General screening method for high activity targeted inhibitors

Abstract

This application discloses a universal screening method for highly active targeted inhibitors, belonging to the field of drug screening technology. The universal screening method includes the following steps: S1, selecting the complete amino acid sequences of human and non-human animal target proteins; S2, obtaining the standard protein three-dimensional structure file based on the amino acid sequence of the human target protein; S3, importing the standard protein three-dimensional structure file of the human target protein into the DrugRep virtual screening server to obtain a candidate list of inhibitors for the human target protein; the same operation is performed on non-human animal target proteins; S4, performing intersection analysis on the inhibitor candidate lists of human and non-human animal target proteins to obtain a core inhibitor set. By synergistically utilizing the structural commonalities of human and non-human animal target proteins, core inhibitors with high cross-species activity can be rapidly screened, improving the efficiency of translating preclinical research results into clinical applications.

Description

Technical Field

[0001] This application belongs to the field of drug screening technology, specifically relating to a universal screening method for highly active targeted inhibitors based on cross-species protein structure comparison. Background Technology

[0002] Targeted inhibitor development is a key direction in precision medicine. By specifically binding to the active sites of target proteins to regulate protein function, it provides an important means for precise disease treatment. However, traditional development models suffer from significant drawbacks such as long development cycles, high costs, and poor cross-species applicability. In particular, inhibitors designed directly against human target proteins often suffer from significant discrepancies between efficacy evaluations and clinical applications due to differences in molecular characteristics between preclinical animal models (such as rats) and human target proteins, severely hindering the improvement of development success rates.

[0003] The three-dimensional structure of a protein is determined by its amino acid sequence, and structural information is the core basis for inhibitor molecular docking and design. The amino acid sequences of target proteins from humans and clinical experimental animals typically share high homology, with conserved structural regions providing the molecular basis for developing universal, cross-species inhibitors. In recent years, with advancements in bioinformatics and computational biology, a range of tools have strongly supported the efficient screening of targeted inhibitors: the NCBI Protein database enables precise acquisition of amino acid sequences from target proteins across multiple species; protein structure prediction systems such as AlphaFold can rapidly generate high-resolution three-dimensional structural models based on sequences; and drug repositioning virtual screening platforms such as DrugRep can rapidly screen potential inhibitors from existing compound libraries based on protein three-dimensional structures.

[0004] However, existing screening strategies for targeted inhibitors often focus solely on human or single non-human animal proteins, failing to fully integrate and utilize the molecular characteristic correlations between human and non-human animal proteins, and lacking synergistic, cross-species screening strategies. This often leads to insufficient compatibility between selected inhibitors in preclinical animal experiments and actual human applications, making it difficult to quickly obtain ideal candidate compounds that simultaneously possess high activity and cross-species applicability.

[0005] Therefore, there is an urgent need to develop a new method that can synergistically utilize the structural information of target proteins from multiple species for inhibitor screening, in order to improve the consistency and R&D efficiency of inhibitors in translational medicine. Summary of the Invention

[0006] In view of this, the primary objective of this application is to provide a universal screening method for highly active targeted inhibitors, which can rapidly screen core inhibitors with high cross-species activity by synergistically utilizing the structural commonalities of human and non-human animal target proteins, thereby improving the efficiency of translating preclinical research results into clinical applications.

[0007] To achieve the above objectives, this application adopts the following technical solution: One aspect of this application discloses a universal screening method for highly active targeted inhibitors, comprising the following steps: S1. Select the complete amino acid sequences of human and non-human animal target proteins; S2. Based on the amino acid sequence of the human target protein, obtain its standard protein three-dimensional structure file; the same operation is performed for non-human animal target proteins. S3. Import the standard protein three-dimensional structure file of the human target protein into the DrugRep virtual screening server, and obtain a candidate list of inhibitors for the human target protein through molecular docking screening; the same operation is performed for non-human animal target proteins. S4. Perform an intersection analysis on the candidate inhibitor lists of human target proteins and non-human animal target proteins to screen out the core inhibitor set that is common to both.

