GenBank: AB004559) were obtained from the GenBank database, and the three-dimensional structure of hOAT1 was constructed using rOAT1 as a template on the SWISS-MODEL platform, as shown in Figure 2 , the modeling results were evaluated by Ramachandran plot and LDDT score, as shown in Figure 3 , the Ramachandran plot showed that 94.4% of the residues in the modeling results fell in the best region, and the LDDT score was 0.81, indicating that the modeling results were of high quality, and the structure could be used for subsequent research.
[0021] (2) hOAT1 and PFOA molecular docking and molecular dynamics simulation A typical new pollutant PFOA was selected for molecular docking and molecular dynamics simulation with the three-dimensional structure of hOAT1 obtained in step (1). The conformation of the complex with the lowest binding energy obtained after molecular docking was imported into pymol for visual analysis, and the trajectory after molecular dynamics simulation was analyzed to calculate the binding free energy and energy decomposition of amino acid residues, determine the key residues and binding pocket position when the pollutant binds to hOAT1, and provide docking box information for subsequent batch docking.
[0022] (3) Construction of small molecule compound database In order to further explore the possible toxic substances targeting OAT1 protein, the present application carries out data screening based on the existing chemical list of China obtained from the website of the Ministry of Ecology and Environment. In the screening process, the repeated substances, mixtures, inorganic substances and the like are excluded. This is because the toxicity assessment of mixtures and inorganic substances is more complex, and the repeated substances will waste the calculation resources. Through a series of screening and exclusion, a database containing 18905 compounds is finally constructed, most of which are organic substances.
[0023] (4) Virtual screening based on receptor hOAT1 In order to ensure that the virtual screening results can reflect the actual situation as much as possible, the present application adopts a series of rigorous pretreatment steps.
[0024] 1) Extract hOAT1 in the stable frame number in the molecular dynamics simulation in step (2), obtain a reliable receptor structure, and use Autodock Tools to perform water removal and hydrogenation operation, and convert it into pdbqt file format; 2) According to the SMILES code of 18905 small molecule ligands in the database in step (3), run the python code to batch convert it into pdb file format and store it in a folder. Then, open the folder storing the small molecules, use OpenBabel to batch convert the small molecules into pdbqt files and perform water removal and hydrogenation operation; this conversion process realizes the conversion from a simple linear expression to a specific three-dimensional structure, providing an intuitive structure model for subsequent docking work and providing the structure information of the ligand for the present application.
[0025] 3) Batch docking of Autodock Vina Put the receptor and all ligands converted into pdbqt format into the same folder, and also put vina.exe, vina_split.exe and vina_license.trf installed after Autodock or Autodock Vina into the folder; create a new conf.txt text file and fill in the docking related information (receptor pdbqt file name, docking box parameters); create a new dock.txt file and fill in the code to automatically dock the small molecule ligand to the receptor binding pocket in the conf.txt file; change the file suffix from.txt to.bat, and double-click the bat file to start batch docking.
[0026] The folder with the name of the ligand will be generated during the docking process, containing two results of the docking log.txt and out.pdbqt, corresponding to the binding energy and docking mode respectively. After the docking is completed, the result file log.txt is scattered in each folder, a new result.txt text file is created, and the final.txt file is generated by running the batch file by double-clicking, which can be automatically opened for viewing. The final.txt contains the docking binding energy and possible binding mode of 18905 small molecule ligands with the targeted OAT1 protein; the above operations need to be performed in the same folder.
[0027] (5) Virtual screening result data processing The binding energy score is an important indicator for evaluating the interaction strength of chemicals and hOAT1. According to this score, the compounds in the database were ranked. In order to further screen compounds with potential biological activity, the threshold value of binding energy less than-10.5 kcal / mol was set, and 300 toxic substances targeting OAT1 protein were screened out, as shown in Table 1.
[0028] Table 1. List of toxic substances targeting OAT1 protein screened by virtual screening
[0029] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.
Claims
1. A method for high-throughput screening of toxic substances targeting the OAT1 protein, comprising the following steps: (1) Obtain the amino acid sequence of hOAT1 protein and construct a three-dimensional structural model of hOAT1 protein using homology; (2) Perform molecular docking and molecular dynamics simulation on PFOA and the three-dimensional structure model of hOAT1 protein constructed in step (1) to determine the key residues and binding pocket range of pollutants binding to hOAT1; (3) Based on the existing chemical list in China, after removing duplicate substances, mixtures and inorganic substances, construct a compound library and obtain their SMILES codes; (4) The small molecules in the compound library in step (3) are batch-docked with hOAT1 protein using a receptor-based virtual screening method; (5) Rank the substances that target hOAT1 protein by scoring their docking binding energy.
2. The method according to claim 1, characterized in that, The amino acid sequence of the hOAT1 protein mentioned in step (1) was obtained from the GenBank database, GenBank: AF097490. Homology modeling was performed using rOAT1 as a template. The amino acid sequence of rOAT1 was obtained from the GenBank database, GenBank: AB004559.
3. The method according to claim 1, characterized in that, Step (1) Using the SWISS-MODEL platform, with rOAT1 as the template, construct the three-dimensional structure of hOAT1 from the same source.
4. The method according to claim 1, characterized in that, The compound library constructed in step (3) contains 18,905 small molecules, most of which are organic.
5. The method according to claim 1, characterized in that, The receptor-based virtual screening method described in step (4) includes the following steps: 1) Extract hOAT1 from the stable frames of molecular dynamics simulation in step (2) to obtain a reliable receptor structure. Use Autodock Tools to perform dehydration and hydrogenation operations on it and convert it into pdbqt file format. 2) Based on the SMILES codes of small molecule ligands in the compound library in step (3), run Python code to convert them in batches to pdb file format and store them in a folder. Then, open the folder where the small molecules are stored, use Open Babel to convert the small molecules into pdbqt files in batches, and perform dehydration and hydrogenation operations. 3) Place the receptor converted to pdbqt format in step 1) and all ligands converted to pdbqt format in step 2) in the same folder. Also place the vina.exe, vina_split.exe, and vina_license.trf files after installing Autodock or Autodock Vina in the same folder. Create a new text file named conf.txt and fill in the docking information: receptor pdbqt file name and docking box parameters. Then create a new file named dock.txt and fill in the code to automatically dock the small molecule ligands to the receptor binding pocket in the conf.txt file. Change the file extension from .txt to .bat and double-click the bat file to start batch docking.
6. The method according to claim 1, characterized in that, Small molecule compounds in the compound library with a binding energy to the hOAT1 protein of less than -10.5 kcal / mol are defined as toxic substances targeting the OAT1 protein.