Computer-aided drug screening method, system or device based on TUDCA and p53

Abstract

The invention discloses a computer-aided drug screening method, system or device based on TUDCA and p53, the interaction relation of TUDCA and p53 protein is found for the first time, and based on this, the invention provides the computer-aided drug screening method, system and device based on TUDCA and p53 and a computer readable storage medium for the field. The invention provides an efficient, accurate and rapid screening method for research and development of new drugs for resisting sleep deprivation induced ovarian function abnormal change, and has very wide application prospects in screening of new drugs for resisting sleep deprivation induced ovarian function abnormal change.

Description

Technical Field

[0001] The present invention belongs to the technical field of computer-aided drug screening, and in particular, relates to a computer-aided drug screening method, system or device based on TUDCA and p53. Background Art

[0002] Lack of sleep has become commonplace in modern fast-paced life. Whether the source of sleep loss comes from shift work, long-distance air travel or a busy lifestyle, it will have a huge impact on our health. The health hazards caused by lack of sleep in women are significantly manifested in the reproductive system, including menstrual cycle disorders and reproductive dysfunction. Therefore, focusing on revealing the molecular mechanism of female reproductive dysfunction caused by lack of sleep and further formulating protective measures against damage to the female reproductive system can provide new theoretical and technical references for maintaining the health of the female reproductive system under sleep deprivation conditions, which is of great significance for the research and development of related drugs.

[0003] At present, there are few studies on the development of specific drugs for abnormal ovarian function caused by sleep deprivation. Therefore, there is an urgent need to develop safe and effective drugs to resist abnormal ovarian function caused by sleep deprivation. Computer-aided drug design (CADD) is based on computer calculation and simulation technology. By learning the prior knowledge contained in massive drug data and mining the interaction relationship between target targets and candidate drugs, it can quickly select active drug molecules with drug-like properties from millions of molecules. This greatly reduces the blindness of screening candidate drug molecules and improves research and development efficiency. Summary of the invention

[0004] In view of this, in view of the above technical problems existing in the prior art, the purpose of the present invention is to provide a computer-assisted drug screening method, system or device based on TUDCA and p53. Computer-assisted drug screening technology is used to screen drugs that can be used to resist abnormal changes in ovarian function caused by sleep deprivation.

[0005] The above-mentioned object of the present invention is achieved through the following technical solutions:

[0006] The first aspect of the present invention provides a computer-assisted drug screening method based on TUDCA and p53.

[0007] Further, the method comprises:

[0008] Obtain TUDCA structure and p53 protein structure data;

[0009] Selecting the spatial structure of the complex formed by the interaction of the TUDCA and the p53 protein, and determining the binding site of the TUDCA and the p53 protein in the complex as the binding site of the targeted drug;

[0010] A computer-assisted drug screening method is used to obtain candidate drugs targeting the binding site.

[0011] Furthermore, the computer-assisted drug screening method comprises:

[0012] Obtaining the binding site of the TUDCA and the p53 protein in the complex;

[0013] Based on the spatial structure of the binding site between the TUDCA and the p53 protein in the complex, screening small molecule compounds with similar structures in a molecular database;

[0014] The small molecule compounds obtained by screening are subjected to molecular docking with the p53 protein to calculate the affinity or binding energy of the small molecule compounds targeting the receptor p53 protein to obtain the score of each small molecule compound, and the candidate drugs are obtained by ranking according to the score.

[0015] Furthermore, the small molecule compound includes an inorganic small molecule compound, an antibody, a polypeptide, a protein analog, a peptide analog, siRNA, shRNA, dsRNA, microRNA or an antisense nucleic acid.

[0016] Furthermore, the binding site between the TUDCA and the p53 protein in the complex is predicted using a bioinformatics prediction method;

[0017] The bioinformatics prediction method includes a molecular docking method, a prediction method based on sequence and structure information, or a multi-scale protein sequence structure comparison learning method.

[0018] Furthermore, the method further comprises:

[0019] Obtaining a system for expressing p53 protein;

[0020] Treating the p53 protein-expressing system with the screened small molecule compound to verify the effectiveness of the small molecule compound;

[0021] Screening small molecule compounds that inhibit p53 protein expression as candidate drugs;

[0022] Optionally, the system expressing p53 protein includes a cell system expressing p53 protein, a subcellular system expressing p53 protein, a tissue system expressing p53 protein, a solution system expressing p53 protein, an organ system expressing p53 protein or an animal system expressing p53 protein.

[0023] The second aspect of the present invention provides a computer-aided drug screening system based on TUDCA and p53.

[0024] Further, the system comprises:

[0025] Data acquisition unit, acquiring TUDCA structure and p53 protein structure data;

[0026] A site determination unit, which selects the spatial structure of the complex formed by the interaction of the TUDCA and the p53 protein, and determines the binding site of the TUDCA and the p53 protein in the complex as the binding site of the targeted drug;

[0027] A drug screening unit, which uses a computer-assisted drug screening method to obtain candidate drugs targeting the binding site;

[0028] Optionally, the computer-assisted drug screening method comprises:

[0029] Obtaining the binding site of the TUDCA and the p53 protein in the complex;

[0030] Based on the spatial structure of the binding site between the TUDCA and the p53 protein in the complex, screening small molecule compounds with similar structures in a molecular database;

[0031] Performing molecular docking between the screened small molecule compounds and the p53 protein to calculate the affinity or binding energy of the small molecule compounds targeting the receptor p53 protein to obtain the score of each small molecule compound, and ranking them according to the score to obtain candidate drugs;

[0032] Optionally, the small molecule compound includes an inorganic small molecule compound, an antibody, a polypeptide, a protein analog, a peptide analog, siRNA, shRNA, dsRNA, microRNA or antisense nucleic acid;

[0033] Optionally, the binding site between the TUDCA and the p53 protein in the complex is predicted using a bioinformatics prediction method;

[0034] The bioinformatics prediction method includes a molecular docking method, a prediction method based on sequence and structure information, or a multi-scale protein sequence structure comparison learning method;

[0035] Optionally, the method further comprises an effectiveness verification unit, which obtains a system expressing p53 protein; treats the system expressing p53 protein with the screened small molecule compound to verify the effectiveness of the small molecule compound; and screens the small molecule compound that has the ability to inhibit the expression of p53 protein as a candidate drug.

[0036] The third aspect of the present invention provides a computer-aided drug screening device based on TUDCA and p53.

[0037] Furthermore, the device comprises:

[0038] A memory and a processor, wherein the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, the computer-assisted drug screening method based on TUDCA and p53 described in the first aspect of the present invention is implemented.

[0039] A fourth aspect of the present invention provides a computer-readable storage medium.

[0040] Furthermore, the computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the computer-assisted drug screening method based on TUDCA and p53 described in the first aspect of the present invention is implemented.

[0041] A fifth aspect of the present invention provides any of the following products:

[0042] (1) a complex formed by the interaction of TUDCA and p53 protein, wherein the complex formed by the interaction of TUDCA and p53 protein is the complex formed by the interaction of TUDCA and p53 protein in the first aspect of the present invention;

[0043] (2) A drug screened by the computer-assisted drug screening method based on TUDCA and p53 according to the first aspect of the present invention.

