Patent Application
20230187019
The present disclosure relates generally to methods of determining a drug combination targeting a protein or a protein complex, and more particularly, to methods for suppressing the activity of a protein or a protein complex by the drug combination via constituent drugs targeting multiple sites in a protein or a protein complex (multi-site-targeting strategy).
The present application is being filed along with a Sequence Listing in electronic format. The Sequence Listing is provided as a file entitled 210761US-Sequence Listing-yym-wll-chl-bjp-20221227.xml, created on Dec. 23, 2022, which is 20.5 kb in size. The information in the electronic format of Sequence Listing is incorporated herein by reference in its entirety.
Autophagy guards the homeostasis of cells to adapt to various internal and external stresses, such as starvation, oxidation stress, aging organelles, dysfunctional proteins, and invaded pathogens. Abnormally regulated autophagy has been found to be associated with both the initiation and progression of cancers (Singh et al., 2017, Oncogene 37, 1142-1158). Recently, one of the autophagins, autophagy related 4B cysteine peptidase (ATG4B), has received more and more attention due to its potential as a drug target for tumor suppression in view of the increasing evidence of its upregulated expression level in several tumor tissues (Liu et al., 2014, Autophagy 10, 1454-1465; Bortnik et al., 2016, Oncotarget 7, 66970-66988; Rothe et al., 2014, Blood 123, 3622-3634; Han et al., 2010, Oncology Letters 1, 821-826; Peng et al., 2017, Oncogenesis 6, e292) and the slight side effects in ATG4B-defected mice (Read et al., 2010, Veterinary Pathology 48, 486-494).
ATG4B is also known to be one of the proteins involved in formation of autophagosome, a mature vehicle that can fuse with the lysosome for degradation of its enclosed substances (Yang et al., 2010, Nature Cell Biology 12, 814-822). The autophagosome originates from the phagophore, an isolated membrane fragment secreted from surrounding organelles, such as endoplasmic reticulum (ER) and mitochondria (Lamb et al., 2013, Nature Reviews Molecular Cell Biology 14, 759-774). The extension of the phagophore requires the phosphatidylethanolamine (PE)-bound microtubule-associated-protein 1 light chain 3B (LC3B-II) on the membrane, while the maturation of autophagosome demands the cleavage of LC3B-II to release free-form LC3B (LC3B-I) (Bento et al., 2016, Annual Review of Biochemistry 85, 685-713). ATG4B participates in the formation of autophagosome by, as a cysteine protease, cleaving the last four residues of the full-length LC3B (pro-LC3B, 1-124) to generate LC3B-I (1-120), leaving a C-terminus glycine that is then activated by the El-like enzyme ATG7 (autophagy related 7) and covalently bonded with phosphatidylethanolamine (PE) (LC3B-II) mediated by E2-like enzyme ATG3 (autophagy related 3) and the ATG12-ATG5-ATG16 conjugation system (Maruyama et al., 2017, The Journal of Antibiotics 71, 72-78; Bento et al., 2016, Annual Review of Biochemistry 85, 685-713).
On the other hand, ATG4B is also responsible for the cleavage of LC3B-II for the mature autophagosome and regeneration of LC3B-I for the next cycle of the autophagosome formation (Yu et al., 2012, Autophagy 8, 883-892). It is also suggested that the LC3B-II cleavage capability of ATG4B also serves as a correction mechanism to reverse the mis-conjugation of LC3B on other organelle membranes and maintain the abundance of LC3B-I for autophagosome formation (Nakatogawa et al., 2012, Autophagy 8, 177-186). The ATG4B depletion cells result in accumulated LC3B puncta, which indicates the abnormal accumulation of autophagosome (Liu et al., 2018, Theranostics 8, 830-845; Bortnik et al., 2016, Oncotarget 7, 66970-66988).
A couple of ATG4B inhibitors identified by in silico drug screening have been reported. Akin et al. performed structure-based virtual screening on closed-form ATG4B to dock 139,735 compounds from the National Cancer Institute (NCI) database and identified compound, NSC185058, which docked at the exit of the active site nearby the ATG4B N-terminus with IC50 of 51 μM (Akin et al., 2014, Autophagy 10, 2021-2035). Bosc et al. used both an opened form and closed form of the ATG4B structure to dock 230,000 compounds from the NCI database and 500,000 compounds from ChemBridge database. They identify a hit compound that also docked at a similar pocket on the closed-form ATG4B and was optimized as a resulting compound, LV-320, with IC50 of 24.5 μM and Kd of 16 μM (Bosc et al., 2018, Scientific Reports 8, 11653). Another compound, 5130, computationally screened from a customized library of 7,249 non-commercial compounds and predicted to bind the similar groove formed by the folded N-terminal tail was reported to specifically target on ATG4B with IC50 of 3.24 μM (Fu et al., 2018, Autophagy 15, 295-311). Liu et al. combined molecular docking and molecular dynamics (MD) simulations to screen 1,312 FDA-approved drugs on the open-form ATG4B structure and repurposed an antifungal drug, tioconazole, as an ATG4B inhibitor by blocking the active site entry for LC3 C-terminus with IC50 of 1.79 μM (Liu et al., 2018, Theranostics 8, 830-845).
Designing drugs specifically targeting at the protein-protein interface (PPI) is a drug development strategy that is receiving high attention. At present, there have been successful cases of drug development in the field of immunology. By targeting the PPI of tumor necrosis factor-α (TNF-α) and its receptor, three FDA-approved drugs have been found to inhibit their biochemical reactions (Palladino et al., 2003, Nature Reviews Drug Discovery 2, 736-746; Eng, G. P., 2016, “Optimizing biological treatment in rheumatoid arthritis with the aid of therapeutic drug monitoring.” Dan. Med. J. 63(11)). In fact, the design of PPI blockers requires some prerequisites; for example, the buried surface area (BSA) of the PPI does not exceed 4,000 Å2 (Ran et al., 2018, Curr. Opin. Chem. Biol. 44, 75-86). Generally, the PPI of a protein that is too large or that has a smooth interface is easily regarded as “undruggable” (Ran et al., 2018, Curr. Opin. Chem. Biol. 44, 75-86). In the past, researchers used computer docking technology analysis to dock a large number of different small molecules as probes on the surface of a protein, and then classified the position of each docking result to find the “druggable” hot spots belonging to this protein for future medications.
However, it is still in the progress of finding effective drugs and formulations applicable to clinical treatment, e.g., drugs targeting different sites of a protein target, such as ATG4B and 3CLpro, to achieve a better therapeutic effect on the diseases associated with the protein target.
While several ATG4B inhibitors have been reported, their potency was generally moderate, with the IC50 values ranging from 80 nM to 51 μM. However, there are at least two alternative sites on ATG4B that could be druggable from the observation of the crystallized ATG4B-LC3B complex (
Drug combination is a strategy that combines two or more drugs functioned by targeting different drug targets or pathways in one formulation to achieve an enhanced therapeutic effect while lowering down the dose usage of each individual drug. This concept might also apply to drugs targeting different sites on the same target protein for an enhanced inhibition on its biological function.
The present disclosure provides a multi-site-targeting strategy which presents a concept aiming to utilize existing pharmaceutical sources or newly synthesized compounds by designing formulations to target at least two potential druggable sites in a single protein or protein complex target. If the combination of multiple compounds can achieve a better inhibitory effect on a protein target than that of individual binders alone, the dosage and thus the toxicity of each compound could be reduced while achieving a similar or better therapeutic effect than that from individual compound alone. The multi-site-targeting strategy can ideally apply to any protein target, given the druggable binding sites available. This can promote the exhaustive use of the available medicinal space to known or new protein targets for human diseases and other emerging infectious diseases.
