Screening method and application of gamma-secretase inhibitor

Abstract

The invention discloses a screening method and application of a gamma-secretase inhibitor, and belongs to the technical field of anti-cancer lead compound screening. The screening method comprises the following steps: S1, designing and constructing a gamma-secretase pharmacophore; s2, ligand pretreatment; s3, carrying out ADME / T evaluation; s4, recognizing active sites; s5, molecular docking; and S6, carrying out MM-GBSA analysis. 30 natural-source gamma-secretase inhibitors are obtained through effective screening, the gamma-secretase inhibitors are regulators of gamma-secretase substrates Notch, and Notch signal channels can regulate and control EMT processes and mitochondrial apoptosis, so that the Notch signal channels are blocked through the gamma-secretase inhibitors, and cells can be blocked in cancer cell proliferation and angiogenesis; therefore, screening of the gamma-secretase inhibitor and determination of the anti-cancer activity of the gamma-secretase inhibitor are of great significance.

Description

Technical Field

[0001] The present invention relates to the technical field of anticancer lead compound screening, and in particular to a screening method for a gamma-secretase inhibitor and an application thereof. Background Art

[0002] Currently, breast cancer (BC) has become the most common cancer in the world. According to the expression of different receptors, breast cancer is divided into three subtypes: hormone receptor-positive type (luminal type), epidermal growth factor receptor 2-positive type (HER-2-positive type) and triple-negative breast cancer (TNBC). At present, neoadjuvant chemotherapy is an important treatment for TNBC. The chemotherapy regimen mainly includes the combined use of taxanes (such as paclitaxel), anthracyclines (such as doxorubicin) and platinum drugs (such as cisplatin). After neoadjuvant chemotherapy, about 20% of TNBC patients can achieve pathological remission, but the overall survival rate of 80% of patients who do not achieve pathological remission is still low, and its biggest drawback is "drug resistance". More importantly, once TNBC develops chemical resistance, it will have a stronger invasive ability, greatly increasing the mortality rate of patients. Therefore, it is of great clinical significance to provide new ideas for the treatment of triple-negative breast cancer resistance and discover new triple-negative breast cancer resistance targeted drugs. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides a method for screening γ-secretase inhibitors, which effectively screened and obtained 30 γ-secretase inhibitors from natural sources, and provided support for subsequent research on γ-secretase inhibitors from natural sources.

[0004] In order to achieve the above technical effects, the present invention is implemented by the following technical scheme: A method for screening γ-secretase inhibitors, characterized in that it comprises the following steps:

[0005] S1. Design and construction of the γ-secretase pharmacophore: Search and screen the crystal structures of the γ-secretase complex protein carrying natural ligands (the crystal structures corresponding to the PS1 protein) in the PDB database, import the best matching γ-secretase protein crystal structure (PDB: 6LR4) into the LigandScout software, and construct the active pocket and key amino acid sites based on the γ-secretase catalytic core PSEN-1 according to the structure and active site information of γ-secretase.

[0006] S2. Ligand preprocessing: The 2D sdf structure files of the eight compound libraries were processed using the LigPrep module in the Schrödinger software to generate all 3D chiral conformations;

[0007] S3. ADME / T evaluation: QikProp module in Schrödinger software was used to predict all ADME / T parameter scores of ligand compounds, and a total of 51 type parameters were obtained; the five principles of Lipinski were used for evaluation;

[0008] S4. Active site identification: Using the Receptor Grid Generation module in the Schrödinger software, set the most appropriate Enclosing box to perfectly enclose the binding site of the natural ligand, and on this basis obtain the active pocket of the protein;

[0009] S5. Molecular docking: Each processed ligand in the eight compound libraries is docked with the active site of PS1 protein in turn. The lower the score, the lower the binding free energy of the compound with the protein and the higher the binding stability.

[0010] S6. MM-GBSA analysis: The lowest-scoring ligand and the active site of the PS1 protein were subjected to MM-GBSA calculation analysis. MM-GBSA dG Bind can represent the binding free energy of the small molecule and the protein. The lower the binding free energy, the higher the binding stability of the ligand and the PS1 protein.