[0008] This application has at least the following beneficial effects: The universal screening method proposed in this application effectively solves the problem of inconsistency between preclinical animal experiments and human application effects in traditional methods, improving both screening reliability and efficiency. Specifically, this application innovatively integrates protein structure prediction with DrugRep virtual screening technology to achieve rapid prediction from sequence to candidate inhibitors, significantly shortening the R&D cycle and reducing experimental costs. More importantly, compared with previous methods, this application employs a synergistic screening strategy using human and non-human animal target proteins, resulting in inhibitors with high predictive activity in both species. Furthermore, by using structural conservation as the screening basis and combining it with a two-species cross-validation strategy, the false positive rate is significantly reduced, improving the reliability of candidate inhibitors. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating a general screening method in a preferred embodiment of this application.

[0010] Figure 2 This is the core inhibitor set and key inhibitor of the A2M target protein screened in Example 2 of this application.

[0011] Figure 3 The inhibitory effects of five key inhibitors on the A2M target protein in HepG2 and BRL-3A cells were studied.

[0012] Figure 4 Cell viability of HepG2 and BRL-3A cells after treatment with different concentrations of Sonidegib.

[0013] In the figure, the comparisons between groups were performed using a variance test, ns: P> 0.05, : P <0.05, : P <0.01, : P <0.001. Detailed Implementation

[0014] The embodiments of this application will be clearly and completely described below. The technical solutions in the embodiments described below are exemplary and only possible technical implementations of this application, not all possible implementations. Those skilled in the art can combine the embodiments of this application to obtain other embodiments without creative effort, and these embodiments are also within the protection scope of this application.

[0015] This application discloses a universal screening method for highly active targeted inhibitors. The core concept of this method lies in performing virtual screening on human and non-human animal target proteins in parallel based on the same computational rules, thereby identifying common inhibitory structures (i.e., a core inhibitor set) that can act on both types of target proteins simultaneously. This allows for the preliminary elimination of compounds with strong species specificity that are unfavorable for subsequent animal experimental validation during the virtual screening stage. Furthermore, by employing structural conservation as the screening basis and combining it with a two-species cross-validation strategy, the false positive rate is significantly reduced, the reliability of candidate inhibitors is improved, and the success rate and translational potential of the screened candidate drugs in animal models are significantly enhanced.

[0016] The general screening method in this application mainly includes the following steps: S1. Select the complete amino acid sequences of human and non-human animal target proteins.

[0017] In this step, by selecting the amino acid sequences of target proteins from two species, the aim is to subsequently screen for compounds that inhibit target proteins across species, thereby improving the universality of the method and building a bridge between preclinical experimental animals and clinical research.

[0018] The amino acid sequence is the primary structural description of a protein, determining its spatial conformation. In this step, the complete amino acid sequence of a human target protein refers to the sequence information consisting of a linear arrangement of amino acid residues corresponding to a specific target protein derived from humans (Homo sapiens). The complete amino acid sequence of a non-human animal target protein refers to the amino acid sequence of a protein derived from a selected non-human animal species that is functionally or structurally homologous to or corresponds to the human target protein. Specifically, the non-human animal source is typically selected as preclinical experimental animals, such as rodents like rats and mice, and can also be non-human primates, such as macaques and whelps; this application does not exhaustively limit the scope. In some specific examples of this application, the non-human animal source is a rat.

[0019] In practice, this amino acid sequence information can be obtained from public bioinformatics databases. For specific examples, it can be retrieved and downloaded from authoritative databases such as the National Center for Biotechnology Information (NCBI) Protein Database and the UniProt Database. Typically, the sequences are saved and used in FASTA format.

[0020] Selecting complete amino acid sequences ensures that the subsequently constructed three-dimensional structural model contains complete structural domains, avoiding the loss of key binding pockets or allosteric sites due to sequence truncation, thus guaranteeing the comprehensiveness and accuracy of virtual screening.

