A virtual screening method of an anti-tumor drug targeting TEAD

Abstract

The application discloses a kind of virtual screening method of antitumor drug with TEAD as target point, belong to antitumor field, a kind of virtual screening method of antitumor drug with TEAD as target point, including following steps: S1: with TEAD2 as receptor protein, the binding site of receptor protein and ligand is predicted;S2: the establishment of pharmacophore model is carried out to binding site, and the pharmacophore that plays a key role in the process of protein and ligand combination is screened out, the key pharmacophore obtained by prediction is used to match and screen Specs database, ligand is sorted according to pharmacophore score, and the ligand that is selected in the front of matching order can be realized, based on the principle and method of key pharmacophore model, dominant water molecule docking and conformation scoring, virtual screening is carried out to Specs database, and the compound with good pharmacophore matching and better docking capacity is screened out, it has good application value and prospect in the development field of antitumor drug with TEAD as target point.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of anti-tumor, more particularly, to a virtual screening method of an anti-tumor drug targeting TEAD. BACKGROUND

[0002] TEAD2 is one of the main members of the TEAD family, TEAD (Transcriptional enhanced associate domain) transcriptional enhanced associate domain protein is the final effector of the Hippo signaling pathway, TEAD cannot activate transcription and needs the help of auxiliary activators (such as YAP, TAZ, Vals, p160 protein), the mammalian TEAD protein family includes TEAD1, TEAD2, TEAD3, TEAD4, among which TEAD1 promotes the expression of heart-specific genes and is considered to be important for the differentiation of myocardium, the role of TEAD2 is not clear, but it may be involved in the gene regulation of neural development, so far the specific function of TEAD3 has not been reported, the main function of TEAD4 is related to embryo implantation, however, almost all tissues express at least one TEAD gene, and some tissues express all four genes, the expression of TEAD family is up-regulated in many types of cancer, including gastric cancer, colorectal cancer, breast cancer and prostate cancer, which is related to the poor survival rate of patients;

[0003] The Hippo signaling pathway is an important regulator of cell growth, proliferation and migration, TEAD is located at the core of the Hippo pathway and is essential for regulating organ growth and wound repair, the dysregulation of TEAD and its regulatory cofactor Yes-related protein (YAP) has been implicated in many human cancers and hyperproliferative pathologies, therefore, the YAP-TEAD complex is a promising therapeutic target,

[0004] However, the screening method in the prior art cannot effectively screen out compounds that can destroy the YAP-TEAD protein-protein interaction and inhibit the activity of TEAD, so as to more effectively develop lead compounds TEAD inhibitors for the treatment of cancer and other hyperproliferative disorders. SUMMARY

[0005] 1. Technical problems to be solved

[0006] In view of the problems in the prior art, the present application aims to provide a virtual screening method for an anti-tumor drug targeting TEAD, which can realize the principles and methods of key pharmacophore modeling, explicit water molecule docking and conformation scoring, and screen out compounds with good pharmacophore matching and good docking ability from the Specs database, and has good application value and prospect in the development of anti-tumor drugs targeting TEAD.

[0007] 2. Technical solution

[0008] To solve the above problems, the present application adopts the following technical solution.

[0009] A virtual screening method for an anti-tumor drug targeting TEAD, comprising the following steps:

[0010] S1: Taking TEAD2 as a receptor protein, predicting the binding site of the receptor protein and ligand;

[0011] S2: Establishing a pharmacophore model for the binding site, screening out a key pharmacophore playing a key role in the binding process of the protein and ligand, using the predicted key pharmacophore to match and screen the Specs database, sorting the ligands according to the pharmacophore score, and selecting ligands with high matching and sorting;

[0012] S3: Predicting the explicit water molecules in the binding pocket of the receptor protein, and using molecular docking software to add an explicit water model for the ligands screened by the key pharmacophore;

[0013] S4: Sorting the docking conformations by conformation scoring, and preliminarily determining the target TEAD drug.

[0014] Further, the Specs database is a pre-established database, which internally stores specific data and pharmacophore scores of various ligands for matching and querying by the key pharmacophore.

[0015] Further, the pharmacophore score is inversely proportional to the matching effect of the pharmacophore constructed by the ligand and TEAD.

[0016] Further, the pharmacophore model is constructed by using align-it software to screen out the key pharmacophore model.

