Antifungal small molecule screening method taking CYP51 protein as target spot and application

Abstract

The invention discloses an antifungal small molecule screening method taking CYP51 protein as a target spot and application, and relates to the technical field of bioinformatics. The method comprises the following steps: constructing a small molecule database for batch molecular docking by utilizing a TCMSP database; performing primary screening on the natural small molecules in the database based on bioavailability and drug-likeness; performing first virtual screening according to a calculation result of the scoring function; matching the pharmacophore model obtained according to the test set molecules and the training set to obtain a final pharmacophore model; and according to the final pharmacophore model, carrying out virtual screening on the natural small molecule database to obtain small molecules with antifungal activity. The screened elemicin and hederasaponin can be developed into a novel antifungal drug which acts on fungal pathogens, a new treatment approach is provided, and the problems of limitation and drug resistance of existing drugs are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of bioinformatics, and in particular to a method for screening antifungal small molecules using CYP51 protein as a target and an application thereof. Background Art

[0002] Fungi are microorganisms that are widely present in nature and are commonly found in soil, air, water, and on human skin and mucous membranes. Fungal infections can cause mild epidermal infections to severe systemic infections. Depending on the site of infection, fungal infections can be divided into superficial fungal infections and invasive fungal infections. Superficial fungal infections mainly affect human skin and hair, with mild symptoms; while invasive fungal infections invade the internal tissues, organs, and blood of the human body, leading to impaired immune function, and even life-threatening in severe cases.

[0003] Common invasive fungi include Candida albicans and Aspergillus. After Candida albicans infects the human body, it mainly endangers health by producing virulence factors. Oral infection not only affects the oral environment, but can also spread to the internal organs and stomach through the digestive tract and respiratory tract, aggravating the condition. There are approximately 150 million cases of severe fungal infections and 1.5 million deaths worldwide each year. Due to the lack of effective monitoring and timely treatment, this number may be seriously underestimated. In addition, the diagnosis of fungal infections is very challenging, and the world is relatively lacking in mycological research and antifungal therapy development.

[0004] Currently used antifungal drugs are often associated with significant side effects, including kidney and liver toxicity. At the same time, fungal pathogens are becoming increasingly resistant to these drugs, especially to key triazole and polyene drugs. Therefore, the development of new antifungal drugs is particularly important. These new drugs may not only provide alternative treatment options, especially for difficult-to-treat, multidrug-resistant fungal infections, but also increase the safety of treatment by improving drug selectivity and reducing toxicity to host cells.

[0005] In the prior art, there are Candida albicans) The main antifungal drugs include: 1) Azoles, mechanism of action: by inhibiting the synthesis of ergosterol in the fungal cell membrane. Representative drugs: imidazoles (such as ketoconazole, clotrimazole, miconazole) and triazoles (such as fluconazole, itraconazole, voriconazole). Application: commonly used to treat superficial fungal infections or candidal infections of the skin and mucous membranes. 2) Polyene drugs, mechanism of action: interact with sterols in the fungal cell membrane, change the permeability of the cell membrane, cause the loss of cell contents, and cause the death of the bacteria. Representative drug: amphotericin B. 3) Allylamine drugs, mechanism of action: by inhibiting squalene epoxidase, blocking the synthesis of fungal cell membranes. Representative drug: terbinafine. 4) Echinocandins, mechanism of action: by inhibiting β-1,3-D-glucan synthase, affecting the synthesis of fungal cell walls. Representative drugs: caspofungin, micafungin. 5) Flucytosine drugs, mechanism of action: by inhibiting thymidine synthase, blocking DNA synthesis, affecting the growth of fungal cells. Representative drug: 5-flucytosine. 6) Nikkomycin drugs, mechanism of action: by inhibiting chitin synthase, blocking the synthesis of fungal cell walls; other antifungal drugs: Representative drugs: Sphingomyelin antifungal drugs, Iturins, Syringomycins, etc. Mechanism of action: Affect the physiological activities of fungal cells through different mechanisms.

[0006] Disadvantages of existing antifungal drugs: 1) Resistance problem: With the widespread use of antifungal drugs, some fungi have developed resistance to existing drugs, which makes treatment more difficult. 2) Drug side effects: Many existing antifungal drugs have side effects, including liver toxicity, kidney problems and other systemic reactions, which limit their long-term or widespread use. 3) Drug selectivity: Existing antifungal drugs may be toxic to both fungi and host cells, which limits their selectivity and therapeutic effects. Treatment cost: Some antifungal drugs are expensive, which increases the financial burden on patients, especially when long-term treatment is required. 5) Limited drug types: The types of antifungal drugs currently available on the market are relatively limited, which limits doctors' treatment options when facing different types of fungal infections. 6) Long treatment cycle: Some fungal infections require long-term treatment, and existing drugs may take weeks or even months to take effect, which increases patient discomfort and treatment costs. 7) Drug delivery problem: The distribution and delivery efficiency of some antifungal drugs in the body is not high, which makes it difficult for drugs to reach the infection site and affects the treatment effect. 8) Patient compliance issues: Due to issues such as long treatment cycles and side effects, patient compliance may be poor, which will affect treatment outcomes and prognosis. Summary of the invention

[0007] In view of the shortcomings of the above-mentioned background technology, the present invention provides a method for screening antifungal small molecules targeting CYP51 protein and its application. The elemenine and ivy saponin screened by the present invention can be used to develop a new antifungal drug, which acts on fungal pathogens, provides a new treatment approach, and overcomes the limitations and drug resistance problems of existing drugs.

[0008] The first object of the present invention is to provide a method for screening antifungal small molecules targeting CYP51 protein, comprising the following steps: Use the TCMSP database to build a small molecule database for batch molecular docking; Conduct initial screening of natural small molecules in the database based on bioavailability and drug-like properties; Using the Candida albicans CYP51 protein as the receptor and the natural small molecules after the initial screening as the ligand, we used AutoDock vina for molecular docking, and used the scoring function in the software to calculate the energy of the ligand and receptor binding process. Based on the calculation results of the scoring function, we conducted the first virtual screening. 5TZ1 was set as the receptor protein, and the natural small molecules after the initial screening were used as ligands, which were docked by LibDock and CDOCKER in turn for the second virtual screening; The intersection of the compounds in the first virtual screening and the compounds in the second virtual screening is taken to obtain potential active compound I; Obtain drugs that act on CYP51 and divide them into test sets and training sets according to their activity; Based on the natural small molecule database after the initial screening, the pharmacophore features of the active compounds in the training set are extracted to establish the corresponding pharmacophore model; Match the pharmacophore model obtained from the test set molecules with the pharmacophore model obtained from the training set to obtain the final pharmacophore model; According to the final pharmacophore model, the natural small molecule database after the initial screening is screened to obtain potential active compounds II; According to the potential active compound I and the potential active compound II, the intersection is taken again to obtain the candidate compound; The candidate compounds were subjected to hydrogen bond interaction docking with CYP51 and activity verification on the test bacteria, and then screened again to obtain small molecules with antifungal activity.

