Method for screening medicinal components of arundina graminifolia aiming at cysteinyl leukotriene and inflammation receptor mediated by cysteinyl leukotriene derivative

Abstract

The invention discloses a computer simulation evaluation method for effective components of arundina graminifolia of an inflammation receptor mediated by cysteinyl leukotriene and derivatives thereof. The computer simulation evaluation method is used for screening active components for inhibiting skin inflammatory pruritus in arundina graminifolia. Comprising the following steps: acquiring homologous protein information of an inflammation receptor based on homologous modeling; establishing an arundina graminifolia chemical component library; evaluating pharmacokinetics and toxicity information based on the molecular descriptors; screening out arundina graminifolia components with potential drug effects through molecular docking; the interaction mode is analyzed under the dynamic condition, the binding free energy is calculated through MM / GBSA and MM / PBSA, the binding strength is quantified, and the anti-inflammatory active ingredients are determined. The method not only can rapidly and accurately predict potential pharmacodynamic substances, stability and interaction with a target spot of the arundina graminifolia for resisting inflammatory itching, but also can save time and medicinal material resources, provides important support for optimization and structural design of a lead compound, and greatly improves the research and development efficiency, progress and success rate.

Description

Technical Field

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

[0002] Itch is an unpleasant physical sensation transmitted by peripheral sensory neurons that triggers the desire to scratch and affects most people. There is increasing evidence that inflammatory mediators are released into the skin's immune cells, keratinocytes, and mucous membranes. Chronic itching affects sleep, mood, and quality of life and is a major unresolved health problem. Itch occurs in the skin and other cell types, activating those specific peripheral sensory neurons, causing itching. Itch occurs in the skin and is a health problem that affects more than 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 not only participate in a variety of inflammatory pathological processes by activating cysteinyl leukotriene receptors (including CysLT1 and CysLT2, both of which are classic 7-transmembrane G protein-coupled receptors), but also mediate smooth muscle spasms, capillary dilation and leakage, increased glandular secretion, changes in airway structure and other reactions. The administration of cysLTs to animals and human subjects will 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, it is also an important cytokine regulator and has extensive interactions with other inflammatory mediators, participating in the proliferation and differentiation regulation of various cells including keratinocytes and tumor cells. Therefore, CysLTs play a key role in the pathogenesis of various inflammatory diseases and itch sensation (Sasaki F, Yokomizo T. The leukotriene receptors as therapeutic targets of inflammatory diseases[J]. International Immunology, 2019, 31(9): 607-15.).

[0004] Keratinocytes (KC) are the main constituent cells of the epidermis and the first tissue structure that the body contacts with various exogenous substances. They can release endogenous itch factors such as cytokines, amines, neuropeptides, and nerve growth factors. As the main constituent cells of the epidermis, KC can not only synthesize CysLTs, but also become one of the target cells of CysLTs in the skin due to its expression of CysLTRs. In addition, the synthesis of CysLTs and the expression of CysLTRs by KC are significantly enhanced under inflammatory conditions. Recent studies have found that KC not only plays an important role in the regulation of skin inflammation, but also has nonspecific phagocytic function and antigen presenting cell function. Changes in its biological behavior will directly affect the occurrence and prognosis of many skin diseases, so it has become an important target for treatment. CysLTR1 and CysLTR2 are expressed in skin tissues, mainly distributed in the epidermis, hair follicles, sebaceous glands, sweat glands, vascular walls and smooth muscles. There is no significant difference in the expression and distribution of the two types of receptors. 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 has a role in 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] Arundina graminifolia (D.Don) Hochr. has the functions of clearing away heat and detoxifying, promoting diuresis and relieving jaundice, clearing the lungs and relieving cough, removing dampness and relieving pain. It is called "Wen Shang Hai" and "Bai Yang Jie" by the Dai people. It is the most commonly used detoxifier in Dai medicine in Xishuangbanna, Yunnan. It is used to treat food poisoning, snake bites, jaundice, rheumatism, urinary tract infections, etc. It is widely used in Dai medicine preparations, such as Yajie Tablets and Baijie Capsules, which are used to relieve symptoms such as edema and food poisoning; such as Da La Tang for the treatment of rheumatoid arthritis; Yajie Shaba for the treatment of diabetes insipidus; and Yajie Gahan for the detoxification and beauty capsules. Moreover, the Bulang and Wa people use Arundina graminifolia to treat cough, tracheitis, pneumonia and tuberculosis. In addition, it is also commonly used as a lubricant, antibacterial agent, moisturizer and rheumatism treatment drug in traditional medicine in countries such as India. As a plant with multiple pharmacological activities, the main active ingredients of Bamboo Leaf Orchid 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 much research attention due to its effects on the inflammatory process mediated by leukotrienes and their derivatives. The active compounds in Bamboo orchid are believed to work by regulating the release of inflammatory mediators and the response of immune cells. They may inhibit certain links in the leukotriene pathway or regulate its activity, thereby reducing local inflammation levels and reducing the severity of related symptoms. This may help alleviate common skin problems such as itchy scalp, oily scalp, dandruff and itchy skin, which are usually associated with lipid metabolism, scalp inflammation, immune disorders, aging, etc.

