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Potent Human Telomerase Inhibitors: Molecular Dynamic Simulations, Multiple Pharmacophore-based Virtual Screening, and Biochemical Assays Faezeh Shirgahi Talari, Kowsar Bagherzadeh, Sahand Golestanian, Michael Jarstfer, and Massoud Amanlou J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.5b00336 • Publication Date (Web): 03 Nov 2015 Downloaded from http://pubs.acs.org on November 21, 2015

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Journal of Chemical Information and Modeling

Potent Human Telomerase Inhibitors: Molecular Dynamic Simulations, Multiple PharmacophoreBased Virtual Screening, and Biochemical Assays Faezeh Shirgahi Talaria,b, Kowsar Bagherzadehc, Sahand Golestaniana, Michael Jarstferb*, Massoud Amanloua*

aDepartment of Medicinal Chemistry, Faculty of Pharmacy and Pharmaceutical Sciences Research Center, Tehran University of Medical Sciences, Tehran, Iran; bEshelman School of Pharmacy, Division of Chemical Biology and Medicinal Chemistry, University of North c Carolina, Chapel Hill, North Carolina, USA; Razi Drug Research Center, Iran University of

Medical Sciences, Tehran, Iran. *Both authors; Michael Jarstfer and Massoud Amanlou, have the same contributions as the corresponding authors *Corresponding author: [email protected]

Keywords: Cancer, Human telomerase enzyme, Inhibitors, Docking, Pharmacophore-based virtual screening, Molecular dynamic simulations

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Abstract Telomere maintenance is a universal cancer hallmark, and small molecules that disrupt telomere maintenance generally have anti-cancer properties. Since the vast majority of cancer cells utilize telomerase activity for telomere maintenance, the enzyme has been considered as an anticancer drug target. Recently, rational design of telomerase inhibitors was made possible by the determination of high resolution structures of the catalytic telomerase subunit from a beetle, and subsequent molecular modeling of the human telomerase complex. A hybrid strategy including docking, pharmacophore-based virtual screening and molecular dynamics simulations (MDS) were used to identify new human telomerase inhibitors. Docking methodology was applied to investigate the ssDNA telomeric sequence and two well-known human telomerase inhibitors (BIBR1532 and MST-312) modes of interactions with hTERT TEN domain. Subsequently molecular dynamic simulations was performed to monitor and compare hTERT TEN domain, TEN-ssDNA, TEN-BIBR1532, TEN-MST-312, and TEN-ssDNA-BIBR1532 behavior in a dynamic environment. Pharmacophore models were generated considering the inhibitors manner in TEN domain anchor site. These exploratory studies identified several new potent inhibitors whose IC50 values were generated experimentally in a low micromolar range with the aid of biochemical assays, including both the direct telomerase and the telomeric repeat amplification protocol (TRAP) assays. The results suggest that the current models of human telomerase are useful templates for rational inhibitor design.


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INTRODUCTION Cancer is one of the most common causes of human death1-5. Historically, cancer therapy has included surgery, chemotherapy, radiation, and hormone therapy. Currently, the state of the art has increased attention to targeted therapy. Targeted therapies more precisely attack cancerous cells preferentially by focusing on pathways specifically required for cancer cell proliferation611

. One cancer hallmark is immortalization. Cancer cells can achieve immortality by virtue of

their ability to maintain telomeres. Telomeres are the physical ends of linear chromosomes and cannot be fully replicated by canonical DNA replication machinery. To overcome this end replication problem, most cancers up regulate the enzyme telomerase12-17. Telomerase activity is observed in ~90 percent of cancer cells, but its activity is low or absent in somatic cells18-21. Telomerase adds DNA repeats, 5’-TTAGGG in human, to the 3΄-ends of the singlestranded G-rich overhang chromosome ends by reverse transcribing its integral RNA template22. Therefore, telomerase-based therapeutic approaches target a major survival factor for cancer cell immortality and represent a nearly universal anti-cancer drug approach23. Telomerase is a unique reverse transcriptase that processively adds telomere repeats to chromosomes ends using the human telomerase reverse transcriptase (hTERT) and human telomerase RNA (hTR) subunits24-26. Direct targeting of telomerase as well as the telomeric ssDNA have been considered as anti-cancer drug strategies. The two best-studied inhibitors include GRN163L and BIBR1532. GRN163L (Imetelstat) is an oligonucleotide that functions as a template antagonist and is currently in several phase I/II clinical trials, and the small molecule BIBR1532 has been studied in preclinical test, but has not proceeded the clinical trials yet27-30.The absence of small molecule clinical candidates that target telomerase provide motivation for developing new approaches towards their development. hTERT is made up of 4 main functional domains: RT, TEN, TRBD and CTE. The RT domain functions as the active site and TEN domain functions as an anchor site for the primer and nascent DNA product31-38. The telomerase reaction cycle has three steps: recognition, elongation and translocation, followed by repeated rounds of elongation/translocation. During the translocation step telomerase dissociates from the newly synthesized DNA and realigns it with the template to synthesize a new repeat. One of the important characteristics of the telomerase mechanism is the role of TEN domain in processive elongation39. Evidence suggests that TEN domain undergoes a series of conformational changes that regulate the telomerase reaction cycle40. TEN domain directly repositions the newly synthesized DNA in the telomerase active site41, facilitates DNA translocation42 which in part makes it necessary for cellular immortalization43. Structurally, TEN domain is characterized by a deep groove on its 3 ACS Paragon Plus Environment


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surface, which appears to function as a single-stranded DNA binding site42,44. The groove contains conserved key amino acids that appear to assist in stabilizing the primer-template 41-46

duplex and reposition the 3′-end of the primer during repeat addition processivity