[0009] Preferably, before docking with LibDock and CDOCKER in sequence, the steps include: The natural small molecule structure was optimized and then preprocessed using AutoDock Tools to obtain the ligand.pdbqt file; the CYP51 protein of Candida albicans was exported as protein.pdbqt format; a box with a spacing of 1.000Å was created using GridOption; the coordinates of the box were recorded and saved as a config.txt file; and docking was performed after the settings were completed.

[0010] Preferably, a corresponding pharmacophore model is established based on Discovery studio 2019 Client software, including: Preparation of training set molecules: Open the natural small molecule database in Sample | Tutorial | Pharmacophore, define the Principal and Max OmitFeat attributes, select heading, select Add Attributes, open AddAttributes, add attributes, and define rules; Extraction of pharmacophore characteristic elements: Display the structures of all active compounds; Select Feature Mapping in Pharmacophore | Edit and Cluster Features, select the characteristic elements to be matched, and set them as default values; Run; Select Current Features in Edit and Cluster Features to view all types of pharmacophore characteristic elements; Construction of common characteristic pharmacophore: Pharmacophore | Automatically create pharmacophore, select common characteristic pharmacophore generation; set Input Ligands to ligands:All; select POS_IONIZABLE and RING_AROMATIC; set ConformationGeneration to BEST, Maximum Conformation to 10, Energy Threshold to 10, and other parameters to default. Generate up to 10 conformations for each small molecule to characterize the conformational space of the small molecule; Run.

[0011] Preferably, the final pharmacophore model is obtained by verifying and confirming the matching of the test set compounds and the CAI index.

[0012] Preferably, screening is performed based on hydrogen bonding interactions, including: After docking the candidate compound with CYP51, the docking file, ligand file and receptor file are superimposed to obtain the docked composite system, observe the hydrogen bond interaction between different docking conformations and proteins, and obtain the candidate compound.

[0013] Preferred small molecules for obtaining antifungal activity include hederaconin and elemenin.

[0014] Preferably, the small molecule database for batch molecular docking is constructed using the TCMSP database, including: The database used was the TCMSP database. By crawling the mol2 structure files of active molecules, a mol2 database containing 13,729 monomer components of traditional Chinese medicine was obtained. The small molecules were optimized and saved in mol2 format. The structures of the compounds were converted using OpenBabel software and saved in PDBQT format. A folder was created for the small molecules in PDBQT format to construct a small molecule database for batch molecular docking.

[0015] Preferably, the initial screening is to screen the natural small molecules in the database for bioavailability>30% and drug-likeness>18%. The second object of the present invention is to provide an application of the small molecules screened by the above method in the preparation of antibacterial drugs.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides an antifungal small molecule screening method with CYP51 protein as a target and an application thereof. The present invention combines two screening tools, AutoDock vina and Discovery studio, and obtains a target small molecule compound after taking the intersection of screening results, thereby greatly improving the accuracy and efficiency of screening, and making the target small molecule compound finally obtained more targeted and active; taking CYP51, a key enzyme in the synthesis pathway of ergosterol, a unique component of fungal cell membrane, as a screening target, has stronger targeting and specificity, can more accurately intervene in the synthesis pathway of fungal cell membrane, and achieve more effective antifungal effect; by simultaneously adopting two screening means, molecular docking and pharmacophore model, the molecular structure and pharmacophore characteristics can be comprehensively considered, and the screening results are more comprehensive, stable and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is the minimum inhibitory concentration of hedera saponin (A), elemenin (B), and fluconazole (C) against Candida albicans; Figure 2 is the effect of hedera saponin and fluconazole on the microscopic morphology of Candida albicans; Figure 3 It is the effect of hederagenoside and fluconazole on the internal microstructure of Candida albicans cells; Figure 4 Effects of hederagenoside (A) and fluconazole (B) on the biofilm formation stage of Candida albicans; Figure 5 is the effect of hedera saponin (A) and fluconazole (B) on the mature stage of Candida albicans biofilm; Figure 6It is the inhibitory effect of hedera saponin and fluconazole on hyphae formation of Candida albicans; Figure 7 The effects of hedera saponins (A) and fluconazole (B) on the surface hydrophobicity of Candida albicans. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand and implement the technical solution of the present invention, the present invention is further described below in conjunction with specific embodiments and drawings, but the embodiments are not intended to limit the present invention.

[0019] The purpose of the present invention is to provide an antifungal small molecule screening method and application targeting CYP51 protein. The necessity and advantages of developing natural product small molecule antifungal drugs are as follows: 1) New mechanism of action: Natural products usually have unique chemical structures and mechanisms of action, which may provide new targets for the development of new antifungal drugs. 2) Overcoming drug resistance: Due to the diversity and complexity of natural products, they may help overcome the drug resistance problem of existing antifungal drugs. 3) Lower side effects: Natural products usually have lower side effects because they have evolved in nature into compounds with lower toxicity to organisms. 4) Eco-friendly: Using natural products as drug sources can reduce the environmental impact during chemical synthesis, which is in line with the principles of green chemistry and sustainable development. 5) Drug diversity: Nature provides a rich library of compounds, which provides a broad resource for the development of antifungal drugs with different structures and functions. 6) Rapid screening: Modern technologies such as high-throughput screening (HTS) can quickly screen candidate molecules with antifungal activity from a large number of natural products. 7) Cost-effectiveness: Some natural products may have lower production costs, which can make antifungal drugs more economical and improve patient accessibility. 8) Modern application of traditional medicine: Many natural products have been used in traditional medicine, and modern research can verify their efficacy and transform them into modern drugs. 9) Synergistic effect: Some natural products may have synergistic effects with other drugs or treatments to improve the therapeutic effect. 10) Innovation potential: The discovery and development of natural products can promote innovation in the field of new drug research and development and provide new ideas for solving the limitations of existing antifungal drugs.