[0044] A sixth aspect of the present invention provides the application of any of the following aspects:

[0045] (1) An application of a complex, wherein the complex is a complex formed by the interaction between TUDCA and p53 protein as described in the fifth aspect of the present invention, and the application includes: application of the complex in screening drugs for resisting abnormal changes in ovarian function caused by sleep deprivation;

[0046] (2) Use of TUDCA in the preparation of a drug for resisting abnormal changes in ovarian function caused by sleep deprivation.

[0047] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0048] The present invention studies the effect and mechanism of TUDCA intervention in resisting abnormal ovarian function caused by sleep deprivation, and discovers for the first time the interactive relationship between TUDCA and p53 protein. Based on this, the present invention provides a computer-assisted drug screening method, system, device and computer-readable storage medium based on TUDCA and p53 in the art. The present invention provides an efficient, accurate and rapid screening method for the research and development of new drugs for resisting abnormal ovarian function caused by sleep deprivation, and has very broad application prospects in the screening of new drugs for resisting abnormal ovarian function caused by sleep deprivation. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0050] Figure 1 : A schematic diagram of a computer-assisted drug screening method based on TUDCA and p53 provided in an embodiment of the present invention;

[0051] Figure 2 : A schematic diagram of a computer-assisted drug screening system based on TUDCA and p53 provided in an embodiment of the present invention;

[0052] Figure 3 : A schematic diagram of a computer-aided drug screening device based on TUDCA and p53 provided in an embodiment of the present invention;

[0053] Figure 4 : Analysis of changes in the estrous cycle of female mice induced by sleep deprivation, including: A: estrous cycle distribution of female mice in the normal control group and the sleep deprivation group (one representative result is shown in each group); B: Comparison of estrous cycle duration of female mice in the normal control group and the sleep deprivation group. n = 6 per group, ****, P < 0.0001;

[0054] Figure 5 : Effects of sleep deprivation on follicular development, A: HE staining to observe the histological changes of ovaries in the normal control group (a, b) and the sleep deprivation group (c, d), the magnification is 40×, the ruler indicates 20μm; B: The percentage of ovarian atretic follicles is the total number of atretic follicles / total number of follicles×100%; C: The percentage of ovarian antral follicles atresia is the total number of atretic follicles / total number of antral follicles×100%; n=5 for each group, ***, P<0.001, ****, P<0.0001;

[0055] Figure 6: Expression analysis of apoptosis of granulosa cells in female mice induced by sleep deprivation, where A: TUNEL immunofluorescence images of female mice in the normal control group (a, b, c) and the sleep deprivation group (d, e, f), green signal is TUNEL positive staining, blue signal (DAPI) is cell nucleus staining, scale indicates 100 μm; B: Quantification of the average pixel intensity relative to the follicle area. n = 3 per group, **, P < 0.01;

[0056] Figure 7 :Sleep deprivation induces activation of p53 pro-apoptotic signaling pathway in female rat ovaries, where A: Western Blot was used to detect the expression levels of p53 and its pro-apoptotic target genes BAX, PUMA, NOXA, BID and anti-apoptotic proteins Bcl-2 and Bcl-xL in female rats after sleep deprivation; BE: Quantitative analysis of the above protein expression levels. n = 6 per group, *, P < 0.05;

[0057] Figure 8 : Analysis of the mRNA expression levels of BAX and PUMA genes in mice induced by sleep deprivation, where A: RT-PCR was used to detect the expression levels of BAX and PUMA mRNA in ovarian tissue after sleep deprivation; BC: Quantitative analysis of BAX and PUMA mRNA expression levels. n = 3 per group, **, P < 0.01, ***, P < 0.001;

[0058] Fig. 9 :Analysis of the changes in the mRNA levels of downstream apoptosis-related target genes in ovarian granulosa cells transfected with p53 siRNA; Analysis of the expression levels of the mRNAs of downstream apoptosis target genes BAX and PUMA in ovarian granulosa cells GC transfected with p53 siRNA; Fig.10 : The expression levels of p53, BAX, and PUMA proteins in female mice induced by TUDCA after sleep deprivation, where A: The expression levels of p53, BAX, and PUMA proteins in female mice pretreated with normal control group, TUDCA group, and solvent control group were detected after sleep deprivation; BD: The quantitative analysis results of each protein. n = 6 in each group, *, P < 0.05, **, P < 0.01;

[0059] Fig.11 : Analysis of changes in the reproductive cycle of female mice induced by TUDCA after sleep deprivation, including: A: Distribution of reproductive cycle of female mice pretreated with normal control group, TUDCA group and solvent control group after sleep deprivation; B: Comparison of the length of reproductive cycle of female mice pretreated with normal control group, TUDCA injection group and solvent control group after sleep deprivation. n = 6 in each group, ***, P < 0.001, ****, P < 0.0001;

[0060] Fig.12: Effects of TUDCA pretreatment on follicular development in female mice after sleep deprivation, A: Histological changes of ovaries in female mice in normal control group (a, b), TUDCA group (e, f) and solvent control pretreatment group (c, d), magnification 40×, scale 20μm; B: The percentage of ovarian atretic follicles among the three groups is the total number of atretic follicles / total number of follicles × 100%; C: The percentage of ovarian antral follicle atresia is the total number of atretic follicles / total number of antral follicles × 100%; n = 6 in each group, **, P < 0.01, ****, P < 0.0001;

[0061] Fig.13 : Expression analysis of apoptosis of granulosa cells in female mice induced by TUDCA pretreatment after sleep deprivation, where A: TUNEL immunofluorescence images of female mice pretreated with normal control group (a, b, c), TUDCA group (g, h, i) and solvent control (d, e, f), green signal is TUNEL positive staining, blue signal (DAPI) is cell nucleus staining, scale indicates 100 μm; B: Quantification of the average pixel intensity of the three groups relative to the follicle area. n = 3 per group, *, P < 0.05, **, P < 0.01. DETAILED DESCRIPTION

[0062] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0063] In some of the processes described in the specification and claims of the present invention and the above-mentioned drawings, multiple operations appearing in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as S101, S102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel.

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0065] Figure 1 1 is a flow chart of a computer-assisted drug screening method based on TUDCA and p53 provided by an embodiment of the present invention. Specifically, the method comprises the following steps:

[0066] S101: Obtain TUDCA structure and p53 protein structure data;

[0067] In one embodiment, the TUDCA refers to tauroursodeoxycholic acid (TUDCA), whose chemical name is 3α,7β-dihydroxycholanoyl-N-taurine, and is a conjugated bile acid formed by the condensation between the carboxyl group of ursodeoxycholic acid (UDCA) and the amino group of taurine.

[0068] In one embodiment, the p53 gene is a tumor suppressor gene located on human chromosome 17p13.1, encoding a 53kD nuclear phosphorylated protein consisting of 393 amino acids, referred to as p53 protein. The p53 gene is a negative regulator in the cell growth cycle and is related to important biological functions such as cell cycle regulation, DNA repair, cell differentiation, and cell apoptosis.

[0069] In one embodiment, the TUDCA structural data can be obtained using conventional methods for obtaining small molecule structural information known to those skilled in the art.

[0070] Exemplarily, the TUDCA structural data can be obtained by the following method: searching the database pubchem (https: / / pubchem.ncbi.nlm.nih.gov / compound / 5365872#section=3D-Conformer) or chemspider (http: / / www.chemspider.com / Chemical-Structure.10947.html?rid=7a636e35-5283-484a-97e0-f556d928c8fa) according to the Chinese name, English name and CAS number of TUDCA to obtain the TUDCA structural data.

[0071] In one embodiment, the p53 protein structure data can be obtained using conventional methods for obtaining protein structure information known to those skilled in the art.