[0021] S2. Based on the amino acid sequence of the human target protein, obtain its standard protein three-dimensional structure file; the same operation is performed for non-human animal target proteins.

[0022] Since virtual screening relies on the matching calculations between the compound and the target protein's three-dimensional spatial structure, it is essential to obtain the target protein's three-dimensional structural coordinate file. This step can be further refined into the following two steps: Step S21: Import the amino acid sequence of the human target protein into the online protein structure prediction system, and obtain the stored protein crystallography information file of the target protein through structure prediction.

[0023] An online protein structure prediction system refers to a server platform that can predict the three-dimensional folding pattern of proteins based on amino acid sequences using algorithms. By leveraging a high-precision structure prediction system, the aim is to overcome the limitation of lacking experimentally resolved structures for some target proteins, providing reliable spatial configurations for subsequent molecular docking and ensuring the accuracy of virtual screening.

[0024] As a specific, but not limiting, example, the online protein structure prediction system described can be AlphaFold2 (or its derivative ColabFold platform), RoseTTAFold, or I-TASSER, etc. Taking AlphaFold2 as an example, the FASTA format sequence obtained in step S1 is pasted or uploaded to the prediction interface. After submitting the task, the server uses a deep learning model to generate the coordinate positions of each amino acid residue in the protein in three-dimensional space. After the prediction is completed, the system outputs a file storing protein crystallographic information, typically in PDB or mmCIF format. This file contains information such as atom type, atom coordinates, residue number, and temperature factor, constituting the data carrier of the protein's three-dimensional structure.

[0025] Step S22: Upload the stored protein crystallography information file of the human target protein to the online format conversion system, complete the format conversion, and obtain the standard protein three-dimensional structure file of the protein.

[0026] Because different molecular docking software or virtual screening servers have varying compatibility with input file formats, standardizing the data format ensures that the subsequent virtual screening platform can correctly read and process the target protein structure information, guaranteeing the compatibility and smooth operation of the computational process. As a specific, but not limiting, example, an online format conversion system could be SCIENCE CODONS, Open Babel online service, or online PDB file repair and format conversion tools. By uploading the file obtained in step S21 and selecting the target output format for conversion, the standard three-dimensional protein structure file for that protein can be obtained.

[0027] Furthermore, in this application, the standard protein three-dimensional structure file can be any one of PDB, mmCIF, or MOL2. It can be adapted according to the experimental purpose, research needs, and the selected system. Other types can be selected based on server update availability; this application does not impose specific limitations on this. In some specific examples, the standard protein three-dimensional structure file is a PDB file.

[0028] Among them, "non-human animal-derived similar operation" refers to obtaining similar information by following the same operation steps and operation logic.

[0029] Step S3: Import the standard protein three-dimensional structure file of the human target protein into the DrugRep virtual screening server, and obtain a candidate list of inhibitors for the human target protein through molecular docking screening; the same operation is performed for non-human animal target proteins.

[0030] In this step, DrugRep is used. It is a publicly available, web-based, interactive virtual screening server for drug relocation, integrating an open-source molecular docking engine (such as AutoDock Vina) and a compound library processing module. After importing standard protein 3D structure files, the server typically performs preprocessing automatically, including hydrogenation, charge assignment, and identification of docking pockets (usually covering the entire protein surface or a predefined active region if not specified by the user). Virtual screening efficiently narrows down the pool of candidate compounds, reducing the cost and time of subsequent experimental screening.

[0031] Furthermore, the molecular docking screening employs a combination of rigid docking and flexible side-chain docking. Specifically, the main backbone of the protein receptor remains fixed during docking (rigid), but the side chains of key amino acid residues around the binding pocket are allowed to rotate freely within a certain angular range (flexible) to accommodate the induced fit effect of different ligand molecules. This semi-flexible docking strategy achieves a good balance between computational accuracy and computational resource consumption, considering both receptor adaptability and avoiding excessive computational load due to the introduction of full flexibility.