[0011] Furthermore, in S1, according to the structure and active site information of γ-secretase, constructing the active pocket and key amino acid sites based on the catalytic core PSEN-1 of γ-secretase specifically comprises using the Protein Preparation Wizard module of Schrödinger software to perform protein pretreatment, regeneration state of natural ligand, hydrogen bond distribution optimization, protein energy minimization and water removal on the obtained protein crystal structure.

[0012] Further, in S1, the screening used MM-GBSA dG Bind <-30 kcal / mol as the screening standard; after screening and removing duplicates, a total of 30 compounds were obtained; the names of the 30 compounds were: Dibenzazepine, Z-VAD(OMe)-FMK, Semagacestat, Dapt, Torachrysone 8-glucoside, Furowanin, Cephapirin sodium, Millewanin G, Calpeptin, Kushenol X, RO4929097, Pyrocatechol monoglucoside, Brivanib alaninate, Ancitabine hydrochloride, Aurintricarboxylic acid, Linagliptin, Nicaraven, MG-132, Calpain Inhibitor III, Lck inhibitor 2, Eragidomide, MK-2048, Ubenimex, Erythrinin C, Calpain inhibitor I, Protirelinacetate, Silibinin B. Atovaquone, 8-Lavandulylkaempferol, CID 13846690.

[0013] Further, in S2, the eight compound libraries are:

[0014] Selleck·FDA-Approved Drug Library;

[0015] TargetMol·Natural Compound Library;

[0016] MedChemExpress·Natural Product Library;

[0017] Pharmacodia (medicine crossing) Natural Product Library;

[0018] BioBioPha BBP(Xili)·Natural Product Library;

[0019] APExBIO·Anti-cancer-Compound-Library Plus;

[0020] Selleck·Preclinical / Clinical Compound Library: L2000;

[0021] MedChemExpress·Immunology / Inflammation Compound Library.

[0022] Furthermore, in S5, the ligands were molecularly docked with the active site of PS1 protein using HTVS, SP and XP respectively, and the docking accuracy was gradually improved.

[0023] Another object of the present invention is to provide an application of a γ-secretase inhibitor, characterized in that the γ-secretase inhibitor is used in triple-negative breast cancer.

[0024] Furthermore, the application of the γ-secretase inhibitor in triple-negative breast cancer is specifically that the γ-secretase inhibitor blocks the Notch signaling pathway, which can block cells in the proliferation and angiogenesis of triple-negative breast cancer cells.

[0025] The beneficial effects of the present invention are:

[0026] The present invention uses a natural source inhibitor screening method based on the γ-secretase pharmacophore, and effectively screens and obtains 30 natural source γ-secretase inhibitors, and the γ-secretase inhibitor is a regulator of the γ-secretase substrate Notch. The Notch signaling pathway can regulate the EMT process and mitochondrial apoptosis. Therefore, by blocking the Notch signaling pathway through these γ-secretase inhibitors, cells can be blocked in cancer cell proliferation and angiogenesis; therefore, the screening of γ-secretase inhibitors and the determination of their anti-cancer activity are of great significance and have important value in the treatment of breast cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0028] Figure 1 2D and 3D images of the docking of Dibenzazepine and PS1 of the present invention;

[0029] Figure 2 2D and 3D images of the docking of Z-VAD(OMe)-FMK and PS1 of the present invention;

[0030] Figure 32D and 3D images of Semagacestat and PS1 docking of the present invention;

[0031] Figure 4 This is the first display diagram of the active pocket and key amino acid sites based on the γ-secretase catalytic core PSEN-1 of the present invention;

[0032] Figure 5 This is the second display diagram of the active pocket and key amino acid sites based on the γ-secretase catalytic core PSEN-1 of the present invention;

[0033] Figure 6 The third display diagram of the active pocket and key amino acid sites of the γ-secretase catalytic core PSEN-1 of the present invention;

[0034] Figure 7 This is a technical roadmap for virtual screening of the PS1 protein, a catalytic subunit of γ-secretase, according to the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Example 1

[0037] A method for screening a γ-secretase inhibitor comprises the following steps:

[0038] 1. Design and construction of γ-secretase pharmacophore (protein pretreatment):

[0039] The crystal structure of PS1 protein was obtained from the RCSB PDB database (PDB ID: 6LR4Chain B). The protein crystal structure was processed by Protein Preparation Wizard module of Schrödinger software for protein pretreatment, natural ligand regeneration state, hydrogen bond distribution optimization, protein energy minimization and water removal.