[0072] Exemplarily, the p53 protein structure data can be obtained by the following method: obtaining the p53 protein structure according to the RCSB PDB database (https: / / www.rcsb.org / ), obtaining the p53 protein structure according to the AlphaFold protein structure database (https: / / alphafold.ebi.ac.uk), or using ColabFold to predict the p53 protein structure online (https: / / github.com / sokrypton / ColabFold).

[0073] In one embodiment, the contents of the present invention include: (1) Under sleep deprivation conditions, multiple indicators such as the estrous cycle, follicular development, and survival of granulosa cells (GC) of female mice are detected to evaluate the effects of sleep deprivation on the reproductive function of female animals; (2) The ovary is an important executive organ of female reproductive function. Granulosa cells play a key role in the biological function of the ovary. Therefore, we then explored the signal transduction pathways and mechanisms of sleep deprivation-induced abnormal changes in the function of ovarian granulosa cells in female mice; (3) Based on the key signal molecules provided by the results of the mechanism study, the corresponding drugs are selected for intervention experiments to detect their antagonistic effects on the abnormal function of ovarian granulosa cells induced by sleep deprivation.

[0074] In one embodiment, the present invention adopts the following research methods: female mice are placed in a sleep deprivation apparatus for sleep deprivation treatment, and the estrous cycle changes of female mice are detected by vaginal cytology smear method, and the monitoring is continued for 20 days; the development of female mouse follicles is observed by hematoxylin-eosin staining method; and the apoptosis reaction of female mouse ovarian granulosa cells is observed by TUNEL staining method. The expression of p53 and its downstream target genes (BAX, PUMA, NOXA, BID, Bcl-2, Bcl-xL) is detected by immunoblotting technology and RT-PCR method; p53 siRNA is transfected in ovarian granulosa cells by cell transfection technology to detect the correlation between the induced expression of p53 and its target genes; Tauroursodeoxycholic acid (TUDCA) is injected into the tail vein of mice to observe the effect and mechanism of TUDCA intervention in resisting abnormal changes in ovarian function caused by sleep deprivation.

[0075] In one embodiment, the present invention first constructs a 72h sleep deprivation animal model. The specific construction method is as follows: female mice are divided into two groups, a normal control group and a sleep deprivation group, according to a random number table method, and the number of mice in each group of each experiment is ≥6. The mice in the control group are raised normally in the animal room, and the mice in the sleep deprivation group are placed in an experimental cage equipped with a sleep deprivation instrument, with a size of 50.8cm×16.51cm×30.48cm, and the operating parameters are set: exercise 6s, rest 3s, speed 2m / min, and the mice in the sleep deprivation group are observed during the period to ensure the awake state. After the mice in the experimental group are sleep deprived for 72h, phenotypic detection is performed or samples are taken for sample analysis.

[0076] In the drug treatment experiment, female mice were divided into a control group, a sleep deprivation group, and a sleep deprivation + TUDCA administration group according to the random number table method. The administration group was pretreated with TUDCA (500 mg / kg) injected into the tail vein before sleep deprivation, and the drug was administered once a day; the control group and the sleep deprivation group were injected with the same volume of saline. The number of mice in each group of each experiment was ≥6, and they were placed in an experimental cage equipped with a sleep deprivation instrument, with the same size and parameter settings as above. After 72 hours of sleep deprivation, the mice in the sleep deprivation group and the TUDCA pretreatment group were subjected to phenotypic detection or sample analysis.

[0077] In one embodiment, the present invention adopts the following method to observe the changes in the estrous cycle of female mice: vaginal cytology is used to observe the changes in the estrous cycle of female mice. At 10 am every day, 10 μL of physiological saline is sucked with a pipette and injected into the mouse vagina. The secretions are sucked back and forth several times and smeared on a clean glass slide. After the smear is dried, it is fixed with 95% ethanol for 15 minutes, and then stained with hematoxylin for 15 minutes. After rinsing, it is placed on a rack to dry, and then the cell morphology is observed under a microscope to determine the estrous cycle of the female mouse. The observation is continued for 20 days and records are kept.

[0078] In one embodiment, the present invention uses hematoxylin-eosin staining to perform staining analysis on ovarian tissue. The specific experimental method is as follows: after removing the fat and connective tissue around the ovary, the ovarian tissue is immediately placed in 4% paraformaldehyde tissue fixative, and HE staining and scanning are performed by Beijing Sewell Company.

[0079] In one embodiment, the present invention uses TUNEL staining to detect cell apoptosis, and the specific experimental method is as follows: (1) Dewaxing and hydrating paraffin sections: the sections are placed in xylene I for 20 minutes, xylene II for 20 minutes for dewaxing, and then placed in anhydrous ethanol I for 3 minutes, anhydrous ethanol II for 3 minutes, 90% alcohol for 5 minutes, and 80% alcohol for 5 minutes for hydration, and washed with distilled water for 2 minutes; (2) Cell permeabilization: the sections are dried, and the tissue area is circled with a tissue pen, and a drop of 20 μg / mL proteinase K (prepared with 10 mM Tris-HCL) is added to the area, incubated at 37°C for 22 minutes, and washed with PBS for 3 times. This step cleans the proteinase K; (3) TUNEL staining: Wash twice with PBS, add 50 μL TUNEL detection solution (prepared with 5 μL (4) DAPI nuclear staining: add DAPI to stain the cell nucleus, incubate at 37°C for 10 min, wash three times with PBS, and seal with an anti-fluorescence quencher; (5) observe under a fluorescence microscope.

[0080] In one embodiment, the present invention uses the following method to extract primary granulosa cells: (1) 21-30 day old C57BL / 6 young mice are used, cervical dislocation is used to kill, and the ovaries are removed in a clean bench and placed in sterile PBS buffer; (2) the surrounding fat tissue is removed under a stereo microscope, the ovaries are placed in a medium dish, washed with PBS buffer for 3 times, and then washed with DMEM culture medium for 3 times, transferred to a small dish, and cultured in a 37°C incubator for 15 minutes; (3) under a stereo microscope, the ovarian tissue is punctured with a 1 mL syringe, Release the ovarian granulosa cells; (4) Add 1-2 mL of hyaluronidase at a concentration of 1 g / L, blow repeatedly, and then place in a constant temperature incubator for digestion for 20 minutes; (5) Filter the digested cell suspension with a 70 μm cell filter, transfer to a 15 mL centrifuge tube, and centrifuge at 1500 rpm for 5 minutes; (6) Discard the supernatant, add 2 mL of red cell breaking solution, resuspend and mix, place at 37°C for 5 minutes, then neutralize with 2 mL of PBS, and centrifuge at 1500 rpm for 5 minutes; (7) Discard the supernatant, add 2 mL of PBS to wash the precipitate once, and centrifuge at 1500 rpm for 5 minutes; (8) Discard the supernatant, add DMEM culture medium containing 10% FBS, adjust the density and inoculate in a six-well plate, change the medium after adhesion the next day, and then culture normally.

