A method for screening effective ingredients of Bamboo Leaf Orchid targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives

Abstract

The present invention discloses a computer simulation evaluation method for the effective components of Bamboo Leaf Bluegrass (Zodiaceae) targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives, for screening active components from Zodiaceae that inhibit inflammatory pruritus. The method includes obtaining homologous protein information of inflammatory receptors based on homology modeling; establishing a library of Zodiaceae chemical components; evaluating pharmacokinetic and toxicological information based on molecular descriptors; screening Zodiaceae components with potential pharmacological effects through molecular docking; analyzing their interaction patterns under kinetic conditions; and calculating binding free energy and quantifying binding strength using MM / GBSA and MM / PBSA to identify the anti-inflammatory active components. This method not only enables the rapid and accurate prediction of potential Zodiaceae pharmacological substances, stability, and interactions with targets for anti-inflammatory pruritus, but also saves time and medicinal material resources, providing important support for the optimization and structural design of lead compounds, significantly improving R&D efficiency, progress, and success rate.

Description

Technical Field

[0001] The present invention relates to the technical field of drug screening, and in particular to a method for screening effective ingredients of Bambusa chinensis targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives. Background Art

[0002] Itch is an unpleasant physical sensation transmitted by peripheral sensory neurons that triggers the desire to scratch and affects most people. Growing evidence suggests that inflammatory mediators are released into the skin's immune cells, keratinocytes, and mucous membranes. Chronic itch can affect sleep, mood, and quality of life, and is a major unresolved health issue. Itch is a health problem that affects over 20% of people worldwide.

[0003] Leukotrienes (LTs) are a family of lipid inflammatory mediators produced by the metabolism of arachidonic acid by 5-lipoxygenase. As the final direct effector of many inflammatory reactions, CysLTs activate cysteinyl leukotriene receptors (including CysLT1 and CysLT2, both of which are classic 7-transmembrane G protein-coupled receptors), not only participating in a variety of inflammatory pathological processes, but also mediating smooth muscle spasm, capillary dilation and leakage, increased glandular secretion, and changes in airway structure. Administration of CysLTs to animals and human subjects can reproduce many characteristics of allergic inflammation and asthma (Ogawa Y, Calhoun WJ. The role of leukotrienes in airway inflammation [J]. Journal of Allergy and Clinical Immunology, 2006, 118 (4): 789-98.). In addition, CysLTs are also important cytokine regulators and have extensive interactions with various other inflammatory mediators, participating in the regulation of proliferation and differentiation of various cells including keratinocytes and tumor cells. Therefore, CysLTs play a key role in the pathogenesis of various inflammatory diseases and itch (Sasaki F, Yokomizo T. The leukotriene receptors as therapeutic targets of inflammatory diseases [J]. International Immunology, 2019, 31 (9): 607-15.).

[0004] Keratinocytes (KCs) are the primary constituent cells of the epidermis and the first tissue structures exposed to various exogenous substances. They release endogenous itch-causing factors such as cytokines, amines, neuropeptides, and nerve growth factor. As the primary constituent cells of the epidermis, KCs not only synthesize CysLTs but also become targets of CysLTs in the skin due to their expression of CysLTRs. Both CysLT synthesis and CysLTR expression are significantly enhanced in KCs under inflammatory conditions. Recent studies have revealed that KCs not only play a crucial role in regulating skin inflammation but also possess nonspecific phagocytic and antigen-presenting cell functions. Alterations in their biological behavior directly influence the pathogenesis and prognosis of numerous skin diseases, making them important therapeutic targets. Both CysLTR1 and CysLTR2 are expressed in skin tissue, primarily distributed in the epidermis, hair follicles, sebaceous glands, sweat glands, vascular walls, and smooth muscle. There is no significant difference in the expression and distribution of the two receptor types. Subacute eczema and psoriasis vulgaris cause severe itching. The expression of CysLTR1 and CysLTR2 in the lesions of patients was significantly higher than that in normal controls (P<0.05), but there was no significant difference in the expression and distribution of the two types of receptors. Targeting CysLT signaling may be a promising approach to treat inflammatory itch (Voisin T, Perner C, Messou MA, et al. The CysLT2R receptor mediates leukotriene C4-driven acute and chronic itch[J]. Proceedings of the National Academy of Sciences, 2021, 118(13): e2022087118.). The results suggest that LTs are involved in the pathogenesis of inflammatory skin diseases such as eczema, psoriasis, and atopic dermatitis, which is based on the existence of their receptors (Sasaki F, Yokomizo T. The leukotriene receptors as therapeutic targets of inflammatory diseases [J]. International Immunology, 2019, 31 (9): 607-15.).

[0005] Bamboo orchid (Arundina graminifolia (D. Don) Hochr.) has the properties of clearing heat and detoxifying, promoting diuresis and relieving jaundice, clearing the lungs and relieving coughs, and removing dampness and alleviating pain. Known in Dai as "Wen Shang Hai" or "Bai Yang Jie" (a hundred solutions), it is the most commonly used antidote in Dai medicine in Xishuangbanna, Yunnan, treating food poisoning, snake bites, jaundice, rheumatism, and urinary tract infections. It is widely used in Dai medicinal preparations, such as Ya Jie Pian and Bai Jie Capsules, which relieve edema and food poisoning symptoms; Da La Tang for rheumatoid arthritis; Ya Jie Sha Ba for diabetes insipidus; and Ya Jie Ga Han, a detoxification and beauty capsule. Furthermore, the Bulang and Wa peoples use bamboo orchid to treat coughs, tracheitis, pneumonia, and tuberculosis. Furthermore, in traditional medicine in countries like India, it is often used as a lubricant, antibacterial agent, moisturizer, and rheumatism remedy. As a plant with multiple pharmacological activities, Bamboo Orchid's main active ingredients include stilbenes, ketones and phenolic acids, which show anti-inflammatory, antibacterial, antioxidant, anti-cholestasis and other pharmacological effects. Therefore, it has broad application potential in the treatment of various diseases.

[0006] Bamboo orchid has attracted considerable research attention due to its effects on inflammatory processes mediated by leukotrienes and their derivatives. The active compounds in Bamboo orchid are believed to work by regulating the release of inflammatory mediators and immune cell responses. This may inhibit certain steps in the leukotriene pathway or modulate its activity, thereby reducing local inflammation and alleviating the severity of related symptoms. This may help alleviate common skin conditions such as itchy scalp, oily scalp, dandruff, and itchy skin, which are often associated with lipid metabolism, scalp inflammation, immune disorders, and aging.