[0007] The screening and development of inflammatory receptor antagonists mediated by leukotrienes and their derivatives is one of the current research hotspots. However, due to the complexity and diversity of these mediators in cellular metabolism, drug-based research faces challenges in cost, cycle, and risk, such as capturing intermediates in their metabolic processes and observing the epigenetic phenomena of the active ingredients of Bamboo Leaf Orchid and their intermolecular interactions. These challenges require a lot of time, energy, and resource investment, but future results may bring significant returns, promote the discovery of active substances and the development of new products, and thus benefit mankind.

[0008] With the rapid development of science and technology, cutting-edge technologies such as computer simulation, AI big data processing and data engineering are profoundly changing the landscape of medical research and clinical practice, and have opened up new paths for the research of traditional Chinese medicines such as bamboo orchid in solving problems such as itchy scalp, oily scalp, dandruff and skin itching. Computer simulation technology has accelerated the drug discovery and verification process, reduced experimental costs and improved research efficiency. AI big data processing technology can analyze massive experimental data and identify key bamboo orchid components that affect treatment effects; data engineering provides data management support for the above technologies, ensuring the accuracy, integrity and security of the data, and promoting the research progress of bamboo orchid in anti-inflammatory itching of the skin. Through these modern scientific and technological means, the research progress of Chinese medicinal materials in the treatment of skin inflammation has been greatly accelerated, revealing the exact mechanism of action of bamboo orchid in skin inflammation mediated by leukotrienes and their derivatives, providing new prospects for its clinical application.

[0009] However, there are still technical challenges in the screening process for inflammatory receptor antagonists 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 dealing with the structural diversity and biological effects of natural drugs, 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 view of the above problems, the present invention proposes a new computer simulation evaluation method to screen the active ingredients in Bamboo Leaf Orchid 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 chemical components of Bamboo Leaf Orchid is established, and molecular descriptors are combined to evaluate pharmacokinetic and toxicity information 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 of the drug to 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 problem in the screening of CysLTs-related inflammatory antagonists, and provides a scientific basis for Bamboo Leaf Orchid 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 Orchid through computer simulation with homology modeling, virtual screening, kinetic simulation and binding energy calculation as the core. Through the method of the present invention, time and resources can be saved, the biological activity, stability and interaction with biological targets of potential pharmacological substances can be predicted quickly and accurately, and the efficiency and success rate of new drug research and development can be improved, providing important support and guidance for drug design and optimization, and accelerating 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 Leaf Orchid targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives, comprising the following steps:

[0014] A. Obtain information data of 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 the molecular descriptors of each bamboo leaf orchid component, obtaining the pharmacokinetic and toxicity information based on the molecular descriptors, performing pharmacokinetic evaluation, screening the bamboo leaf orchid effective components, and forming the bamboo leaf orchid effective component complex;