[0020] In order to screen two drugs, the present invention screened CYP51 (14α-demethylase), a member of the P450 enzyme family, plays an important role as a key rate-limiting enzyme in the ergosterol synthesis pathway, and is therefore also a target for antifungal drugs. An ideal antifungal drug should be highly selective, targeting fungal cells, while having minimal toxicity to human cells. Therefore, screening drugs for targets that are unique to fungi but do not exist in human cells or have large homology differences can significantly reduce the side effects of drugs on the human body. The CYP51 protein selected in the present invention has significant differences in homology between humans and fungi, and can be used as an antifungal target for virtual screening.

[0021] In order to achieve the above object, the present invention provides a first aspect of a method for screening antifungal small molecules targeting CYP51 protein, comprising the following steps: Use the TCMSP database to build a small molecule database for batch molecular docking; Conduct initial screening of natural small molecules in the database based on bioavailability and drug-like properties; The crystal structure of Candida albicans CYP51 protein (PDB number: 5TZ1) was used as the receptor and the natural small molecules after the initial screening were used as ligands. Molecular docking was performed using AutoDock vina, and the scoring function in the software was used to calculate the energy of the ligand and receptor binding process. The first virtual screening was performed based on the calculation results of the scoring function. 5TZ1 was set as the receptor protein, and the natural small molecules after the initial screening were used as ligands, which were docked by LibDock and CDOCKER in turn for the second virtual screening; The intersection of the compounds in the first virtual screening and the compounds in the second virtual screening is taken to obtain potential active compound I; Obtain drugs that act on CYP51 and divide them into test sets and training sets according to their activity; Based on the natural small molecule database after the initial screening, the pharmacophore features of the active compounds in the training set are extracted to establish the corresponding pharmacophore model; Match the pharmacophore model obtained from the test set molecules with the pharmacophore model obtained from the training set to obtain the final pharmacophore model; According to the final pharmacophore model, the natural small molecule database after the initial screening is screened to obtain potential active compounds II; According to the potential active compound I and the potential active compound II, the intersection is taken again to obtain the candidate compound; The candidate compounds were subjected to hydrogen bond interaction docking with CYP51 and activity verification on the test bacteria, and then screened again to obtain small molecules with antifungal activity.

[0022] Among them, before docking with LibDock and CDOCKER, it includes: The natural small molecule structure was optimized and then preprocessed using AutoDock Tools to obtain the ligand.pdbqt file; the CYP51 protein of Candida albicans was exported as protein.pdbqt format; a box with a spacing of 1.000Å was created using GridOption; the coordinates of the box were recorded and saved as a config.txt file; and docking was performed after the settings were completed.

[0023] The corresponding pharmacophore model was established based on Discovery studio 2019 Client software, including: Preparation of training set molecules: Open the natural small molecule database in Samples | Tutorials | Pharmacophore, define the Principal and Max OmitFeat attributes, select heading, select Add Attributes, open AddAttributes, add attributes, and define rules; Extraction of pharmacophore characteristic elements: Display the structures of all active compounds; Select Feature Mapping in Pharmacophore | Edit and Cluster Features, select the characteristic elements to be matched, and set them as default values; Run; Select Current Features in Edit and Cluster Features to view all types of pharmacophore characteristic elements; Construction of Common Feature Pharmacophore: Pharmacophores | Create Pharmacophores Automatically, select Common Feature Pharmacophore Generation; set Input Ligands to ligands: All; select POS_IONIZABLE and RING_AROMATIC; set Conformation Generation to BEST, Maximum Conformation to 10, Energy Threshold to 10, and other parameters to default. Generate up to 10 conformations for each small molecule to characterize the conformational space of the small molecule; Run.

[0024] The final pharmacophore model is obtained by verifying and confirming the matching of the test set compounds and the CAI index.

[0025] Screening based on hydrogen bonding interactions, including: After docking the candidate compound with CYP51, the docking file, ligand file and receptor file are superimposed to obtain the docked composite system, observe the hydrogen bond interaction between different docking conformations and proteins, and obtain the candidate compound.

[0026] The candidate compounds were used to test the activity of the test bacteria to determine the final small molecule with antifungal activity.

[0027] Small molecules that obtain antifungal activity include hederagenosides and elemenin.

[0028] The TCMSP database is used to build a small molecule database for batch molecular docking, including: The database used was the TCMSP database. By crawling the mol2 structure files of active molecules, a mol2 database containing 13,729 monomer components of traditional Chinese medicine was obtained. The small molecules were optimized and saved in mol2 format. The structures of the compounds were converted using OpenBabel software and saved in PDBQT format. A folder was created for the small molecules in PDBQT format to construct a small molecule database for batch molecular docking.

[0029] The initial screening is to screen the natural small molecules in the database with bioavailability > 30% and drug-like properties > 18%.

[0030] The second method of the present invention provides a use of the above-screened small molecules in the preparation of antibacterial drugs.

[0031] It should be noted that the experimental methods used in the present invention are all conventional methods unless otherwise specified; the reagents and materials used are all commercially available unless otherwise specified.

[0032] A method for screening antifungal small molecules targeting CYP51 protein comprises the following steps: 1. Acquisition and preprocessing of CYP51 protein structure The protein structure of CYP51 of Candida albicans was obtained from the PDB database, and 5TZ1 with a lower resolution value was preferred to improve the accuracy of screening.

[0033] To facilitate the subsequent molecular docking study of the target protein, the CYP51 protein was optimized using the AutoDock Tools software. The processed protein file was saved in PDBQT format for subsequent use.

[0034] 2. Obtaining protein domains and related active sites By searching for CYP51 protein in the RCSB PDB database and combining it with the literature, we found that in the docking model of CYP51 protein and substrate, the three amino acid residues Tyr ll8, Ser 378, and His 310 played an important role in the active pocket and affected the antifungal activity of the compound.

[0035] (1) According to the existing active site, set the three amino acid residues Tyr ll8, His 310, and Ser 378 to be included in the active site, and use Discovery studio 2019 Client to define the binding site of the receptor. The specific steps are as follows: (2) Prepare the system: In the Tools Explorer, open Receptor-Ligand Interaction | Define and Edit Binding Site, select Define Receptor, and define the Candida albicans protein structure (PDB ID 5TZ1) as the receptor molecule.