[0007] The screening and development of antagonists for inflammatory receptors mediated by leukotrienes and their derivatives is a current research hotspot. However, due to the complexity and diversity of these mediators in cellular metabolism, drug-based research faces challenges in cost, timeliness, and risk. These challenges, such as capturing intermediates in their metabolic processes and observing the epigenetic phenomena of the active ingredients of Bamboo Scented Orchid and their intermolecular interactions, require significant investment of time, effort, and resources. However, future achievements could bring significant rewards, promoting the discovery of active substances and the development of new products, thus benefiting humanity.

[0008] With the rapid advancement of science and technology, cutting-edge technologies such as computer simulation, AI big data processing, and data engineering are profoundly transforming the landscape of medical research and clinical practice, opening up new avenues for the study of traditional Chinese medicines such as Bamboo Leaf Orchid in addressing issues such as itchy scalp, oily scalp, dandruff, and skin itching. Computer simulation technology accelerates drug discovery and validation, reduces experimental costs, and improves research efficiency. AI big data processing technology can analyze massive amounts of experimental data and identify key Bamboo Leaf Orchid components that influence therapeutic efficacy. Data engineering provides data management support for these technologies, ensuring data accuracy, integrity, and security, and promoting research on Bamboo Leaf Orchid's anti-inflammatory effects on itchy skin. These modern technologies have significantly accelerated the research progress of traditional Chinese medicines in treating skin inflammation, revealing the precise mechanism of action of Bamboo Leaf Orchid in skin inflammation mediated by leukotrienes and their derivatives, offering new prospects for its clinical application.

[0009] However, technical challenges still exist in the screening process for antagonists of inflammatory receptors mediated by cysteinyl leukotrienes (CysLTs) and their derivatives. Due to the complex interactions between CysLTs and inflammatory receptors, traditional computer simulations have difficulty accurately predicting binding sites and binding strengths. At the same time, molecular docking and pharmacokinetic models have difficulty handling the structural diversity and biological effects of natural medicines, and screening results may be biased. For inflammatory skin diseases, drugs need to have good pharmacokinetic properties and low toxicity, which places higher demands on existing simulation tools.

[0010] In response to the above-mentioned problems, the present invention proposes a new computer simulation evaluation method to screen for active ingredients in Bamboo Leaf Blue that inhibit skin inflammatory itching. High-precision homologous protein information of inflammatory receptors is obtained through homology modeling, which enhances the accuracy of molecular docking. A library of Bamboo Leaf Blue chemical components is established, and combined with molecular descriptors, pharmacokinetic and toxicity information is evaluated to ensure that the screening results are more biologically relevant. Finally, the binding free energy is calculated by MM / GBSA and MM / PBSA, the binding strength between the drug and the target is quantified, and the anti-inflammatory active ingredients are accurately screened. This method significantly improves the efficiency and accuracy of drug screening, solves the difficult problem in the screening of CysLTs-related inflammatory antagonists, and provides a scientific basis for Bamboo Leaf Blue extract as a potential drug for treating skin itching. Summary of the Invention

[0011] The purpose of the present invention is to propose a method for obtaining the pharmacological substances and interaction binding patterns in Bamboo Herb through computer simulation based on homology modeling, virtual screening, kinetic simulation, and binding energy calculation. The method of the present invention can save time and resources, quickly and accurately predict the biological activity, stability, and interaction with biological targets of potential pharmacological substances, and improve the efficiency and success rate of new drug research and development, provide important support and guidance for drug design and optimization, and accelerate the process of drug research and development.

[0012] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0013] A method for screening effective ingredients of Bamboo Herb targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives, comprising the following steps:

[0014] A. Obtain information on homologous proteins of inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives based on homology modeling to form homologous protein receptors;

[0015] B. Establish information data of Bamboo Leaf Orchid component database;

[0016] C. Using the information data of the Bamboo Leaf Orchid component library, obtaining molecular descriptors of each Bamboo Leaf Orchid component, obtaining pharmacokinetic and toxicity information based on the molecular descriptors, performing pharmacokinetic evaluation, screening Bamboo Leaf Orchid medicinal components, and forming Bamboo Leaf Orchid medicinal component complexes;

[0017] D. Using the homologous protein receptor, perform batch molecular docking with the Bamboo Leaf Blue medicinal component complex, perform affinity analysis, and screen out Bamboo Leaf Blue components with good affinity as candidate drugs;

[0018] E. Using the homologous protein receptor, docking with the positive drug ligand formed by the positive drug information data to determine the binding site, and selecting the bamboo leaf component with high consistency in the binding site among the candidate drugs as the primary drug;

[0019] F. Use a computer to simulate uniform environmental conditions, simulate the binding of the primary selected drugs to the homologous protein receptors under the uniform environmental conditions, perform binding free energy analysis, and screen out the Bambusa japonica components with good binding as the preferred drugs.

[0020] A comprehensive evaluation was conducted on the effective components of Bamboo Leaf Orchid targeting cysteinyl leukotriene receptors, and affinity, binding stability and binding free energy were used as comprehensive indicators to determine the effective components in Bamboo Leaf Orchid.

[0021] The present invention discloses a computer simulation evaluation method for the effective components of Bamboo Leaf Orchid targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives, and screens out active components in Bamboo Leaf Orchid that inhibit skin inflammatory pruritus. The method comprises obtaining information on homologous proteins of inflammatory receptors based on homology modeling; establishing a Bamboo Leaf Orchid chemical component library; obtaining pharmacokinetic and toxicity information based on molecular descriptors; screening Bamboo Leaf Orchid effective components through molecular docking to obtain Bamboo Leaf Orchid components with potential efficacy; performing interaction pattern analysis on the Bamboo Leaf Orchid components with potential efficacy under kinetic conditions; and determining the anti-inflammatory active components in Bamboo Leaf Orchid by quantifying the binding strength based on MM / GBSA and MM / PBSA calculations of binding free energy. The present invention can not only quickly and accurately predict the potential effective substances, stability, and interaction with targets of Bamboo Leaf Orchid for anti-inflammatory pruritus, but also save time and medicinal material resources, provide important support and guidance for lead compound optimization and structural design, and significantly improve the efficiency, progress, and success rate of research and development.

[0022] Preferably, step A comprises the following steps:

[0023] A1. Obtain the protein crystal sequence of human inflammatory receptor from the gene protein database;

[0024] A2. Using a homology modeling server, a backbone is constructed on the structurally conserved region of the protein crystal sequence, and a structurally variable region is generated using the homology structure to obtain an inflammatory receptor homologous protein template;

[0025] A3. Screen the inflammatory receptor homologous protein template using sequence similarity, sequence coverage, acquisition method, resolution, sequence alignment, and binding ligand as indicators to obtain the best inflammatory receptor homologous protein template. Perform homology modeling based on the best inflammatory receptor homologous protein template to obtain inflammatory receptor homologous protein structural information data to form the homologous protein receptor.