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

[0018] E. Using the homologous protein receptor to dock with the positive drug ligand formed by the positive drug information data, determining the binding site, and selecting the bamboo leaf orchid component with high consistency of 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 with 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 invention discloses a computer simulation evaluation method for the efficacy components of bamboo orchid targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives, and screens out active components in bamboo orchid that inhibit skin inflammatory pruritus, including obtaining inflammatory receptor homologous protein information based on homology modeling; establishing a bamboo orchid chemical component library; obtaining pharmacokinetic and toxicity information based on molecular descriptors; screening bamboo orchid efficacy components through molecular docking to obtain bamboo orchid components with potential efficacy; performing interaction mode analysis on bamboo orchid components with potential efficacy under kinetic conditions; and determining the anti-inflammatory active components in bamboo orchid by calculating the binding free energy and quantifying the binding strength based on MM / GBSA and MM / PBSA. The invention can not only quickly and accurately predict the potential efficacy substances, stability and interaction with the target of bamboo 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 greatly improve the efficiency, process 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. constructing a main chain on the structurally conserved region of the protein crystal sequence through a homology modeling server, generating a structurally variable region using the homology structure, and obtaining an inflammatory receptor homologous protein template;

[0025] A3. The inflammatory receptor homologous protein templates are screened based on sequence similarity, sequence coverage, acquisition method, resolution, sequence alignment, and binding ligand as indicators to obtain the best inflammatory receptor homologous protein template, and homology modeling is performed based on the best inflammatory receptor homologous protein template to obtain inflammatory receptor homologous protein structure information data to form the homologous protein receptor.

[0026] Preferably, in step C, the pharmacokinetic evaluation indexes 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 the molecular descriptor information, the pharmacokinetic evaluation of the active ingredients of Bamboo Leaf Orchid was carried out with 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.

[0028] Preferably, step D comprises: 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 using the binding energy information of the batch molecular docking with the Bamboo Orchid medicinal ingredient complex, with 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 value as the candidate drugs.

[0030] Preferably, the positive drug comprises KNT.

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

[0032] F1. Obtain the topological structure information of the primary drug 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 and the homologous protein receptor in the center of the simulation box, and perform energy minimization;

[0033] F2. Use a force field model to describe the interaction force between atoms in the system, set a solvent model, place the complex system in a dodecahedral water solvent box, ensure that the system is electrically neutral as a whole, and form 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 composite system;

[0037] F6. By calculating the solvation free energy of the primary drug and the homologous protein receptor in different solvent models, and finding 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 result 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.

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

[0040] gmx_MMPBSA is a tool for performing final state free energy calculations using GROMACS. It is a program written in Python. In gmx_MMPBSA, the preset human body temperature and ion concentration are set to calculate the binding energy of MM / PBSA and MM / GBSA.

[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, methoxyfurantanol, trihydroxymethoxyflavin, isothiocyanate, and loncovin.

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

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

[0044] 2. The present invention first obtains qualified bamboo orchid components through pharmacokinetic and toxicity information, screens bamboo orchid medicinal components through molecular docking, obtains bamboo orchid components with potential medicinal effects, and then analyzes the interaction pattern of bamboo orchid components with potential medicinal effects under kinetic conditions to further obtain the effectiveness ranking of specific bamboo orchid medicinal components for inflammatory receptors. Finally, the binding strength is quantified by calculating the binding free energy based on MM / GBSA and MM / PBSA to obtain the final effectiveness ranking of specific bamboo orchid medicinal components for 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 orchid medicinal components, and accelerating the process of drug research and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the process 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 ligand KNT;

[0050] Figure 6 This is a conformational superposition diagram, where the initial conformation of KNT is the blue structure, and the docked KNT is the colored structure. The gray helix is ​​the protein in AutoDock 4, the brown helix is ​​the protein in AutoDock Vina, and the green helix is ​​the protein in LeDock;

[0051] Figure 7 are 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] Fig. 9 This is the best docking conformation of CysLT1R-Zhuyelan active ingredients, where: blue structures represent amino acid residues, orange represents potential active ingredients of Zhuyelan, gray lines represent hydrophobic interactions, blue lines represent hydrogen bonds, green lines represent π-π stacking, and red lines represent π-cation interactions;

[0054] Fig.10 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] Fig.11 is the contact map of Complex-1 residues;

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

[0057] Fig.13 This is the energy contribution diagram of each amino acid residue of 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 further described in 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 the 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 bamboo leaf orchid component library;

[0062] Step 3: Pharmacokinetic and toxic 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 the UniProt protein coding gene protein database;