[0036] (3) Find the site: In the Tools Explorer, open Receptor-Ligand Interactions | Define and Edit Binding Site, select Get site from current selection, and record the center coordinates of the cavity for subsequent docking.

[0037] 3. Obtaining the lowest energy conformation of CYP51 protein In the molecular docking process, in order to simulate the real motion process, the ligand and the receptor are both moving under the Newton force field. However, due to the large number of receptor amino acids and the complex spatial structure, the receptor is often guaranteed to be stationary during the docking process, while the ligand will bind to the receptor in different conformations at the active pocket. Therefore, in order to improve the accuracy of the screening, the present invention uses Gromacs to perform molecular dynamics simulation on 5TZ1 in advance, simulates the motion of 5TZ1 under the Newton force field, and determines the lowest energy conformation of CYP51. In the simulation process, the parameter standard of RMSD is introduced.

[0038] RMSD (Root Mean Square Deviation) is a measure used to quantify the difference between two sets of spatial points. In the field of computational chemistry and molecular modeling, RMSD is often used to compare the similarities or differences between two molecular structures, especially in molecular docking, structure prediction, simulation and other molecular dynamics studies. RMSD can be calculated using the following formula (1): (1) Where n is the number of atoms; A i and B i are the masses of the i-th atom in the two groups respectively; || A i -B i || 2 It is i The Euclidean distance between atoms.

[0039] 4. Selection and processing of TCMSP database The database used in the present invention is the TCMSP (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform) database. By crawling the mol2 structure files of active molecules, a mol2 database containing 13,729 monomer components of traditional Chinese medicine is obtained, and the small molecules are optimized and saved in mol2 format. The structure of the compound is converted using OpenBabel software and saved in PDBQT format. A folder is created for the small molecules in PDBQT format to construct a small molecule database for batch molecular docking.

[0040] 5. Preliminary screening of TCMSP database OB value (oral bioavailability) is bioavailability. Bioavailability can indicate the proportion of drugs entering the human circulation. DL value (drug-like properties) refers to the similarity of a compound to known drugs. Compounds with drug-like properties have the potential to become drugs and are called drug-like molecules. After the database was built, the natural small molecules in the TCMSP database were initially screened for OB>30% and DL>18%.

[0041] 6. Receptor-based virtual screening Molecular docking (Receptor-based virtual screening) was performed using AutoDock vina: Based on the protein structure of Candida albicans CYP51 and the principle of energy minimization, the software built-in scoring function (Vina score) was used to sort the ligands. The scores were obtained by calculating the interaction energy and binding free energy between the ligand and the receptor. The lower the score, the stronger the binding ability. During the docking process, the protein structure remained rigid, and natural small molecule compounds could change freely. Finally, according to the sorting results of the scores, the docking binding energy of the control drug fluconazole was used as the threshold, that is, compounds with scores lower than fluconazole were considered to have potential activity. Compounds with docking binding energy less than or equal to -7.8 kcal / mol were retained to narrow the scope, and 147 compounds with potential activity were finally screened out.

[0042] It should be noted that AutoDock Vina uses a built-in scoring function to evaluate the binding energy of proteins and ligands. The scoring function is pre-set in the software. Users only need to input the structural data of proteins and ligands into the software, and AutoDock Vina will automatically use the built-in scoring function to calculate their binding energy.

[0043] After the optimization, the natural small molecule structure was preprocessed using AutoDock Tools to obtain the ligand.pdbqt file. The CYP51 protein of Candida albicans was exported as protein.pdbqt format. A box with a spacing of 1.000Å was created using GridOption. The coordinates of the box were recorded and saved as config.txt file. After the settings were completed, docking was performed.

[0044] LibDock operation steps: Select the Input Receptor parameter and set 5TZ1 as the receptor protein; select the database in Input Ligands; select the coordinates and radius of the active center in Input Site Sphere; select UserSpecified in Docking Preferences and change the specific parameters of the docking calculation as needed; set the Max Hits toSave parameter to 10 in Docking Preferences; after the settings are completed, run the run, and after the run is completed, 1951 potentially active compounds can be obtained.

[0045] The CDOCKER operation steps are as follows: The 1951 data obtained by LibDock docking were docked with CDOCKER. Select Receptor-LigandInteractions | Dock Ligands to open the corresponding parameter panel of CDOCKER. Set Input Receptor to 5TZ1. Select the database for Input Ligands. Select the coordinates and radius of the active center for Input Site Sphere. Set Pose Cluster Radius to 0.5 for Top Hits. Leave the rest as default. Click Run and sort the scoring results in the same way. Use the docking binding score of fluconazole as the threshold to retain compounds with a score greater than or equal to 138. A total of 235 docking results were generated.

[0046] These 235 compounds were intersected with the 147 compounds screened by AutoDock vina, and finally 25 compounds with potential activity were obtained.

[0047] 7. Pharmacophore-based virtual screening Collect literature reports on drugs that act on CYP51 and query their IC values ​​on the Binding DataBase database 50 or K d Then, according to their activity, these drugs are divided into two data sets, namely test set and training set. The pharmacophore features of the active compounds in the training set are extracted to establish the corresponding pharmacophore model. The test set molecules are responsible for matching the pharmacophore model obtained from the training set, so as to analyze and compare the reliability of the established pharmacophore model based on the score. The pharmacophore model is established using Discovery studio 2019 Client software. The specific steps are as follows: (1) Preparation of training set molecules: Open the natural small molecule database in Samples | Tutorials | Pharmacophore, define the Principal and Max OmitFeat attributes, select heading, select Add Attributes, open AddAttributes, add attributes, and define the rules as shown in Table 2 and Table 3: Table 2 Principal attributes

[0048] Table 3 Max OmitFeat attributes

[0049] (2) Extraction of pharmacophore characteristic elements: Display the structures of all active compounds; Select Feature Mapping in Pharmacophore | Edit and Cluster Features, select the characteristic elements to be matched, and set them as default values; Run; Select Current Features in Edit and Cluster Features to view all types of pharmacophore characteristic elements.

