Screening of small molecule inhibitors targeting c-maf and their anti-myeloma applications

Abstract

The application discloses a kind of screening and anti-myeloma application of targeting c-Maf small molecule inhibitor, especially provide the application of sorafenib or glimepiride in preparation anti-myeloma drug.Aspect.The application discloses a kind of method for screening targeting c-Maf small molecule inhibitor, it includes the steps based on molecular virtual screening docking.It is found that potential compound glimepiride and sorafenib can inhibit the activity of c-Maf by the method of the application through virtual screening, molecular dynamics (MD) simulation, molecular mechanics and MM / GBSA free energy calculation and experimental analysis, both can induce the cycle stagnation and cell apoptosis of myeloma cell.The method of the application can screen more targeting c-Maf small molecule inhibitor, it is also helpful to further structure optimization based on sorafenib and glimepiride in the future, and design c-Maf small molecule inhibitor with better activity.

Description

Technical Field

[0001] This invention belongs to the fields of medicinal chemistry and computational chemistry, specifically relating to the screening of small molecule inhibitors targeting c-Maf and their application in anti-myeloma. Background Technology

[0002] Multiple myeloma (MM) is an incurable malignant hematologic malignancy that can progress from undetermined monoclonal immunoglobulinosis (MGUS) to smoldering myeloma. Studies have shown that the transcription factor c-Maf is dysregulated in myeloma, with approximately 25% of myeloma cells and 6% of myeloma patients exhibiting t(14;16) c-Maf gene translocations, and 50% of myeloma cells showing c-Maf overexpression. c-Maf gene rearrangements are clinically relevant in myeloma patients, with patients exhibiting t(14;16) translocations having shorter overall survival. Furthermore, c-Maf overexpression can promote myeloma drug resistance, while c-Maf silencing can induce chemosensitivity of myeloma cells to proteasome inhibitors. In summary, c-Maf is an important driver gene in myeloma, playing a crucial role in early MM progression. Therefore, developing novel c-Maf inhibitors will contribute to the discovery of novel molecularly targeted drugs for multiple myeloma.

[0003] Evidence suggests that glucocorticoids, digoxin C, and the USP5 inhibitor WP1130 can regulate the development and progression of myeloma by reducing c-Maf protein levels or inhibiting the transcriptional activity of the c-Maf gene. However, current research on direct targeting of the transcription factor c-Maf is limited. Therefore, it is necessary to utilize the crystal structure of c-Maf for molecular docking-based virtual screening to develop potential small-molecule inhibitors targeting c-Maf. Summary of the Invention

[0004] The purpose of this invention is to provide, based on existing technology, the application of sorafenib or glimepiride in the preparation of antimyeloma drugs.

[0005] Another objective of this invention is to discover potential compounds that can inhibit c-Maf activity based on existing FDA-approved drugs through virtual screening, molecular dynamics (MD) simulation, molecular mechanics and MM / GBSA free energy calculation and experimental analysis, and to verify that they can induce cell cycle arrest and apoptosis in myeloma cells.

[0006] The objective of this invention can be achieved through the following measures:

[0007] The use of sorafenib or glimepiride in the preparation of antimyeloma drugs.

[0008] Applications of sorafenib or glimepiride in the preparation of small molecule inhibitors targeting c-Maf.

[0009] A pharmaceutical composition for treating myeloma, comprising sorafenib or glimepiride as the active ingredient, supplemented with pharmaceutically acceptable excipients.

[0010] The pharmaceutical compositions of the present invention can be formulated into liquid, solid, semi-solid, or gaseous preparations by pharmaceutically acceptable methods. Liquid preparations include solutions, injections, lotions, liniments, etc.; solid preparations include powders, pills, tablets, films, etc.; semi-solid preparations include ointments, gels, suppositories, pastes, etc.; and gaseous preparations include aerosols, sprays, etc.