[0017] Further, the prediction method of the explicit water molecules is to predict the explicit water molecules using WatVina receptor, and to perform molecular docking based on the explicit water molecules using WatVina.

[0018] Further, the score of the conformation scoring is inversely proportional to the molecular docking effect.

[0019] 3. Beneficial effects

[0020] Compared with the prior art, the present application has the advantages that:

[0021] The present application is based on the principle and method of key pharmacophore model, explicit water molecule docking and conformation scoring, virtual screening of Specs database, screening out compounds with good pharmacophore matching and good docking ability, which has good application value and prospect in the field of anti-tumor drug development with TEAD as target. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 The protein crystal structure of TEAD2 (PDB ID: 6cdy) of the present application;

[0023] Figure 2 The binding site of the receptor protein of the present application;

[0024] Figure 3 The key pharmacophore model formed by the receptor protein of the present application;

[0025] Figure 4 The schematic diagram of molecular docking analysis of the receptor protein and the compound of the present application;

[0026] Figure 5 The conformation scoring analysis schematic diagram of the present application. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application; obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments; based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0028] Embodiment 1:

[0029] Please refer to Figures 1-4 The virtual screening method of the anti-tumor drug with TEAD as target, as shown in the figure, includes the following steps:

[0030] Step 1: Taking TEAD2 (PDB ID: 6cdy) as the receptor protein, the binding site of the receptor protein and the ligand is predicted;

[0031] Step 2: The pharmacophore model of the binding site is established, and the pharmacophore that plays a key role in the binding process of the protein and the ligand is screened out; the predicted key pharmacophore is used to match and screen the Specs database; the ligands are sorted according to the pharmacophore score, and the ligands with high matching order are selected;

[0032] The ligand is ranked according to the matching effect of the ligand and the pharmacophore constructed by the TEAD from good to bad.

[0033] The pharmacophore score is inversely proportional to the matching effect of the ligand and the pharmacophore constructed by the TEAD, that is, the greater the negative value of the pharmacophore score, the better the matching of the ligand and the pharmacophore constructed by the TEAD, that is, the ligand is ranked according to the pharmacophore score from large to small.

[0034] Here, the Specs database is a pre-established database that stores specific information of various ligands and pharmacophore scores (scores after matching of ligands and pharmacophores) for key pharmacophore matching query;

[0035] Meanwhile, the pharmacophore model is constructed by align-it software to screen the key pharmacophore model, and the pharmacophore model is:

[0036] The distance between the triazole ring center of the original ligand of 6CDY and the nearest N on the amino acid LYS 357 is 3.69Å, forming mutual hydrophobic interaction to construct a pharmacophore HYBL;

[0037] The distance between the benzene ring center of the original ligand of 6CDY and the nearest C on the amino acid ILE 408 is 3.87Å, forming mutual hydrophobic interaction to construct a pharmacophore HYBL;

[0038] The distance between the N atom on the triazole ring of the original ligand of 6CDY and the nearest C on the amino acid LEU 383 is 3.57Å, and the N atom acts as a hydrogen bond donor to form hydrogen bond interaction to construct a pharmacophore HDON;

[0039] The distance between the N atom on the benzene ring of the original ligand of 6CDY and the nearest C on the amino acid VAL 329 is 3.61Å, and the N atom can act as a hydrogen bond donor to form hydrogen bond interaction to construct a pharmacophore HDON;

[0040] The distance between the O atom of the original ligand of 6CDY and the nearest H on the amino acid GLN 401 is 2.32Å, forming mutual hydrogen bond interaction to construct a pharmacophore HACC;

[0041] The distance between the N atom on the triazole ring of the original ligand of 6CDY and the nearest N on the amino acid LYS 357 is 4.23Å, and the N atom can act as a hydrogen bond acceptor to form hydrogen bond interaction to construct a pharmacophore HACC;

[0042] The distance between the center of the six-membered ring of the original ligand of 6CDY and the center of the benzene ring of the amino acid PHE 428 is 4.57Å, forming hydrophobic interaction to construct a pharmacophore HYBL;

[0043] 6CDY original ligand six-membered ring center and the distance of the benzene ring center on the amino acid PHE 406 is 6.27 angstrom to form a hydrophobic interaction to build a pharmacophore HYBL;