[0050] (3) Construction of Common Feature Pharmacophore: Pharmacophores | Create Pharmacophores Automatically, select Common Feature Pharmacophore Generation; set Input Ligands to ligands: All; select POS_IONIZABLE and RING_AROMATIC; set Conformation Generation to BEST, Maximum Conformation to 10, Energy Threshold to 10, and other parameters to default. Generate up to 10 conformations for each small molecule to characterize the conformational space of the small molecule; Run.

[0051] (4) Pharmacophore result analysis: Open the Report page; select View Aligned Ligands 01 to view the matching of the training set molecules with the first-ranked pharmacophore; the final decision on which pharmacophore model to select is a subjective process. The present invention uses the matching of the test set compounds and the CAI index to verify and determine. The CAI system is mainly based on four parameters: A, Y, N and CAI, and its calculation formula is as follows (2, 3, 4, 5): (2) (3) (4) (5) Where D represents the total number of compounds in the test set; A i Indicates the number of active compounds in the test set; H a Indicates the number of active compounds hit by the pharmacophore model; H trepresents the number of compounds hit by the pharmacophore model; A represents the hit rate of active ingredients; Y represents the effective hit rate; N represents the effective identification index, and CAI represents the comprehensive evaluation index; the pharmacophore model 1 with the highest CAI index is selected as the optimal pharmacophore model.

[0052] Pharmacophore-based screening: The pharmacophore model obtained above was used to screen the TCMSP compound database through the Search 3D Database module of the Discovery studio 2019Client software. The screening results were sorted according to the Fit Value value, and the Fit Value threshold was set to 3.5. Finally, 57 potentially active compounds were obtained.

[0053] The 57 compounds with potential activity screened based on the pharmacophore were intersected with the 25 compounds with potential activity obtained by docking to obtain candidate compounds.

[0054] The candidate compounds were subjected to hydrogen bond interaction with CYP51 and activity verification on the test bacteria, and then screened again to obtain small molecules with antifungal activity. The details are as follows: 8. Screening based on hydrogen bonding interactions To further improve the accuracy of the screening, referring to the mode of action of fluconazole and CYP51, the candidate compounds with similar binding modes to fluconazole in the active pocket were analyzed, and the hydrogen bond interactions between the candidate compounds and CYP51 in the active area were analyzed, in order to obtain candidate compounds with more reliable antifungal activity and improve the hit rate of the screening. The interaction was analyzed using Discovery studio 2019 Client software, and the specific steps are as follows: Prepare the composite system: After the candidate compound and CYP51 are docked, the docking composite system can be obtained by superimposing the docking file, ligand file and receptor file, which is stored in PDBQT format.

[0055] Analysis results: Observe the hydrogen bonding interactions between different docking conformations and proteins, and select candidate compounds based on the number and sites of hydrogen bonds formed.

[0056] 9. Verification of the activity of candidate compounds on the tested strains In order to detect the antifungal activity of the candidate compounds against Candida albicans, the experimental methods and standards provided in GB / T 38483-2020 were referred to, and the inhibitory effects of the candidate compounds on Candida albicans under the conditions of 128 μg / mL, 256 μg / mL, 512 μg / mL, and 1024 μg / mL were determined. The specific experimental steps are as follows: The activated Candida albicans culture was centrifuged at 3000g for 1 min, the supernatant was discarded, and the culture was washed with PBS buffer. The supernatant was discarded again, and the mixture was centrifuged again for 3 times, and then the culture was resuspended in PBS. The final concentration of the culture was adjusted to 1×10 7 CFU / ml, 180 μL EPD medium was added to each well of the 96-well plate, and 20 μL of candidate compound solution was added to each well to make the final concentrations 128 μg / mL, 256 μg / mL, 512 μg / mL, and 1024 μg / mL, respectively. 2 μL of bacterial solution was added to each well, and only bacterial solution without drug treatment was used as a control; after culturing on a constant temperature shaker (28°C, 120rpm) for 12h, 24h, and 36h, the absorbance value (optical density, OD) at 600nm was measured with a multifunctional microplate reader, and OD was taken. 600 The value is less than the minimum concentration of 100% of the growth control (the absorbance value of the blank medium should be subtracted from each well), and no fungal colonies grow in the well, which is considered to be the candidate compound with antifungal activity. Repeat three times, with fluconazole as the control.

[0057] Through virtual screening of molecular docking and pharmacophore models, screening based on hydrogen bond interactions, and verification of the activity of candidate compounds against test strains, two new natural antifungal small molecules, ivy saponin and elegans, with good antifungal activity were obtained.

[0058] In order to illustrate the relevant properties of the small molecules screened by the present invention, they are described in conjunction with the accompanying drawings.

[0059] Inhibitory effects of the two screened compounds on fungi-MIC Candida albicans can be divided into superficial infection and deep infection. The clinical symptoms of oral infection are dry mouth, stickiness, burning sensation of oral mucosa, pain, loss of taste, etc. It can also spread to the internal organs and gastrointestinal tract, aggravating the condition. After Candida albicans infects the human body, it mainly harms human health by producing virulence factors including yeast-hyphae phenotypic conversion, biofilm formation, and cell surface hydrophobicity. Candida albicans in hyphae form are more toxic and have stronger invasive ability in the process of invading the host; biofilms wrap yeast cells, pseudohyphae cells, and hyphae cells through the cytoplasmic matrix and attach them to biological or non-biological surfaces, making Candida albicans have a strong infection ability. Currently, the commonly used antifungal drugs in clinical practice cannot effectively inhibit biofilms; and the hydrophobicity of the cell surface can promote the adhesion of Candida albicans.