[0081] In one embodiment, the present invention uses the following method to perform cell transfection: (1) cells are plated at 2×10 5 The cells were inoculated at a density of 100 μg / mL in a six-well plate, and the cell density was preferably grown to 30-40% the next day; (2) Several autoclaved test tubes were taken and marked, and 5 μL of Lipofectamine RNA iMAX and the desired siRNA were placed in 250 μL Opti-MEM for dilution, and allowed to stand at room temperature for 5 min; (3) 250 μL of siRNA system was added dropwise to 250 μL of iMAX system, shaking while adding, for a total of 500 μL of transfection system, and allowed to stand at room temperature for 20 min; (4) The old culture medium was discarded, and the cells were washed 1-2 times with PBS buffer, and 1.5 mL of complete culture medium was added; (5) 500 μL of the mixed transfection reagent was added dropwise to the culture plate, shaken evenly, and placed in a constant temperature incubator for continued culture.

[0082] In one embodiment, the following method is used for RT-PCR detection: (1) RNA extraction: ① Tissue treatment: Take an appropriate amount of ovarian tissue and add 1 mL of Tizol to a homogenizer tube, let it stand at room temperature for 5 minutes, homogenize it in a homogenizer at 3500 rpm / min to completely lyse the tissue, and transfer it to an RNase-free EP tube; ② Add 200 μL of chloroform, shake vigorously for 15 seconds, and let it stand at room temperature for 3-4 minutes; ③ Centrifuge at 4°C, 12000 rpm for 15 minutes; ④ Remove the supernatant, add 500 μL of isopropanol, gently invert to mix, and let it stand at room temperature for 10-30 minutes; ⑤ Centrifuge at 4°C, 12000 rpm Centrifuge for 15 minutes; ⑥ Discard the supernatant, be careful to retain the precipitate, and wipe the edge of the EP tube dry; ⑦ Wash the bottom precipitate with 75% ethanol (anhydrous ethanol: DEPC = 3:1); ⑧ 4℃, 7500rpm, centrifuge for 10 minutes; ⑨ Discard the supernatant, 4℃, 7500rpm, centrifuge for 5 minutes; ⑩ Discard the supernatant, precipitate in the clean bench for 5-10 minutes, add 20-30μLDEPC water according to the amount of RNA extracted, and measure the concentration and purity of RNA with an ultra-micro spectrophotometer. (2) RNA reverse transcription into cDNA and primer design: ① RNA reverse transcription into cDNA, the reaction system is shown in Table 1 below. Add the system on ice, centrifuge after addition, and the reaction procedure is shown in Table 2 below. ② Mouse primer sequences are shown in Table 3 below (primers are synthesized from Beijing Qingke Biological Co., Ltd.). (3) RT-PCR reaction: The reaction system is shown in Table 4 below, and the reaction procedure is shown in Table 5 below. (4) Electrophoresis identification: Prepare agarose gel of appropriate concentration and perform gel image analysis.

[0083] Table 1 Reaction system

[0084]

[0085]

[0086] Table 2 Reaction procedure

[0087]

[0088] Table 3 Mouse primer sequences

[0089]

[0090] Table 4 Reaction system

[0091]

[0092] Table 5 Reaction procedure

[0093]

[0094] In one embodiment, the following method is used for protein immunoblotting (Western Blot) analysis: (1) Preparation of protein samples: ① Extraction of cell protein: When the cells grow to a density of 80%-90%, discard the cell culture medium and wash twice with PBS buffer. According to the cell density and number, add Ip buffer containing protease inhibitor (PI), phenylmethylsulfonyl fluoride (PMSF) and sodium vanadate (Na3VO4), blow evenly and place on ice for lysis for 30 minutes, scrape the cells with a scraper and transfer them to a 1.5mL EP tube, centrifuge at 4°C, 12000rpm for 20 minutes, and absorb the supernatant into a new EP tube; Extraction of ovarian tissue protein: According to the ratio of 0.1g tissue to 1mL RIPA lysis buffer, take an appropriate amount of ovarian tissue, add RIPA lysis buffer, methylsulfonyl fluoride (PMSF) and sodium vanadate (Na3VO4) to a homogenizer. Homogenize in a homogenizer at 3000r / min for 10 cycles, then place on ice for lysis for 10 minutes, and repeat this step until the tissue is completely lysed. Centrifuge at 4°C, 9000 rpm for 5 min, transfer the homogenate to a new EP tube, and continue centrifugation at 4°C, 12000 rpm for 20 min. Repeat this process and take the middle layer to clarify the sample. (2) BCA protein quantification: ① Prepare standards with different concentration gradients, namely: 1 mg / mL, 0.5 mg / mL, 0.25 mg / mL, 0.125 mg / mL, and 0 mg / mL; ② Prepare the sample to be tested and take it out, 19 μL PBS + 1 μL sample to be tested (diluted 20 times); ③ Prepare BSA reagent mixture, A solution: B solution = 50:1, 200 μL / sample; ④ Add 200 μL BSA reagent mixture to the standard and the sample to be tested, vortex to mix, centrifuge instantly, incubate at 37°C for 30 min, detect the absorbance at 570 nm with an enzyme reader, and draw a standard curve. The sample protein concentration can be calculated according to the formula; ⑤ Adjust the total protein amount according to the calculated protein concentration, add 5× loading buffer, vortex to mix, boil for 10 min, and store at -20°C for a short time. (3) Immunoblotting experiment: ① SDS-PAGE gel preparation: Prepare SDS-PAGE gel of appropriate concentration according to the molecular weight of the target protein; ② Electrophoresis: Install the prepared gel into the electrophoresis tank, make sure there is no leakage, add protein sample and protein pre-stained Maker, and perform electrophoresis at a constant voltage of 60V. After seeing the protein pre-stained Maker separate, adjust the voltage to 120V. When bromophenol blue reaches the bottom of the gel, the electrophoresis can be terminated; ③ Transfer: Prepare pre-cooled transfer solution, transfer chuck, sponge, filter paper, and PVDF membrane (activated in methanol for 30s in advance). According to the sandwich method, place sponge-filter paper-gel-PVDF membrane-filter paper-sponge on the black (negative) side of the transfer chuck in sequence. This step is performed in the transfer solution. Be careful to exhaust air bubbles.Transfer at 60V constant voltage for 3h; ④ Blocking: After taking out the PVDF membrane, wash it once with TBST, put it in 5% skim milk or BSA blocking solution, and block it at room temperature for 1h; ⑤ Incubate the primary antibody: select 5% skim milk / BSA blocking solution to dilute the primary antibody according to the ratio in the instructions, and incubate it at 4℃ overnight. Discard the primary antibody, wash the membrane 3 times with TBST, 10min / time; ⑥ Incubate the secondary antibody: select the corresponding secondary antibody according to the primary antibody, and incubate it with 5% skim milk at room temperature for 1h. Discard the secondary antibody, wash the membrane 5 times with TBST, 10min / time; ⑦ Develop: Add an appropriate amount of ECL substrate and collect data with a developer.

[0095] S102: selecting the spatial structure of the complex formed by the interaction of the TUDCA and the p53 protein, and determining the binding site of the TUDCA and the p53 protein in the complex as the binding site of the targeted drug;

[0096] In one embodiment, the binding site between the TUDCA and the p53 protein in the complex is predicted using a bioinformatics prediction method;

[0097] The bioinformatics prediction method includes a molecular docking method, a prediction method based on sequence and structure information, or a multi-scale protein sequence structure comparison learning method.

[0098] In one embodiment, the molecular docking is a computational chemistry method for simulating the binding mode between small molecules and proteins. This method predicts the binding site by placing small molecule compounds on the binding site of the protein and calculating their interaction energy. The DFSite algorithm combines molecular docking and local features, describes features from three levels of amino acids, atoms and binding sites themselves, and uses a linear regression algorithm to train the scoring function to improve the accuracy of the prediction.