[0068] (2) constructing a main chain 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 Bamboo Orchid using ChemDraw and convert them into three-dimensional conformations;

[0075] (2) Use AutoDockTools software to obtain the 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 according to the following steps:

[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 times;

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

[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 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 through 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) A dodecahedron simulation box filled with water molecules was constructed, with the edge of the box 1 nm away from the edge of the molecule. The system contained one protein and one ligand. The complex system of the inflammatory protein and the active ingredient of the bamboo orchid was placed in the center of the box, and Na ions or Cl ions were added to ensure that the system was 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 protein with 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) Setting the step size to 1 fs to 2 fs, performing a dynamic simulation for a time of 50 ns to 200 ns, collecting system coordinates every 10 ps to 50 ps, ​​and collecting 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 ion 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] Embodiment 1:

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

[0099] Search CysLT1R in the UniProt protein-coding gene protein database and select 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, the above sequence was input and the model was started. 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, totaling 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 manifested 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, the hydrophobic interaction is participated by amino acid residues Y104, Y108, F150, P176, L189, V192, and L260; the hydrogen bonding sites include R79, Y104, S193, and R253. In addition, Y108 and the ligand undergo π-π stacking interaction, while R253 participates in π-cation interaction.

[0114] The interaction sites between CysLT1R homologous proteins and KNT proligands are as follows Figure 5 As shown in C, the interaction and amino acid residue sites are completely consistent with the 6rz4.1.A template protein, which further illustrates that the biological activity of the homologous protein is consistent with the template protein, and has research value for subsequent docking model exploration. This paper will use the CysLT1R homologous protein as a receptor to conduct research on the CysLT1R-antagonist model, 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 docking conformations that satisfied the distance with the original ligand less than 0.2 nm 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 ranges from -10.08 kcal / mol to -6.93 kcal / mol, with a difference of 3.15 kcal / mol, indicating that this method can obtain relatively good affinity analysis for molecular docking. In AutoDock Vina, the affinity ranges from -12.2 kcal / mol to -8.66 kcal / mol, with a difference of 3.54 kcal / mol, indicating that this method has a good affinity analysis for the CysLT1R-antagonist system. In LeDock, the affinity ranges from -9.99 kcal / mol to -6.18 kcal / mol, with a difference of 3.81 kcal / mol. The molecular docking affinity is low and the scores vary greatly. The scoring shows that this method cannot well describe the affinity of the CysLT1R-antagonist system.

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

[0121] Conformation superposition analysis

[0122] The PyMOL software was used to plot the top ten docking conformations with the original ligands, and the bond angle torsion and position shift of the docked ligands and the original ligands were observed. Figure 6 shown.

[0123] In AutoDock 4, the KNT molecules generated by the 1st, 2nd, 4th, 5th, 6th, 7th, and 8th dockings are basically consistent with the KNT molecules of the original conformation, while the bond angles of the 3rd, 9th, and 10th conformations are distorted to a large extent, and the overlap degree with the original conformation is low. This shows 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 by docking are basically consistent with the original conformation KNT molecules, the bond angles are slightly distorted, and the overlap is basically consistent, among which the first one is almost overlapping. This shows that the software algorithm can accurately describe the docking of the CysLT1R-antagonist model. In LeDock, the KNT molecules generated by the 1st, 2nd, 5th, 7th, and 8th dockings are basically consistent with the original conformation KNT molecules, among which the first conformation is almost overlapping with the original ligand. The bond angles of the 3rd, 4th, 6th, 7th, 9th, and 10th conformations are distorted to a large extent, and the overlap degree with the original conformation is low. This indicates that the software algorithm cannot accurately describe the docking situation of the CysLT1R-antagonist model.

[0124] In summary, among the software algorithms of 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] Conformation superposition RMSD analysis

[0126] In order to eliminate false positive results in molecular docking, this chapter analyzes the binding mode by comparing the overlap between the KNT conformation generated by docking and the original ligand KNT conformation, combining the overlap diagram and RMSD value of the docking results. At the same time, the average RMSD between the docked KNT and the original conformation is calculated, and the relevant results are shown in Table 2.

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

[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 best performance of the docked conformation and the original ligand RMSD was obtained by AutoDock Vina software, followed by LeDock software, and finally AutoDock 4 software.