The druggable binding sites located in the three-dimensional structure of a protein target can be identified by both experimental techniques, such as X-ray crystallography and the chemical shift perturbation data based on nuclear magnetic resonance (NMR) spectroscopy, and computational methods, such as protein-protein docking and molecular dynamics (MD) simulations. The identification of allosteric sites could require mutation screening for protein function. In addition, computational methods, such as time-dependent and independent linear response theory (LRT), model the atomic displacement inside correlated atomic motion upon remote perturbation, and thus can probe frequently communicating residues, e.g., the possible allosteric sites. Furthermore, MD simulation and elastic network models could explore the conformational space of a target protein for docking to screen and discover effective drugs that would not otherwise be found using a single protein conformation. With the alternative druggable sites, the present disclosure provides a general framework for designing a multi-site-targeting strategy to suppress drug targets in general.
In one aspect, the present disclosure relates to a method for determining a drug combination targeting different sites of a protein or a protein complex, comprising: identifying a first binding site and a second binding site of the protein or the protein complex based on a three-dimensional structure thereof, wherein the first binding site and the second binding site are different sites in the three-dimensional structure of the protein or the protein complex; identifying a first drug interacting with the first binding site; identifying a second drug interacting with the second binding site; and combining the first drug and the second drug to provide at least one of a synergistic effect and an additive effect in suppressing an activity of the protein or the protein complex. In some embodiments, the drug combination including the first drug and the second drug provides a better effect than that of the first drug or the second drug alone.
In exemplary embodiments of the method of the present disclosure, the first binding site and the second binding site are independently a main functional site, an orthosteric site, an active site, a main substrate-binding site, an allosteric site, a recognition site or a site at the protein-protein interface.
In exemplary embodiments of the method of the present disclosure, the first drug and the second drug are independently selected from the group consisting of a newly synthesized compound, an FDA-approved drug, an FDA-approved biologic, a drug metabolite, a prodrug, an experimental small molecule, an experimental biologic, an experimental polypeptide, and any combination thereof. In some embodiments, at least one of the first drug and the second drug is an anticancer drug. In some embodiments, at least one of the first drug and the second drug is an antiviral drug (e.g., nelfinavir and boceprevir). In some embodiments, at least one of the first drug and the second drug is a non-anticancer drug such as an anthelmintic drug (e.g., moxidectin), an antibacterial drug (e.g., norvancomycin) or an antifungal drug (e.g., tioconazole).
In exemplary embodiments of the method of the present disclosure, the three-dimensional structure is an ensemble structure of the protein or the protein complex.
In exemplary embodiments of the method of the present disclosure, the structure of the protein or the protein complex can be found by a crystal structure, an NMR-determined structure, a cryo-electron microscopy (EM)-resolved structure, a simulation-sampled structure, structures predicted from a structural prediction algorithm, or a combination thereof.
In exemplary embodiments of the method of the present disclosure, the identifying of the first drug comprises selecting the first drug from at least one first dataset containing molecular entities. In some embodiments, the identifying of the second drug comprises selecting the second drug from at least one second dataset. In some embodiments, the first dataset and the second dataset are the same or different. In some embodiments, the first dataset and the second dataset are independently a drug library, a genomic dataset, a proteomic dataset, a biochemical dataset or a population dataset.
In exemplary embodiments of the method of the present disclosure, the selecting comprises interacting the molecular entities of the dataset with at least one of the first binding site and the second binding site, and ranking the affinity of the molecular entities (i.e., compounds, drugs, etc.) to at least one of the first binding site and the second binding site by experimental and/or theoretical methods, for example, nuclear magnetic resonance (NMR) spectroscopy, isothermal titration calorimetry, docking energy, distances between poses and the binding site, entropy calculations, molecular dynamics (MD) simulations, normal mode analysis (NMA), or any combination thereof.
In exemplary embodiments of the method of the present disclosure, an allosteric site can be determined by analysis of atomic displacement and/or correlated atomic motion derived from the molecular dynamics (MD) simulations, normal mode analysis (NMA), linear response theory (LRT), or any combination thereof.
In exemplary embodiments of the method of the present disclosure, poses are reported for the molecular entities docked by AutoDock Vina and/or AutoDock.
In exemplary embodiments of the method of the present disclosure, the ranking is performed by normalized ranking, logarithm of odds (LOD) scoring or a combination thereof.
In exemplary embodiments of the method of the present disclosure, the normalized ranking is performed based on at least one of docking affinity, number of contacts, and an extent of poses concentrated in at least one of the first binding site and the second binding site.
In exemplary embodiments of the method of the present disclosure, the logarithm of odds scoring is performed based on at least one of docking affinity, a distance of the molecular entity to the first binding site or the second binding site, and a size of poses cluster.
In one aspect, the present disclosure relates to a method for treating an ATG4B-related disease or a 3CL protease (3CLpro)-related disease in a subject in need thereof, comprising administering an effective amount of the drug combination obtained from the aforementioned methods.
In exemplary embodiments of the method of the present disclosure, the drug combination comprises at least two selected from the group consisting of aclacinomycin A, boceprevir, daclatasvir, dihydroergocristine, ethynyl estradiol, Evans blue, moxidectin, netupitant, norvancomycin, ponatinib, temsirolimus, tioconazole, tat-N7 peptide, and tat-N9 peptide.
In exemplary embodiments of the method of the present disclosure, the ATG4B-related disease is breast cancer, colorectal cancer, neural glioma cancer, gastric cancer, pancreatic cancer or melanoma.
In this disclosure, where a document, act or item of knowledge is referred to or discussed, this reference or discussion is not an admission that the document, act or item of knowledge or any combination thereof was at the priority date, publicly available, known to the public, part of common general knowledge, otherwise constitutes prior art under the applicable statutory provisions, or is known to be relevant to an attempt to solve any problem with which this specification is concerned.
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
For a fuller understanding of this disclosure, reference should be made to the following detailed descriptions, taken in connection with the accompanying drawings.
In the following description of the embodiments, reference is made to the accompanying drawings, which form a part thereof, and within which are shown by way of illustrative embodiments by which the disclosure may be practiced. It is to be understood that other embodiments may also be utilized and structural changes may be made without departing from the scope of the disclosure.
As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the content clearly dictates otherwise. As used herein, the term “and” is intended to be inclusive unless otherwise indicated. As used herein, the term “or” is generally employed in its sense including “and/or” unless the context clearly dictates otherwise.
As used herein, the term “about” refers to a degree of deviation for a property, composition, amount, value or parameter as identified, such as deviations based on experimental errors, measurement errors, approximation errors, calculation errors, standard deviations from a mean value, routine minor adjustments, and so forth.
As used herein, the terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to”) unless otherwise noted.
As used herein, the terms “composition” and “composite” are used interchangeably. As used herein, the term “ensemble structure” is well-known in the structural determination by nuclear magnetic resonance (NMR), the ensemble of structures rather than a single structure, with perhaps several members, all of which fit the NMR data and retain good stereochemistry, is deposited with the Protein Data Bank. Comparisons between the models in this ensemble provide some information on how well the protein conformation is determined by the NMR constraints. It should be noted that all the sequences corresponding to NMR-determined ensemble structures have the same sequences (one protein with variable conformations). The structural ensemble here, additionally, refers to different proteins with variations in sequence and/or length but having similar main chain conformations, in addition to those structures, such as from NMR determinations or from molecular dynamics simulations, having the same sequence but differing in structure due to natural shape fluctuations.
Identification of ATG4B-LC3 recognition interfaces
The ATG4B-LC3 recognition interfaces were defined based on the crystallized complex structure deposited under the PDB code of 2Z0D. By defining the contact as two heavy atoms that were within 4 Å, the recognition interface was described as those heavy atoms that formed the intermolecular contacts between ATG4B and LC3B as shown in
In silico drug screening for 2016 FDA-approved drugs on both ATG4B and LC3 interfaces was performed by molecular docking and molecular dynamics (MD) simulations. 2016 FDA-approved drugs were obtained from MedChemExpress (MCE), and the protonation state of which was determined at pH 7 by the Chemicalize (Swain, 2012, J. Chem. Inf. Model. 52, 613-615). The missing residues of ATG4B and LC3 were modeled by SWISS-MODEL (Kiefer et al., 2009, Nucleic Acids Res. 37, D387-92) using the existing x-ray crystal structure (PDB ID: 2Z0D) as template.