[0040] 2. Ligand pretreatment:

[0041] The 2D sdf structure files of the eight compound libraries were processed using the LigPrep module in Schrödinger to generate all 3D chiral conformations.

[0042] 3. ADME / T evaluation:

[0043] QikProp module was used to predict all ADME / T parameter scores of ligand compounds, and a total of 51 type parameters were obtained, among which Lipinski's five principles were mainly used for evaluation.

[0044] 4. Active site identification:

[0045] The Receptor Grid Generation module is used to set the most appropriate Enclosing box to perfectly encapsulate the binding site of the natural ligand, and on this basis, the active pocket of the protein is obtained.

[0046] 5. Molecular docking:

[0047] Each processed ligand in the eight compound libraries was molecularly docked with the active site of the PS1 protein in turn (HTVS, SP and XP were used respectively, and the docking accuracy was gradually improved). The lower the score, the lower the binding free energy of the compound with the protein and the higher the binding stability.

[0048] 6. MM-GBSA analysis:

[0049] The lowest-scoring ligand and the active site of the PS1 protein were subjected to MM-GBSA calculation analysis. MM-GBSA dG Bind can approximately represent the binding free energy of small molecules and proteins. The lower the binding free energy, the higher the binding stability of the ligand and the PS1 protein.

[0050] Specific results interpretation

[0051] According to the ADME / T results, RuleOfFive is the number of violations of Lipinski's five principles. The rules are mol_MW<500, QPlogPo / w<5, donorHB≤5, accptHB≤10. Compounds that meet these rules are considered to be drug-like, that is, the smaller the parameter number, the better the drugability. A total of 35,143 ligand compounds were included in the eight compound libraries. Through ADME / T evaluation, it was found that 19,486 ligand compounds were more likely to be clinical development drugs, and then these compounds were screened by HTVS.

[0052] According to the HTVS docking results, a total of 17,731 small molecules can interact with PS1 protein. We used the docking score ≤-6 as the screening criterion, of which 2,957 compounds met this criterion. These compounds were then screened using standard precision (SP).

[0053] According to the SP docking results, we used the docking score ≤-7.5 as the screening criterion. A total of 366 compounds met this criterion. We will subsequently perform extra precision (XP) screening on these compounds.

[0054] According to the XP results, for these 366 compounds, the XP docking score ≤-9 was used as the screening criterion, and a total of 42 compounds met this criterion. We will subsequently perform MM-GBSA analysis on these compounds.

[0055] According to the MM-GBSA analysis results, with MM-GBSA dG Bind < -30 kcal / mol as the screening criterion, after removing duplicates, there are 30 compounds in total. We show the XP Gscore and MM-GBSA binding free energy results of the 30 compounds.

[0056] We will display the top three compounds (marked in red) in 2D and 3D.

[0057] As shown in Table 1.

[0058]

[0059]

[0060]

[0061] The comprehensive results of ADME / T evaluation, XP docking, MM-GBSA analysis and 3D docking view showed that Dibenzazepine, Z-VAD(OMe)-FMK and Semagacestat have stable binding with PS1 and better drugability.

[0062] 2D and 3D images of dibenzazepine docking with PS1 ( Figure 1 ); Dibenzazepine penetrates into the binding pocket of PS1 protein, and the residues VAL261, ALA434, PRO433, LEU432, ALA431 of PS1 protein form hydrophobic forces on Dibenzazepine. The ligand forms one hydrogen bond with residues GLY382 and ASH358, two hydrogen bonds with residue LEU432, and two hydrogen bonds and one π-Cation (π cation) bond with residue LYS380.