[0134] Ligand binding site analysis

[0135] In this chapter, the amino acids R79, Y104, Y108, F150, P176, L189, V192, S193, R253 and L260 of the original conformation 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] According to the analysis of amino acid residues in the docking results of AutoDock 4, the following types of interactions were observed: The amino acid residues involved in the hydrophobic interaction include Y104, Y108, F112, F150, F158 and P176. R79, Y104, S193, and R253 formed hydrogen bond interactions with the ligand. The π-π stacking between Y104 and the ligand can further enhance the binding between the ligand and the receptor, while R253 participates in the π-cation interaction. It contains 10 amino acid residues, and the hydrogen bond, π-π stacking, and π-cation interaction are consistent with the initial conformation, while 4 hydrophobic interactions are consistent, with a residue site coverage rate of 80%.

[0139] According to the best docking results in AutoDock Vina, amino acid residue analysis was performed. It was observed that the hydrophobic interaction mainly involved Y108, F112, F150, F158, V186, L189, V192, R253, L257 and L260. In addition, the hydrogen bonds formed included Y104, S193, R253. In addition, π-π stacking was observed between Y104 and Y108, while R253 was involved in π-cation interaction. It contains 12 amino acid residues, with a residue site coverage rate of 90%.

[0140] In the LeDock docking results, amino acid residues Y108, F112, F150 and V192 participated in the hydrophobic interaction. R79, Y104, S193, R253 formed hydrogen bonds with the ligand. Y104, Y108 and F158 formed π-π stacking with the ligand, and R253 participated in the π-cation interaction. It contains 9 kinds of amino acid residues, and the hydrogen bonds, π-π stacking, and π-cation interactions are consistent with the initial conformation, while 3 hydrophobic interactions are consistent, and the residue site coverage rate is 80%.

[0141] Affinity evaluation, conformational superposition analysis, conformational superposition 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 simulations, such as Figure 7As shown in the figure, the simulation temperature was set to 310K, the simulation time was 100ns, the RMSD value remained stable around 0.28nm, and the overall volatility was less than 0.1nm, indicating that the protein structure did not undergo significant structural changes during the simulation. RMSF indicates that the specific regions of the protein have high flexibility, which plays an important role in the function of the protein.

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

[0145] 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;

[0146] The molecular descriptors of the Bamboo Orchid medicinal substances were used through ADMETlab 2.0 software to obtain pharmacokinetic and toxicological information, including: based on the molecular descriptor information, the pharmacokinetic and toxicological theoretical evaluation and research of the Bamboo Orchid medicinal ingredients were carried out according to 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 Bamboo Leaf Orchid medicinal substances through molecular docking, including:

[0148] Use the GridBox tool to set 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] 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 that different categories of compounds showed significant differences. 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 bamboo leaf orchid phenanthrene was -10.27 kcal / mol, that of scutellaria baicalensis was -9.98 kcal / mol, and that of isoshanin was -9.82 kcal / mol, showing strong affinity.

[0161] The average affinity of ketone compounds is -8.60 kcal / mol. The affinity of flavonoid compounds ranges from -6.95 to -9.98 kcal / mol, with an average of -8.87 kcal / mol; the affinity of diphenyl ketone compounds ranges from -9.13 to -10.42 kcal / mol, with an average of -9.81 kcal / mol; the affinity of fluorenone compounds ranges from -7.39 to -8.91 kcal / mol, with an average of -7.87 kcal / mol; the affinity of other ketone compounds ranges 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 acid compounds is -8.14 kcal / mol. The affinity of benzofuran and benzopyran phenolic acid compounds ranges from -8.05 to -9.92 kcal / mol, with an average of -8.82 kcal / mol; the affinity of other phenolic acid compounds ranges from -5.64 to -9.31 kcal / mol, with an average of -7.70 kcal / mol. The affinity of methoxyfurantanol is -9.92 kcal / mol, which is relatively outstanding.