. The

important role of TEN domain in telomerase activity41-43, 47-55, suggests it would be a good target for inhibitor design. While no small molecule inhibitor hTERT binding site has been identified, some evidence suggests that BIBR1532, one of the best characterized small molecule telomerase inhibitors, may target TEN domain56-59. Telomerase treated with BIBR1532 exhibits decreased repeat addition processivity, suggesting increased rate of product release, and/or a decreased rate of the conformational changes that dictate translocation56. Kinetic analysis demonstrated BIBR1532 is noncompetitive with deoxyribonucleotides (dNTP), suggesting it does not target the active site57-60. Together, the data suggest that TEN domain would be an effective target site for rational design of telomerase inhibitors and that BIBR1532 may target TEN domain. In this study, we combined computer-aided drug design (CADD) strategies with the application of the reported homology model of hTERT49 to identify several new human telomerase inhibitors. The inhibition activity of the identified compounds were then confirmed by performing biochemical assays. The success of the platform suggests that rational structurebased design can be used to develop potent human telomerase inhibitors.

RESULTS AND DISCUSSION Current models of human telomerase suggest that the ribonucleoprotein (RNP) can exists between two limiting conformations including a state after primer extension with the primer/DNA product 3΄-end aligned with the 5΄ terminus of the templating domain (opened) and a state after translocation with the primer 5΄-end aligned with the 3΄-end of the template (closed)46. TEN domain plays a significant role in primer alignment and assists in converting telomerase between these two states. In the first step of telomerase cycle TEN domain assists in stabilizing the primer in the active site of the hTERT ring (RT-TRBD-CTE) and after each round of telomere synthesis TEN domain is repositioned relative to the catalytic site in the open state to facilitate primer-realignment46,49. It has been speculated that TEN domain can play a key role through repeated extension cycles (elongation) by constraining the RNA: DNA heteroduplex throughout DNA synthesis49 as well as assisting in re-establishing contact between the nascent DNA product and the template during repeat addition DNA synthesis42. Importantly, it has been suggested Gln169 has a critical role in allowing conformational 4 ACS Paragon Plus Environment

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changes required between TEN domain and the hTERT ring since Gln169 mutants cannot easily convert between the open and closed states42. Notably, in the generated homology model of human telomerase structure employed here, Gln169 forms two key hydrogen bonds including one with the backbone carbonyl group of Pro174 and the other with the backbone amino group of Leu175 as interactions which are responsible to stabilize TEN domain structural features49. Based on the importance of TEN domain, we utilized a rational process to identify novel human telomerase inhibitors that target TEN domain. Our effort focused on finding ligands that reside between amino acids Gln169, Pro174 and Leu175, which forms a TEN domain surface groove key residues. We propose that such ligands will disturb interactions between hTERT TEN domain and the nascent DNA product by preventing required TEN domain motions that facilitate switching between the open and closed states. CAVER 3.0.1.61 was applied in order to identify those residues that structure the tunnels leading into TEN domain anchor site. Possible cavities for DNA interactions were computed with the application of eleven snap shots (the energy minimized structure and every 1 ns of simulations snap shots) obtained from 10 ns of MDS performed for the individual hTERT TEN domain by assigning maximum probe radius as 9 Å, shell depth 4 Å, and clustering threshold of 3.5 Å. Only one tunnel with bottleneck of 1.6 Å was obtained (Figure 1). The result suggests that Val10, Gly106, Gly109, Gly110, Thr128, Ala130, Phe159, Tyr168, Gln169, Val170, Pro174, Leu175 and Tyr176 form a substrate/ligand entrance tunnel within TEN domain.

Docking and Molecular Dynamics Simulations (MDS) studies Among limited number of inhibitors reported for human telomerase, the mechanism of inhibition of only one, BIBR153257-59, has been well-studied. The crystallographic data of human telomerase and BIBR1532 complex is not available, so the exact interaction site of the ligand with the enzyme is unknown. However, all the evidence from the hTERT TEN domain function together with investigations on BIBR1532 mechanism of action (MOA) suggests a hypothesis that the TEN domain is an intercalation site for BIBR1532. Further, there are evidences at hand which prove MST-31285 compound62-64 ability in inhibiting human telomerase enzyme directly. Lack of well-characterized telomerase inhibitors has prevented a more complete dataset of ligands for ligand-based pharmacophore modeling or 3DQSAR studies. Docking methodology was used to predict the desire ligands as well as the substrate modes of interactions with the protein and also to calculate the ligands binding energies with the active 5 ACS Paragon Plus Environment


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pocket. In order to evaluate the docking methodology several known telomerase inhibitors were first docked over the entire hTERT molecule and then the hTERT TEN domain. The calculated docking binding energies over hTERT TEN domain and the target compounds experimentally reported IC50 values exhibited a correlation value of R2 = 0.52 which is acceptable and similar to other similar docking studies65-69 (Table 1). In the next step, molecular dynamics simulations methodology was employed to monitor BIBR1532 and MST-312 behavior in the hTERT TEN domain binding site. In addition, binding modes of telomeric ssDNA, 5΄-TTAGGG with TEN domain was also monitored by MDS. Docking studies show that telomeric ssDNA interacts with TEN domain through the formation of four hydrogen bonds with Arg6, Thr128, Gln169 and Gln177. Hydrophobic and ionic interactions with Tyr176 are observed as well. Telomeric ssDNA was also predicted to engage ionic interactions with Arg3 and Arg6 that are located in TEN domain entrance tunnel (Figure 2a). The docking binding energy for TEN-ssDNA interaction was obtained to be -11 kcal/mol. The docking studies predict several interactions between BIBR1532 and hTERT TEN domain, including hydrogen bonds between the amino group of BIBR1532 and a Gln169 backbone carbonyl group and the carboxyl group of BIBR1532 and Leu175 backbone amino group. BIBR1532 is more involved with the ssDNA-binding groove of TEN domain surface through ionic interactions with a negative ionizable area provided by Gln169 and Tyr176 and also hydrophobic interactions with Pro174 and the residues that are involved in the domain entrance tunnel. The docking binding energy for BIBR1532-TEN domain interaction was obtained to be -9 kcal/mol. Based on the performed docking studies, it is proposed that BIBR1532 interactions with Gln169, Pro174 and Leu175 restrict conformational changes in hTERT TEN domain that gives rise to the observed inhibition activity (Figure 2b). MST-312 orientations and binding configurations in hTERT TEN domain are different from those of BIBR1532. Major interactions include hydrogen bonding with Thr128, Gln169 and Tyr176 as well as hydrophobic interactions with Thr128, Ala130, Pro174, Leu175 and Tyr176. Docking predicted a binding energy value of almost three kcal/mol weaker than that of BIBR1532 for MST-312 (-6 kcal/mol) (Figure 2c). Ten ns of MDS were performed for each ssDNA /ligand-protein complex and the individual domain itself. Additionally, MDS were performed on hTERT TEN domain in the presence of the telomeric ssDNA, and BIBR1532. The obtained results were analyzed after RMSD plot observations (Figure 3). The backbone RMSD of hTERT TEN domain in all the three computed complexes were monitored with respect to the energy minimized structure of each as 6 ACS Paragon Plus Environment