[0099] In one embodiment, the prediction method based on sequence and structure information combines a prediction method based on structure (such as RNAsite_str) and a prediction method based on sequence (such as RNAsite_seq). First, multiple sequences are aligned using the BLASTN tool, and then weights are assigned using the Henikoff–Henikoff scheme, ultimately forming a prediction method based on sequence and structure information, RNAsite, which achieves a relatively ideal effect.

[0100] In one embodiment, the multi-scale protein sequence structure comparative learning method predicts the interaction between compounds and proteins through a comparative learning framework (PSC-CPI). The PSC-CPI model is divided into two parts: a compound encoder and a protein encoder, which extract representations from molecular graphs and protein sequence structures, respectively. By predicting the contact map between protein residues and compound atoms, the paired representations of compounds and proteins are learned, and an interaction strength prediction module is introduced to predict binding affinity.

[0101] In one embodiment, the present invention demonstrates that sleep deprivation induces disturbances in the estrous cycle of female mice. The estrous cycle is an important indicator of the reproductive function of female animals. Therefore, we used the vaginal cytology smear method to evaluate the changes in the estrous cycle of female mice before and after sleep deprivation. The mice in the experimental group were continuously sleep deprived for 72 hours, and were continuously monitored for 20 days starting on the 4th day to detect changes in the estrous cycle. The type, morphology, and number of smear cells were used to determine whether the female mice were in proestrus, estrus, metestrus, or diestrus. The results showed that the average estrous cycle of the mice in the control group was 4.78±0.37 days, and the estrous cycle was extended to 6.68±0.57 days (±0.57 days) after sleep deprivation. Figure 4 A, B, P < 0.0001). This result shows that sleep deprivation can induce estrous cycle disorders in female mice.

[0102] In one embodiment, the present invention proves that sleep deprivation induces abnormal follicular development and increases the rate of follicular atresia. In order to further study the effect of sleep deprivation on the function of ovarian tissue in female mice, we used HE staining to perform morphological analysis on the ovarian tissue structure. The ovaries of female mice in the control group and the experimental group were fixed, dehydrated, and made into tissue sections for HE staining and microscopic observation. The results showed that the follicles of female mice in the control group developed normally, while the follicles of female mice in the sleep deprivation group were more likely to be atretic during the development stage, with shrinkage of the zona pellucida and collapse of the follicle wall ( Figure 5 A). According to statistics, the percentage of follicular atresia in the normal control group was 44.03±8.07%, and the percentage of follicular atresia in the sleep deprivation group was 70.52±3.67% ( Figure 5 B, P < 0.001); the percentage of atresia of antral follicles in the normal control group was 51.60 ± 2.30, and the percentage of atresia of antral follicles in the sleep deprivation group was 77.56 ± 2.05% ( Figure 5 C, P < 0.0001). This result shows that the follicular atresia rate and the antral follicle atresia rate of female mice increased significantly after sleep deprivation.

[0103] In one embodiment, the present invention demonstrates that sleep deprivation induces apoptosis of ovarian granulosa cells. According to the above research results, sleep deprivation can induce an increase in the follicular atresia rate of female mice, suggesting that there may be an increase in ovarian granulosa cell apoptosis. For this reason, we used terminal deoxynucleotidyl transferase mediated nick end labeling (TUNEL) to further detect changes in apoptosis of granulosa cells in ovarian follicles. TUNEL-positive cells were analyzed under a fluorescence microscope. In order to quantify the fluorescence intensity in the follicles, we used ImageJ software to determine the average pixel intensity of the corresponding follicle area. The results showed that the TUNEL staining fluorescence intensity of the granulosa cells in the sleep deprivation group was higher than that in the control group ( Figure 6 A), and statistically significant ( Figure 6 B, P<0.01). This result suggests that sleep deprivation causes an increase in the apoptosis response of ovarian granulosa cells in female mice.

[0104] In one embodiment, the present invention demonstrates that sleep deprivation induces changes in the expression levels of p53 and its modified proteins in mouse ovaries. In order to explore the molecular mechanism of sleep deprivation-induced apoptosis of ovarian granulosa cells, we detected the expression level of signal protein p53, which plays a key role in granulosa cell apoptosis. The results showed that the expression level of p53 protein in the ovaries increased significantly after sleep deprivation ( Figure 7 A, B, P<0.05). At the same time, the expression levels of p53 downstream pro-apoptotic proteins BAX and PUMA were significantly upregulated ( Figure 7 A, C, D, P < 0.05), while the expression levels of other pro-apoptotic proteins (NOXA, BID) and anti-apoptotic proteins (Bcl-2, Bcl-xL) did not change significantly ( Figure 7 A, E). The above results indicate that sleep deprivation induces the activation of p53 and its downstream specific pro-apoptotic signaling pathways.

[0105] In one embodiment, the present invention demonstrates that sleep deprivation induces changes in the mRNA expression levels of p53-related apoptosis target genes in mice. To further confirm the above results, we detected the mRNA expression levels of positive target genes BAX and PUMA. The results showed that the expression levels of BAX and PUMA in the ovaries of mice were upregulated after sleep deprivation, which was consistent with the trend of protein expression changes ( Figure 8 AC, P<0.01, P<0.001). This result suggests that sleep deprivation induces the expression of pro-apoptotic target genes BAX and PUMA at the transcriptional level, which is mediated by p53.

[0106] In one embodiment, the present invention demonstrates that inhibition of p53 expression significantly down-regulates the expression of its downstream target genes BAX and PUMA mRNA. In order to explore whether there is a causal relationship between p53 and the up-regulation of BAX and PUMA expression, we transfected p53 siRNA in primary ovarian granulosa cells. The results showed that when p53 expression was inhibited, the mRNA transcription of BAX and PUMA was also inhibited ( Fig. 9 ). This result suggests that p53 can mediate the expression of pro-apoptotic genes BAX and PUMA at the transcriptional level. The P53 / BAX-PUMA pathway may play an important role in sleep deprivation-induced ovarian granulosa cell apoptosis and ovarian dysfunction.

[0107] In one embodiment, the present invention demonstrates that TUDCA pretreatment inhibits sleep deprivation-induced ovarian p53 / BAX-PUMA pathway activation. Tauroursodeoxycholic acid (TUDCA) has been shown to inhibit apoptosis by targeting to prevent BAX from being transported to mitochondria, then inhibiting cytochrome c release, antagonizing the initiation of apoptosis cascades. Through literature research, it was found that TUDCA can regulate apoptosis responses in PC12 neuronal cells by targeting the E2F-1 / p53 / BAX pathway, and TUDCA can also reduce PUMA upregulation and BAX activation in HuH-Ntcp cells to inhibit hepatocyte lipopoptosis. In addition, TUDCA has been approved by the FDA for the treatment of patients with hepatobiliary diseases and neurological diseases (such as Alzheimer's disease and Parkinson's disease). However, its role in the ovarian system is still unclear. In order to further explore the contribution of the p53 / BAX-PUMA pathway in mediating ovarian dysfunction, we gave female mice tail vein injections of TUDCA before sleep deprivation, and treated them for 3 consecutive days to explore whether this drug can antagonize sleep deprivation-induced ovarian dysfunction responses. The results showed that sleep deprivation induced the activation of p53, BAX, and PUMA proteins in the ovaries of female mice, and the above reactions were significantly inhibited after pretreatment with TUDCA ( Fig.10 AD, P<0.05, P<0.01), the results were statistically significant.