For each FDA-approved drug, two sets of docking were performed by Vina (Trott O. and Olson A. J., 2010, J. Comput. Chem. 31, 455-461)— one on ATG4B and the other on LC3. 20 docking poses of each ligand-receptor (FDA drug and ATG4B/LC3) pair were clustered by hierarchical clustering (Zepeda-Mendoza et al., 2013, Encyclopedia of Systems Biology 43, 886-887) using the pairwise RMSD of the poses as the distance. The interface residues on ATG4B and LC3 were defined as the residues contacting any heavy atom from the other protein within 4 Å, and the contact number of a drug pose was defined as the number of heavy atoms from the interface residues staying within 4 Å from the pose.
A cluster that contains many docking poses indicates a higher conformational entropy of the drug at its bound state, which favors the binding free energy because of a lessened decrease of entropy upon drug-protein complexation. Every drug had its largest cluster of poses from the docking, which served as the representative cluster for the drug. Only those drugs whose representative cluster was larger (had more poses) than the average size of the representative clusters of all the drugs were kept (
The drugs that remained in both ATG4B's and LC3's docking runs were subject to further analysis. As discussed above, contact number can roughly suggest pairwise atom interactions. Assuming a small interface where all the interface residues in ATG4B interact with all the interface residues in LC3, the product (multiplication) of a drug's contact with both proteins, rather than the sum of the two, is more indicative on how many pairwise atomic interactions are blocked by the drug. Here, the contact number of a drug to a protein's interface residues was calculated from the average contact number of all the poses of the largest cluster for the drug. Out of 667, the top 100 drugs having the largest products of contacts with both proteins were selected for the following MD simulations on both protein-drug complexes and binding free energy calculations by MM/GBSA.
The simulation package OpenMM (Eastman et al., 2017, PLoS Comput Biol. 13, e1005659) with AMBER ff14SB force field (Maier et al., 2015, J. Chem. Theory Comput. 11, 3696-3713) was used to examine the binding stability for the top-ranked 100 drugs on both ATG4B and LC3 interfaces, and the results were reported in Table 1 below. The drugs that left the interface within 10 ns were discarded (
1The ranking order is based on “Contact Number.”
2The total MM/GBSA is the sum of ATG4B and LC3 binding free energy.
3Contact ratio is the smallest value of simulation contact number divided by docking pose contact number. The contact ratio lower than 0.3 is considered as the drug has left the interface of protein, marked in italic and bold.
1The ranking order is based on the total MM/GBSA.
2The total MM/GBSA is the sum of ATG4B and LC3 binding free energy.
The sequence of glutathione S-transferase-tagged LC3B (GST-LC3B) cloned into the pGEX-6p vector was kindly provided by Dr. Nobuo N Noda (Noda et al., 2008, Genes to Cells 13, 1211-1218). To express LC3B, the vector was transformed into the expression host E. coli BL21 (DE3). A single colony was picked and incubated overnight in 10 mL Luria broth (LB) medium containing 10 μL of 1 mM ampicillin at 37° C. The cultured medium was then mixed with 1 L LB medium (or M9 minimal medium containing 15NH4Cl for 15N labeling) and continuously grown to reach an OD600 of 0.6 to 0.7. The expression of GST-LC3B was induced by 1 mM isopropyl-O-D-thiogalactopyranoside (IPTG) at 25° C. for 10 hours and then stopped at 4° C. for another 20 minutes. The suspended bacterial cells were centrifuged, and the pellet was re-suspended in the 15 mL of binding buffer A containing 140 mM NaCl, 2.7 mM KCl, 10 mM NaHPO4, 1.8 mM KH2PO4 at pH 7.3. The bacterial cells were broken by Sonicator (Qsonica Q125 Sonicator) in an ice bath and centrifuged. The supernatant containing GST-LC3B was then collected.
The GST-LC3B was first purified by using AKTA FPLC system with the GSTPrep FF 16/10 column (GE Healthcare Life Sciences) and elution buffer B containing 50 mM Tris-HCl and 10 mM reduced glutathione at pH 7.3. The elute containing GST-LC3B (
To assay the ATG4B catalytic activity and the inhibition effect of vinorelbine, the full-length LC3B was cloned and purified with C-Myc appended on the C-terminus and S-tag on the N-terminus. To perform the cleavage assay, 1 nM ATG4B, 250 nM C-Myc-LC3B-S-tag, and 10 μM of vinorelbine were added in the buffer composed of 150 mM NaCl, 50 mM Tris, 1 mM DTT (pH 8.0) to a final volume of 100 μL. The reagents were incubated at 37° C. for 30 minutes and then subjected to western blotting. The intensity of the band in the gel was calculated by using Image Studio Lite from LI-COR Biosciences or ImageJ.
The 1H-15N HSQC, 13C-15N HNCA, and 13C-15N HNCOCA spectra were collected using the triple-resonance cryogenic probe on Varian 700 MHz NMR spectrometer at 25° C. All the acquired spectra were initially processed using the VnmrJ Varian software (version 2.3), and the resulting FID file was converted to the UCSF file for the Sparky software (version 3.13) that was used to analyze the collected spectra. The resonance assignments of the 15N-labelled LC3B were done by overlapping the 2D HSQC spectra to that of BMRB 26881. Missing residues were manually assigned by two 3D experiments (HNCA and HNCOCA). To identify the vinorelbine binding interface on LC3B, the 1H-15N HSQC spectra of the 15N-labelled LC3B and vinorelbine titration at a molar ratio of 1:1 were superimposed. The chemical shift perturbation sites were identified by displaced cross-peaks on the spectra.
The cell viability was measured using the MTT assay or CellTiter-Glo Luminescent Cell Viability Assay. For MTT assay, mouse AK4.4 (1,000 cells/well) and human AsPC-1 (1,500 cells/well) cells were seeded in 96-well plates and cultured in serum-free medium (SFM) containing indicated concentrations of drugs for 48 hours. To measure the cell viability, 15 μL MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, 5 mg/mL in PBS) was added in each well and kept at 37° C. for three hours. The precipitated crystals were then dissolved in 50 μL DMSO, and the absorbance at 570 nm was measured by Multiskan GO Microplate Spectrophotometer (Thermo Scientific, USA) to estimate the cell survival rate. To quantify the drug combination effect, the combination index was calculated when the cell viability was reduced by 50% using the following Equation (1):
where IC50,A and IC50,B were the inhibition concentrations of drugs A and B when the cells remained 50% of the viability; CA,50 and CB,50 were the concentrations of drugs A and B when treated in combination and resulting in a 50% reduction of the cell viability. The CI50 near unity indicates an additive effect, CI50<1 suggests a synergistic effect and CI50>1 represents an antagonism effect.
Cells were seeded in a 12-well plate (105 cells/well) and cultured in serum-free medium (SFM) with different concentrations of drugs for six hours. The plate was then washed with phosphate-buffered saline (PBS), and cells were lysed with 100 μL radioimmunoprecipitation assay buffer (RIPA buffer) for each well. The cell extracts were mixed with 4×dye (900 μL of 4×Laemmli sample buffer+100 μL of 2-mercaptoethanol) at 95° C. for ten minutes and separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). The fractions were then transferred to a polyvinylidene fluoride (PVDF) membrane and immersed in 5% nonfat milk at room temperature for one hour. Afterward, the transfer membrane was incubated with protein-specific primary antibodies at 4° C. overnight, washed by PBST (0.1% Tween 20 in PBS), and then incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies at room temperature for one hour. The blots washed by PBST were then detected by enhanced chemiluminescence (ECL) for the quantification of proteins.
The mouse models of pancreatic ductal adenocarcinoma (PDAC) were established by in situ implanting the AK4.4 cells (105 per mouse) mixed with Matrigel into five to six-week-old FVB/NJNarl mice provided by National Laboratory Animal Center (NLAC), NARLabs, Taiwan. The drugs with different doses (10 mg/kg and 20 mg/kg) were administered via oral or intraperitoneal (IP) injection into the xenografted mice for six times at indicated days after the implantation. The mice were sacrificed 2 days after the last administration (Day 16). Proteins in tumor tissues were quantified by western blotting.