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the reference structure and plotted as a function of the simulations time to see if the systems have converged to the equilibration state. The backbone RMSD of TEN domain in complex with the telomeric ssDNA stabilizes earlier and is lower in value than those calculated for hTERT TEN domain in complex with the studied inhibitors. This observation can be referred to the facts that not only the ssDNA is the native substrate of TEN domain, but also its larger size that offers more opportunity for the telomeric ssDNA to interact with the binding groove of hTERT TEN domain. The RMSD values of hTERT TEN domain binding groove participant residues that are calculated after 10 ns of MDS and with respect to the initial energy minimized corresponds (Figures 4a and 4b) further confirms the above mentioned observation. The target residues deviations in the presence of the ssDNA is similar to that of the individual TEN domain, while the presence of BIBR1532 and MST-312 induce significant deviations to the target residues. Further, considering the studied inhibitors root mean square deviations (Figure 3b), it is clear that the ligands stabilize in the binding groove after almost 2.5 ns of molecular dynamic simulations and stay stable for the rest of the study. The root-mean-square ﬂuctuations (RMSF) of the residues were graphed to better follow the enzyme residues ﬂexibility throughout the simulation time (Figure 4c and 4d). RMSF calculations of the key amino acids further confirms the conformational alternations occurs in hTERT TEN domain ssDNA binding groove as a result of the studied inhibitors binding (Figures 4c and 4d). Considering Figure 4c BIBR1532 mainly interacts with residues 104 to 112 which are located in deeper parts of the groove in compare with residues 98 to 103 that are more accessible. Interestingly, significant fluctuations obtained for residue Gln169 in TEN-BIBR1532 and TEN-MST-312 complexes (Figure 4d) show that BIBR1532 and MST-312 are capable of disturbing TEN domain conformation (especially residues Gln169, Pro174 and Leu175) needed for binding of the nascent telomeric ssDNA product. This disruption may transfigure the enzyme active site conformation required for the catalysis. Figure 4d reveals RMSF value of < 0.1 nm indicating that residues 169 to 175 lose their flexibility in the presence of the inhibitors especially in the presence of BIBR1532. Superimposition of TEN domain and TEN-BIBR1532 complex as well as TEN domain and TEN-MST-312 complex provides insight into possible binding modes (Figures 5a and 5b). As it can be seen in Figure 5a, the hydrogen binding interactions between BIBR1532 amino group and the backbone carbonyl group of residue Gln169 prevents significant displacement in this residue orientation in compare with that in the absence of the ligand. Also, a significant translocation is observed in Pro174 and Leu175 to better conduct the electrostatic π- π stacking 7 ACS Paragon Plus Environment


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interactions between the pyrrolidine group of Pro174 and the benzene ring of BIBR1532 benzoate fragment as well as the hydrogen binding interactions between Leu175 backbone amino group and the carboxylate group of the benzoate fragment. These interactions disturb the orientations of the three amino acids toward each other and as a result leads to a shallower groove. Other notable interactions include an electrostatic σ-π and hydrophobic interactions between the naphthalene fragment of the ligand and residues Leu131 and Thr128, respectively. MST-312 binding perturbs the putative ssDNA binding groove (Figure 5b). The displacement of residue Gln169 is considerable and hydrophobic interactions between residues Pro174 and Leu175 and MST-312 ligand diaminobenzene and catechol fragments, respectively, disturb conformation of the ssDNA binding groove which is shaped by intramolecular interactions of key Gln169, Pro174 and Leu175 amino acids. Additionally, electrostatic π-π and hydrogen binding interactions between the ligand catechol fragments and Tyr176 and Thr128 residues aids firm stabilization of MST-312 in the grove. MDS of T E N - telomeric ssDNA complex suggests that the binding is stabilized through four hydrogen bonds (Figure 5c). One hydrogen bond is formed between the purine fragment of the first guanine base in the telomeric sequence of TTAGGG and the backbone amino group of residue Gln169, another between the hydroxyl side-chain group of residue Thr128 and the carbonyl group of the same purine fragment, the third is seen between the hydroxyl group of Tyr176 and the purine fragment of the adenine base, and the forth is formed between the guanidine group of residue Arg108 and the ssDNA backbone. The structural changes associated with the ligands binding were compared to those resulting from the ssDNA binding (Figures 6a and 6b). No significant difference were seen in Gln169 orientation while notable differences are observed for residues Pro174, Leu175 and Tyr176, which shows that the presence of the ligands drastically rearranges the intervening loop of TEN domain ssDNA-binding groove. Interactions of the hTERT TEN domain with the telomeric ssDNA was also monitored in the presence of BIBR1532 (Figure 7). The results show that the telomeric ssDNA cannot enter TEN domain’s ssDNA binding groove appropriately in the presence of BIBR1532. Instead, the ssDNA establishes interactions (mainly by hydrogen bond formations and hydrophobic interactions) with exterior residues which are distant and distinct from the ssDNA binding site. The backbone RMSD plots of TEN domain in complex with ssDNA and BIBR1532-ssDNA and the ssDNA in the absence and presence of BIBR1532 shows that the final ssDNA-TEN state formed slower in the presence of BIBR1532 (Figures 8a and 8b). Further, RMSD plot of BIBR1532 shows that it reaches its final sate faster in the presence of the ssDNA (Figure 8c). 8 ACS Paragon Plus Environment