[0108] In one embodiment, the present invention demonstrates that inhibiting the activation of the ovarian p53 / BAX-PUMA pathway can antagonize the reproductive cycle disorder effects induced by sleep deprivation in female mice. We then analyzed the effect of TUDCA pretreatment on the reproductive cycle disorder effects induced by sleep deprivation in female mice. After the mice in the experimental group were injected with TUDCA or its solvent (normal saline) through the tail vein, they were continuously sleep deprived for 72 hours. The results showed that the average estrous cycle of mice in the non-stressed normal control group was 4.61±0.47 days, the estrous cycle of mice in the solvent control group was extended to 6.60±0.55 days after sleep deprivation, and the estrous cycle of female mice in the TUDCA treatment group was 5.23±0.22 days ( Fig.11A, B, P < 0.001, P < 0.0001). This result shows that TUDCA treatment can significantly antagonize the reproductive cycle disorder effect induced by sleep deprivation in female mice and basically restore it to normal levels.

[0109] In one embodiment, the present invention demonstrates that inhibiting the activation of the ovarian p53 / BAX-PUMA pathway can antagonize the abnormal follicular development response induced by sleep deprivation in female mice. Subsequently, we analyzed the percentage of follicular atresia in each group of mice. The results showed that the follicular atresia rate of female mice in the normal control group without stress was 56.05±9.74%, the follicular atresia rate of mice in the solvent control group after sleep deprivation was 73.6±7.17%, and the atresia rate of female mice in the TUDCA treatment group was 55.40±8.49% ( Fig.12 A, B, P < 0.01). We also analyzed the percentage of antral follicle atresia: the percentage of antral follicle atresia in female mice in the normal control group without stress was 52.8 ± 2.59%, the percentage of antral follicle atresia in the solvent control group after sleep deprivation was 77.56 ± 2.50%, and the percentage of antral follicle atresia in female mice in the TUDCA treatment group was 56.6 ± 2.70% ( Fig.12 C, P<0.0001). The above results show that TUDCA can significantly antagonize the abnormal follicular development induced by sleep deprivation.

[0110] In one embodiment, the present invention demonstrates that inhibiting the activation of the ovarian p53 / BAX-PUMA pathway can antagonize the apoptotic response of ovarian granulosa cells in female mice induced by sleep deprivation. Based on the above results, we used the TUNEL method to detect the effect of TUDCA pretreatment on the apoptotic response of granulosa cells in ovarian follicles of mice after sleep deprivation. The results showed that the green fluorescence intensity of granulosa cells in the sleep-deprived mice in the TUDCA injection group was lower than that in the sleep-deprived group, and there was statistical significance ( Fig.13 A, B, P<0.05, P<0.01). These results suggest that TUDCA can target the p53 / BAX-PUMA pathway to antagonize the apoptosis response of ovarian granulosa cells in female mice.

[0111] The above results prove that sleep deprivation induces reproductive dysfunction in female mice (estrous cycle delay, increased follicular atresia rate, and apoptosis of ovarian granulosa cells), and sleep deprivation induces upregulation of the activation level of p53 activation and its dependent apoptosis response signaling pathway in ovarian granulosa cells, while preventive administration of TUDCA can antagonize the activation reaction of p53 / BAX-PUMA signaling pathway induced by sleep deprivation, and then antagonize the reproductive dysfunction induced by sleep deprivation (estrous cycle delay, increased follicular atresia rate, and apoptosis of ovarian granulosa cells). TUDCA has biological safety and has potential application value in alleviating the harmful effects of female reproductive health caused by insufficient sleep. That is, the present invention proves the interactive relationship that TUDCA antagonizes the reproductive dysfunction induced by sleep deprivation by inhibiting p53. Based on this, the present invention has developed a computer-assisted drug screening method based on TUDCA and p53 for the first time in screening, which also proves the effectiveness and feasibility of the computer-assisted drug screening method based on TUDCA and p53 provided by the present invention in drug screening.

[0112] S103: using a computer-assisted drug screening method to obtain a candidate drug targeting the binding site;

[0113] In one embodiment, the computer-assisted drug screening method comprises:

[0114] Obtaining the binding site of the TUDCA and the p53 protein in the complex;

[0115] Based on the spatial structure of the binding site between the TUDCA and the p53 protein in the complex, screening small molecule compounds with similar structures in a molecular database;

[0116] The small molecule compounds obtained by screening are subjected to molecular docking with the p53 protein to calculate the affinity or binding energy of the small molecule compounds targeting the receptor p53 protein to obtain the score of each small molecule compound, and the candidate drugs are obtained by ranking according to the score.

[0117] In one embodiment, the small molecule compound includes an inorganic small molecule compound, an antibody, a polypeptide, a protein analog, a peptide analog, siRNA, shRNA, dsRNA, microRNA or an antisense nucleic acid. It should be noted that the present invention has no particular limitation on the specific type of the small molecule compound, and any small molecule compound that may be used in the computer-assisted drug screening method provided by the present invention and may produce a corresponding effect falls within the protection scope of the present invention.

[0118] In one embodiment, the method further comprises:

[0119] Obtaining a system for expressing p53 protein;

[0120] Treating the p53 protein-expressing system with the screened small molecule compound to verify the effectiveness of the small molecule compound;

[0121] Screen small molecule compounds that inhibit p53 protein expression as candidate drugs.

[0122] In one embodiment, the system expressing p53 protein includes a cell system expressing p53 protein, a subcellular system expressing p53 protein, a tissue system expressing p53 protein, a solution system expressing p53 protein, an organ system expressing p53 protein or an animal system expressing p53 protein.

[0123] In one embodiment, in molecular docking, affinity refers to how tightly a molecule binds to a receptor. A high affinity means that the binding is more stable, while a low affinity means that the binding is less stable. Affinity is usually calculated, for example, by calculating the binding free energy (ΔG) or the binding constant (Kd).

[0124] In the process of molecular docking, affinity depends on the interaction between molecules and receptors, including hydrogen bonds, van der Waals forces, electrostatic interactions, etc. These interactions jointly determine the binding mode between molecules and receptors, thus affecting affinity.

[0125] In order to evaluate affinity, some scoring systems or scoring methods are usually used to quantify the interaction between molecules and receptors. These scoring methods are based on different algorithms and physical models, which can reflect the binding energy, interaction type and affinity between molecules and receptors.

[0126] In one embodiment, the modes of drug screening using computer-assisted drug screening technology include one or more of the following: protein-small molecule docking, protein-protein docking, and protein-nucleic acid docking.

[0127] In one embodiment, the protein-small molecule docking refers to a computational simulation process of docking the structures of proteins and small molecules (such as drug molecules) together through certain algorithms and programs. This process can be used to study the interaction between proteins and small molecules, as well as possible biological functions.

[0128] In protein-small molecule docking, software such as DOCK is usually used for computational simulation. DOCK is a highly automated drug design software that can achieve docking between small molecule ligands and biomacromolecule receptors. It uses a fragment-based scoring method to achieve fast and accurate docking. The basic algorithm of DOCK software includes two stages. The first stage is the low-precision stage, which mainly searches for a rough match between small molecule ligands and biomacromolecule receptors. The second stage is the high-precision stage, in which all side chain conformations are considered to calculate more accurate interaction energy. In the low-precision stage, DOCK software will randomly translate and rotate small molecule ligands, perform a certain number of rigid body moves, and then calculate the interaction energy. After outputting the lowest conformation, it enters the high-precision stage. In the high-precision stage, the program will perform more optimization and adjustments to achieve more accurate docking.