[0163] In summary, different classes of compounds showed diverse performance in terms of affinity, which suggests that they may play an important role in the interaction with CysLT1R. During the screening of docking results, the binding site was ensured to fall within the active pocket, and the top 10% compounds with the strongest affinity were analyzed, including formyloxybenzoin A, bamboo orchid, formyloxybenzoin C, formyloxybenzoin G, deliflavone B, shanshetin, formyloxybenzoin D, methoxyfurantanin, trihydroxymethoxyflavin, isoshanin and loncosil. These compounds are of great significance to the biological activity and pharmacological properties of CysLT1R and can be used as potential active ingredients of 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] Analysis of action type and active site

[0166] Optimal docking conformation of CysLT1R-Bamboo Leaf Orchid active ingredients

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

[0168] When analyzing the interaction between compounds and CysLT1R, it was found that different compounds exhibited diverse types of interactions. Specifically, baicalin, methoxyfurantanin, and isothiocyanate interacted mainly through hydrophobic interactions and hydrogen bonds. Formyloxybenzoin A, formyloxybenzoin C, formyloxybenzoin G, formyloxybenzoin D, trihydroxymethoxyflavin, and loncovin interacted with CysLT1R through hydrogen bonding, π-π stacking, and hydrophobic interactions. In addition, deliflavone B and shanshetin exhibited π-cationic interactions in addition to the above-mentioned types of interactions.

[0169] Specifically, the hydrophobic interaction involves the energy change of bound water released by desolvation when CysLT1R binds to the active ingredients of Bamboo Orchid. Hydrogen bonding stabilizes the binding through the NH…O bond formed in the α-helix of CysLT1R, and this interaction is of great significance to the binding stability of CysLT1R and the active ingredients of Bamboo Orchid. The π-π stacking interaction is a weak interaction between the aromatic ring in the active ingredients of Bamboo Orchid and the aromatic amino acids (such as tyrosine and phenylalanine) in CysLT1R, and the π-cation interaction also involves a non-covalent weak interaction between the aromatic ring and the cation.

[0170] Interaction type and residue status

[0171] The PLIP binding sites of the potential active ingredients in eleven species of Bamboo Orchid were analyzed, with R79, Y104, Y108, F150, P176, L189, V192, S193, R253 and L260 as the key amino acid residues in the active sites of positive drugs. 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 in affinity, formyloxybenzoin A, bamboo leaf orchid and formyloxybenzoin C all formed four hydrogen bonds with CysLT1R, and the amino acid residues involved in these hydrogen bonds included Y104, Q164, H190, S193, Y249 and R253. Sanshocin and isothiocarpin 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. In addition, deliflavone B, methoxyfurantanol, trihydroxymethoxyflavin and loncovin formed only one hydrogen bond, involving residues including Y104, S193 and R253. These data suggest that the active substances of Bamboo Sphaeranthus containing more hydrogen bonds bind more stably to CysLT1R.

[0176] In addition, the number of key amino acid residues is also an important indicator for evaluating the binding of CysLT1R to active substances of Bamboo Orchid. False positive results can be avoided to a certain extent by analyzing these key residues. Among the Bamboo Orchid compounds ranked in the top 10% in affinity, formyloxybenzoin A, formyloxybenzoin C and loncosilver all contain six key amino acid residues; formyloxybenzoin D contains five key amino acid residues; formyloxybenzoin G and shanshetin contain four key amino acid residues; Bamboo Orchid phenanthene, deliflavone B and trihydroxymethoxyflavin contain three key amino acid residues; while methoxyfurantanin and isoshanin contain one and two key amino acid residues, respectively, and fail to meet the screening criteria.

[0177] In summary, this chapter selects nine bamboo orchid compounds, including formyloxybenzoin A, bamboo orchid phenanthroline, formyloxybenzoin C, formyloxybenzoin G, deliflavone B, scalycin, formyloxybenzoin D, trihydroxymethoxyflavin and loncosil as active ingredients, which are defined as potential bamboo orchid pharmacodynamic substances. These compounds have excellent performance in affinity, interaction type and the number of key amino acid residues, and can effectively exclude false positive results. In order to further study the pharmacological properties and bioavailability of these active ingredients, this study will establish multiple evaluation indicators and conduct pharmacokinetic simulation analysis.