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Analysis of the number of hydrogen bonds formed showed that BIBR1532 decreasing the propensity for hydrogen formation between the hTERT TEN domain and telomeric ssDNA substrate (Figure 9).

Pharmacophore Model Generation, Pharmacophore-based and Docking-based virtual screening Since no crystallographic data of human telomerase and BIBR1532 or MST-312 complexes are available, pharmacophore models were generated using computational approaches70-73. In this study, a structure-based pharmacophore modeling strategy was employed to generate pharmacophore models which precisely describe physicochemical and structural features (functional groups), to enable the obtained set of compounds to interact properly in the target receptor ligand-binding pocket. It has to be mentioned that when the three-dimensional structure of the target and its ligands is unknown, pharmacophore approaches play a predominant role among the compound selection filters but in this situation the best way could be generating different pharmacophore models to cover all probable physicochemical and structural features72,73. AutoDock Tools (ADT) software ranks the docked conformations by energy, lowest (best) to highest, and it clusters conformations with similar structure or energy. The lowest energy conformations with RMSD values lower than 0.5 are the seeds for first cluster. ADT cluster analysis of TEN-ligands (BIBR1532 and MST-312) docking log files revealed that each ligand resided in six exclusive conformations in the cluster with the optimum docking binding energy. Docking interactions analysis showed several modes of conducive interactions between the ligands and the pocket key residues for each individual conformation. TEN domain anchor site key amino acids were analyzed including compounds’ binding modes in the hTERT TEN domain binding groove and mapping pharmacophore features, such as hydrogen bond acceptors (HBA) and donors (HBD), hydrophobic areas (HY), positive and negative ionizable area (respectively PI and NI), and aromatic rings (AR). As a matter, six particular pharmacophore models were generated from the obtained poses using LigandScout v3.01. Subsequently, the similar modes of the ligands interactions in the anchor site were aligned over to have those critical pharmacophore features that are common in the both inhibitors for that target pose (Figure 10). All of the mapped models not only project those pharmacophore characteristics that simulate TEN-ligands (BIBR1532 and MST-312) desirable interactions, but also comprise exclusive intervals and angles in between the features to include the stereochemistry and geometry specifications required. According to Figure 10, hydrogen bond donor and acceptors as well as hydrophobic interactions are the dominant pharmacophore 9 ACS Paragon Plus Environment


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points present in all models while polar and electrostatic interactions are also observed in some. The models were then assigned individually as filters for pharmacophore-based virtual screening over the ZINC database.

Docking studies of the identified compounds Pharmacophore-based virtual screening elicited out a total number of 20 compounds (Figure 11) that were further evaluated with the aid of ADT and LigandScout. The virtually screened obtained ligands were first evaluated according to their pharmacophoric fitness score. Then the compounds went through docking studies to better evaluate their binding energies and poses of interaction with TEN domain. Finally, 6 compounds were selected according to their docked structures, orientations, binding modes, calculated binding energies and their predicted ki values (Figure 10), to be assayed experimentally. Each set of the identified compounds was first sorted out based on the involved members binding energies and capability to interact with Gln169 strongly. Their ability to interact with Pro174 and Leu175 which can lead to impair the fundamental intramolecular hydrogen bonds i n between Gln169 with Pro174 and Leu175 were studied as well. Further, the compounds ability to interact with other key residues of TEN domain surface ssDNA groove including the ones observed via CAVER studies was considered and finally the compounds shape, the number of aromatic rings and carboxylate group were pondered. The compound that had all the key factors were chosen to be further evaluated experimentally. Two compounds were obtained from pharmacophore model one virtual screening. Compounds 1a and 1b are stabilized in TEN domain anchor site with similar orientations and binding configurations. The two major interactions of these compounds include hydrogen bindings with Gln169 and Leu175 but compound 1a is more involved with the cavity through ionic interactions with Pro174 and Leu175 (Figure 10a). Since 1a better alter Gln169, Pro174 and Leu175 conformations towards each other and its lower docking binding energy in compare with 1b, compound 1a was chosen to go under experimental study. Through docking studies of the obtained compounds via model two, it was seen that compound 2b is better positioned in the groove with better lower docking binding energy regarding compound 2a which is probably due to its semi-planner structure. Compound 2b is stabilized in TEN domain by forming four hydrogen bonds. Interactions of compound 2b with Gln169 through two hydrogen bond formation involves both backbone and sidechain carbonyl groups of Gln169 which may lead to disturb intramolecular Gln169, Pro174 and Leu175 hydrogen bonds strongly. The two other hydrogen bonds are observed between 10 ACS Paragon Plus Environment