[0044] 6CDY original ligand six-membered ring center and the distance of the benzene ring center on the amino acid PHE 386 is 6.63 angstrom to form a hydrophobic interaction to build a pharmacophore HYBL;

[0045] Step 3: Predict the dominant water molecules in the binding pocket of the receptor protein, and use molecular docking software to add a dominant water model to the ligand screened by the key pharmacophore;

[0046] Here, the prediction of the dominant water molecules is to predict the dominant water molecules using WatVina receptor, and to perform molecular docking based on the dominant water molecules using WatVina;

[0047] Among them, the key pharmacophore model formed by the receptor protein:

[0048] TEAD2 protein is the receptor, and its three-dimensional crystal structure is derived from RCSB PDB protein database (PDB ID: 4KIK); which contains a ligand, and the pharmacophore is generated by align-it, and then the database is virtually screened based on the pharmacophore, and the top 20000 small molecules are selected according to the pharmacophore matching results. Among them, HYBL is aromatic and lipophilic; HDON is a hydrogen bond donor; HACC is a hydrogen bond acceptor, Figure 3 The medium brown represents HYBL, the blue represents HDON, and the purple represents HACC.

[0049] Step 4: Sort the docking conformations by conformation scoring to preliminarily determine the target TEAD drug;

[0050] Here, the score of the conformation scoring is inversely proportional to the molecular docking effect, that is, the greater the negative value of the conformation scoring, the better the molecular docking result.

[0051] Example 2:

[0052] Please refer to Figure 4 On the basis of example 1, a WatVina prediction process is disclosed, that is, to predict the dominant water molecules by WatVina, and the WatVina molecular docking parameters are:

[0053] exhaustiveness arg (=6) exhaustiveness of the global search;

[0054] population arg (=5) population size for genetic algorithm;

[0055] ga_search arg (=5) amplitude for ga searching loop size;

[0056] num_modes arg (=20) maximum number of binding modes to generate;

[0057] rmsd arg (=1.5) modes clustering cutoff;

[0058] energy_range arg (=3) maximum energy difference between the bestbinding mode and the worst one displayed (kcal / mol).

[0059] In summary, based on the key pharmacophore model, the principle and method of explicit water molecule docking and conformation scoring, the virtual screening of Specs database is carried out, and the compounds with good pharmacophore matching and good docking ability are screened out, which has good application value and prospect in the development field of anti-tumor drugs taking TEAD as a target.

[0060] The above describes only the preferred specific embodiments of the present application; however, the protection scope of the present application is not limited to this. Any skilled person in the art, according to the technical solution and the improvement concept of the present application, should be covered within the protection scope of the present application, by equivalent replacement or change within the technical range disclosed by the present application.

Claims

1. A virtual screening method for antitumor drugs targeting TEAD, characterized in that: It includes the following steps: S1: Using TEAD2 as the receptor protein, predict the binding sites of the receptor protein and ligand; S2: Establish a pharmacophore model for the binding site, screen out the pharmacophores that play a key role in the binding process of protein and ligand, use the predicted key pharmacophores to match and screen the Specs database, sort the ligands according to the pharmacophore score, the pharmacophore score is inversely proportional to the pharmacophore matching effect, and select the ligands with higher matching ranking. The matching ranking is sorted from good to bad according to the matching effect of the ligand and the pharmacophore constructed by TEAD. S3: Predict the dominant water molecules in the receptor protein binding pocket and use molecular docking software to perform molecular docking with a dominant water model on ligands that have passed the key pharmacophore screening. S4: The docking conformations are ranked by conformation scoring. There is an inverse relationship between the conformation score and the molecular docking effect. The larger the negative value of the conformation score, the better the molecular docking result, and the target TEAD drug is preliminarily determined.

2. The virtual screening method for antitumor drugs targeting TEAD according to claim 1, characterized in that: The Specs database is a pre-established database that stores detailed information on various ligands and pharmacophore scores, which are used for matching queries of key pharmacophores.

3. The virtual screening method for antitumor drugs targeting TEAD according to claim 1, characterized in that: The pharmacophore model was constructed using the align-it software, and key pharmacophore models were selected.

4. The virtual screening method for antitumor drugs targeting TEAD according to claim 1, characterized in that: The method for predicting primordial water molecules involves using the WatVina receptor to predict primordial water molecules and then using WatVina for molecular docking based on primordial water molecules.

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