[0026] Preferably, in step C, the pharmacokinetic evaluation indicators include one or more of the five drug-like principles, lipid-water partition coefficient, P-glycoprotein inhibition probability, human intestinal absorption HIA probability, intestinal epithelial cell permeability, and blood-brain barrier probability.

[0027] Based on molecular descriptor information, the pharmacokinetic evaluation of the active ingredients of Bamboo Leaf Orchid was carried out using the five drug-like principles, lipid-water partition coefficient, P-glycoprotein inhibition probability, HIA probability of human intestinal absorption, intestinal epithelial cell permeability, and blood-brain barrier probability as indicators, and the active ingredients that meet the five drug-like principles, are easily absorbed, and are not easy to penetrate the blood-brain barrier were screened out.

[0028] Preferably, step D includes: setting docking parameters around the active site of the homologous protein receptor, determining the size and position of the docking active area grid box, and screening the candidate drugs by computer with the binding energy information of the batch molecular docking of the Bamboo Orchid medicinal component complex, using the affinity and active site binding information as indicators; the molecular docking includes semi-flexible docking, that is, protein flexibility and small molecule rigidity are docked.

[0029] Preferably, the affinity and active site binding information as indicators include: ensuring that the binding site falls within the active pocket, using an affinity less than -7 kal / mol to indicate effective docking, and selecting the top 10% of components with the smallest negative values ​​as the candidate drugs.

[0030] Preferably, the positive drug includes KNT.

[0031] Preferably, the operation of step F includes:

[0032] F1. Obtain the topological structure information of the primary drug candidate and the atomic topological structure information of the homologous protein receptor, construct a regular dodecahedron simulation box filled with water molecules, place the complex system of the primary drug candidate and the homologous protein receptor in the center of the simulation box, and perform energy minimization;

[0033] F2. Using a force field model to describe the interaction forces between atoms in the system, setting a solvent model, placing the complex system in a dodecahedral water solvent box, ensuring that the system is electrically neutral as a whole, and forming the uniform environmental conditions;

[0034] F3. Minimize the energy of the entire system and restrict the ligand constraints to eliminate unreasonable geometric configurations and high energy states;

[0035] F4. Allowing the system to reach temperature equilibrium and pressure equilibrium, obtaining the binding state of the complex system under dynamic conditions, and analyzing the binding state results;

[0036] F5. Build a virtual environment and prepare the topology file, input file, trajectory file, and index file of the complex system;

[0037] F6. By calculating the solvation free energy of the primary drug and the homologous protein receptor in different solvent models, and calculating the free energy contributed by each amino acid, a binding free energy analysis is performed to screen out the bamboo leaf orchid component with good binding as the preferred drug.

[0038] Preferably, in step F4, the binding state analysis comprises analyzing one or more of the root mean square error, root mean square fluctuation, solvent accessible surface area, radius of gyration, residue contact map, principal component analysis, and free energy profile of the complex.

[0039] Preferably, in step F5, the virtual environment includes a gmx_MMPBSA virtual environment; in step F6, the different solvent models for calculating the solvation free energy include one or more of MM / PBSA and MM / GBSA.

[0040] gmx_MMPBSA is a Python program that performs final-state free energy calculations using GROMACS. It calculates MM / PBSA and MM / GBSA binding energies using a preset environment, such as human body temperature and ion concentration.

[0041] The bamboo orchid medicinal ingredients obtained by the above-mentioned bamboo orchid medicinal ingredient screening method for inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives include one or more of formyloxybenzoin A, bamboo orchid phenanthroline, formyloxybenzoin C, formyloxybenzoin G, deliriflavonoids B, scutellarin, formyloxybenzoin D, methoxyfurantoin, trihydroxymethoxyflavin, isoamycin, and loncovin.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. The present invention uses the information of inflammatory receptor homologous proteins obtained based on homology modeling, compared with directly using inflammatory receptor proteins;

[0044] 2. The present invention first obtains qualified Bamboo Leaf Orchid components through pharmacokinetic and toxicity information, screens Bamboo Leaf Orchid medicinal components through molecular docking, obtains Bamboo Leaf Orchid components with potential medicinal effects, and then analyzes the interaction pattern of Bamboo Leaf Orchid components with potential medicinal effects under kinetic conditions to further obtain the effectiveness ranking of specific Bamboo Leaf Orchid medicinal components targeting inflammatory receptors. Finally, based on MM / GBSA and MM / PBSA calculations, the binding free energy is quantified by binding strength to obtain the final effectiveness ranking of specific Bamboo Leaf Orchid medicinal components targeting inflammatory receptors, thereby saving time and resources, achieving rapid and accurate prediction of the biological activity, stability and interaction with biological targets of potential medicinal substances, and improving the efficiency and success rate of new drug research and development, providing important support and guidance for the design and optimization of drugs, especially Bamboo Leaf Orchid medicinal components, and accelerating the process of drug research and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic flow diagram of the present invention;

[0046] Figure 2 is the template protein sequence alignment obtained by cysteine ​​leukotriene receptor type 1 homology modeling;

[0047] Figure 3 It is the structure and active site of CysLT1R homologous protein;

[0048] Figure 4 is the Ramachandran plot of CysLT1R protein;

[0049] Figure 5 A is the two-dimensional structure of KNT, B is the interaction site map between the 6rz4.1.A template protein and the original ligand KNT, and C is the interaction site map between the CysLT1R homologous protein and the original KNT ligand;

[0050] Figure 6 This is a conformational overlay diagram, where the initial KNT conformation is shown in blue, and the docked KNT structure is shown in colored. The gray helices represent the protein in AutoDock 4, the brown helices represent the protein in AutoDock Vina, and the green helices represent the protein in LeDock.