To create an ensemble structure of LC3B, both the homologous crystal structures and sampled structures using MD simulations were collected. For the homologous crystal structures, the amino acid sequence of LC3B (residue IDs: 5-120) in the crystal structure, PDB code: 2Z0DB, was used to search for the homologous sequences using SWISS-MODEL web server. Among the 1,428 structures found by SWISS-MODEL, 56 structures were retained that shared >95% sequence similarity with the query sequence. The redundant homologous structures were removed that include the ones exactly come from the query sequence and the duplicated ones presented in different biological assemblies or chains (the structure from the biological assembly 1 and chain A for each found PDB ID were chosen). Finally, the structures of which the alignment of the sequence covering the original query sequence of 116 residues were kept, which resulted in seven homologous structures.
To sample alternative conformations of LC3B, MD simulation run for 100 ns using the crystal structure from PDB code, 2Z0DB (residue IDs: 5-120), as the initial conformation. The preparation of simulation system and the running of simulation were performed using AMBER20. The protein was parameterized by ff14SB force field. The protonation states of ionizable residues were adjusted based on the pKa values predicted under pH=7 by PDB2PQR server. The protein was solvated in TIP3P water model, with 10 Å of water layer on each side of the water box. Sodium and chloride ions were added to neutralize the system and reach a final concentration of 100 mM. The simulation protocol was similar as that for protein-drug simulation described above. For the 100 ns production run, the snapshots were sampled every 20 ps, which resulted in 5,000 snapshots in total.
To summarize the ensemble structures using few representative conformations, the seven homologous structures and 5,000 sampled conformations were iteratively superposed to their mean structure in each of the iteration until the root-mean-square deviation (RMSD) of the mean structures between consecutive iteration was <10−6 Å. From the superposed structures, a 3n by 3n covariance matrix was constructed for the 3n Cartesian elements of the n superposed heavy atoms:
C
3n×3n
=QQ
T
where
m is the number of conformations in the ensemble structures, {right arrow over (ql)} is the Cartesian coordinates for conformation i, {right arrow over (q)}
is the average of the Cartesian coordinates among m conformations. Eigenvalue decomposition of the covariance matrix projected all the ensemble structures to the first two principal components (
To identify potential ATG4B allosteric modulators, ensemble docking was performed by docking 2016 FDA-approved drugs on the three representative alternative conformations from the ensemble structures. All the hydrogens were added with the protonation state respecting to the pKa values under pH=7 by PDB2PQR server. All the PDBQT files for the protein receptors and drugs required for docking were generated using AutoDock Tools. AutoDock Vina was used to perform the docking experiment. Global docking was performed by setting the docking box covering all the protein structure, with a 10 Å margin on each side of the box. The exhaustiveness was set to 50. AutoDock Vina allowed at most 20 poses to be reported for each docked drug.
To rank the drugs from the docking results, two ranking methods were applied, i.e., normalized ranking and logarithm of odds (LOD) scoring. In normalized ranking, the docking poses with high docking affinity (≤−7 kcal/mol) were first retained. Then, three features were used to rank the drugs, including docking affinity (kcal/mol), number of contacts, and the extent of the pose located in the allosteric site. The number of contacts was defined as the number of heavy atoms that were within 4 Å between the docked drug and the residues (51-54 and 58-59) of the LC3B, which were used to interact with the ATG4B N-terminal tail (
where y represented one of the three features, Ry represented the ranking of feature y among the sampled docking poses for a drug, and n was the total number of sampled docking poses of a drug. The pose with the highest TP score was specified as the representative pose of a drug among the sampled docking poses for that drug. Then, the 2016 drugs were ranked based on a drug score (D score) calculated by the three features of the representative poses with a similar manner:
where z represented one of the three features, Rz represented the ranking of feature z among the 2016 drugs, and m (=2016) was the total number of drugs.
In the logarithm of odds (LOD) scoring, three features to rank the drugs were used, i.e., docking affinity (kcal/mol), the distance to the drug target site, and the size of poses cluster. For the distance to the drug target site, it was taken the sum of distances of drug center of mass to LC3B (residue IDs: 51-54 and 58-59) and ATG4B N-terminal tail (residues IDs: 5-14), which interacted with the LC3B for the intermolecular allosteric regulation. For the size of poses cluster, the docked poses for each drug were clustered if any pair of heavy atoms between two poses was nearby each other (<1 Å) or can be connected through intermediate poses that were nearby each other. The number of top-ranked poses (within top 10) was then counted as the size of the cluster. For each pose from all the sampled docked poses of 2016 drugs, a score was given by the sum of the logarithm of odds score from the three features:
where x represented a sampled docked pose, and f represented the feature generation function that took an input pose x and returned the corresponding feature value. f∈{the docking affinity, the distance to the drug target site, the size of poses cluster}. PfT was the value distribution of feature f for a docked pose sampled from a true binder. PfF was the value distribution of feature f for a docked pose sampled from a decoy. Thus, the LOD score represented how more likely a sampled pose was resulted from a true binder or decoy. The distribution of PfT and PfF was derived from the docking results of the 2016 drugs on 16 selected crystallized complex structures composing of proteins as drug targets and FDA-approved drugs that was also included in the 2016 drugs according to Westbrook et al., 2019 (Table 3). The 2016 drugs including the one originally in the complex were then docked to the proteins where the co-crystallized drugs had been removed. The docked pose of the co-crystallized drug that was most similar to the one in the complex structure was defined as true binder. The other docked poses and other docked poses from other 2015 drugs were treated as decoys. The feature values from these sampled docked poses were used to build the distribution of PfT and PfF. The score for each drug was assigned by the highest LOD score among the sampled docking poses and was used for ranking the 2016 drugs.
1The chemical ID for the access of the summary page of the drug in Protein Data Bank.
2The protein chain ID in the PDB entry used in this study.
3The residue ID of the drug in the selected chain ID.
4The annotated experimental binding affinity associated with the PDB entry from Binding MOAD (*), BindingDB ($), or PDBBind (#).
The two ranking methods described above were applied to the docking results performed on each of the three alternative conformations, which resulted in six sets of ranked drug lists (Table 4). The top-ranked drugs were chosen for testing their effects on the inhibition of cancer cell viability from the following rules:
Choice 1: the top three-ranked drugs in each of the six ranked drug lists, including ponatinib, suramin, ergotamine, dolutegravir, conivaptan, moxidectin, nilotinib, ledipasvir, paritaprevir, aclacinomycin A, ethynyl estradiol, closantel, saquinavir, dihydroergocristine, itraconazole, daclatasvir, (+)-butaclamol, and temsirolimus;
Choice 2: the drugs ranked within top 20 in four out of the six ranked drug lists, including suramin, ergotamine, nilotinib, ledipasvir, itraconazole, and Evans blue;
Choice 3: the drug ranked within top five in both the ranked drug lists resulted from the docking results using the conformation in the crystal structure, gliquidone;
Choice 4: the drug ranked within top 20 by either of the two ranking methods from both the docking results using the conformation in the crystal structure and alternative conformation I, CP-640186;
Choice 5: the drug with most docked poses of which the docking affinity were <−9 kcal/mol among the 2016 drugs when docked on alternative conformation II.
Table 4 below illustrates the top 20 drugs in each of the six ranked drug lists resulted from three conformations of LC3B and two ranking methods. The drugs chosen for further assay of inhibition on cancer cells viability by Choice 1 were indicated by the symbol “@”; the drugs chosen by Choice 2 were marked as “#”; the drugs chosen by Choice 3 were shown in the symbol of “+”; the drugs chosen by Choice 4 were indicated by the symbol “$”, and that chosen by Choice 5 was shown in the symbol “&”. The drugs showed >50% inhibition of the viability on HCT116 cells were highlighted in italic. The drugs showed >10% differences in inhibition of cell viability between shATG4B/HCT116 and shCtrl/HCT116 were highlighted in bold.