[0011] This invention discloses a method for screening small molecule inhibitors targeting c-Maf, which includes a virtual screening step based on molecular docking, comprising:

[0012] (1) Using the crystal structure of c-Maf as the receptor protein, a drug screening library was selected. The three-dimensional structure of the drug molecules was preprocessed using the dbtranslate module in Sybyl-X2.1. Then, the Surflex molecular docking module Sybyl-X2.1 was used for virtual screening based on molecular docking.

[0013] (2) The binding sites are formed by the binding bags formed on the A chain of the c-Maf crystal structure by amino acids Arg294, Asn298, Tyr301, Ala302, Cys305 and Arg306; during the preparation of the receptor, hydrogen atoms missing from c-Maf are added through a biopolymer module to remove all water molecules;

[0014] (3) Initiate molecular optimization before and after docking, and select targets through docking score ranking and cluster analysis.

[0015] In step (1), preferably, FDA-approved drugs are selected as the screening library.

[0016] In step (2), preferably, the binding sites are formed by the binding pockets formed on the A chain of the c-Maf crystal structure by the amino acids Arg294, Asn298, Tyr301, Ala302, Ser304, Cys305, Arg306, Lys308 and Arg309.

[0017] In step (2), preferably, the maximum number of conformations of the docking parameters is set to 20, the maximum step size after optimization is 1000 steps, and the energy gradient is 0.005 kcal / (mol*Å).

[0018] Following step (3), further screening of small molecule inhibitors targeting c-Maf can be conducted through molecular dynamics simulations, molecular mechanics and MM / GBSA free energy calculations, and experimental analysis. Specifically, this may include:

[0019] 1) Detect the cytotoxicity of RPMI-8226 treated with different doses of compounds and calculate the IC50 of each compound. 50 The values ​​were used to identify potential small molecule inhibitors targeting c-Maf; for example, in Example 2 of this application, the cytotoxicity of RPMI-8226 treated with different doses of compounds was tested, and the IC50 of each compound was calculated. 50 The effects of the above compounds on c-Maf transcriptional activity were further examined using a luciferase reporter system. The results showed that both sorafenib and glimepiride significantly inhibited the luciferase activity of the MARE promoter, and that glimepiride and sorafenib reduced the expression of c-Maf protein.

[0020] 2) To detect the effects of the screened compounds on myeloma cell function in order to determine their effects and mechanisms; for example, in Example 3 of this application, flow cytometry showed that glimepiride and sorafenib can promote cell cycle arrest and apoptosis in myeloma cells by reducing c-Maf protein expression.

[0021] 3) The docking prediction binding mode of the screened compounds can be optimized. Simulations are performed on each c-Maf / ligand complex, and the root mean square deviation (RMSD) value of c-Maf is calculated to further evaluate the binding stability of the c-Maf / ligand complex. For example, in Example 4 of this application, we performed a 100 ns MD simulation on each c-Maf / ligand complex and calculated the root mean square deviation (RMSD) value of c-Maf. Figure 4 A). The RMSD plot shows that the two systems reach equilibrium in a very short time. Furthermore, the root mean square fluctuation (RMSF) of each residue was calculated to further evaluate the binding stability of the c-Maf / ligand complex. Figure 4 B) The results show that the fluctuations of each system in the range of 50 to 100 ns are smaller and more stable.

[0022] 4) The binding free energy of the c-Maf / ligand complex was calculated using existing formulas via MM / GBSA free energy calculations. MM / GBSA free energy decomposition analysis was performed using the mmpbsa module in Amber 12 to investigate the detailed binding interaction between c-Maf and the inhibitor. The binding free energy can be distributed across each residue of the receptor. The binding interaction of each ligand-residue pair consists of three energy terms: van der Waals contribution (ΔEvdw), electrostatic contribution (ΔEele), and desolvation term (ΔEdesolvation), including a polar term (ΔGGB) and a nonpolar term (ΔGSA). Finally, it was determined whether the screened compounds could strongly bind to the A chain of c-Maf, and the role of different amino acids in ligand binding was assessed. For example, Example 5 of this application demonstrates that glimepiride and sorafenib can strongly bind to the A chain of c-Maf, and indicates that the amino acids contributing the most in the c-Maf / Glimepiride system are Arg294, Tyr301, and Arg306.