[0051] Figure 7 A. Root mean square error RMSD and B. Root mean square fluctuation RMSF of CysLT1R protein;

[0052] Figure 8 is the affinity value of the compound in Bamboo Leaf Orchid;

[0053] Figure 9 This is the optimal docking conformation of CysLT1R-Zhuyelan active ingredients, where: blue structure represents amino acid residues, orange represents potential active ingredients of Zhuyelan, gray line represents hydrophobic interaction, blue line represents hydrogen bonding, green line represents π-π stacking, and red line represents π-cation interaction;

[0054] Figure 10 This is a comparison chart of the root mean square error, root mean square fluctuation, solvent accessible surface area, and gyration radius of Complex-1 and CysLT1R;

[0055] Figure 11 is the contact map of Complex-1 residues;

[0056] Figure 12 This is the FEL analysis diagram and the lowest energy conformation of Complex-1;

[0057] Figure 13 This is the energy contribution diagram of each amino acid residue in MM / PBSA of Complex-1. DETAILED DESCRIPTION

[0058] To make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be described in further detail below with reference to the accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0059] like Figure 1As shown, a method for screening the active ingredients of Bamboo Leaf Orchid targeting inflammatory receptors mediated by leukotrienes and their derivatives is used to screen out the active ingredients in Bamboo Leaf Orchid that inhibit skin inflammatory itching, specifically including:

[0060] Step 1: Obtain protein data through homology modeling;

[0061] Step 2: Establish a component library of Bamboo Leaf Orchid;

[0062] Step 3: Pharmacokinetic and toxicity component analysis;

[0063] Step 4: Virtual screening based on molecular docking;

[0064] Step 5, molecular dynamics simulation;

[0065] Step 6: Binding free energy analysis;

[0066] The step 1 is specifically implemented according to the following steps:

[0067] (1) Obtain the protein crystal sequence of human inflammatory receptor from UniProt protein coding gene protein database;

[0068] (2) constructing a backbone on the structurally conserved region of the target sequence in step (1) using the SWISS MODEL homology modeling server, generating a structurally variable region using the homologous structure, and obtaining a homologous protein;

[0069] (3) Select the best template based on similarity, coverage, acquisition method, resolution, and binding ligand to obtain protein structure information;

[0070] (4) Check the structural integrity and analyze the geometric conformation of the generated structural model;

[0071] (5) Ramachandran plot analysis of residues to test the rationality of protein conformation;

[0072] (6) Molecular dynamics simulation of protein stability and amino acid root mean square fluctuations.

[0073] The step 2 is specifically implemented according to the following steps:

[0074] (1) Draw 102 molecular structures of Bambusa chinensis using ChemDraw and convert them into three-dimensional conformations;

[0075] (2) Use AutoDockTools software to obtain charge energy and conformation optimization information based on the coordinate information of each atom and output it as a file in pdbqt format.

[0076] The step 3 is specifically implemented as follows:

[0077] (1) Obtain molecular descriptors of the structure of Bamboo Leaf Orchid compounds using ChemDraw software;

[0078] (2) The molecular descriptors were input into ADMETlab 2.0 software to obtain the pharmacokinetic and toxicity data of the active ingredients of Bamboo Leaf Orchid and perform data analysis.

[0079] The step 4 is specifically implemented according to the following steps:

[0080] (1) Use the GridBox tool to set docking parameters around the active site of the receptor protein, determine the size and position of the grid box, and set the number of dockings to 10 to 30;

[0081] (2) All ligand molecules were docked with receptor proteins using AutoDock Vina software to obtain docking conformations and record relevant information such as docking scores and binding sites;

[0082] (3) Ensure that the binding site falls within the active pocket, and use an affinity of less than -7 kal / mol to indicate effective docking. Select the top 10% of components with the smallest negative value and define them as potential pharmacological substances of Bamboo Leaf Orchid.

[0083] The step 5 is specifically implemented according to the following steps:

[0084] (1) Preprocessing: Use the force field model to describe the interaction force between the atoms in the system, set the solvent model, and generate the top file and other position restriction files of the inflammatory receptor protein through the gmx pdb2gmx command;

[0085] (2) Obtain the topological structure information of the ligand molecule using Acpype software;

[0086] (3) Combine the receptor protein and the ligand, modify the gro file and top file, and generate the complex file.

[0087] (4) Construct a dodecahedron simulation box filled with water molecules, with the edge of the box 1 nm away from the edge of the molecule. The system contains one protein and one ligand. The complex system of the inflammatory protein and the active ingredient of the bamboo orchid is placed in the center of the box, and Na ions or Cl ions are added to ensure that the system is electrically neutral as a whole.

[0088] (5) Use the gmx grompp command to perform energy minimization to eliminate unreasonable geometric configurations and high energy states.

[0089] (6) combining the protein with the ligand, water, and ions;

[0090] (7) Use a thermostat to perform NVT ensemble pre-equilibrium simulations, and then perform NPT ensemble finished product simulations;

[0091] (8) Set the step size to 1 fs to 2 fs, perform dynamic simulations for 50 ns to 200 ns, collect system coordinates every 10 ps to 50 ps, ​​and collect coordinate, energy, pressure and other data;

[0092] The step 6 is specifically implemented according to the following steps:

[0093] (1) Build the gmx_MMPBSA virtual environment under the Linux system and prepare the topology file, input file, trajectory file, and index file of the dynamic simulation complex system;

[0094] (2) The binding free energy of the complex was calculated by setting the interval sampling frame number to 10 to 100 frames, the ion concentration to 0.15 (M), and the ionic strength to 0.15 (M). The amino acid residue information within 0.3 nm to 0.6 nm between the receptor and the ligand was calculated and recorded.

[0095] (3) Considering the influence of solvent effect on binding free energy, the solvation free energy was calculated using MM / PBSA and MM / GBSA solvent models respectively.

[0096] (4) Analyze the binding energy data and analyze the contribution of each amino acid residue to the binding energy.

[0097] Example 1:

[0098] Taking the cysteine ​​leukotriene type 1 receptor (CysLT1R) as an example, the conformation of the inflammatory receptor was established based on homology modeling, including:

[0099] Searching for CysLT1R in the UniProt protein-coding gene database, we selected the human protein crystal Q9Y271, expressed from gene ENSG00000173198, with the following sequence:

[0100] MDETGNLTVSSATCHDTIDDFRNQVYSTLYSMISVVGFFGNGFVLYVLIK

[0101] TYHKKSAFQVYMINLAVADLLCCVCTLPLRVVYYVHKGIWLFGDFLCRLSTYA

[0102] LYVNLYCSIFFMTAMSFFRCIAIVFPVQNINLVTQKKARFVCVGIWIFVILTSSP

[0103] FLMAKPQKDEKNNTKCFEPPQDNQTKNHVLVLHYVSLFVGFIIPFVIIIVCYT

[0104] MIILLTLLKKSMKKNLSSHKKAIGMIMVVTAAFLVSFMPYHIQRTIHLHFLHNE

[0105] TKPCDSVLRMQKSVVITLSLAASNCCFDPLLYFFSGGNFRKRLSTFRKHSLSS

[0106] VTYVPRKKASLPEKGEEICKV;

[0107] Using the SWISS-MODEL homology modeling server, we input the above sequence and started to build the model. A total of 1760 templates were found to match the target sequence.