[0060] 1. Determination of the minimum inhibitory concentration of candidate compounds against Candida albicans The steps for measuring the minimum inhibitory concentration (MIC) of ivy saponins and elegans against Candida albicans are as follows: Determination of minimum inhibitory concentration in YEPD medium: The activated Candida albicans culture was centrifuged for 1 min (3000 g), the supernatant was discarded, and the culture was washed with PBS buffer for 3 times and then resuspended in PBS. The concentration of the diluted culture was 1×10 7 CFU / ml for standby, add 180μL YEPD medium to each 96-well plate; add 20μL of ivy saponin solution to each well, so that the final concentrations are 0.5 μg / mL, 1 μg / mL, 2 μg / mL, 4 μg / mL, 8 μg / mL, 16 μg / mL, 32 μg / mL, 64 μg / mL, 128 μg / mL, 256 μg / ml; add 20μL of elemenine solution to each well, so that the final concentrations are 0.5 μL / mL, 1 μL / mL, 2 μL / mL, 4 μL / mL, 8 μL / mL, 16 μL / mL, 32 μL / mL, 64 μL / mL, 128 μL / mL, 256 μL / ml; add 2 μL of diluted bacterial solution to each well. The control was made with bacterial solution only without drug treatment. After culturing in a constant temperature shaker (28°C, 120 rpm) for 12h, 24h, and 36h, the absorbance (optical density, OD) of each well at 600nm was measured by a multifunctional microplate reader, and the minimum concentration at which OD600 was less than 100% of the growth control was taken as the MIC of hederaconin and elemenin. The experiment was repeated three times, with fluconazole as the control.

[0061] See also Figure 1 As shown in the figure, the minimum inhibitory concentration results of hedera saponins, elegans and fluconazole against Candida albicans show that hedera saponins and elegans have good antifungal activity, with minimum inhibitory concentrations of 32 μg / mL and 16 μL / mL, respectively. In particular, when the concentration of elegans is greater than 0.5 μL / mL, 50%-70% of the growth of Candida albicans can be inhibited within 24 hours, and when the concentration of hedera saponins is greater than 8 μg / mL, 50% of the growth of Candida albicans can be inhibited within 24 hours. However, after 24 hours of treatment with fluconazole 128 μg / mL, there is still a large amount of bacterial growth, indicating that fluconazole can only inhibit Candida albicans and cannot completely kill the fungus. After 36 hours of treatment, hedera saponins (32 μg / mL) and elegans (16 μL / mL) can completely inhibit the growth of Candida albicans, indicating that these two natural small molecules have bactericidal functions.

[0062] 2. Scanning electron microscope (SEM) observation of the surface microstructure of Candida albicans Scanning electron microscope was used to observe the changes in the microscopic structure of the bacterial surface after treatment with hedera saponin and fluconazole. Untreated Candida albicans cells were used as controls. The specific operation steps are as follows: (1) Centrifuge (8000 rpm, 5 min) to collect Candida albicans that have grown to the logarithmic phase, wash three times with PBS (pH 7.0, 10 mM) buffer, and adjust the bacterial concentration to OD 600 Adjust to 0.6 for standby use.

[0063] (2) After the cells were treated with hederagenin and fluconazole for 12 h, the cells were collected by centrifugation at 8000 rpm for 5 min and washed three times with PBS (pH 7.0, 10 mM) buffer. 4% glutaraldehyde electron microscopy fixative was added to the collected precipitate and fixed overnight at 4°C.

[0064] (3) Fixation: The treated cells were centrifuged to collect the precipitate, and then rinsed three times with PBS (pH 7.4, 10 mM) buffer, each time for 15 min.

[0065] (4) Dehydration: Rinse the above bacterial precipitate with 30%, 50%, 70%, 80%, 90%, 95% and 100% alcohol (twice) in sequence, each time for 15 minutes, and finally use isoamyl acetate to replace the alcohol for 15 minutes.

[0066] (5) Drying: Drying in a critical point dryer.

[0067] (6) Conductive treatment: The sample was placed on a conductive adhesive and gold was sprayed on the sample stage of an ion sputtering instrument. The microstructure of the bacterial surface was observed under a scanning electron microscope.

[0068] See also Figure 2 As shown in the figure, the microscopic morphology of Candida albicans was observed after treatment with hederaconin and fluconazole. The bacteria of the control group CK maintained a regular and typical oval shape (red arrow), with a smooth surface and a complete and full cell membrane. After treatment with hederaconin and fluconazole, the surface of the bacteria (blue arrow) was rough, concave, and irregular. Comparison of hederaconin and fluconazole showed that the surface of the bacteria was deformed after treatment with 8 μg / mL of hederaconin, and the surface roughness and deformation of the bacteria were higher than those of fluconazole at 32 μg / mL, indicating that hederaconin has a stronger ability to destroy the cell structure of Candida albicans than fluconazole, which is an important reason why hederaconin has a higher antifungal ability than fluconazole.

[0069] 3. Transmission electron microscopy (TEM) observation of the internal microstructure of Candida albicans cells Transmission electron microscope was used to observe the changes in the microscopic structure of cells after treatment with hedera helix saponin and fluconazole. The sample was prepared as follows: (1) The bacterial solution in the logarithmic growth phase was centrifuged at 8000 rpm. After 5 min, the bacteria were collected and washed three times with PBS (pH 7.0, 10 mM) buffer. The concentration of the bacterial solution was adjusted to OD 600 0.6 is reserved.

[0070] (2) The bacterial suspension was incubated with hedera saponin and fluconazole for 12 hours, and the cells were collected after centrifugation at 8000 rpm for 5 minutes. The cells were washed three times with PBS (pH 7.0, 10 mM) buffer. The collected precipitate was fixed with 4% glutaraldehyde electron microscopy fixative at room temperature for 2-4 hours, and then placed in a 4°C refrigerator for overnight fixation.

[0071] (3) Agar pre-embedding: Collect bacterial precipitates by centrifugation, wash with PBS and centrifuge, repeat 3 times. Prepare 1% agarose solution and add it to the EP tube. Before the agarose solidifies, pick up the precipitate and suspend it in the agarose.

[0072] (4) Fixation: Fix the samples treated above with 1% osmium acid (prepared by diluting PBS) at room temperature in the dark for 2 h. Rinse with PBS (pH 7.0, 10 mM) buffer 3 times, 15 min each time.

[0073] (5) Dehydration: Dehydrate and rinse the samples with 0%, 50%, 70%, 80%, 90%, 95%, 100%, and 100% alcohol for 20 min, and then rinse with 100% acetone twice for 15 min each time.

[0074] (6) Infiltration embedding: acetone: 812 embedding agent = 1:1, at 37°C, infiltration for 4 hours; acetone: 812 embedding agent = 1:2, at 37°C, infiltration overnight; pure 812 embedding agent at 37°C, infiltration for 8 hours. Add pure 812 embedding agent to the embedding plate, insert the sample into the embedding plate and place it in an oven at 37°C overnight.

[0075] (7) Polymerization: Place the embedded plate in a 60°C oven for 48 hours, then remove the resin block and set aside.