[0023] This invention also includes the application of the binding sites formed by the six amino acids Arg294, Asn298, Tyr301, Ala302, Cys305 and Arg306 on the A chain of the c-Maf crystal structure in the c-Maf / Sorafenib and c-Maf / Glimepiride systems in screening small molecule inhibitors targeting c-Maf.

[0024] The present invention also includes the application of the binding sites formed by amino acids Arg294, Tyr301 and Arg306 on the A chain of the c-Maf crystal structure in screening small molecule inhibitors targeting c-Maf.

[0025] Based on existing FDA-approved drugs, this application, through virtual screening, molecular dynamics (MD) simulations, molecular mechanics and MM / GBSA free energy calculations, and experimental analysis, identified glimepiride and sorafenib as potential compounds that can inhibit c-Maf activity. Both can induce cell cycle arrest and apoptosis in myeloma cells. The method described in this application can screen for more small molecule inhibitors targeting c-Maf and will also facilitate future structural optimization of sorafenib and glimepiride to design c-Maf small molecule inhibitors with even better activity. Attached Figure Description

[0026] Figure 1 This outlines a virtual screening and experimental evaluation process for c-Maf inhibitors based on molecular docking.

[0027] Figure 2 To screen the effects of compounds on c-Maf activity;

[0028] In the figure, (A) the IC50 of RPMI-8226 cells treated with different concentrations of the compound was calculated. 50 Value; (B) Dual-luciferase reporter gene activity assay: effects of erlotinib, sorafenib, sildenafil citrate, vemurafenib, fluvastatin and glimepiride on c-Maf transcriptional activity; (C) Western blot detection of c-Maf protein expression in myeloma cells stimulated by glimepiride and sorafenib;

[0029] Figure 3 The effects of glimepiride and sorafenib on myeloma cell cycle and apoptosis;

[0030] In the figure, (A) flow cytometry was used to detect changes in the cell cycle of RPMI-8226 cells after treatment with glimepiride and sorafenib; (B) a bar chart showed the distribution of RPMI-8226 cell subsets after treatment with glimepiride and sorafenib; (C) flow cytometry was used to detect changes in apoptosis in RPMI-8226 cells after treatment with glimepiride and sorafenib; (D) a bar chart showed the changes in the apoptosis rate of myeloma cells stimulated by the two treatments.

[0031] Figure 4 The dynamic stability of the c-Maf / ligand system was evaluated using RMSD and RMSF calculations.

[0032] In the figure, (A) shows the time evolution of the total base RMSD of c-Maf in the two systems; (B) shows the RMSF of each residue in the two systems to further evaluate the binding stability of the complex at each RMSF of c-Maf in the two systems.

[0033] Figure 5 The MM / GBSA free energy decomposition diagrams of the total base binding free energy in the c-Maf / Sorafenib (A) and c-Maf / Glimepiride (B) systems are shown.

[0034] The image shows a cartoon model illustrating the c-Maf structure, while rod-shaped models represent sorafenib and glimepiride, respectively; the bases of both that bind significantly (<-0.5 kcal / mol) to c-Maf are also labeled. Detailed Implementation

[0035] The present invention can be better understood from the following embodiments. However, those skilled in the art will readily understand that the descriptions of the embodiments are for illustrative purposes only and should not, and will not, limit the invention as detailed in the claims.

[0036] Example 1

[0037] 1. Experimental Methods

[0038] Virtual screening based on molecular docking:

[0039] The Surflex molecular docking module Sybyl-X2.1 is used for molecular docking-based virtual screening. Using the crystal structure of c-Maf as the receptor protein, FDA-approved drugs were selected as the screening library. Since only two-dimensional structural information was available, compounds in the FDA-approved database were preprocessed using the dbtranslate module in Sybyl-X2.1.