[0108] The top 10 template proteins in the global model quality assessment were selected for analysis, such as Figure 2 As shown, based on similarity, residue coverage, acquisition method, resolution, and whether it contains a binding ligand, 6rz4.1.A was selected as the template protein. The protein was obtained by X-ray with a resolution of Contains the ligand Pranlukast;

[0109] Based on the template protein, homology modeling was performed to obtain the homologous protein structure and active site of CysLT1R. Figure 3 As shown, Figure 3 A shows the active pocket of the protein, with the center coordinates in three-dimensional space being (43.23, 26.72, 60.70) and the unit cell size being Figure 3 B shows the site of the active pocket, which contains 47 amino acid residues;

[0110] The generated protein structure was analyzed by Ramachandran plot of residues. Figure 4 The Ramachandran plot showed that 98.26% of the amino acid residues were in the allowed region, and no amino acid residues were in the non-allowed region, indicating that the conformational space distribution of the protein model was reasonable.

[0111] Contact with ligand

[0112] The activity of CysLT1R protein is reflected by its main ligand KNT, which is a CysLTs receptor antagonist that selectively binds to the receptors of LTC4, LTD4, and LTE4 to antagonize their effects. Its molecular structure is as follows Figure 5 As shown in A.

[0113] The contact between 6rz4.1.A template protein and KNT is as follows Figure 5As shown in Figure B, hydrophobic interactions are facilitated by amino acid residues Y104, Y108, F150, P176, L189, V192, and L260; hydrogen bonding sites include R79, Y104, S193, and R253. Furthermore, Y108 and the ligand undergo π-π stacking, while R253 participates in π-cation interactions.

[0114] The interaction sites between CysLT1R homologous proteins and KNT proligands are as follows Figure 5 As shown in Figure C, the interaction patterns and amino acid residue positions are completely consistent with those of the 6rz4.1.A template protein, further demonstrating that the biological activity of the homologous protein is consistent with that of the template protein, which is valuable for subsequent docking model research. This paper will conduct research related to the CysLT1R-antagonist model using the CysLT1R homologous protein as the receptor, analyze different algorithms, and explore the software algorithm that is most suitable for this system.

[0115] Affinity evaluation

[0116] In this study, AutoDock 4, AutoDock Vina, and LeDock software based on different algorithms were used to dock CysLT1R with the original ligand KNT. The docked conformations that met the requirement of a distance of less than 0.2 nm to the original ligand were ranked by affinity. The smaller the negative value, the stronger the reaction and the better the binding effect. The top ten conformations are shown in Table 1.

[0117] Table 1 Affinity of CysLT1R-KNT (kcal / mol)

[0118]

[0119] In AutoDock 4, the affinity ranged from -10.08 kcal / mol to -6.93 kcal / mol, with a difference of 3.15 kcal / mol, indicating that this method can provide relatively good affinity analysis for molecular docking. In AutoDock Vina, the affinity ranged from -12.2 kcal / mol to -8.66 kcal / mol, with a difference of 3.54 kcal / mol, indicating that this method provides good affinity analysis for the CysLT1R-antagonist system. In LeDock, the affinity ranged from -9.99 kcal / mol to -6.18 kcal / mol, with a difference of 3.81 kcal / mol. The molecular docking affinities were low and the scores varied widely, indicating that this method cannot well describe the affinity of the CysLT1R-antagonist system.

[0120] In summary, AutoDock Vina gave the lowest affinity value, followed by AutoDock 4, and LeDock had the highest affinity value, so AutoDock Vina was the preferred software algorithm.

[0121] Conformational overlay analysis

[0122] The top ten docking conformations ranked by affinity were plotted with the original ligand using PyMOL software to observe the bond angle torsion and position shift between the docked ligand and the original ligand. Figure 6 shown.

[0123] In AutoDock 4, the KNT molecules generated for docking in the first, second, fourth, fifth, sixth, seventh, and eighth stages are largely consistent with the native conformation, while the bond angles of the third, ninth, and tenth stages show significant distortion and low overlap with the native conformation. This indicates that the software algorithm can be used to describe the docking of the CysLT1R-antagonist model to a certain extent. In AutoDock Vina, the ten KNT molecules generated for docking are largely consistent with the native conformation, with minor distortions in the bond angles and consistent overlap, with the first one showing near overlap. This indicates that the software algorithm can accurately describe the docking of the CysLT1R-antagonist model. In LeDock, the KNT molecules generated for docking in the first, second, fifth, seventh, and eighth stages are largely consistent with the native conformation, with the first stage showing near overlap with the native ligand. However, the bond angles of the third, fourth, sixth, seventh, ninth, and tenth stages show significant distortion and low overlap with the native conformation. This indicates that the software algorithm cannot accurately describe the docking situation of the CysLT1R-antagonist model.

[0124] In summary, among the software algorithms for the CysLT1R-antagonist model, the conformation of KNT after docking by AutoDock Vina software was basically consistent with the original ligand, followed by AutoDock 4 software, and finally LeDock software.

[0125] Conformational superposition RMSD analysis

[0126] To eliminate false positives in molecular docking, this chapter analyzes the binding mode by comparing the docked KNT conformation with the original ligand KNT conformation. The docking results are combined with the RMSD values ​​and the overlay plots to analyze the binding mode. The average RMSD between the docked KNT and the original conformation is also calculated. The relevant results are shown in Table 2.

[0127] Table 2 RMSD analysis of KNT after docking and original conformation

[0128]

[0129]

[0130] In the AutoDock 4 docking results, the RMSD values ​​of the top ten conformations are The average value is There are large structural differences between the conformations.

[0131] In contrast, the top ten docking results of AutoDock Vina showed better alignment, and the RMSD values ​​of the top ten conformations were The average RMSD value is Most conformations overlap well with the original ligand, but some conformations still have large RMSD values ​​and certain differences in the overlap quality.

[0132] The RMSD values ​​of the top ten conformations of the top ten docking results of LeDock are The average RMSD value is It is between AutoDock 4 and AutoDock Vina. Some conformations show small RMSD values, while others have large RMSD values, and the alignment quality varies.

[0133] In summary, the AutoDock Vina software performed best in terms of RMSD between the docked conformation and the original ligand, followed by the LeDock software, and finally the AutoDock 4 software.

[0134] Ligand binding site analysis

[0135] In this chapter, amino acids R79, Y104, Y108, F150, P176, L189, V192, S193, R253 and L260 of the original conformation, totaling 10 binding sites, were set as key amino acid residues. The binding site situations after docking by the three software algorithms are shown in Table 3.