[0129] In one embodiment, the protein-protein docking refers to a computational simulation process of docking the structures of two proteins together through certain algorithms and programs. This process can be used to study the interaction between proteins and possible biological functions.

[0130] In protein-protein docking, RosettaDock is a commonly used docking software that uses a fragment-based scoring method to achieve fast and accurate protein docking. The software can achieve precise adjustment of side chain conformations during docking and consider a variety of complex interactions, such as hydrogen bonds, ionic bonds, hydrophobic interactions, etc. The basic algorithm of RosettaDock includes two stages. The first stage is the low-precision stage, which mainly searches for the degree of adaptation of the backbone shapes between the two proteins. The second stage is the high-precision stage, in which all side chain conformations are considered to calculate more accurate interaction energy. In the low-precision stage, the program randomly translates and rotates a component of a protein molecule, performs a certain number of rigid body moves, and then calculates the interaction energy. After outputting the lowest conformation, it enters the high-precision stage. In the high-precision stage, the program performs 50 MCMPCycles: rearranges the conformation and minimizes the interaction energy, which is used as the initial starting conformation.

[0131] In one embodiment, the protein-nucleic acid docking refers to a computational simulation process of docking the structures of proteins and nucleic acids (e.g., DNA or RNA) together through certain algorithms and programs. This process can be used to study the interactions between proteins and nucleic acids, as well as possible biological functions.

[0132] In protein-nucleic acid docking, software such as NAflex is usually used for computational simulation. NAflex is a software developed specifically for nucleic acid structure prediction and design, which can achieve accurate modeling and docking of DNA or RNA molecules. NAflex software uses a fragment-based scoring method to achieve fast and accurate docking. It takes into account a variety of complex interactions, such as hydrogen bonds, ionic bonds, hydrophobic interactions, etc., and can achieve precise adjustment of side chain conformations during docking. The basic algorithm of NAflex software includes two stages. The first stage is the low-precision stage, which mainly searches for rough matches between proteins and nucleic acids. The second stage is the high-precision stage, in which all side chain conformations are considered and more accurate interaction energies are calculated. In the low-precision stage, NAflex software randomly translates and rotates nucleic acid molecules, performs a certain number of rigid body moves, and then calculates the interaction energy. After outputting the lowest conformation, it enters the high-precision stage. In the high-precision stage, the program will perform more optimization and adjustments to achieve more accurate docking.

[0133] In one embodiment, drug virtual screening is an important method in the field of targeted drug design and is also a drug screening method well known to those skilled in the art. The process is roughly as follows:

[0134] First, based on the known complex structure of the drug and the target protein, the binding mode of the drug and the target protein is studied to determine the key amino acid residues. This step helps to understand how the drug interacts with the target protein and provides a basis for subsequent virtual screening.

[0135] Next, a large number of small molecule compounds are virtually screened using computer-aided drug design methods. In this process, the binding ability of small molecule compounds to target proteins will be predicted and evaluated. Generally, small molecules with low binding energy and good shape complementarity will be selected as potential drug candidates.

[0136] Finally, the screened candidate drugs are further experimentally verified to confirm their interaction with the target protein and their biological activity.

[0137] In one embodiment, in drug virtual screening, the binding site refers to the region on the target protein that binds to the small molecule compound. In order to perform receptor-based virtual screening, it is first necessary to determine the binding site of the target protein. This is usually achieved by analyzing and studying the structure of the target protein.

[0138] Once the binding site is determined, the computer-aided drug design method can be used to dock the compounds in the small molecule compound library with the target protein. During the docking process, the computer simulates the interaction between the compound and the binding site and evaluates their binding ability. According to the docking results, small molecule compounds with strong binding ability to the target protein can be screened as potential drug candidates. The determination of drug virtual screening binding sites is crucial to the success of drug design. Therefore, when determining the binding site, it is necessary to consider a variety of factors, such as the structural characteristics of the target protein, the binding mode of known ligands, etc. At the same time, in the virtual screening process, the binding site also needs to be reasonably processed and optimized to improve the accuracy and efficiency of the screening.

[0139] In one embodiment, computer-aided drug screening is a technology for drug screening using computer-aided drug design methods, which can help researchers quickly screen candidate drugs with strong binding ability to target proteins and potential efficacy from a large number of small molecule compounds.

[0140] In one embodiment, molecular docking is a method for drug design based on the characteristics of receptors and the interaction between receptors and drug molecules. It mainly studies the interaction between molecules (such as ligands and receptors) and predicts their binding mode and affinity. This method is widely used in the early stages of drug development and can help researchers quickly screen out compounds with potential efficacy.

[0141] Molecular docking methods mainly focus on spatial matching and energy matching. Spatial matching refers to the geometric complementarity between the drug molecule and the receptor protein, while energy matching refers to the minimization of the interaction between the drug molecule and the receptor protein. For the calculation of geometric matching, grid point calculation, fragment growth and other methods are usually used, while energy calculation uses simulated annealing, genetic algorithm and other methods.

[0142] According to the degree and method of simplification, molecular docking methods can be divided into rigid docking, semi-flexible docking and flexible docking. In the rigid docking method, the molecular conformations involved in the docking do not change during the calculation process, only the spatial position and posture of the molecules are changed. Semi-flexible docking allows some conformations to change during the calculation. Flexible docking allows more conformational changes.

[0143] In one embodiment, the molecular libraries used in drug virtual screening are mainly the following:

[0144] ZINC: contains more than 250 million commercially available compounds for small molecule virtual screening.

[0145] PubChem: contains bioactive substances for small molecule virtual screening.

[0146] DrugBank: Contains drugs and small molecules for drug design and discovery.

[0147] ChEMBL: Contains small molecules and is used for drug discovery and chemical genomics research.

[0148] ChemDB: Contains a large number of known small molecules for chemical genomics research and drug discovery.

[0149] HMDB: Contains a large number of known small molecules for use in chemical genomics research and drug discovery.

[0150] BindingDB: Contains a large number of known small molecules for chemical genomics research and drug discovery.

[0151] SMPDB: Contains a large number of known small molecules for chemical genomics research and drug discovery.

[0152] In addition, there are some commercial databases such as ChemDiv, Enamine, Lifechemicals, Specs, Chembridge, Maybridge, Microsource, Vitas-M and Interbioscreen, which are also often used for drug virtual screening.