Conivaptan
@
Moxidectin
@
Ledipasvir
@#
Paritaprevir
@
@
@
Daclatasvir
@
Temsirolimus
@
Evans Blue
#
Evans Blue
#
Evans Blue
#
Evans Blue
#
Ledipasvir
#
Ledipasvir
#
Ledipasvir
#
Netupitant
&
To further improve the peptides for the allosteric inhibition of ATG4B, in silico methods were used including Docking and MD simulations for the designs of new peptides. First, the ATG4B N-terminal tail was retrieved from the crystal structure (residues 5-18, SEQ ID NO: 18 (TLTYDTLRFAEF), N-term-12), and the first three residues (TLT) from the N-terminal and four residues (RFAE) in the middle of the peptide were removed due to their flexibilities and fewer contacts to LC3B observed from the crystal structure and MD simulations (Table 5). For example, Table 5 below illustrates the contact number and RMSF of residues of N-term-12 to LC3B in the crystal structure and a 30 ns MD simulation. The crystal structures of LC3B and N-term-12 were derived from PDB code: 2Z0D. N-term-12 refers to “SEQ ID NO: 18 (TLTYDTLRFAEF)” (residue IDs: 5-16).
1The contact number was defined as the number of heavy atoms in the residue that were within 4 Å to LC3B.
2The contact number difference was defined as the difference of the contact number between each snapshot and the crystal structure.
3RMSF: the root-mean-square fluctuation of the Cα atom in the 30 ns MD simulations.
To replace the removed four residues (RFAE) and link the remaining segments, linkers of one, two, or three glycines (G) were considered. To determine the suitable length of the linker, AutoDock Vina was used with default parameters to dock the peptides connected by these three linkers into LC3B. The number of native contacts of N-term-12 was defined as the number of heavy atoms on LC3B that were within 4 Å of the fourth to seventh (YDTL) and last (F) residues of N-term-12. Then, the remained native contacts (normalized by the number of native contacts to give a ratio between zero and one) were calculated for each pose of the docked peptide using the same set of residues (YDTL and E). The results indicated that the one with two glycines as the linker (SEQ ID NO: 14 (YDTLGGF)) had pose retaining the highest native contacts as N-term-12 (Table 6), and the length of the GG linker was the shortest one that can accommodate the gap (8.14 Å) between residues of L and F after removing the middle residues, RFAE, from N-term-12. Specifically, Table 6 illustrates the native contact ratio of docked peptides containing different lengths of glycine linker. The number of native contacts was defined as the number of heavy atoms in LC3B that were within 4 Å of N-term-12 in the crystal structure.
1The binding affinity reported from AutoDock Vina.
2The native contact ratio was the number of retaining native contacts in the docked pose divided by the number of native contacts formed by N-term-12. The value was between zero and one.
Next, point mutation on each residue of the above peptide was performed to the other 19 residues, and 134 peptides, including SEQ ID NO: 14 (YDTLGGF) (7×19+1=134), were again subject to docking screening on LC3B's LIR binding site using AutoDock Vina. All the outputted poses were first compared to the binding mode of N-term-12 by calculating the RMSD values between the Cα atoms of the first four and last residues in the docking poses, and the fourth to seventh (YDTL) and the last residues (F) of the N-term-12. The docking poses that were similar enough to the N-term-12 (RMSD <10 Å) were retained, and then sorted based on AutoDock Vina-predicted binding affinities (Table 7). Specifically, Table 7 illustrates the results of point-mutated peptides/poses (20 docking poses for each peptide) docked to LC3B. The upper part of the table listed the top-ranked peptides poses having the smallest RMSD from the position of ATG4B's N-terminal tail (SEQ ID NO: 20 (YDTLRFAEF)) and rank-ordered by Vina predicted affinity. Those prioritized poses that belong to the “wild type” peptide, SEQ ID NO: 14 (YDTLGGF), were collected and shown separately at the bottom of the table. Peptides marked with an asterisk (*) indicated the selected peptides subject to further 100 ns MD simulations for binding stability assessment to LC3B. The top-ranked three peptides (SEQ ID NO: 1 (YDYLGGF), SEQ ID NO: 2 (YDTLGIF), SEQ ID NO: 3 (YDTLYGF), Table 7, upper, indicated by arrow) and the top-ranked pose of SEQ ID NO: 14 (YDTLGGF) (Table 7, bottom, indicated by arrow), as a control, were selected for further 100 ns simulations to assess their binding stabilities. The simulation results suggested that SEQ ID NO: 2 (YDTLGIF) was a promising candidate, compared with the other three, to form stable interactions with LC3B as the peptide is stable through the 100 ns simulation. This peptide, as an interference of the inter-molecule allosteric regulation by LC3B, is thus chosen for the ATG4B cleavage assays to assess its inhibition to ATG4B.
1RMSD Ctrl: root-mean-square deviation of peptide to ATG4B’s N-terminal tail (SEQ ID: 20 (YDTLRFAEF)) by considering the C of the first four and last residues.
All the peptides used in this study were synthesized and purchased from LifeTein LLC and Kelowna International Scientific Inc.
System preparation and defining the interface residues of 3CLpro Protein targets were prepared by performing a MD simulation of 200 ns starting from the homodimer 3CLpro (PDB id: 6Y2E). Consequently, the chain A was extracted from the last frame, and additional 100 ns simulation was performed. Finally, the last frame of the simulation (End of Monomer Simulation) was termed EMS. The EMS monomer served as the target conformation for screening the interface drug (by global docking using Autodock Vina).
To find drugs blocking the combination of two monomers, the interface residue of the homodimer was first defined by the contact number where a contact was the atom-atom distance small than or equal to 4 Å. The interface residues of the 3CLpro homodimer were defined by the contact number of a residue larger than six. Nine interfacial residues in the domain II SER121, PRO122, SER123, GLY124, VAL125, SER139, PHE140, LEU141, and GLU166, and six residues in the N-terminus SERI, GLY2, ARG4, METE, ALAI, and PROS were found.
AutoDock Vina was used for drug screening, where each of the 2016 FDA-approved drugs was docked in the protein and sampled for 20 poses. The docking box was set to cover the whole structure of the protein target with an additional 5 Å margin patched on each side of the docking box. Exhaustiveness of 20 was set to give comprehensive screening results. The procedure was termed as the global docking. Consequently, the global docking was used on the EMS structure, and the output docking poses were submitted to further drug clustering analysis.
Considering that the contribution of conformational entropy could facilitate the binding free energy, the poses of global docking were clustered for each drug by the hierarchical clustering method. The number of poses in each cluster for each drug was calculated, and the number of poses in the largest cluster was termed as the largest cluster size (LCS). The mean LCS of all the FDA drugs was termed MLCS. The drugs whose LCS are smaller or equal to the rounded MLCS were removed. The remaining drugs were served as candidate drugs for further drug contact analysis.
Next, interface drugs having high contact with domain II and N-terminus were chosen. A contact was formed if a heavy atom in the drug is <4 Å from a heavy atom of interface residues defined in the above section. The top 50 drugs having the highest contact number were selected for further MD simulations and MM/PB(GB)SA analysis.
Huang's method (Huang et al., 2019, bioRxiv) is performed by the normal mode-based td-LRT for the closed form of ATG4B (PDB code: 2CY7), where perturbation forces are given to perturb each of 41 CA atoms (within 8 Å of the active site) along 133 uniformly distributed directions. Consequently, the calculated CGCM was shown in
Drug Docking and MD Simulations to Repurpose FDA Approved Drugs that Target Site 1 of ATG4B
The ATG4B structure adopting an active enzyme conformation (or the “open form”; PDB ID: 2Z0D) was taken for screening of FDA drugs that bind the identified allosteric site. All the missing loops were patched, and the catalytically inert mutation, H280A, was back-mutated with the aids of SWISS-MODEL web service (Waterhouse et al., 2018, Nucleic Acids Research 46, 296-303).