[0032] Furthermore, the docking scoring employs a comprehensive scoring function. This scoring function is an empirical formula based on a weighted combination of force field interaction energies (such as van der Waals forces, electrostatic interactions, and hydrogen bonding) with desolvation effects, ligand torsional energies, and other factors. The value output by the comprehensive scoring function is expressed as the binding free energy (kcal / mol); a lower value indicates a more stable binding between the acceptor and ligand and a stronger affinity.

[0033] The specific screening criteria identified compounds with binding energies ≤ -7.0 kcal / mol as inhibitor candidates. In this field, a binding energy of -7.0 kcal / mol is generally considered a commonly used empirical threshold for potential biological activity. By setting this threshold, a large number of noisy molecules with weak binding forces or non-specific binding can be filtered out, significantly narrowing the range of compounds for subsequent analysis and improving data processing efficiency. It should be understood that the threshold of -7.0 kcal / mol is only a relatively optimal empirical value, and in practical applications, it can be appropriately relaxed or tightened according to the properties of the target protein, for example, set to ≤ -6.5 kcal / mol or ≤ -8.0 kcal / mol.

[0034] After the docking calculations are completed, the server outputs a candidate list of inhibitors for human target proteins. This list typically includes a compound ID (such as PubChem CID or ZINC ID), compound name, a schematic diagram of the two-dimensional molecular structure, and the corresponding docking score (binding energy value). The same procedure is followed for non-human animal target proteins to obtain their respective candidate inhibitor lists.

[0035] S4. Perform an intersection analysis on the candidate inhibitor lists of human target proteins and non-human animal target proteins to screen out the core inhibitor set that is common to both.

[0036] The specific intersection analysis method compares the candidate inhibitor lists for both human and non-human animal target proteins according to compound name or CID number. The CID number (PubChem Compound Identifier) ​​is a unique compound identifier in the PubChem database of the National Institutes of Health (NIH), possessing uniqueness and cross-platform consistency. Using CID numbers for comparison is preferred to eliminate errors caused by synonyms. Compounds appearing in both lists are extracted as the core inhibitor set. This step aims to improve the success rate of translation between preclinical animal models and human applications by identifying compounds with conserved inhibitory activity across species. This minimizes the translation gap caused by species differences in target sites, resulting in drugs effective in animal experiments but ineffective in humans, or effective in human cells in vitro but ineffective in animal models. This significantly enhances the species universality of candidate drugs and their reference value in preclinical research.

[0037] By extracting all compounds from this set as priority candidate compounds for subsequent experimental validation and drug development, a clear and focused list of candidate molecules is provided for drug development, improving research efficiency and success rate.

[0038] In a preferred embodiment, the general screening method further includes a step of screening key inhibitors.

[0039] After obtaining the core inhibitor set, although these compounds all have binding potential on human and animal targets, their binding strength may differ. To focus on the molecules with the strongest human activity, the key inhibitors are selected from the core inhibitor set based on compounds that score highly in the virtual screening of human target proteins.

[0040] In some specific examples, compounds in the core inhibitor set can be sorted from low to high according to their docking binding energy to human target proteins (i.e., from strong to weak binding affinity), and the top 5, top 10, top 20, or top 50 compounds can be selected as key inhibitors. This operation ensures that the candidates that ultimately enter experimental validation have the optimal predicted affinity for human targets, taking into account both species universality and human activity intensity.

[0041] As a preferred embodiment, the general screening method further includes the step of screening highly active targeted inhibitors from key inhibitors through in vitro experiments.

[0042] While the computational results of virtual screening are instructive, they must be validated through biological experiments to confirm their actual pharmacological activity. In vitro experiments were conducted by treating human and non-human animal cells with key inhibitors, and the inhibitory effect on target proteins was then assessed to obtain highly active targeted inhibitors.