[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, and the top file of CysLT1R and other position restriction files were generated by the gmx pdb2gmx command;

[0181] The topological structure information of formyloxybenzoin A was obtained by 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. The edge of the box is 1nm away from the edge of the molecule and the volume is 653.251nm 3, the system contains 1 protein, 1 ligand, 19703 water molecules, the inflammatory protein and bamboo orchid medicinal ingredient complex system 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 protein with ligand, 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 1 bar pressure balance state;

[0188] Set the step size to 2fs, the time to 50ns, collect the system coordinates every 10ps, and 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 Fig.10 As shown;

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

[0191] The free energy profile was obtained based on principal component analysis, and the lowest energy conformation was obtained based on the index file, such as Fig.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 under 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 starting frame number of the binding free energy set to 1, the ending frame number to 50001, the sampling interval to 50 frames, the temperature to 310 K, the ion concentration to 0.15 (M), and the ion strength to 0.15 (M). The amino acid residue information within 0.6 nm of the receptor and ligand was calculated and recorded;

[0195] The above calculation results were combined to obtain the total binding free energy of the ligand-protein complex and 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 Fig.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 loncovin in bamboo orchid were the effective ingredients for CysLT1R.

[0198] The above disclosure is only the preferred embodiment of the present invention, which 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 Leaf Orchid targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives, characterized in that: The steps include: A. Obtain information data of 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 the molecular descriptors of each bamboo leaf orchid component, obtaining the pharmacokinetic and toxicity information based on the molecular descriptors, performing pharmacokinetic evaluation, screening the bamboo leaf orchid effective components, and forming the bamboo leaf orchid effective component complex; D. Using the homologous protein receptor to carry out batch molecular docking with the Bamboo Leaf Orchid medicinal component complex, perform affinity analysis, and screen out Bamboo Leaf Orchid components with good affinity as candidate drugs; E. Using the homologous protein receptor to dock with the positive drug ligand formed by the positive drug information data, determining the binding site, and selecting the bamboo leaf orchid component with high consistency of 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 with 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 the effective components of Bamboo Leaf Orchid 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. constructing a main chain on the structurally conserved region of the protein crystal sequence through a homology modeling server, generating a structurally variable region using the homology structure, and obtaining an inflammatory receptor homologous protein template; A3. The inflammatory receptor homologous protein templates are screened based on sequence similarity, sequence coverage, acquisition method, resolution, sequence alignment, and binding ligand as indicators to obtain the best inflammatory receptor homologous protein template, and homology modeling is performed based on the best inflammatory receptor homologous protein template to obtain inflammatory receptor homologous protein structure information data to form the homologous protein receptor.

3. The method for screening the effective components of Bamboo Leaf Orchid targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives as claimed in claim 2, characterized in that: In step C, the pharmacokinetic evaluation indexes 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 the effective components of Bamboo Leaf Orchid targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives as claimed in claim 1, characterized in that: Step D comprises: 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 and the binding energy information of the batch molecular docking of the Bamboo Orchid medicinal component complex, taking the affinity and active site binding information as indicators; the molecular docking comprises semi-flexible docking.

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

6. The method for screening the effective components of Bamboo Leaf Orchid 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 the effective components of Bamboo Leaf Orchid 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 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 and the homologous protein receptor in the center of the simulation box, and perform energy minimization; F2. Use a force field model to describe the interaction force between atoms in the system, set a solvent model, place the complex system in a dodecahedral water solvent box, ensure that the system is electrically neutral as a whole, and form 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 composite system; F6. By calculating the solvation free energy of the primary drug and the homologous protein receptor in different solvent models, and finding 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.

8. The method for screening the effective components of Bamboo Leaf Orchid targeting inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives as claimed in claim 7, characterized in that: In step F4, the binding state result 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 the effective components of Bamboo Leaf Orchid 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 Leaf Orchid medicinal ingredient obtained by the Bamboo Leaf Orchid medicinal ingredient screening method for inflammatory receptors mediated by cysteinyl leukotrienes and their derivatives as claimed in claim 1, characterized in that: It includes one or more of formyloxybenzoin A, bamboo orchid, formyloxybenzoin C, formyloxybenzoin G, deliflavone B, scalycin, formyloxybenzoin D, methoxyfurantanol, trihydroxymethoxyflavin, isothiocyanate, and loncosil.

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