[0136] Table 3 Binding site information

[0137]

[0138] Based on amino acid residue analysis of the AutoDock 4 docking results, the following interaction types were observed: Hydrophobic interactions involved amino acid residues Y104, Y108, F112, F150, F158, and P176. R79, Y104, S193, and R253 formed hydrogen bonding interactions with the ligand. Y104 and the ligand formed π-π stacking, a π-cation interaction that further enhanced ligand-receptor binding, while R253 participated in π-cation interactions. The structure contained 10 amino acid residues, and hydrogen bonding, π-π stacking, and π-cation interactions were well consistent with the initial conformation. Four hydrophobic interactions were consistent, resulting in 80% residue coverage.

[0139] Based on the optimal docking result in AutoDock Vina, amino acid residue analysis was performed. Hydrophobic interactions were observed primarily involving Y108, F112, F150, F158, V186, L189, V192, R253, L257, and L260. Furthermore, hydrogen bonds were formed involving Y104, S193, and R253. Furthermore, π-π stacking was observed between Y104 and Y108, while R253 participated in a π-cation interaction. The site contains 12 amino acid residues, representing 90% residue coverage.

[0140] LeDock docking results revealed that amino acid residues Y108, F112, F150, and V192 participated in hydrophobic interactions. R79, Y104, S193, and R253 formed hydrogen bonds with the ligand. Y104, Y108, and F158 formed π-π stacking with the ligand, while R253 participated in π-cation interactions. The structure contained nine amino acid residues, and hydrogen bonding, π-π stacking, and π-cation interactions were well consistent with the initial conformation. Three hydrophobic interactions were consistent, resulting in 80% residue coverage.

[0141] Affinity evaluation, conformational overlay analysis, conformational overlay RMSD analysis, and ligand binding site analysis are all methods for evaluating models, among which affinity evaluation is the key factor.

[0142] In summary, AutoDock 4 has the best matching degree of amino acid residue interaction types, followed by LeDock, and finally AutoDock Vina. AutoDock Vina has the best amino acid residue site coverage, followed by LeDock, and finally AutoDock 4.

[0143] The generated protein structure was subjected to molecular dynamics simulation, e.g. Figure 7As shown in the figure, the simulation temperature was set at 310K and the simulation time was set to 100ns. The RMSD value remained stable around 0.28nm, with an overall fluctuation of less than 0.1nm, indicating that the protein structure did not undergo significant structural changes during the simulation. The RMSF indicates that specific regions of the protein have high flexibility, which is important for the protein's function.

[0144] Establish a library of Bamboo Orchid components, including: drawing 102 Bamboo Orchid molecular structures using ChemDraw and converting them into three-dimensional conformations;

[0145] AutoDockTools software was used to obtain charge energy and conformation optimization information based on the coordinate information of each atom and output it as a file in pdbqt format;

[0146] The molecular descriptors of the active ingredients of Bamboo Leaf Orchid were used with ADMETlab 2.0 software to obtain pharmacokinetic and toxicological information. This included: based on the molecular descriptor information, theoretical pharmacokinetic and toxicological evaluation and research of the active ingredients of Bamboo Leaf Orchid were conducted using the five-drug principle, lipid-water partition coefficient, P-glycoprotein inhibition probability, human intestinal absorption HIA probability, intestinal epithelial cell permeability, and blood-brain barrier probability;

[0147] Screening of effective substances of Bamboo Leaf Orchid by molecular docking, including:

[0148] Use the GridBox tool to set the docking parameters around the active site of the receptor protein and determine the size and position of the grid box. The docking parameter information is as follows:

[0149] receptor=CysLT1R.pdbqt

[0150] center_x=42.23

[0151] center_y=26.72

[0152] center_z=60.7

[0153] size_x=24.4

[0154] size_y=27.6

[0155] size_z=29.6

[0156] num_modes=20

[0157] Using a Python script loop, each ligand molecule was docked with the cysteine ​​leukotriene type 1 receptor. All ligand molecules were automatically docked with CysLT1R, 20 docking conformations were obtained, and relevant information such as docking scores and binding sites were recorded.

[0158] Interaction affinity

[0159] The CysLT1R-Bamboo Leaf Orchid molecular docking was performed in AutoDock Vina software, and the average of multiple results was taken. The affinity of CysLT1R-Bamboo Leaf Orchid molecules ranged from -5.39 to -11.4 kcal / mol. Figure 8 shown.

[0160] When analyzing the affinity of the compounds, we observed significant differences among different classes of compounds. The average affinity of stilbene compounds was -8.35 kcal / mol. The affinity of bibenzyl compounds ranged from -7.6 to -9.12 kcal / mol, with an average of -8.26 kcal / mol; the affinity of stilbene compounds ranged from -7.72 to -9.20 kcal / mol, with an average of -8.32 kcal / mol; the affinity of phenanthrene compounds ranged from -5.39 to -10.27 kcal / mol, with an average of -8.50 kcal / mol. The average affinity of quinone compounds was -8.44 kcal / mol. Among them, the affinity of scutellaria baicalensis was -10.27 kcal / mol, that of scutellaria baicalensis was -9.98 kcal / mol, and that of isoamycin was -9.82 kcal / mol, showing strong affinity.

[0161] The average affinity of ketones was -8.60 kcal / mol. The affinity of flavonoids ranged from -6.95 to -9.98 kcal / mol, with an average of -8.87 kcal / mol. The affinity of benzophenones ranged from -9.13 to -10.42 kcal / mol, with an average of -9.81 kcal / mol. The affinity of fluorenones ranged from -7.39 to -8.91 kcal / mol, with an average of -7.87 kcal / mol. The affinity of other ketones ranged from -6.28 to -9.05 kcal / mol, with an average of -7.40 kcal / mol. It is particularly noteworthy that the affinities of all dibenzoyl ketone compounds are higher than their average, among which the affinity of formyloxybenzoin A is -10.14 kcal / mol, the affinity of formyloxybenzoin C is -10.23 kcal / mol, and the affinity of formyloxybenzoin D is -10.10 kcal / mol, showing significant biological activity.

[0162] The average affinity of phenolic acids was -8.14 kcal / mol. Benzofuran and benzopyran-type phenolic acids had affinities ranging from -8.05 to -9.92 kcal / mol, with an average of -8.82 kcal / mol. Other phenolic acids had affinities ranging from -5.64 to -9.31 kcal / mol, with an average of -7.70 kcal / mol. Methoxyfurantanol exhibited a particularly strong affinity, at -9.92 kcal / mol.

[0163] In summary, different classes of compounds exhibited diverse affinity properties, suggesting that they may play a significant role in the interaction with CysLT1R. During docking screening, the binding site was ensured to fall within the active pocket, and the top 10% of compounds with the strongest affinity were analyzed. These compounds included formyloxybenzoin A, baculin, formyloxybenzoin C, formyloxybenzoin G, deliflavone B, scutellarin, formyloxybenzoin D, methoxyfurantoin, trihydroxymethoxyflavin, isoamyl, and loncovin. These compounds have important implications for the biological activity and pharmacological properties of CysLT1R and may serve as potential active ingredients for CysLT1R.