[0043] The specific processing method is as follows: human cell lines and corresponding animal cell lines are seeded separately in multi-well plates, and key inhibitors of varying or fixed concentrations are added and incubated for a specified time. Subsequently, the inhibitory effect on the target protein is detected. Specific detection methods include, but are not limited to, Western blotting and enzyme-linked immunosorbent assay (ELISA), etc., which are not specifically limited in this application. If a compound can significantly inhibit the function of the target protein in both human and animal cells, and the degree of inhibition is dose-dependent, then the compound can be identified as a highly active targeted inhibitor.

[0044] In addition, to rule out false positive inhibitory effects caused by the screening of highly active targeting inhibitors through broad cytotoxicity rather than specific targeting, the in vitro experiments also included treating human and non-human animal cells with different concentrations of highly active targeting inhibitors, and then detecting cell viability to verify the cytotoxicity of the screened highly active targeting inhibitors.

[0045] Specifically, cell viability can be detected using established experimental methods in this field, such as the MTT assay, CCK-8 assay, or ATP luminescence assay, but is not limited to these. The half-maximal inhibitory concentration (IC50) is calculated by plotting a cell viability-drug concentration curve. 50 ) or median toxic concentration (CC) 50 If the effective concentration of the target protein inhibiting the protein is much lower than the concentration that produces cytotoxicity (i.e., a wide therapeutic window), then the highly active targeted inhibitor demonstrates its safety potential, thus completing the final screening and confirmation of the highly active targeted inhibitor. This validation step ensures that the final target compound not only has high affinity across species but also a low risk of off-target toxicity.

[0046] The present application will be further illustrated below with reference to specific embodiments. It should be noted that the specific embodiments below are for illustrative purposes only and do not limit the scope of the present application in any way.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the specification of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application.

[0048] In addition, unless otherwise specified, methods without detailed conditions or steps are conventional methods, and the reagents and materials used are commercially available.

[0049] Example 1: A general screening method for highly active targeted inhibitors This embodiment exemplifies the highly active targeted inhibitor screening method described in this application, and the specific process is as follows: Figure 1 As shown, it includes the following steps: (1) Obtain the amino acid sequence of the target protein: Select two species from the NCBI Protein (https: / / www.ncbi.nlm.nih.gov / protein / ?term) database and obtain the complete amino acid sequences of the human and rat target proteins.

[0050] (2) Predicting protein structure: The amino acid sequence of the human target protein was submitted to the protein structure prediction system AlphaFold Server (https: / / AlphaFoldserver.com / ). The AlphaFold system, based on deep residual neural networks and multiple sequence alignment (MSA) information, predicted the distance between amino acids and the main chain / side chain angle, generated a three-dimensional spatial conformation model, and output it as a CIF file. At the same time, the amino acid sequence of the rat target protein was submitted to AlphaFold Server in the same way for homology modeling and output as a CIF format file.

[0051] (3) File format conversion: Import the human target protein CIF file into the online format conversion system SCIENCE CODONS (https: / / sciencecodons.com / ) and convert it into the standard PDB format file widely used in molecular docking; at the same time, upload the rat target protein CIF file to the SCIENCE CODONS system in the same way and convert it into PDB format.

[0052] (4) Virtual Screening: The human and rat target protein PDB files were imported into the DrugRep (http: / / cao.labshare.cn:10180 / DrugRep / php / index.php) virtual screening platform. The DrugRep platform is based on a molecular docking algorithm to calculate the spatial complementarity and molecular mechanical interactions between the target protein binding site and each small molecule in the compound library, including van der Waals forces, electrostatic forces, and hydrogen bonding forces, to obtain a binding free energy score. Based on this score, high-affinity candidate compounds were screened to obtain candidate inhibitor lists for human and rat target proteins, respectively.

[0053] (5) Intersection screening: The candidate inhibitor lists of human and rat target proteins obtained in step (4) are compared and analyzed, and their intersection is calculated to obtain a set of compounds that have potential high activity against both human and rat target proteins.