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side-chain hydroxyl group of Thr128 and backbone amino group of Leu175 with the two carbonyl groups of 2b (Figure 10b). Six compounds were extracted based on pharmacophore model three. All the six identified compounds give good docking binding energies while that of compound 3f is almost 2 kcal/mol better than the others. 3f is stabilized in TEN domain groove via hydrogen bond formation with Ala130, Gln169 and Tyr176, and also an electrostatic σ-π interaction with Gln169 through its para-fluorobenzene fragment (Figure 10c). In addition this compound contains indole and pyrimidine- 2,4,6-trion fragments that are similar to purine and pyrimidine bases of DNA structure so it can be a good competitor for the telomeric ssDNA to approach TEN domain ssDNA groove. Seven compounds were obtained through virtually screening model four which are almost in the same range of docking binding energy and modes of interactions with the key residues. Compound 4c was chosen based on its similar structure to BIBR1532 in compare to the other six compounds. Compound 4c interacts with TEN domain groove through four hydrogen bonds formation with Thr128, Ala130, Gln169 and Leu175. Ionic interactions of ligand’s carboxylate group are also observed with Pro174, Leu175 and Tyr176 (Figure 10d). Applying pharmacophore model five, two compounds were identified. Compounds 5a and 5b are similar while 5b has a naphthalene fragment. Compound 5b is very interesting to be assayed because it has a scaffold very similar to that of BIBR1532, with an extra hydroxyl group on its naphthalene fragment. Compound 5b interacts with TEN domain groove through three hydrogen bonds formation; two hydrogen bonds with Gln169 carbonyl group and one with Leu175 backbone amino group. Ionic interactions of ligand’s carboxylate group are also observed with Leu175 and Tyr176 (Figure 10e). From the three compounds which were obtained through pharmacophore model six, compound 6b was selected due to its lower docking binding energy. In addition it is a good chance to assay the effect of the existence of an ethene-1,1-diyldibenzene fragment instead of the naphthalene fragment in BIBR1532 structure. This ligand interacts with TEN domain through two hydrogen bonds formation in which the backbone amino group of Gln169 and the side-chain hydroxyl group of Thr128 are involved (Figure 10f). Also compound 6b intercalations in TEN domain groove through hydrophobic interactions with Val10, Ala107, Thr126, Thr128, Leu131, Leu153, Ala167, Gln169, Pro174, Leu175 and Tyr176 shows this ligand can be fit and trapped in this area so well. Insertion of this compound with its specific mode of interaction in the telomeric ssDNA groove efficiently displace Pro174 and Leu175 positions towards each other as well as Gln169 backbone amino group. 11 ACS Paragon Plus Environment


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Confirmation of telomerase inhibition Hits from the virtual screen were purchased from MolPort and assayed for telomerase inhibition using the PCR-based TRAP assay. BIBR1532 as a positive control for telomerase inhibition (reported IC50 = 100 nM57 and 5 µM59, 2.5 µM (Table 2)). Four of the predicted compounds inhibition activity were confirmed by the TRAP assay with IC50 values in the low micromolar range (Figure 12 and Table 2). Like BIBR1532, it appears these compounds are more effective at inhibiting synthesis of longer telomerase products suggesting that they too block the processivity of telomerase presumably by inhibiting the translocation step. Finally, inhibition was confirmed using a direct telomerase assay. As we expected, each of the predicted ligands displayed potent telomerase inhibition activity at 20 µM concentration of the ligand.

CONCLUSIONS Here in this study reported experimentally obtained evidences were applied to accelerate the design new set of compounds with the ability of inhibiting enzyme telomerase over activity that would specifically prevent processivity procedure performed by TEN domain. To achieve the goal, a combined computer aided drug design technique was applied by coupling different strategies to make the rational drug design approach more precise and to acquire significant development in pharmaceutical output. Therefore, interactions of the already introduced compounds with the ability of inhibiting the enzyme rigorously were thoroughly monitored with the aid of docking methodology and classical molecular dynamic simulations to gain a clearer picture of the microscopic phenomenon leading the effective inhibition behavior for the target ligands. The obtained results suggests the critical role of residues 169 to 175, especially Gln169, Pro174 and Leu175, in hTERT TEN domain-telomerci ssDNA binding. Summarizing the observation through MDS studies it is concluded that BIBR1532 binds the active groove similar to that of telomeric ssDNA except that due to its smaller size, it does not conduct interactions with the entrance residues while MST-312 stabilizes in the pocket in a different mode from both the telomeric ssDNA and BIBR1532. The semi-planar structure of BIBR1532 enables the ligand to ride into the groove and lock TEN domain in the closed state by generating interactions with the groove residues, especially Gln169, that consequently disturb the active loop conformation. This observation that has been previously monitored through inducing site direct mutations in residue Gln169 further confirms the hypothesis of the key role of this residue in the open and close states of the hTERT TEN domain active groove in 12 ACS Paragon Plus Environment

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telomerase activity .The observations were then applied to design structure-based pharmacophore models to be later employed through virtual screening process. Six new set of compounds were gained employing the six generated models. The compounds went through docking-based virtual screening procedure and after ensuring their potency to generate effective interactions with TEN domain ssDNA binding groove, the best ligand from each set went under experimental studies. The experimentally generated IC50 values are in a range of 980 nM to 3.98 µM that proves the potency of the introduced hits to accelerate human telomerase inhibition. Also, the

models

generated

offer

a

template

for

better

understanding the structural mechanism of telomerase inhibition by BIBR1532 and MST-312, as well as developing more accurate models for next generation rational design of telomerase inhibitors. Future work will include mutagenesis of the key residues of the hTERT TEN domain to confirm their impact on binding of inhibitors BIBR1532, MST-312 along with the new inhibitors reported here. Additionally, kinetic analysis including competition assays of the inhibitors will be used to further validate the pharmacophore modeling, which presumes that the inhibitors bind in a similar fashion. In conclusion, this study once again demonstrates the value of computational methodologies, including virtual screening of large databases with pharmacophore models, to not only hasten, but also reduce the cost, of the lead identification stage of the drug development process.