[0040] The binding pockets formed by Arg294, Asn298, Tyr301, Ala302, Ser304, Cys305, Arg306, Lys308, and Arg309 on the A-chain of the c-Maf crystal structure may be potential binding sites for c-Maf inhibitors. During receptor preparation, hydrogen atoms missing from c-Maf were added using a biopolymer module, and all water molecules were removed. The maximum number of conformations for docking parameters was set to 20, the optimized maximum step size was 1000 steps, and the energy gradient was 0.005 kcal / (mol*Å).

[0041] Molecular optimization is initiated before and after docking, and targets are selected based on docking score ranking and cluster analysis. Docking parameters are set to default values, and the target is selected based on docking score ranking and cluster analysis.

[0042] Experimental Results

[0043] We conducted a virtual screening based on molecular docking of an FDA-approved drug library. Figure 1 Through docking score ranking and cluster analysis, six compounds were selected: erlotinib, sorafenib, sildenafil citrate, vemurafenib, fluvastatin, and glimepiride (see [link to relevant data]). Figure 1 Experimental evaluations were conducted, and novel c-Maf small molecule inhibitors sorafenib and glimepiride were successfully identified, which will contribute to the development and application of small molecule drugs targeting c-Maf.

[0044] Example 2

[0045] 1. Experimental Methods

[0046] Cell viability assay:

[0047] Cell seeding, 1x10 cells per well 4 RPMI-8226 cells were seeded in 96-well plates and treated with different doses of the corresponding compounds;

[0048] After 48 hours, add 5 μl of CCK8 reagent and incubate at 37°C for 2 h;

[0049] Measure the absorbance at 450 nm and calculate IC. 50 value.

[0050] Dual reporter gene activity assay:

[0051] HEK293T cells were transiently transfected with c-Maf expression plasmid, MAF response element (MARE) luciferase reporter gene plasmid and Renilla internal control plasmid using PolyJet transfection reagent manufactured by Novizan.

[0052] After 6 hours, replace the culture medium with fresh medium and add the 6 compounds to the corresponding wells, then let it stand for another 48 hours.

[0053] Cells were collected, lysed using the lysis buffer provided with the kit, and then luciferase activity was measured according to the protocol of the luciferase reporter gene assay kit.

[0054] Immunoblot detection:

[0055] RPMI-8226 cells were seeded in 6-well plates and stimulated with glimepiride and sorafenib compounds at given concentrations for 48 hours.

[0056] Whole-cell extracts were prepared using NP40 lysis buffer, and proteins were separated using 10% SDS-PAGE gel and transferred to a PVDF membrane.

[0057] Cut PVDF membranes and block them with 5% skim milk powder. Incubate overnight with anti-c-Maf and anti-β-actin antibodies, respectively. Wash the membranes with TBST and apply secondary antibodies.

[0058] The membrane is washed with TBST, treated with ECL reagent, and then exposed.

[0059] Experimental Results

[0060] We examined the cytotoxicity of RPMI-8226 cells treated with different doses of the compounds and calculated the IC50 for each compound. 50 value( Figure 2A). The effects of the above compounds on c-Maf transcriptional activity were further examined using a luciferase reporter system. The results showed that both sorafenib and glimepiride significantly inhibited luciferase activity in the MARE promoter ( Figure 2 B). Subsequently, we also found that glimepiride and sorafenib also reduced the expression of c-Maf protein (B). Figure 2 C). Therefore, sorafenib and glimepiride may be potential small molecule inhibitors targeting c-Maf.

[0061] Example 3

[0062] 1. Experimental Methods

[0063] Cell cycle detection:

[0064] RPMI-8226 cells were seeded in 6-well plates and stimulated with given concentrations of glimepiride and sorafenib.

[0065] 48 hours later, cells were collected and stained with propidium iodide (PI).