Hydrogen atoms of ionizable residues in the ATG4B structure were added or removed per their protonation states, calculated by PDB2PQR web service (Dolinsky et al., 2007, Nucleic Acids Research 35, 522-525). A set of 2016 FDA-approved drugs compiled from the catalogs of MedChemExpress (MCE) FDA-Approved Drug Library (Cat. No.: HY-L022) and Screen-Well FDA Approved Drug Library (Version 1.5) of Enzo Life Sciences, Inc. were used for the drug screening. The 3D structures of drugs were built by BIOVIA Discovery Studio (Dassault Systèmes BIOVIA, Discovery Studio Modeling Environment, Release 2017, San Diego: Dassault Systèmes, 2016) with appropriate adjustment of the protonation state of all ionizable functional groups under pH=7. The docking software, AutoDock Vina (Trott O. and Olson A. J., 2010, J. Comput. Chem. 31, 455-461), was used to perform virtual drug screening. The PDBQT files required as the input files were generated using AutoDock Tools (Morris et al., 2009, J. Comput. Chem. 30, 2785-91). The search box was adjusted to cover the whole protein structure for global search, with a space of 5 Å thickness padded from each side of the box to the protein. The exhaustiveness was set to 100. For each drug, 20 docking poses were allowed to be generated by AutoDock Vina.
To analyze the docking result and choose the drug candidates for repurposing as ATG4B intramolecular allosteric drugs, in addition to the binding affinity obtained for each docking pose, the distance, as a second feature, from the pose to the target sites was calculated. Within the top 10 ICCs having the highest communication scores (CSs), those >10 Å away from the active sites and spatially clustered were selected to discover a single allosteric site comprising TRP27, TYR33, ARG31, LYS39, LYS32 and ILE28 as shown in the red spheres of
where feature f represents the pose affinity or distance. Rf represents the rank of a pose according to the f feature (the highest rank is 1; the lowest is N). N is the total number of the retained poses, which is 7 in this case.
Protein expression and purification for the study of prediction of the allosteric sites in ATG4B Human ATG4B with N-terminal 6xHis-tag and human LC3B with both N-terminal 6xHis-tag and C-terminal S-tag were cloned into pETDuet-1 expression plasmids as previously described (Shu, C W et al., 2010, Autophagy 6, 936-947), subsequently transformed into E. coli BL21 (ECOS 21, Yeastern Biotech Co., FYE207-40VL). A single colony harboring the correctly in-frame inserted gene was inoculated into an overnight culture that was then diluted 100-fold in fresh Luria-Bertani broth with 50 μg/mL ampicillin for growing at 37° C. with 200 rpm agitation for 2 to 3 hours until the optical density of the culture at 600 nm reaches 0.4 (around 3×108 cells/mL). The recombinant proteins were then induced by 0.1 mM isopropyl β-D-1-thiogalactopyranoside (IPTG) and cultivated at 20° C. for another 5 hours.
The expression cell culture was harvested by 12,000×g centrifugation at 4° C. for 20 minutes, and the cell pellets of 1 liter broth were resuspended in a 10 mL lysis buffer (50 mM Tris-HCl, pH 7.4, 300 mM NaCl, 20 mM imidazole). The 10 mL cells were lysed by sonication and centrifuged at 38,500×g for 15 minutes at 4° C., and the supernatant containing the 6xHis-tagged recombinant proteins was filtered by 0.8 μm membrane before subjected to 3 mL Ni-NTA-agarose (Qiagen, 30250) gravity column. Subsequently, the sample buffer comprising 10 mM Tris-HCl, pH 7.4, 150 mM NaCl, 10 mM β-mercaptoethanol, and 0.1% Triton X-100 was applied throughout the purification, including pre-washing by 20 mM imidazole and collection of elution with 100 mM imidazole. The ATG4B and LC3B proteins were concentrated and frozen with 20% glycerol. The protein purity was verified >90% by Coomassie blue staining on SDS-PAGE, and the quantity was measured by bicinchoninic acid assay.
ATG4B enzyme activity assay was analyzed by immunoblotting. The purified recombinant ATG4B (5 to 10 nM) was incubated with 500 nM C-terminal S-tagged LC3B in 100 μL reaction buffer containing 50 mM Tris-HCl, pH 8.0, 150 mM NaCl, and 1 mM dithiothreitol at 37° C. for 2 hours. Reactions were stopped by addition of 5X SDS-sample buffer and 95° C. heated for 5 minutes before loaded to 12% SDS-PAGE gel for electrophoresis. Afterwards, the samples were then transferred to nitrocellulose membranes (PALL, Biotrace NT 66485) for immunoblotting analyses. The membrane was blocked with 5% skim milk (Sigma-Aldrich, 70166) in TBST buffer (TBS with 0.05% Tween-20) for 1 hour at room temperature with mild shaking, and then incubated with anti-S-tag (Bethyl, A190-135A), anti-c-Myc (Sigma-Aldrich, C3956) or anti-ATG4B (Sigma-Aldrich, A2981) primary antibodies in TBST buffer containing 5% BSA for overnight at 4° C. with mild shaking. The proteins were then probed with peroxidase conjugated mouse anti-rabbit IgG secondary antibody (Santa Cruz, sc-2357-CM) with 5% skim milk in TBST buffer for 1 hour at room temperature. The membranes were then treated with enhanced chemiluminescence (ECL) reagent (GE Healthcare, RPN2232) for band intensity detection by the ImageQuant LAS 4000 Imaging System (Cytiva, USA). The intensities of remaining substrate LC3B after enzymatic cleavage were quantified by the software ImageJ.
Three tumor cells were tested, i.e., HCT116 (colorectal cancer), AsPC-1 (pancreatic cancer) and MDA-MB-468 cell lines (breast cancer). Human colorectal carcinoma cell line HCT116 (catalog number: BCRC 60349, Bioresource Collection and Research Center (BCRC), Hsinchu City, Taiwan) was maintained in the culture medium: McCoy's 5a medium (catalog number: 16-600-082, Gibco, Waltham, Mass.) with 1.5 mM L-glutamine (Gibco) and 10% fetal bovine serum (FBS, Gibco). Human pancreatic adenocarcinoma cell line AsPC-1 (catalog number: BCRC 60494, BCRC) was maintained in the culture medium: RPMI 1640 medium (catalog number: 11875168, Gibco) with 2 mM L-glutamine, 4.5 g/L glucose (Gibco), 10 mM HEPES (Gibco), 1 mM sodium pyruvate (Gibco) and 10% fetal bovine serum. Human breast adenocarcinoma cell line MDA-MB-468 (catalog number: ATCC HTB-132, American Type Culture Collection (ATCC), Manassas, Va.) was maintained in the culture medium: Leibovitz's L-15 medium (catalog number: 11415064, Invitrogen, Waltham, Mass.) with 2 mM L-glutamine and 10% fetal bovine serum. All cells were incubated in a humidified atmosphere without CO2 in air at 37° C. to serve as the control for the following assays.
For cell viability assay, tumor cells (1×105/mL) were suspended in the medium without testing drugs and then inoculated in 96-well plates (100 μL per well) for attachment first. After a 12-hour incubation to allow for cell attachment, the conditioned medium was withdrawn. Then, fenretinide and moxidectin (10 μM) were added to the culture medium (100 μL per well) to treat cells for two additional days. Cell proliferation was quantified by using a Premix WST-1 Cell Proliferation Assay System (Takara, Japan). According to the manufacturer's protocol, cell proliferation was determined by measuring the optical density (OD) after the reaction for 3 hours by recording the absorbance at 450 nm using a plate reader (Multiskan Go, Thermo Fisher Scientific).
The FRET (fluorescence resonance energy transfer)-based assay was designed for 3CLpro activity assay and explained in
The following examples provide various non-limiting embodiments and properties of the present disclosure.