EXPERIMENTAL SECTION Computational Methodology; Tunnels into the anchor site 61

CAVER 3.0.1 software

was used to monitor TEN domain to make a rational map of

pathways in to our hypothetic active binding pocket and also to identify the involved residues. Possible entrances were carved by assigning maximum probe radius as 9 Å, shell depth 4 Å, and clustering threshold 3.5 Å. further, the clustering threshold was decreased to look for more potential channels into the active site.

Docking Simulation Strategy AutoDockTools (ADT) 1.5.4 package74 was used for preparing all input files including human telomerase homology modeled TEN domain pdb file and ligands. Polar hydrogens were added and partial atomic charges were assigned by Kollman-united charges method. The built structure was saved in PDBQT format to be delivered to AutoDockTools as the input file. 13 ACS Paragon Plus Environment


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The grid box of 126×126×126 Å (x, у and z) was assigned on the macromolecule binding pocket with the spacing of 0.375 Å. Docking calculation parameters were assigned as follow: number of Lamarckian job=100; initial population=100; maximum number of energy evaluation=25×105; maximum generations=27,000; mutation rate of 0.2 across-over rate of 0.80; and all other parameters were kept as their default value.

The grid maps were

calculated and the docking procedure was performed by Autogrid 4.2 and Autodock 4.2, respectively. The two-dimensional structures of the ligands were generated applying Marvin Sketch v5.7, ChemAxon, and all hydrogens were added. The three-dimentional structures of molecules were modeled, energy minimized, and saved in mol2 format by HyperChem 8. Non-polar hydrogens were merged by ADT and atomic charge were assigned according to Gasteiger-Marsili charge.

Pharmacophore model generation and virtual screening TEN domain together with BIBR1532 and MST-312 complexes went through pharmacophore 75

model generation. LigandScout v3.01

was employed for structure-based pharmacophore

generation, refinement, mapping pharmacophore features, and finally virtual screening. The 76

final models were applied to ZINC compound database . The extracted compounds inhibitory activity was investigated with docking studies. Eventually 6 compounds were selected and were further studied via biochemical assays including TRAP and human telomerase direct assays.

Classical MD Simulations GROMACS MD package77 (Ver. 4.5.5) was used to simulate five individual systems; the individual TEN domain, TEN-ssDNA, TEN-BIBR1532, TEN-MST-312, and TEN-ssDNABIBR1532. The behavior of the individual TEN domain was monitored followed by the investigation of the domain conformational changes in the presence of the two prominent direct teleomerase inhibitors, including BIBR1532 and MST-312 in a dynamic environment. Further, telomeric ssDNA sequence of TTAGGG behavior in TEN domain ssDNA binding groove was monitored molecular dynamically as the domain main substrate. Docking methodology was used to obtain suitable ligand-protein complex, substrate-protein orientations to start MDs with and also to calculate their binding energy. The ligand’s topology files were generated via PRODRG web server78. The telomeric ssDNA structure was elicited from a crystallography structure (with PDB ID: 4P1D) and then energy 14 ACS Paragon Plus Environment

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minimized with the aid of Hyperchem 8. The converged structure was then applied to the docking studies and the corresponding topology file was generated by GROMACS software and Amber force field (amber99sb-ildn). The initial structures were built from TEN domain homology modeled PDB file49 and the initial orientation of the ligands and the telomeric ssDNA towards the human telomerase enzyme target domain was obtained from the former docking studies. In order to simulate TEN-ssDNA-BIBR1532 system, the telomeric ssDNA was docked over the obtained TEN-BIBR1532 complex after 10 ns of simulations, and the structure with the optimum modes of energy and key interactions was applied for the following molecular dynamic simulations. The enzyme acid and basic residues protonation state was chosen considering their local environment. The complex was immersed in a dodecahedronshaped box (x, у and z) with the minimum distance of 1 nm between the protein surface and the box walls, followed by solvation in a simple point charge water model molecules. Periodic boundary conditions were assigned in all directions. The system net charge was neutralized by adding Na+ counter ions which were randomly substituted by water molecules in suitable electrostatic potential positions. The whole system was then submitted to energy minimization employing steepest descent algorithm with tolerance of 1000 kJ/mol/nm. After convergence, the system went through NVT ensemble MD simulations for 20 ps. MD simulations was carried out employing NPT in a periodic boundary condition. Amber force field (amber99sb-ildn) was assigned to the protein. Berendsen barostat and thermostat were applied to keep the pressure and temperature constant at 1 bar and 300 K with a coupling time of τp=0.5 ps, and τT=0.1 ps, respectively. Long-range electrostatic interactions were calculated with the particle mesh Ewald method. The bond lengths were restrained employing LINCS, allowing an integration step of 1 fs. The MD simulations were extended for 10 ns at constant pressure and temperature conditions.

Data analysis and presentation The ligand–protein interactions were analyzed and visualized employing LigandScout v3.01, ADT and Discovery studio v3.579 and PyMOL software80. PyMOL was also employed to depict the docking poses and the MD simulation figures.

Experimental studies All small molecules tested as telomerase inhibitors were purchased from MolPort.