[0153] Figure 2 Schematic diagram of a computer-assisted drug screening system based on TUDCA and p53 provided by an embodiment of the present invention. Specifically, the system includes:

[0154] Data acquisition unit, acquiring TUDCA structure and p53 protein structure data;

[0155] A site determination unit, which selects the spatial structure of the complex formed by the interaction of the TUDCA and the p53 protein, and determines the binding site of the TUDCA and the p53 protein in the complex as the binding site of the targeted drug;

[0156] A drug screening unit, which uses a computer-assisted drug screening method to obtain candidate drugs targeting the binding site;

[0157] Optionally, the computer-assisted drug screening method comprises:

[0158] Obtaining the binding site of the TUDCA and the p53 protein in the complex;

[0159] Based on the spatial structure of the binding site between the TUDCA and the p53 protein in the complex, screening small molecule compounds with similar structures in a molecular database;

[0160] Performing molecular docking between the screened small molecule compounds and the p53 protein to calculate the affinity or binding energy of the small molecule compounds targeting the receptor p53 protein to obtain the score of each small molecule compound, and ranking them according to the score to obtain candidate drugs;

[0161] Optionally, the small molecule compound includes an inorganic small molecule compound, an antibody, a polypeptide, a protein analog, a peptide analog, siRNA, shRNA, dsRNA, microRNA or antisense nucleic acid;

[0162] Optionally, the binding site between the TUDCA and the p53 protein in the complex is predicted using a bioinformatics prediction method;

[0163] The bioinformatics prediction method includes a molecular docking method, a prediction method based on sequence and structure information, or a multi-scale protein sequence structure comparison learning method;

[0164] Optionally, the method further comprises an effectiveness verification unit, which obtains a system expressing p53 protein; treats the system expressing p53 protein with the screened small molecule compound to verify the effectiveness of the small molecule compound; and screens the small molecule compound that has the ability to inhibit the expression of p53 protein as a candidate drug.

[0165] Figure 3 Schematic diagram of a computer-assisted drug screening device based on TUDCA and p53 provided by an embodiment of the present invention. Specifically, the device includes:

[0166] A memory and a processor, wherein the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, the computer-assisted drug screening method based on TUDCA and p53 as described above is implemented.

[0167] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the computer-assisted drug screening method based on TUDCA and p53 as described above is implemented.

[0168] The present invention also provides any of the following products:

[0169] (1) a complex formed by the interaction of TUDCA and p53 protein, wherein the complex formed by the interaction of TUDCA and p53 protein is the complex formed by the interaction of TUDCA and p53 protein as described above;

[0170] (2) A drug screened using the computer-assisted drug screening method based on TUDCA and p53 as described above.

[0171] The embodiments of the present invention also provide any of the following applications:

[0172] (1) An application of a complex, wherein the complex is a complex formed by the interaction of TUDCA and p53 protein as described above, and the application includes: application of the complex in screening drugs for resisting abnormal changes in ovarian function caused by sleep deprivation;

[0173] (2) Use of TUDCA in the preparation of a drug for resisting abnormal changes in ovarian function caused by sleep deprivation.

[0174] The validation results of this validation example show that assigning intrinsic weights to indications can moderately improve the performance of the method relative to the default setting.

[0175] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0176] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0177] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0178] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0179] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0180] A person skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a disk or an optical disk, etc.

[0181] The above is a detailed introduction to a computer device provided by the present invention. For a person skilled in the art, according to the concept of the embodiments of the present invention, there may be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A computer-assisted drug screening method based on TUDCA and p53, characterized in that: The method comprises: Obtain TUDCA structure and p53 protein structure data; Selecting the spatial structure of the complex formed by the interaction of the TUDCA and the p53 protein, and determining the binding site of the TUDCA and the p53 protein in the complex as the binding site of the targeted drug; A computer-assisted drug screening method is used to obtain candidate drugs targeting the binding site.

2. The computer-assisted drug screening method based on TUDCA and p53 according to claim 1, characterized in that: The computer-assisted drug screening method comprises: Obtaining the binding site of the TUDCA and the p53 protein in the complex; Based on the spatial structure of the binding site between the TUDCA and the p53 protein in the complex, screening small molecule compounds with similar structures in a molecular database; The small molecule compounds obtained by screening are subjected to molecular docking with the p53 protein to calculate the affinity or binding energy of the small molecule compounds targeting the receptor p53 protein to obtain the score of each small molecule compound, and the candidate drugs are obtained by ranking according to the score.

3. The computer-assisted drug screening method based on TUDCA and p53 according to claim 2, characterized in that: The small molecule compound includes an inorganic small molecule compound, an antibody, a polypeptide, a protein analog, a peptide analog, siRNA, shRNA, dsRNA, microRNA or an antisense nucleic acid.

4. The computer-assisted drug screening method based on TUDCA and p53 according to claim 1, characterized in that: The binding site between the TUDCA and the p53 protein in the complex is predicted using a bioinformatics prediction method; The bioinformatics prediction method includes a molecular docking method, a prediction method based on sequence and structure information, or a multi-scale protein sequence structure comparison learning method.

5. The computer-assisted drug screening method based on TUDCA and p53 according to claim 1, characterized in that: The method further comprises: Obtaining a system for expressing p53 protein; Treating the p53 protein-expressing system with the screened small molecule compound to verify the effectiveness of the small molecule compound; Screening small molecule compounds that inhibit p53 protein expression as candidate drugs; Optionally, the system expressing p53 protein includes a cell system expressing p53 protein, a subcellular system expressing p53 protein, a tissue system expressing p53 protein, a solution system expressing p53 protein, an organ system expressing p53 protein or an animal system expressing p53 protein.

6. A computer-assisted drug screening system based on TUDCA and p53, characterized in that: The system comprises: Data acquisition unit, acquiring TUDCA structure and p53 protein structure data; A site determination unit, which selects the spatial structure of the complex formed by the interaction of the TUDCA and the p53 protein, and determines the binding site of the TUDCA and the p53 protein in the complex as the binding site of the targeted drug; A drug screening unit, which uses a computer-assisted drug screening method to obtain candidate drugs targeting the binding site; Optionally, the computer-assisted drug screening method comprises: Obtaining the binding site of the TUDCA and the p53 protein in the complex; Based on the spatial structure of the binding site between the TUDCA and the p53 protein in the complex, screening small molecule compounds with similar structures in a molecular database; Performing molecular docking between the screened small molecule compounds and the p53 protein to calculate the affinity or binding energy of the small molecule compounds targeting the receptor p53 protein to obtain the score of each small molecule compound, and ranking them according to the score to obtain candidate drugs; Optionally, the small molecule compound includes an inorganic small molecule compound, an antibody, a polypeptide, a protein analog, a peptide analog, siRNA, shRNA, dsRNA, microRNA or antisense nucleic acid; Optionally, the binding site between the TUDCA and the p53 protein in the complex is predicted using a bioinformatics prediction method; The bioinformatics prediction method includes a molecular docking method, a prediction method based on sequence and structure information, or a multi-scale protein sequence structure comparison learning method; Optionally, the method further comprises an effectiveness verification unit, which obtains a system expressing p53 protein; treats the system expressing p53 protein with the screened small molecule compound to verify the effectiveness of the small molecule compound; and screens the small molecule compound that has the ability to inhibit the expression of p53 protein as a candidate drug.

7. A computer-assisted drug screening device based on TUDCA and p53, characterized in that: The device comprises: A memory and a processor, wherein the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, the computer-assisted drug screening method based on TUDCA and p53 described in any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer-assisted drug screening method based on TUDCA and p53 according to any one of claims 1 to 5 is implemented.

9. Any of the following products: (1) A complex formed by the interaction between TUDCA and p53 protein, characterized in that: The complex formed by the interaction of TUDCA and p53 protein is the complex formed by the interaction of TUDCA and p53 protein in claim 1; (2) A drug screened by the computer-assisted drug screening method based on TUDCA and p53 according to any one of claims 1 to 5.

10. Any of the following applications: (1) An application of a composite, characterized in that: The complex is a complex formed by the interaction of TUDCA and p53 protein as described in claim 9, and the application includes: application of the complex in screening drugs for resisting abnormal changes in ovarian function caused by sleep deprivation; (2) Use of TUDCA in the preparation of a drug for resisting abnormal changes in ovarian function caused by sleep deprivation.

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