[0063] 2D and 3D images of Z-VAD(OMe)-FMK docking with PS1 ( Figure 2); Z-VAD(OMe)-FMK penetrates into the binding pocket of PS1 protein, and the residues ALA434, LEU432, ALA431, VAL379, LEU381, etc. of PS1 protein form hydrophobic forces on Z-VAD(OMe)-FMK. The ligand forms one hydrogen bond with residues GLY382 and ASP275, and two hydrogen bonds with residues ASH385, LYS380, and LEU432.

[0064] 2D and 3D diagrams of Semagacestat docking with PS1 ( Figure 3 ); Semagacestat penetrates deep into the binding pocket of PS1 protein, and PS1 protein residues VAL261, ILE287, VAL272, LEU282, TYR77, etc. form hydrophobic forces on Semagacestat. The ligand forms one hydrogen bond with residues ASH385 and GLY382, two hydrogen bonds with residue LEU432, and two hydrogen bonds and one π-Cation (π cation) bond with residue LYS380.

[0065] Example 2

[0066] A method for screening γ-secretase inhibitors, comprising the following steps:

[0067] 1. Design and construction of γ-secretase pharmacophore:

[0068] like Figure 4-6 As shown, the crystal structures of γ-secretase complex proteins carrying natural ligands were searched and screened in the PDB database (https: / / www.rcsb.org / ), and the best matching γ-secretase protein crystal structure (PDB: 6LR4) was imported into the LigandScout software. According to the structure and active site information of γ-secretase, the active pocket and key amino acid sites based on the γ-secretase catalytic core PSEN-1 were constructed.

[0069] 2. Virtual screening technology roadmap based on PS1 protein, a catalytic subunit of γ-secretase:

[0070] like Figure 7 As shown, the PS1 protein crystal structure is retrieved and processed by first using the receptor grid generation tool of Glide to generate a grid, and then using Glide to perform high-throughput virtual screening (HTVS) of the compounds.

[0071] 3. 35,100 compounds were selected and processed from 8 compound libraries, and ADME / T evaluation was performed first. High-throughput virtual screening (HTVS) of the compounds was also performed using Glide.

[0072] 4. After virtual screening, use Glide to perform standard precision (SP) docking on the shortlisted compounds, and use Glide to perform extra precision (XP) docking on the shortlisted compounds; select the compound with the lowest docking score; and finally perform MM-GBSA calculation.

[0073] 5. Determination of anticancer activity of the γ-secretase inhibitors discovered through screening:

[0074] MTT colorimetric analysis is currently the most commonly used in vitro anti-tumor drug screening method in various laboratories. The principle is that the succinate dehydrogenase in the mitochondria of living cells can reduce exogenous MTT to water-insoluble blue-purple crystals - formazan and deposit in the cells, while dead cells do not have this function. Dimethyl sulfoxide (DMSO) can dissolve formazan in cells. Within a certain range of cell numbers, the amount of MTT crystals formed is proportional to the number of cells and cell viability. The light absorption value is measured by an enzyme marker, which can indirectly reflect the number of living cells. This method has been widely used in various cell proliferation detections, tumor cell growth, activity detection of bioactive factors, large-scale anti-tumor drug screening, cytotoxicity tests, and tumor radiosensitivity determinations; the present invention performs anti-cancer activity determination on the γ-secretase inhibitors found by screening.

[0075] Example 3

[0076] As a potential therapeutic target for breast cancer, γ-secretase inhibitors have attracted the attention of many scholars internationally. The main function of γ-secretase inhibitors is to inhibit the Notch signaling pathway. γ-secretase inhibitors inhibit the activity of γ-secretase and block the conduction of the Notch signaling pathway, thereby inhibiting the proliferation and invasion of tumor cells and exerting an anti-tumor effect. γ-secretase inhibitors can be used in the treatment of breast cancer. Some γ-secretase inhibitors can work on all types of breast cancer, and a few γ-secretase inhibitors only work on triple-negative breast cancer. For example, γ-secretase inhibitors such as NMK-T-057, PF-03084014, LY411575, RO4929097 (RG-4733) and GSI-Ⅰ (Z-LLNle-CHO) are potential candidate drugs for breast cancer, and γ-secretase inhibitors such as Nirogacestat (PF-03084014) and Sulindac sulfide play a role in triple-negative breast cancer. These γ-secretase inhibitors all induce cell apoptosis by inhibiting γ-secretase-mediated Notch signaling activation.