[0066] Following the instructions of the cell cycle assay kit, flow cytometry was used to analyze changes in the cell cycle.

[0067] Apoptosis detection:

[0068] RPMI-8226 cells were seeded in 6-well plates and stimulated with given concentrations of glimepiride and sorafenib.

[0069] 48 hours later, cells were collected and stained with buffers containing FITC and PI, respectively.

[0070] Cells were washed and resuspended, and apoptosis rate was analyzed by flow cytometry.

[0071] Experimental Results

[0072] We further investigated the effects of glimepiride and sorafenib on myeloma cell function. Flow cytometry analysis showed that glimepiride and sorafenib induced G1 phase arrest in RPMI-8226 cells. Figure 3 A and 3B). Annexin V-FITC / PI staining results showed that glimepiride and sorafenib significantly promoted myeloma cell death (A and B). Figure 3(C and 3D). Therefore, glimepiride and sorafenib can promote cell cycle arrest and apoptosis in myeloma cells by reducing c-Maf protein expression.

[0073] Example 4

[0074] 1. Experimental Methods

[0075] MD simulation testing:

[0076] The binding interactions between c-Maf and glimepiride and sorafenib were optimized using Amber 12 MD simulations;

[0077] The c-Maf protein and small molecule compound were parameterized using the standard Amber force field (FF03) and the universal Amber force field (GAFF), respectively, and a 12 Å thick layer of water was placed around the c-Maf / ligand complex.

[0078] In each system, an appropriate amount of Cl- was added to replace the same number of water molecules to make it electrically neutral. The amounts of Cl- added in the two systems were 12 and 18, respectively.

[0079] Optimize all water molecules and Cl- ions, using the steepest descent for 2500 steps with all receptor proteins and ligand atoms constrained, followed by 2500 steps using the conjugate gradient method;

[0080] The amino acid side chains, ligands, water molecules and ions were optimized in 5000 steps. A stepwise heating method was adopted, that is, the entire system was heated from 0K to 300K in 60ps.

[0081] Experimental Results

[0082] To optimize the predicted binding mode of glimepiride and sorafenib, we performed 100 ns MD simulations on each c-Maf / ligand complex and calculated the root mean square deviation (RMSD) value of c-Maf. Figure 4 A). The RMSD plot shows that the two systems reach equilibrium in a very short time. Furthermore, the root mean square fluctuation (RMSF) of each residue was calculated to further evaluate the binding stability of the c-Maf / ligand complex. Figure 4 B) The results show that the fluctuations of each system in the range of 50 to 100 ns are smaller and more stable.

[0083] Example 5

[0084] 1. Experimental Methods

[0085] MM / GBSA free energy calculation:

[0086] The binding free energy of the c-Maf / ligand complex was calculated using existing formulas.

[0087] MM / GBSA free energy decomposition analysis was performed using the mmpbsa module in Amber 12 to investigate the detailed binding interaction between c-Maf and the inhibitor;

[0088] The binding free energy can be distributed to each residue of the receptor. The binding interaction of each ligand-residue pair consists of three energy terms: van der Waals contribution (ΔEvdw), electrostatic contribution (ΔEele), and desolvation term (ΔEdesolvation), which includes a polar term (ΔGGB) and a nonpolar term (ΔGSA).

[0089] Experimental Results

[0090] Our results confirm that both glimepiride and sorafenib strongly bind to the A chain of c-Maf. In the c-Maf / Sorafenib and c-Maf / Glimepiride systems, six amino acids—Arg294, Asn298, Tyr301, Ala302, Cys305, and Arg306—play crucial roles in ligand binding. Figure 5 A). In the c-Maf / Glimepiride system, the amino acids that contribute the most are Arg294, Tyr301, and Arg306. Figure 5 B) The above-mentioned binding mode may help to design c-Maf small molecule inhibitors with better activity through further structural optimization of sorafenib and glimepiride in the future.

[0091] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications may still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions may be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. Application of glimepiride in the preparation of antimyeloma drugs.

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