[0076] (8) Ultrathin sectioning: Use an ultrathin slicer to slice the resin block into 60-80 nm ultrathin sections, and use a 150-mesh square copper mesh to scoop out the sections.

[0077] (9) Staining: Stain the copper mesh in 2% uranyl acetate saturated alcohol solution in the dark for 8 minutes; wash 3 times with 70% alcohol; wash 3 times with pure water; stain 8 minutes with 2.6% lead citrate solution in the dark for 8 minutes; wash 3 times with ultrapure water, dry with filter paper, and place the copper mesh section in a copper mesh box to dry overnight at room temperature. Observe under a transmission electron microscope and collect images.

[0078] See also Figure 3 As shown in the figure, the results of ivy saponin and fluconazole on the internal microstructure of Candida albicans. The interior of the Candida albicans in the control group CK (red arrow) is darker, indicating that there are more electron-dense particles inside the bacteria. At the same time, the bacteria are plump, and the cell wall and cell membrane surface are smooth. After treatment with ivy saponin, the intracellular substances are greatly reduced, large vacuoles appear inside (blue arrows), the surface of the bacteria is irregular, the outer membrane of the cell wall becomes thinner (green arrows), and there are fine cracks on the edges. At 32 μg / mL, deformation is obviously seen, there are local defects, the cell membrane is discontinuous, there are protrusions, and the bacteria are almost empty shells. The degree of damage to the internal structure of the bacteria is much greater than that of fluconazole, which may be an important reason why ivy saponin has bactericidal ability compared with fluconazole.

[0079] 4. Effect of ivy saponins on the biofilm formation ability of Candida albicans XTT was added to the biofilm treated with hederagenin, and the activity of the biofilm was calculated by detecting the content of orange substance in the supernatant. The specific steps were as follows: Biofilm culture: The activated Candida albicans was washed three times with PBS and diluted to 1×10 5 CFU / mL, added to 96-well plates, 180 μL per well, and only RPMI1640 medium was added to column 1 as a control. Hederagen saponin and fluconazole were added to a final concentration of 8 μg / mL, 16 μg / mL, 32 μg / mL, 64 μg / mL, and 128 μg / mL, and cultured at 37°C for 24 h.

[0080] Preparation of XTT reaction solution: Prepare 1 mg / mL XTT saturated solution, filter and sterilize using a 0.22 μm microporous filter membrane and a syringe, and operate in a dark environment. The XTT reaction solution should be prepared before use.

[0081] Biofilm activity assay: discard the upper culture medium of the biofilm plate, wash three times with PBS, add 100 μL of XTT reaction solution to each well, incubate at 37°C in the dark for 2 hours; take 80 μL of the upper orange liquid from each well and use a multifunctional microplate reader to detect the absorbance value (OD490) of each well solution at a wavelength of 490 nm. Biofilm formation rate = (OD490-control OD490) / (growth control OD490-background control OD490) × 100%. Repeat three times.

[0082] See also Figure 4 As shown in the figure, the XTT method can be used to determine the effect of hedera saponins on the biofilm formation process of Candida albicans. At 32 μg / mL, hedera saponins inhibited the formation of Candida albicans biofilm by 50%, and it was concentration-dependent. At 128 μg / mL, the inhibition rate reached 80%. Fluconazole also had a concentration-dependent effect on biofilm formation, but the inhibition was significantly weaker than that of hedera saponins, with an inhibition rate of only 50% at 128 μg / mL. This indicates that hedera saponins have a higher inhibition rate in the early stage of biofilm formation of Candida albicans and can effectively reduce the adhesion ability of Candida albicans.

[0083] 5. Effect of ivy saponins on mature biofilm of Candida albicans To treat mature biofilm of Candida albicans, it is necessary to culture the biofilm for 24 hours and then add the corresponding concentration of drug solution.

[0084] See also Figure 5 As shown in the figure, the effects of hedera saponins and fluconazole on mature biofilms. The inhibition rate of hedera saponins on mature biofilms at 128 μg / mL was 20%, and that of fluconazole was 17%, indicating that hedera saponins and fluconazole had poor abilities to inhibit mature biofilms, which further indicated that the effect of hedera saponins in inhibiting biofilms was in the early stages of Candida albicans biofilms.

[0085] 6. Effect of ivy saponins on the hyphae formation ability of Candida albicans The effect of ivy saponin on the hyphae formation ability of Candida albicans was determined in liquid culture medium. The Candida albicans culture liquid cultured overnight in YEPD medium was centrifuged at 3000g for 5 minutes to collect the cells; the collected cells were resuspended in RPMI-1640 medium (added with 10% FBS) and the concentration of the bacterial suspension was adjusted to 1×10 6 CFU / mL. Add the above Candida albicans cell suspension to a sterile 24-well plate, add 10 μL of bacterial solution and 900 μL of RPMI-1640 medium (added with 10% FBS) to each well. Set up a blank control with only the medium added. Add 100 μL of different concentrations of ivy saponin to make the final concentrations 8 μg / mL, 16 μg / mL, 32 μg / mL, 64 μg / mL, and 128 μg / mL. Use PBS solution instead of the drug as a negative control. Repeat three times, and after shaking culture at 37°C for 24 h, observe the mycelial growth using an inverted microscope.

[0086] See also Figure 6As shown in the results, the hyphae formation ability of Candida albicans has a significant effect on the adhesion of Candida albicans to host cells. The stronger the hyphae formation ability, the stronger the invasion and adhesion ability of Candida albicans. Ivy saponin can completely inhibit the hyphae formation of Candida albicans at a concentration of 128 μg / mL, but at a low concentration, less than 32 μg / mL, it can inhibit some hyphae formation, but there is still hyphae growth. Fluconazole has a certain inhibitory effect on Candida albicans hyphae when the concentration is greater than 16 μg / mL, and even at a concentration of 256 μg / mL, it cannot completely inhibit the formation of hyphae. The inhibition of hyphae can reduce the adhesion of Candida albicans to the host, reduce its invasion and damage to the host, and thus produce an antibacterial effect.