To identify potential drugs that could be repurposed as the ATG4B-LC3B recognition interface blockers, drugs were sought that had a strong association with the recognition interfaces of both ATG4B and LC3B. First, the residues that were in contact (heavy atoms within 4 Å) between ATG4B and LC3B in the crystallized ATG4B-LC3B complex structure (PDB code: 2Z0D) were defined as the recognition interfaces. The resulting interfaces were located at the residues surrounding the entry to the catalytic center (Cys74, Asp278, and His280) for ATG4B and the residues nearby the C-terminal tail for LC3B (
To confirm the molecular mechanism under the vinorelbine-mediated defect of ATG4B on LC3B catalysis, the binding site of vinorelbine on LC3B was examined by comparing the 1H-15N HSQC spectra of LC3B with and without the titration of vinorelbine. When titrating the vinorelbine to LC3B in a 1:1 ratio, seven residues (Glu14, Leu44, Leu47, Phe80, Ser115, Gln116, and Thr118) were observed, showing large chemical shift perturbation (
The cell toxicity of vinorelbine was further assayed by the cell viability assay on three cancer cell lines: human colon cancer HCT116, human pancreatic cancer AsPC-1, and mouse pancreatic cancer AK4.4. Vinorelbine showed effective toxicity to HCT116, with reduced 55%, 63%, and 68% of cell viability when treated in 4 μM, 10 μM, and 20 μM, respectively (
Tioconazole, an FDA-approved drug originally for anti-fungi, has been found to inhibit the enzyme activity of ATG4B and to target the active site of ATG4B that blocks the entry of the substrate LC3B, which sensitizes the tumor to chemotherapy. It is interesting to see whether the combination of vinorelbine and tioconazole, with different underlying mechanisms for the impairment of ATG4B catalysis, can provide improved suppression of cancer cells. The cell viability assays were performed for AK4.4 and AsPC-1 treated by different combinations of the concentration of vinorelbine and tioconazole. To quantify the drug combination effect, the combination index (CI50) was calculated as the sum of the factions of individual drugs' concentrations in combination to the IC50 of the same drugs when 50% of the cell viability was reduced due to the drug combo (see Equation (1)). Smaller than or equal to unity refers to therapeutic synergy or additivity, respectively. For each tested concentration of vinorelbine, the increased concentration of tioconazole resulted in an enhanced reduction of the cell viability of AK4.4 (
1CI50 is calculated based on Equation (1) in the Cell Viability section in MATERIALS AND METHODS
While not observing in AK4.4, a synergistic effect of vinorelbine and tioconazole was observed in AsPC-1, where combining 2.5 μM, 5 μM, and 10 μM of vinorelbine with the same corresponding concentration of tioconazole (2.5 μM, 5 μM, and 10 μM), resulting in 63%, 53%, and 32% cell viability that were lower than that of 68%, 61%, and 46% when 5 μM, 10 μM, and 20 μM vinorelbine alone was used, respectively (
To assess the effect of the treatment of vinorelbine alone, tioconazole alone, or tioconazole combined with vinorelbine or with designed peptides for targeting LIR binding pocket on LC3B, i.e., TAT-N-term-7 (tat-N7, with an amino acid sequence of SEQ ID NO: 16 (YGRKKRRQRRR-GGS-YDTLGIF)) and TAT-N-term-9 (tat-N9, with an amino acid sequence of SEQ ID NO: 17 (YGRKKRRQRRR-GGS-YDTLRFAEF)), on tumors, the mouse tumor xenografts were established and subject to the treatment. Drugs of 5, 10 or 20 mg/kg were treated via pre-oral (P.O.) administration or intraperitoneal (I.P.) injection on days 3, 5, 7, 10, 12, and 14. In addition, 10 mg/kg tat-N7, 10 mg/kg tat-N9 or 20 mg/kg tioconazole were treated to the mouse via intraperitoneal (I.P.) injection. The mouse was sacrificed on day 16, and their body weights and tumor sizes were measured (
In order to access the possible toxicity of tioconazole (Tc), TAT-N-term-9 (Tat-N9), TAT-N-term-7 (Tat-N7), Tc combined with Tat-N9 (Tc+Tat-N9) or Tc combined with Tat-N7 (Tc+Tat-N7), these drugs are administered to healthy FVB/NJNarl mice via six intra-peritoneal (IP) injections throughout 16 days. Tc is a known active site inhibitor for ATG4B, and Tat-N9 and Tat-N7 are intermolecular allosteric drugs for ATG4B. It can be found that Tc caused an 8% increase (P-value =0.025) in the liver (from 5.25% to 5.69% of mouse body weight), while other treatment groups did not show the same effect (
Besides as a substrate of ATG4B, the LC3B had a dual role as an intermolecular allosteric regulator to the activity of ATG4B. The allosteric regulation that could be interfered by the designed peptide inhibitors that competed the ATG4B N-terminal tail binding site on the LC3B required for the regulation. Accordingly, small molecular drugs that could reduce ATG4B activity through the same tricks were designed as the peptide inhibitors functioned. To find such drugs, ensemble docking strategy was applied to screen 2016 FDA-approved drugs. First, three representative alternative conformations of LC3B from crystalized structures of homology proteins were obtained and sampled for conformations in a 100 ns explicit solvent for MD simulation using the crystallized LC3B structure from PDB code, 2Z0DB, as the initial conformation. Principal component analysis and hierarchical clustering on these collected conformations resulted in three representative alternative conformations (
The suppression of drugs for the cancer cell viability was tested on the colorectal cancer, HCT116, and pancreatic cancer, AsPC-1, cell lines. For HCT116, the remaining cell viability upon treatment of 20 μM ponatinib (19%), moxidectin (2%), aclacinomycin A (28%), ethynyl estradiol (47%), Evans blue (35%), and netupitant (3%) showed more than 50% inhibition of the cell viability (
To see whether the observed inhibition of cell viability of the drugs for HCT116 was resulted from the perturbation of ATG4B-LC3B regulatory mechanism and thus the autophagy pathway, an ATG4B silencing strain (shATG4B/HCT116, S strain) and a control strain (shCtrl/HCT116, C strain) were cultured for the cell viability assays of picked drugs. From the resulted remaining cell viability, it showed that 20 μM of ponatinib (S strain: 31%, C strain: 21%), moxidectin (S strain: 1%, C strain: 1%), aclacinomycin A (S strain: 38%, C strain: 28%), ethynyl estradiol (S strain: 70%, C strain: 49%), temsirolimus (S strain: 60%, C strain: 50%), and Evans blue (S strain: 34%, C strain: 35%) could effectively inhibit the cell viability (
Among the above-identified drugs, ponatinib and moxidectin were chosen for further assays due to their consistent inhibition on the viability of cancer cells. The binding site of ponatinib or moxidectin on LC3B was examined by comparing the 1H-15N HSQC spectra of LC3B with and without the titration of ponatinib or moxidectin, and the results were shown in
Further, to confirm the molecular origin of their cell toxicity, the in vitro cleavage assays were performed. As expected, both ponatinib (Pn) and moxidectin (Mx) showed moderate inhibition of 30% and 38% on ATG4B's catalytic activity at 5 μM and 10 μM, respectively (
Furthermore, the allosteric inhibition of ponatinib or moxidectin can be additively combined with the known orthosteric inhibitor, tioconazole, to suppress the viability of cancer cells, AK4.4 (
In addition, the allosteric inhibition of moxidectin (Mx) can be additively combined with the interface blocker, vinorelbine (Vb), to suppress the viability of cancer cells, AK4.4 and AsPC-1 (
The existence of the LC3B-induced intermolecular allosteric regulation of ATG4B can be evaluated by designing peptides that compete with ATG4B's N-terminus for the binding of LC3B. Any decrease of the LC3B-enhanced ATG4B enzyme activity in the presence of these peptides can be interpreted as the existence of positive cooperativity of LC3B-mediated allosteric regulation. Three synthesized peptides were tested. The first was taken from the first 18 residues of ATG4B N-terminus (N-term-18, SEQ ID NO: 19 (MDAATLTYDTLRFAEFED), or N18), which has been shown to interact with LC3B and caused the chemical shift perturbation on the ATG4B N-terminus binding site (also termed LC3-interacting region (LIR) binding site) in previous NMR titration experiments (
To further design a peptide with a higher binding affinity to compete with ATG4B N-terminal tail as the third peptide, the four residues (RFAE) in the middle of the N-term-9 peptide (SEQ ID NO: 20 (YDTLRFAEF)) that were found to be more flexible (root-mean-square fluctuation (RMSF)>1 Å) than other N9 residues (Table 5) during the simulation were removed. The last residue (Phe 16) was retained for its increased contact with LC3B over the 30 ns simulation (Table 5). To re-join the topologically disconnected “YDTL” and “F,” a short linker of glycine (G) repeats was inserted to replace the 4 (RFAE) residues in silico. The length of the linker should cover at least the linear distance between Leul 1 and Phe16 that is around 8 Å.