[0054] (6) Identify core inhibitors: Extract all compounds from the intersection as priority candidate compounds for subsequent experimental verification and drug development.

[0055] Example 2: Screening and validation of key inhibitors of A2M protein This embodiment uses α2-macroglobulin (A2M) as the target protein to exemplify the specific application of the general screening method in Example 1.

[0056] 2.1 Screening of key inhibitors of A2M protein In this embodiment, the screening of key inhibitors of A2M protein first involved obtaining the complete amino acid sequences of human A2M (SEQ ID NO.1) and rat A2M (SEQ ID NO.2), respectively.

[0057] Following the procedure in Example 1, key inhibitors for the A2M protein were screened. The top five compounds (Conivaptan, Ergotamine, Sonidegib, Nilotinib, and Indocyanine) with the highest scores in the virtual screening of human A2M were selected from the core inhibitor set. Figure 2 As described above, it will be further validated as a key inhibitor of A2M.

[0058] 2.2 Validation of key inhibitors of A2M protein 2.2.1 Verification of the inhibitory effect of the core inhibitor on A2M protein (1) Cell culture and treatment HepG2 cells (catalog number: iCell-h092, Cybio (Shanghai) Biotechnology Co., Ltd.) and BRL-3A cells (catalog number: iCell-r003, Cybio (Shanghai) Biotechnology Co., Ltd.) were cultured in DMEM medium (catalog number: iCell-0008, Cybio (Shanghai) Biotechnology Co., Ltd.) containing 10% fetal bovine serum and incubated at 37°C in a 5% CO2 incubator. When the cells reached the logarithmic growth phase, they were seeded into 6-well plates (for qPCR detection) and 96-well plates (for cell viability detection). A control group (DMSO treatment) and five experimental groups (core inhibitor treatment, final concentration 10 μM) were set up, with 3 replicates per group, and the treatment lasted for 24 hours.

[0059] (2) Real-time quantitative PCR detection of A2M mRNA expression level Cells were collected, and total RNA was extracted using the RNA-Quick Purification Kit (ES Science, catalog number: RN001). After concentration and purity testing, an appropriate amount of total RNA was taken and reverse transcribed into cDNA using HiScript III RT SuperMix for qPCR (+gDNA wiper) (Vazyme, catalog number: R323-01). Using cDNA as a template, real-time quantitative PCR (qPCR) was performed using the SYBR Green method. The qPCR reagent was Taq-HS SYBR Green qPCR Premix (Universal) (iScience, catalog number: EG20113M). The reaction system was 20 μL: 10 μL SYBR Green Premix, 0.4 μL each of forward and reverse primers (10 μmol / L), 2 μL cDNA template, and ddH2O to a final volume of 20 μL. GAPDH was used as an internal control gene. -ΔΔCt The relative expression level was calculated using the method described in Table 1. See Table 1 for specific primers.

[0060] Table 1 Primer Information

[0061] The results are as follows Figure 3 As shown, in human HepG2 cells and rat BRL-3A cells, the relative expression level of A2M mRNA in the A2Mi-3 (Sonidegib) treatment group was significantly downregulated compared with the control group (HepG2: P<0.01; BRL-3A: P<0.05), indicating that Sonidegib has a cross-species inhibitory effect on A2M gene expression. The next step is to verify the inhibitory effect of Sonidegib on A2M.

[0062] 2.2.2 Verification of the inhibitory effect of Sonidegib on A2M protein (1) Cell culture and treatment Following the steps in section 2.2.1, after the cells reached the logarithmic growth phase, cell viability was assessed in the 96-well plates. Experimental groups were set up with final Sonidegib concentrations of 0 μM, 1.5 μM, 3 μM, 6 μM, and 12 μM.

[0063] (2) Cell viability detection Cell viability was detected using the CCK-8 assay. After treatment, 10 μL of CCK-8 solution was added to each well and incubated at 37°C for 2 hours. The absorbance at 450 nm was measured using a microplate reader, and the cell viability was calculated.