TRAP assay 15 ACS Paragon Plus Environment


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Telomerase activity in cell extracts was determined by a modified telomerase repeat amplification protocol (TRAP assay)81. In brief, cell lysates were prepared from 4×104 cells in 100 µ L of lysis buffer (10 mM Tris-HCl, pH 7.5, 1 mM MgCl2, 1 mM EGTA, 0.1 mM benzamidine, 5 mM β-mercaptoethanol, 0.5% CHAPS, 10% glycerol) by incubating the suspended cells on ice 30 min followed by three freeze thaw cycles using liquid N2 to freeze the samples. Cell lysate were clarified by centrifugation (10,000 g, 30 min at 4 ºC), and protein levels in the cell extracts were determined by the method of Bradford. Cell extracts (100 ng of total protein) were incubated with 0.1 µ g TS primer (5΄-AAT CCG TCG AGC AGA GTT) in a 25 µ l reaction containing TRAP buffer (200 mM Tris-HCl, pH 8.3, 15 mM MgCl2, 630 mM KCl, 0.5% Tween 20, and 10 mM EGTA) and 50X dNTP Mix ( 2.5 mM each dATP, dTTP, dGTP and dCTP) at 30 ºC for 30 min. Telomerase extension reactions were then heated to 95 ºC for 5 min before the products were amplified by adding 1µ M ACX reverse primer (5΄-GCG CGG [CTTACC]3 CTA ACC-3΄), 0.1 µ g TSNT template (5΄-AAT CCG TCG AGC AGA GTT-3΄ used as a PCR and loading control), 0.5 units Hot Master Taq DNA polymerase (Eppendorf), 5 µ l of 10 × Taq buffer, and dNTPs to a final concentration of 50 µ M. The reactions were then subjected to 33 PCR cycles at 95 ºC for 30 s, 60 ºC for 30 s, 72 ºC for 60 s. Reaction products were separated on a 12.5 % nondenaturing polyacrylamide gel, stained with SYBER Green Ι (Molecular Probes), and imaged on a phosphorimager. Reactions were quantified using ImageQuant and the product intensity for each reaction was normalized to the TSNT internal standard.

Inhibition Studies TRAP assays were conducted as described above with varying concentrations of inhibitors. IC50 values were calculated using the Excel software82. More than six concentrations were used for each compound and assays were performed at least in triplicate. The following four-parameter logistic curve equation was used: ‫ܦ=ݕ‬+

‫ܣ‬−‫ܦ‬ 1 + 10ሺ௫ି௟௢௚஼ሻ஻

where x is the concentration of inhibitor, y is the % inhibition relative to the primer-only control, A is the maximal activity, B is the slope factor, C is the IC50 (i.e., the concentration 16 ACS Paragon Plus Environment

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required for 50 % inhibition), and D is the minimal activity. Inhibition data were plotted as % inhibition vs log inhibitor concentration and fit using the same equation.



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Direct telomerase assay Telomerase activity was measured using a modification of a previously described direct assay83. Each 25 µ L reaction contained 50 mM Tris-HCl, pH 8.0, 50 mM KCl, 1 mM MgCl2, 5 mM β-mercaptoethanol, 1 mM spermidine, 1 µ M human telomere primer (5΄-TTA GGG TTA GGG TTA GGG), 0.5 mM dATP, 0.5 mM dTTP, 2.9 µ M dGTP, 0.33 µ M [α-32P]dGTP (3000 Ci/mmol, 10 µ Ci/µ L; Perkin-Elmer), and 10 µ L of preassembled telomerase. Inhibition studies also included 20 µ M amounts of small molecule inhibitors. Primer extension was carried out at 30 °C for 90 min. After the addition of a

32

P-labeled loading

control (114 nucleotide, 5΄-end labeled DNA oligonucleotide, 1000 cpm per reaction), the primer extension products were extracted with phenol/chloroform/isoamyl alcohol and ethanol precipitated in the presence of 0.6 M NH4OAc and 35 ng/µ L glycogen. Products were precipitated at -80 °C in 2.5 vol of absolute ethanol for 30 min followed by centrifugation at 22000g at 4 °C for 25 min and washing with 2 vol of 70% ethanol. Pellets were resuspended in a suitable volume of TE, and ethanol precipitation was repeated to ensure the removal of all unincorporated [α-32P]-dGTP.

The final pellets were

dissolved in a formamide loading buffer containing 40% formamide, 10 mM Tris-HCl, pH 8.0, 10 mM EDTA, 0.05% xylene cyanol, and 0.05% bromophenol blue. The products were heated at 95°C for 5 min and resolved on a prewarmed, 0.4 mm thick, 20 × 20 cm, 10% polyacrylamide/7 M urea/1× TBE gel. A small amount of the human telomere primer was labeled with [γ-32P]-ATP and T4 polynucleotide kinase (Fisher) and loaded in a separate lane to be used as a marker for the start of primer elongation. The gel was run at 800 V for 1 h in 1× TBE. After drying the gel and exposing it to a phosphorimager screen (Molecular Dynamics) overnight, telomerase activity was imaged using a phosphorimager (Molecular Dynamics Storm 860) and quantified with ImageQuant (version 5.2). The intensities of each band in each sample were summed and normalized to the loading control.

Acknowledgment We are really thankful of Professor Robert L. Jernigan and his coworkers because of their great effort of generating the applied human telomerase model and their generosity of sending that for us to accelerate the rational drug design process. Our study significantly can show and prove their model application capability for human telomerase inhibitors rational design. The financial support of the Research Council of the Tehran University of Medical Sciences is gratefully acknowledged. 18 ACS Paragon Plus Environment