[0164] However, affinity is only one indicator for evaluating the active components of CysLT1R. This chapter will further analyze the interaction types and active sites of these compounds, and use the original ligand as a positive drug to perform key residue site analysis to exclude possible false positive results.

[0165] Action type and active site analysis

[0166] Optimal docking conformation of CysLT1R-Zodiaceae active ingredients

[0167] Visual analysis of the best molecular docking results of eleven active ingredients of Bamboo Herb compounds, such as Figure 9 shown.

[0168] Analysis of compound interactions with CysLT1R revealed diverse interaction patterns. Specifically, baicalin, methoxyfurantanol, and isoshanin primarily interacted through hydrophobic interactions and hydrogen bonding. Formyloxybenzoin A, C, G, D, trihydroxymethoxyflavin, and loncovin interacted with CysLT1R through hydrogen bonding, π-π stacking, and hydrophobic interactions. Furthermore, deliflavone B and shancovin exhibited π-cationic interactions in addition to the aforementioned interaction patterns.

[0169] Specifically, the hydrophobic interaction involves the energy change of bound water released by desolvation when CysLT1R binds to the active ingredients of Bamboo Scented Plant. Hydrogen bonding stabilizes the binding through NH…O bonds formed within the α-helix of CysLT1R, and this interaction is crucial for the stability of the binding between CysLT1R and the active ingredients of Bamboo Scented Plant. π-π stacking is a weak interaction between the aromatic rings in the active ingredients of Bamboo Scented Plant and aromatic amino acids (such as tyrosine and phenylalanine) in CysLT1R. π-cationic interactions also involve weak non-covalent interactions between aromatic rings and cations.

[0170] Interaction type and residue status

[0171] The PLIP binding sites of the potential active ingredients in eleven species of Bamboo Orchid were analyzed. The key amino acid residues of the positive drug active sites were R79, Y104, Y108, F150, P176, L189, V192, S193, R253 and L260. The interaction types and amino acid residues are shown in Table 4:

[0172] Table 4 Interaction types and residues

[0173]

[0174]

[0175] Hydrogen bonding is an important factor in measuring affinity. Among the top three compounds with the highest affinity, formyloxybenzoin A, benzoyloxybenzoin, and formyloxybenzoin C all formed four hydrogen bonds with CysLT1R, involving amino acid residues Y104, Q164, H190, S193, Y249, and R253. Sansciclovine and isosaccharin formed three hydrogen bonds, involving residues Y26, E175, Q164, S193, and R253. Formyloxybenzoin G and formyloxybenzoin D formed two hydrogen bonds, involving residues H190, S193, Y249, and R253. Furthermore, deliflavone B, methoxyfurantoin, trihydroxymethoxyflavin, and loncovin formed only one hydrogen bond, involving residues Y104, S193, and R253. These data suggest that the active substances from Bamboo Shoots with more hydrogen bonds bind more stably to CysLT1R.

[0176] Furthermore, the number of key amino acid residues is an important indicator for evaluating CysLT1R binding to active compounds from the genus Rhamnoides. Analyzing these key residues can, to a certain extent, avoid false positive results. Among the genus Rhamnoides compounds ranked in the top 10% by affinity, formyloxybenzoin A, formyloxybenzoin C, and loncovin each contain six key amino acid residues; formyloxybenzoin D contains five; formyloxybenzoin G and scutellarin contain four; scutellarin, deliflavone B, and trihydroxymethoxyflavin contain three; while methoxyfurantanol and isoamycin contain one and two key amino acid residues, respectively, failing to meet the screening criteria.

[0177] In summary, this chapter selected nine compounds from the plant Bamboo Shoots as active ingredients, defining them as potential pharmacologically active substances from Bamboo Shoots: formyloxybenzoin A, Bamboo Shoots, formyloxybenzoin C, formyloxybenzoin G, deliflavone B, kaempferol, formyloxybenzoin D, trihydroxymethoxyflavin, and loncovin. These compounds demonstrated excellent binding affinity, interaction types, and the number of key amino acid residues, effectively eliminating false-positive results. To further investigate the pharmacological properties and bioavailability of these active ingredients, this study will establish multiple evaluation metrics and conduct pharmacokinetic simulations.

[0178] The complex system of CysLT1R protein and potential pharmacological substances of Bamboo Leaf Orchid was defined as Complex-1, Complex-2, Complex-3, Complex-4, Complex-5, Complex-6, Complex-7, Complex-8, and Complex-9;

[0179] The interaction pattern analysis of candidate active ingredients under kinetic conditions was performed using GROMACS software, taking Complex-1 as an example, including:

[0180] The AMBER03 force field model was used to describe the interaction forces between atoms in the system. The solvent model was set to TIP3P. The top file of CysLT1R and other position constraint files were generated using the gmx pdb2gmx command.

[0181] The topological structure information of formyloxybenzoin A was obtained using Acpype software;

[0182] Combine CysLT1R protein with formyloxybenzoin A, modify the gro file and top file, and generate a complex file;

[0183] Construct a dodecahedron simulation box filled with water molecules, with the edge of the box 1nm away from the edge of the molecule and a volume of 653.251nm 3The system contains 1 protein, 1 ligand, and 19,703 water molecules. The complex system of inflammatory protein and medicinal ingredients of bamboo orchid is placed in the center of the box, and 14 Cl ions are added to ensure that the system is electrically neutral as a whole.

[0184] Energy minimization was performed using the gmx grompp command to eliminate unreasonable geometries and high-energy states.

[0185] Combine proteins with ligands, water, and ions;

[0186] Use NVT thermostat to balance the temperature and make the system reach the equilibrium state of 310K;

[0187] Use NPT constant pressure device to balance the pressure and make the system reach the pressure balance state of 1 bar;

[0188] Set the step size to 2fs, the time to 50ns, and collect the system coordinates every 10ps. Collect coordinate, energy, pressure and other data during the simulation;

[0189] The root mean square error, root mean square fluctuation, solvent accessible surface area, and radius of gyration of Complex-1 were analyzed and compared with those of CysLT1R protein, such as Figure 10 As shown;

[0190] Analyze the distances between amino acids in the Complex-1 system and draw a residue contact map, such as Figure 11 As shown;

[0191] The free energy profile is obtained based on principal component analysis, and the lowest energy conformation is obtained based on the index file, such as Figure 12 As shown;

[0192] Calculate the binding strength based on the binding free energy of MM / GBSA and MM / PBSA. Steps:

[0193] Build the gmx_MMPBSA virtual environment in Linux system and prepare the topology file, input file, trajectory file and index file of the dynamic simulation complex system;

[0194] The molecular dynamics simulation recorded a total of 5000 frames of data, with the binding free energy starting frame number set to 1, the ending frame number set to 50001, the sampling interval set to 50 frames, the temperature set to 310 K, the ion concentration set to 0.15 (M), and the ionic strength set to 0.15 (M). The amino acid residue information within 0.6 nm of the receptor and ligand was calculated and recorded.