[0064] The results are as follows Figure 4 As shown, there was no significant difference in cell viability between the Sonidegib treatment group and the control group (P>0.05), indicating that Sonidegib has no obvious cytotoxicity to cells at this concentration, and its downregulation of A2M is not caused by cell death.

[0065] The above examples verify the accuracy of the general screening method of this application, demonstrating that Sonidegib is a key cross-species inhibitor of A2M.

[0066] It should be understood that this application merely demonstrates and verifies the feasibility and accuracy of the general screening method of this application by using A2M as an example of a target protein. Those skilled in the art can use the general screening method of this application to screen for highly active targeted inhibitors for different target proteins based on experimental purposes and research needs.

[0067] It should be noted that this application is not limited to the above-described embodiments. The above embodiments are merely examples, and any embodiments with the same structure and effect as the technical concept within the scope of this application are included in the technical scope of this application. Furthermore, various modifications that can be conceived by those skilled in the art to the embodiments, and other ways of constructing by combining some of the constituent elements of the embodiments, without departing from the spirit of this application, are also included in the scope of this application.

Claims

1. A universal screening method for highly active targeted inhibitors, characterized in that, Includes the following steps: S1. Select the complete amino acid sequences of human and non-human animal target proteins; S2. Based on the amino acid sequence of the human target protein, obtain its standard protein three-dimensional structure file; the same operation is performed for non-human animal target proteins. S3. Import the standard protein three-dimensional structure file of the human target protein into the DrugRep virtual screening server, and obtain a candidate list of inhibitors for the human target protein through molecular docking screening; the same operation is performed for non-human animal target proteins. S4. Perform an intersection analysis on the candidate inhibitor lists of human target proteins and non-human animal target proteins to screen out the core inhibitor set that is common to both.

2. The general screening method as described in claim 1, characterized in that, The non-human animal source is any one of rats, mice, or non-human primates.

3. The general screening method as described in claim 1, characterized in that, Step S2 includes the following steps: S21. The amino acid sequence of the human target protein is imported into the online protein structure prediction system, and the stored protein crystallography information file of the target protein is obtained through structure prediction. S22. Upload the stored protein crystallography information file of the human target protein to the online format conversion system, complete the format conversion, and obtain the standard protein three-dimensional structure file of the protein. The same procedure applies to the non-human animal-derived target proteins.

4. The general screening method as described in claim 1 or 3, characterized in that, The standard protein three-dimensional structure file can be any one of PDB, mmCIF, or MOL2.

5. The general screening method as described in claim 1, characterized in that, The molecular docking screening adopts a combination of rigid docking and flexible side-chain docking mode. The docking scoring adopts a comprehensive scoring function to screen out compounds with binding energy ≤ -7.0 kcal / mol as inhibitor candidates.

6. The general screening method as described in claim 1, characterized in that, The intersection analysis compares the candidate inhibitor lists for human and non-human animal target proteins according to compound name or CID number, and extracts compounds that appear in both lists to form a core inhibitor set.

7. The general screening method as described in claim 1, characterized in that, The general screening method also includes a step of screening key inhibitors; The screening of key inhibitors involves selecting compounds from the core inhibitor set that score highly in the virtual screening of human target proteins as key inhibitors for the target proteins.

8. The general screening method as described in claim 7, characterized in that, The general screening method also includes the step of screening highly active targeted inhibitors from key inhibitors through in vitro experiments; The in vitro experiment involved treating human and non-human animal cells with key inhibitors, respectively, and then detecting the inhibitory effect on the target protein to obtain highly active targeted inhibitors.

9. The general screening method as described in claim 8, characterized in that, The in vitro experiments also included treating human and non-human animal cells with different concentrations of highly active targeting inhibitors, detecting cell viability, and verifying the cytotoxicity of the screened highly active targeting inhibitors.

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