[0087] 7. Effect of ivy saponins on the hydrophobicity of Candida albicans cell surface Wash the activated Candida albicans three times with PBS and dilute the bacterial solution with RPMI1640 to a concentration of 1×10 5 CFU / mL, 500 μL of the drug was added to 3 mL of bacterial solution to make the final concentrations 8 μg / mL, 16 μg / mL, 32 μg / mL, 64 μg / mL, and 128 μg / mL, respectively, and cultured at 28°C with shaking for 60 min. The bacteria were collected by centrifugation (3000 g, 5 min), resuspended in PBS, and 100 μL of each sample was taken. The absorbance OD at 600 nm was measured by a microplate reader. 600 (A 0 ). Add the remaining PBS suspension of each sample to a test tube, add 1 mL of n-octane, and let stand at room temperature for 30 min. Take 100 μL of the lower aqueous phase after the aqueous phase-chloroform phase separation and measure the OD 600 (A 1 ). Cell surface relative hydrophobicity (CSH) = (1-A 1 / A 0 )×100%.

[0088] See also Figure 7 As shown in the figure, cell surface hydrophobicity (CSH) is one of the important indicators of the pathogenicity of Candida albicans. The higher the cell surface hydrophobicity, the stronger the drug resistance of Candida albicans. The effect of ivy saponins on the surface hydrophobicity of Candida albicans. At a concentration of 128 μg / mL, the inhibition rate of ivy saponins on cell surface hydrophobicity reached 85%, showing concentration dependence. The inhibition rate of fluconazole was 20% at 128 μg / mL, and the inhibition degree was significantly lower than that of ivy saponins. Cell surface hydrophobicity is an important factor in the formation of Candida albicans biofilm. It is speculated that ivy saponins may affect the formation of biofilm by inhibiting the hydrophobicity of Candida albicans cell surface.

[0089] The present invention describes preferred embodiments and their effects. However, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0090] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for screening antifungal small molecules targeting CYP51 protein, characterized in that: The following steps are involved: Use the TCMSP database to build a small molecule database for batch molecular docking; Conduct initial screening of natural small molecules in the database based on bioavailability and drug-like properties; Using the Candida albicans CYP51 protein as the receptor and the natural small molecules after the initial screening as the ligand, we used AutoDockvina for molecular docking, and used the scoring function in the software to calculate the energy of the ligand-receptor binding process. Based on the calculation results of the scoring function, we conducted the first virtual screening. 5TZ1 was set as the receptor protein, and the natural small molecules after the initial screening were used as ligands, which were docked by LibDock and CDOCKER in turn for the second virtual screening; The intersection of the compounds in the first virtual screening and the compounds in the second virtual screening is taken to obtain potential active compound I; Obtain drugs that act on CYP51 and divide them into test sets and training sets according to their activity; Based on the natural small molecule database after the initial screening, the pharmacophore features of the active compounds in the training set are extracted to establish the corresponding pharmacophore model; Match the pharmacophore model obtained from the test set molecules with the pharmacophore model obtained from the training set to obtain the final pharmacophore model; According to the final pharmacophore model, the natural small molecule database after the initial screening is screened to obtain potential active compounds II; According to the potential active compound I and the potential active compound II, the intersection is taken again to obtain the candidate compound; The candidate compounds were subjected to hydrogen bond interaction docking with CYP51 and activity verification on the test bacteria, and then screened again to obtain small molecules with antifungal activity.

2. The method for screening antifungal small molecules targeting CYP51 protein according to claim 1, characterized in that: Before docking via LibDock and CDOCKER, include: The natural small molecule structure was optimized and then preprocessed using AutoDock Tools to obtain the ligand.pdbqt file; the CYP51 protein of Candida albicans was exported as protein.pdbqt format; boxes with a spacing of 1.000Å were created using the Grid Option; the coordinates of the boxes were recorded and saved as the config.txt file; and docking was performed after the settings were completed.

3. The method for screening antifungal small molecules targeting CYP51 protein according to claim 1, characterized in that: The corresponding pharmacophore model was established based on Discovery studio 2019 Client software, including: Preparation of training set molecules: Open the natural small molecule database in Sample | Tutorial | Pharmacophore, define the Principal and Max OmitFeat attributes, select heading, select Add Attributes, open AddAttributes, add attributes, and define rules; Extraction of pharmacophore characteristic elements: Display the structures of all active compounds; Select Feature Mapping in Pharmacophore | Edit and Cluster Features, select the characteristic elements to be matched, and set them as default values; Run; Select Current Features in Edit and Cluster Features to view all types of pharmacophore characteristic elements; Construction of common characteristic pharmacophore: Pharmacophore | Automatically create pharmacophore, select common characteristic pharmacophore generation; set Input Ligands to ligands:All; select POS_IONIZABLE and RING_AROMATIC; set ConformationGeneration to BEST, Maximum Conformation to 10, EnergyThreshold to 10, and other parameters to default. Generate up to 10 conformations for each small molecule to characterize the conformational space of the small molecule; Run.

4. The method for screening antifungal small molecules targeting CYP51 protein according to claim 3, characterized in that: The final pharmacophore model is obtained by verifying and confirming the matching of the test set compounds and the CAI index.

5. The method for screening antifungal small molecules targeting CYP51 protein according to claim 1, characterized in that: Screening based on hydrogen bonding interactions, including: After docking the candidate compound with CYP51, the docking file, ligand file and receptor file are superimposed to obtain the docked composite system, observe the hydrogen bond interaction between different docking conformations and proteins, and obtain the candidate compound.

6. The method for screening antifungal small molecules targeting CYP51 protein according to claim 1, characterized in that: Small molecules that obtain antifungal activity include hederagenosides and elemenin.

7. The method for screening antifungal small molecules targeting CYP51 protein according to claim 1, characterized in that: The TCMSP database is used to build a small molecule database for batch molecular docking, including: The database used was the TCMSP database. By crawling the mol2 structure files of active molecules, a mol2 database containing 13,729 monomer components of traditional Chinese medicine was obtained. The small molecules were optimized and saved in mol2 format. The structures of the compounds were converted using OpenBabel software and saved in PDBQT format. A folder was created for the small molecules in PDBQT format to construct a small molecule database for batch molecular docking.

8. The method for screening antifungal small molecules targeting CYP51 protein according to claim 1, characterized in that: The initial screening is to screen the natural small molecules in the database with bioavailability > 30% and drug-like properties > 18%.

9. Use of a small molecule screened by the method according to any one of claims 1 to 8 in the preparation of an antibacterial drug.

Images