[0195] Combining the above calculation results, the total binding free energy of the ligand-protein complex was obtained to evaluate the binding affinity of Complex-1;

[0196] According to the output file, the binding energy contributed by each amino acid residue is analyzed, such as Figure 13 shown.

[0197] According to the above steps, the affinity, binding stability and binding free energy were comprehensively analyzed, and it was determined that formyloxybenzoin A, bamboo orchid phenanthroline, formyloxybenzoin C, formyloxybenzoin G, deliflavone B, scutellarin, formyloxybenzoin D, trihydroxymethoxyflavin and longcosin in bamboo orchid were the effective ingredients targeting CysLT1R.

[0198] The above disclosure is merely a preferred embodiment of the present invention and certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for screening effective ingredients of Bamboo Herb targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives, characterized in that: The steps include: A. Obtain information on homologous proteins of inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives based on homology modeling to form homologous protein receptors; B. Establish information data of Bamboo Leaf Orchid component database; C. Using the information data of the Bamboo Leaf Orchid component library, obtaining molecular descriptors of each Bamboo Leaf Orchid component, obtaining pharmacokinetic and toxicity information based on the molecular descriptors, performing pharmacokinetic evaluation, screening Bamboo Leaf Orchid medicinal components, and forming Bamboo Leaf Orchid medicinal component complexes; D. Using the homologous protein receptor, perform batch molecular docking with the Bamboo Leaf Blue medicinal component complex, perform affinity analysis, and screen out Bamboo Leaf Blue components with good affinity as candidate drugs; E. Using the homologous protein receptor, docking with the positive drug ligand formed by the positive drug information data to determine the binding site, and selecting the bamboo leaf component with high consistency in the binding site among the candidate drugs as the primary drug; F. Use a computer to simulate uniform environmental conditions, simulate the binding of the primary selected drugs to the homologous protein receptors under the uniform environmental conditions, perform binding free energy analysis, and screen out the Bambusa japonica components with good binding as the preferred drugs.

2. The method for screening effective ingredients of Bamboo Herb targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives as claimed in claim 1, characterized in that: Step A includes the following steps: A1. Obtain the protein crystal sequence of human inflammatory receptor from the gene protein database; A2. Using a homology modeling server, a backbone is constructed on the structurally conserved region of the protein crystal sequence, and a structurally variable region is generated using the homology structure to obtain an inflammatory receptor homologous protein template; A3. Screen the inflammatory receptor homologous protein template using sequence similarity, sequence coverage, acquisition method, resolution, sequence alignment, and binding ligand as indicators to obtain the best inflammatory receptor homologous protein template. Perform homology modeling based on the best inflammatory receptor homologous protein template to obtain inflammatory receptor homologous protein structural information data to form the homologous protein receptor.

3. The method for screening effective ingredients of Bamboo Herb targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives as claimed in claim 2, characterized in that: In step C, the pharmacokinetic evaluation indicators include one or more of the five drug-like principles, lipid-water partition coefficient, P-glycoprotein inhibition probability, human intestinal absorption HIA probability, intestinal epithelial cell permeability, and blood-brain barrier probability.

4. The method for screening effective ingredients of Bamboo Herb targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives as claimed in claim 1, characterized in that: Step D includes: setting docking parameters around the active site of the homologous protein receptor, determining the size and position of the docking active area grid box, and screening the candidate drug by computer with the binding energy information of the batch molecular docking of the bamboo orchid medicinal component complex, using affinity and active site binding information as indicators; the molecular docking includes semi-flexible docking.

5. The method for screening effective ingredients of Bamboo Herb targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives as claimed in claim 1, characterized in that: The affinity and active site binding information are used as indicators, including: ensuring that the binding site falls within the active pocket, using an affinity less than -7 kal / mol to indicate effective docking, and selecting the top 10% of components with the smallest negative values, which are defined as the candidate drugs.

6. The method for screening effective ingredients of Bamboo Herb targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives as claimed in claim 1, characterized in that: The positive drug includes KNT.

7. The method for screening effective ingredients of Bamboo Herb targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives as claimed in claim 1, characterized in that: The operation of step F includes: F1. Obtain the topological structure information of the primary drug candidate and the atomic topological structure information of the homologous protein receptor, construct a regular dodecahedron simulation box filled with water molecules, place the complex system of the primary drug candidate and the homologous protein receptor in the center of the simulation box, and perform energy minimization; F2. Using a force field model to describe the interaction forces between atoms in the system, setting a solvent model, placing the complex system in a dodecahedral water solvent box, ensuring that the system is electrically neutral as a whole, and forming the uniform environmental conditions; F3. Minimize the energy of the entire system and restrict the ligand constraints to eliminate unreasonable geometric configurations and high energy states; F4. Allowing the system to reach temperature equilibrium and pressure equilibrium, obtaining the binding state of the complex system under dynamic conditions, and analyzing the binding state results; F5. Build a virtual environment and prepare the topology file, input file, trajectory file, and index file of the complex system; F6. By calculating the solvation free energy of the primary drug and the homologous protein receptor in different solvent models and calculating the free energy contributed by each amino acid, a binding free energy analysis is performed to screen out the Bamboo Herb component with good binding as the preferred drug.

8. The method for screening effective ingredients of Bamboo Herb targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives as claimed in claim 7, characterized in that: In step F4, the binding state analysis includes analyzing one or more of the root mean square error, root mean square fluctuation, solvent accessible surface area, radius of gyration, residue contact map, principal component analysis, and free energy profile of the complex.

9. The method for screening effective ingredients of Bamboo Herb targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives as claimed in claim 7, characterized in that: In step F5, the virtual environment includes the gmx_MMPBSA virtual environment; in step F6, the different solvent models for calculating the solvation free energy include one or more of MM / PBSA and MM / GBSA.

10. A Bamboo Herb medicinal ingredient obtained by the method for screening Bamboo Herb medicinal ingredients targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives as claimed in claim 1, characterized in that: The invention comprises one or more of formyloxybenzoin A, bamboo orchid, formyloxybenzoin C, formyloxybenzoin G, deliriflavonoids B, scissin, formyloxybenzoin D, methoxyfurantoin, trihydroxymethoxyflavin, isoamycin and loncovin.

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