Claims

1. A method for screening a γ-secretase inhibitor, characterized in that: The following steps are involved: S1. Design and construction of the γ-secretase pharmacophore: Search and screen the crystal structures of the γ-secretase complex protein carrying natural ligands (the crystal structures corresponding to the PS1 protein) in the PDB database, import the best matching γ-secretase protein crystal structure (PDB: 6LR4) into the LigandScout software, and construct the active pocket and key amino acid sites based on the γ-secretase catalytic core PSEN-1 according to the structure and active site information of γ-secretase. S2. Ligand preprocessing: The 2D sdf structure files of the eight compound libraries were processed using the LigPrep module in the Schrödinger software to generate all 3D chiral conformations; S3. ADME / T evaluation: QikProp module in Schrödinger software was used to predict all ADME / T parameter scores of ligand compounds, and a total of 51 type parameters were obtained; the five principles of Lipinski were used for evaluation; S4. Active site identification: Using the Receptor Grid Generation module in the Schrödinger software, set the most appropriate Enclosing box to perfectly enclose the binding site of the natural ligand, and on this basis obtain the active pocket of the protein; S5. Molecular docking: Each processed ligand in the eight compound libraries is docked with the active site of PS1 protein in turn. The lower the score, the lower the binding free energy of the compound with the protein and the higher the binding stability. S6. MM-GBSA analysis: The lowest-scoring ligand and the active site of the PS1 protein were subjected to MM-GBSA calculation analysis. MM-GBSA dG Bind can represent the binding free energy of the small molecule and the protein. The lower the binding free energy, the higher the binding stability of the ligand and the PS1 protein.

2. The method for screening a γ-secretase inhibitor according to claim 1, characterized in that: In S1, according to the structure and active site information of γ-secretase, the active pocket and key amino acid sites based on the catalytic core PSEN-1 of γ-secretase are constructed, specifically, the obtained protein crystal structure is subjected to protein pretreatment, natural ligand regeneration state, hydrogen bond distribution optimization, protein energy minimization and water removal treatment using the Protein Preparation Wizard module of Schrödinger software.

3. The method for screening a γ-secretase inhibitor according to claim 1, characterized in that: In S1, the screening was performed with MM-GBSA dG Bind < -30 kcal / mol as the screening standard; after screening and removing duplicates, a total of 30 compounds were obtained.

4. The method for screening a γ-secretase inhibitor according to claim 1, characterized in that: In S2, the eight compound libraries are: Selleck·FDA-Approved Drug Library; TargetMol·Natural Compound Library; MedChemExpress·Natural Product Library; Pharmacodia (medicine crossing) Natural Product Library; BioBioPha BBP(Xili)·Natural Product Library; APExBIO·Anti-cancer-Compound-Library Plus; Selleck·Preclinical / Clinical Compound Library: L2000; MedChemExpress·Immunology / Inflammation Compound Library.

5. The method for screening a γ-secretase inhibitor according to claim 1, characterized in that: In S5, the ligands were molecularly docked with the active site of PS1 protein using HTVS, SP and XP respectively, and the docking accuracy was gradually improved.

6. A use of a γ-secretase inhibitor, characterized in that Gamma-secretase inhibitors in triple-negative breast cancer.

7. The use of a γ-secretase inhibitor according to claim 6, characterized in that: The application of the γ-secretase inhibitor in triple-negative breast cancer is specifically that the γ-secretase inhibitor blocks the Notch signaling pathway, which can block cells in the proliferation and angiogenesis of triple-negative breast cancer cells.

Images