To determine a suitable length of the linker, the number of native contacts was defined as those heavy atoms of LC3B within 4 Å from the fourth to seventh (YDTL) and last (F) residues of N-term-12 (YDTL) and eleventh (F) residue of N-term-12 in the crystal structure. The residues, Asp19, Ile23, Lys30, Lys49, Lys51, Phe52, Leu53, Va154, Pro55, Va158, Glu62, Leu63, Ile66, Arg70, and Phe108, were found to contact “YDTL” and “F” in the N-term-12, among which the residues, Lys51, Phe52, Leu53, Va154, and Va158, were found perturbed in LC3B's 1H-15N HSQC spectra in the presence of the synthesized N-term-18 with Δp.p.m. >0.2. Then, the retaining native contact ratio for the “YDTL” and “F” was calculated in each of these docked peptides, i.e., SEQ ID NO: 21 (YDTLGF), SEQ ID NO: 14 (YDTLGGF) and SEQ ID NO: 22 (YDTLGGGF), and it was found that the peptide with a GG linker gave a docking pose that recovered the native contact, with a ratio of 0.84 (Table 6). Also, the Cα distance between Leu11 and Phe16 was 8.14 Å, longer than the double of the average Cα distance (3.8 Å), which also suggested the introduction of a GG linker that allowed extending to at least triple of the average Cα distance.
To further optimize the peptide with a double G linker (SEQ ID NO: 14 (YDTLGGF)), point mutation on each residue was performed to the other 19 residues, which resulted in totally 134 (=7×19+1) peptides, including SEQ ID NO: 14 (YDTLGGF). The generated peptides were then subject to docking screening by AutoDock Vina. All the resulting poses were first compared to N-term-18 in the x-ray structure by calculating the Cα atom RMSD of YDTL and F residues in the observed N-term-12 and those in the docking poses of the aforementioned three peptides. The docking poses that were similar enough to the N-terminal tail (RMSD <10 Å) were retained and ranked based on AutoDock Vina-predicted binding affinities (Table 7). The top-ranked three peptides (SEQ ID NO: 1 (YDYLGGF), SEQ ID NO: 2 (YDTLGIF), SEQ ID NO: 3 (YDTLYGF), Table 7, upper, indicated by asterisk) and the most top-ranked pose of SEQ ID NO: 14 (YDTLGGF) (Table 7, bottom, indicated by asterisk), as a control, were selected for further 100-ns simulations to assess their binding stabilities. The simulation results suggested that SEQ ID NO: 2 (YDTLGIF) was a promising candidate to form stable interactions with LC3B as the binding of this peptide that was very stable through the 100 ns simulation with the docking pose of the last snapshot remained similar from 2 ns to the end of the simulation (
The inhibitory effect of the three designed peptides on ATG4B activity was tested using ATG4B cleavage assays and ATG4B activity reporter assays. In the ATG4B cleavage assays, a peptide that can inhibit ATG4B activity will result in darker bands of the full-length fusion protein composed of C-Myc, pro-LC3B, and S-tag, which can be quantified by immunoblotting for S-tag and C-Myc. The full-length LC3B fusion protein was used as the negative control and as the positive control when also incubated with ATG4B (
Since the peptides were designed to target the LIR motif binding pocket on LC3B, their ATG4B inhibition ability was presumed to come from the interference of the interaction between the ATG4B N-terminal tail and LC3B, which shifted the ATG4B back to its closed/inactive conformation through the intermolecular allosteric regulation accompanied by the folded-back N-terminal tail. To confirm the molecular mechanism behind the observed reduction of ATG4B activity by the peptides, NMR spectra were analyzed when 15N-labelled LC3B was titrated with the peptides to identify their binding sites on LC3B. It was observed that N-term-18 introduced a similar pattern of chemical shift perturbation at the LIR motif binding pocket (
The existence of the intermolecular allosteric mechanism of ATG4B induced by LC3B supported by the results revealed a new strategy to design ATG4B inhibitors for cancer therapy. An allosteric drug that is designed for interfering the binding of the ATG4B N-terminal tail by LC3B might affect the tumor growth due to the impairment of the ATG4B enzyme activity. As a proof of the concept, the effect of the designed peptides on the suppression of the cancer cells was tested by the cell viability assays. Different concentrations of N-term-7 and N-term-9 were treated to the colorectal cancer, HCT116, and the pancreatic cancer, AsPC-1, cell lines (
An intramolecular allosteric site of a disease target protein can be predicted and validated via the time-dependent linear response theory (td-LRT)-based method. Details of the method are described by Huang et al. (Huang et al., 2019, bioRxiv). In this Example, the clustered intramolecular communication centers (ICCs) as potential allosteric sites were predicted in a notable distance away from the catalytic center, so as to find the drugs that can bind the sites. A library of 2016 FDA-approved drugs by small molecule docking was screened for this purpose. These drugs were then tested for their in vitro inhibition of ATG4B's function by enzymatic assay and suppression of tumor cell growth.
By perturbing 41 residues within 8 Å from Cys74, the catalytic cysteine of ATG4B, the td-LRT-based method was used to derive the coarse-grained communication map (CGCM) (
#The distance between the mass center of a drug docking pose to its closest heavy atom of allosteric site residues. The distances in the parentheses are the same after a 10-ns MD simulation.
Furthermore, MD simulations for these three drugs were conducted to examine their binding stability and site proximity. The top two results based on the heavy-atom contact and proximity to the allosteric site (
Further, not only top-10 drugs but also top-50 drugs ranked by drug-interface contacts were selected to perform the MD simulation and MM/PB(GB)SA analysis using the standard protocol. From the simulation trajectory, the mean drug-interface contacts were calculated by the average drug-interface contacts over the snapshots of the last 2 ns of MD between the 3CLpro interface and drugs. Top-10 drugs ranked by mean drug-interface contacts were selected for each interface as shown in Tables 18 and 19 below.
Tables 18 and 19 below showed the mean drug-interface contacts between drug and domain-II/N-terminus, and corresponding MD-based energy. The “Contact” referred to the mean drug-interface contacts calculated over the snapshots of the last 2 ns of MD trajectories. The column MMG(P)BSA was the MMG(P)BSA-derived energy in the unit of kcal/mol. It was observed that the top-10 drugs ranked by drug-interface contacts shown in Table 17 may not be top-10 when the ranking was based on the mean drug-interface contacts, and generally, drugs with high mean drug-interface contacts had low MD/MMP(G)BSA derived energy, namely good drug-protein affinity.
From the above results, the drug norvancomycin (02009) revealed high contacts with the N-terminus (orange color in
From the top-2 that had the most contact with 3CLpro's domain-II and N-terminus, four interface drugs, namely anidulafungin (01995), miglitol (00297), ombitasvir (01973) and norvancomycin (02009) were obtained, where norvancomycin was the first drug chosen for the following enzyme activity assay.
A 3CLpro (Cat. #78042-1) assay kit (BPS Bioscience, CA, USA) was used for the single use experiment of the norvancomycin, and it showed 20 to 30% inhibition at 3 to 90 μM (see the yellow bars in the middle of
Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein.
All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as” and “for example”) provided herein, is intended merely to better illuminate the disclosure and does not pose a limitation on the scope of the disclosure unless otherwise indicated.
| Number | Date | Country | |
|---|---|---|---|
| 63289176 | Dec 2021 | US |