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(67) Kim J. H.; Kim J. H.; Lee G. E.; Lee J. E.; Chung I. K. Potent Inhibition of Human Telomerase by Nitrostyrene Derivatives. Mol. Pharmacol. 2003, 63, 1117-1124. (68) Hayakawa, N.; Nozawa, K.; Ogawa, A.; Kato, N; Yoshida, K.; Akamatsu, K.; Tsuchiya, M.; Nagasaka, A.; Yoshida, S. Isothiazolone derivatives selectively inhibit telomerase from human and rat cancer cells in vitro. Biochemistry. 1999, 38, 11501-11507. (69) Mizushina Y.; Takeuchi T.; Sugawara F.;Yoshida H. Anti-Cancer Targeting Telomerase Inhibitors: β-Rubromycin and Oleic Acid. Mini Rev. Med. Chem. 2012, 12, 1135-43. (70) Steindl, T. M.; Schuster, D.; Wolber, G.; Laggner, C.; Langer, T. High-Throughput Structure-Based Pharmacophore Modelling as A Basis for Successful Parallel Virtual Screening. J. Comput. Aid. Mol. Des. 2006, 20, 703-715. (71) Yang, S. Y. Pharmacophore Modeling and Applications in Drug Discovery: Challenges and Recent Advances. Drug Disco. Today. 2010, 15, 444-450. (72) Wolber, G.; Seidel, T.; Bendix, F.; Langer, T., Molecule-Pharmacophore Superpositioning and Pattern Matching in Computational Drug Design. Drug Disco. Today. 2008, 13, 23-29. (73) Chen, Z.; Tian, G.; Wang, Z.; Jiang, H.; Shen, J.; Zhu, W. Multiple Pharmacophore Models Combined with Molecular Docking: A Reliable Way for Efficiently Identifying Novel PDE4 Inhibitors with High Structural Diversity. J. Chem. Inf. Model. 2010, 50, 615-625. (74) Morris, G. M.; Huey, R.; Lindstrom, W.; Sanner, M. F.; Belew, R. K.; Goodsell, D. S.; Olson, A. J. AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility. J. Comput. Chem. 2009, 16, 2785–2791. (75) Wolber, G.; Langer, T. LigandScout: 3-D Pharmacophores Derived from Protein-Bound Ligands and their Use As Virtual Screening Filters. J. Chem. Inf. Model. 2005, 45, 160-169. (76) Irwin, J. J.; Sterling, T.; Mysinger, M. M.; Bolstad ,E. S.; Coleman, R. G. ZINC: A Free Tool to Discover Chemistry for Biology. J. Chem. Inf. Model. 2012, 52, 1757–1768. (77) Pronk S.; Páll, S.; Schulz, R.; Larsson, P.; Bjelkmar, P.; Apostolov, R.; Shirts, M. R.; Smith, J.C.; Kasson, P.M.; van der, S. D; Hess, B.; Lindahl, E. GROMACS 4.5: A HighThroughput and Highly Parallel Open Source Molecular Simulation Toolkit. Bioinformatics. 2013, 1, 845-854. (78) Schüttelkopf, A. W.; Van Aalten, D. M. PRODRG: A Tool for High-Throughput Crystallography of Protein-Ligand Complexes. Acta Crystallogr.D. Biol.Crystallogr. 2004, 60, 1355-1363. (79) Discovery Studio Modeling Environment, version 3.5; Accelrys Software Inc., San Diego, CA, 2013. http://accelrys.com/ (accessed May, 2013) (80) Makarewicz, T.; Kazmierkiewicz, R. Molecular Dynamics Simulation by GROMACS 25 ACS Paragon Plus Environment


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using GUI plugin for PyMOL. J. Chem. Inf. Model. 2013, 53, 1229-1234. (81) Reed, J.; Gunaratnam, M.; Beltran, M.; Reszka, A. P.; Vilar, R.; Neidi, S. TRAP-LIG, a Modified Telomere Repeat Amplification Protocol Assay to Quantitate Telomerase Inhibition by Small Molecules. Anal. Biochem. 2008, 380, 99-105. (82) Sharma, R. K.; Sikarwar, A. MLR Study of IC50 Values for Cephalosporin Derivatives by MS Excel. I. J. RASET. 2013, 1, 85-95. (83) Chen, J. L.; Greider, C. W. Determinants in Mammalian Telomerase RNA that Mediate Enzyme Processivity and Crossspecies Incompatibility. EMBO J. 2003, 22, 304-14.

Legend of the Tables

Table 1. Reported IC50 values and the predicted docking binding energies of telomerase inhibitors Table2. Binding energies obtained through docking studies and properties of the extracted compounds

Table 1 Inhibitor

IC50 (µM)

Binding energy (Kcal/mol)

FJ5002

2.00

-6.01

MOIB

3.50

-6.29

MQMN

22.00

-4.76

MST-199

0.38

-8.35

MST-295

0.75

-8.12

NVN

2.57

-6.39

DPNS

0.40

-7.47

EOIB

2.00

-5.94

Β-Rubromycin

2.00

-7.47

MST-312

0.67

-6

_

-11

TelomericssDNA

Binding energy=Binding energy from the docking studies, IC50=inhibitory concentration 50% (BindingDB (http://www.bindingdb.org))66-69.


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Table 2 Compound

ZINC Database ID HB don HB acc Net charge

Binding energy

IC50

xlogp

MW (g/mol)

1a

ZINC13682481

1

6

-1

-5.20

>4µM

3.59

446.882

2b

ZINC13831130

3

11

0

-6.30

>4µM

-0.74

444.423

3f

ZINC00853309

1

7

0

-7.53

1.87µM

3.32

464.456

4c

ZINC02104260

3

8

-1

-5.80

3.98µM

0.72

410.402

5b

ZINC04962473

2

6

-1

-6.50

2.40µM

4.18

347.350

6b

ZINC04457841

1

5

-1

-8.77

980nM

4.98

403.845

BIBR1532

ZINC13488964

1

4

-1

-9.00

2.30µM

4.57

330.163

Binding energy=Binding energy from the docking studies, HB don =H-bond donor; HB acc =H-bond acceptor, Net charge=the net charge of the compound, xlogP=the octanol/water partition coefficients of organic compounds, IC50=inhibitory concentration 50%, MW=Molecular weight

ACS Paragon Plus Environment


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Legends of the figures

Figure 1. Tunnel (green sphere) identified by CAVER with probe radius as 0.9 Å, shell depth 4 Å, and clustering threshold 3.5 Å, and bottleneck 1.6 Å. Figure 2. Proposed interactions of the ligands with TEN domain observed employing docking methodology; a. telomeric ssDNA, b. BIBR1532, and c. MST. Three dimensional representations; green dashes: H-bond donors, red dashes: H-bond acceptor, yellow sphere: hydrophobic site, green sphere: both hydrophobic and negative-ionisable sites. Two dimensional representations; green arrow: H-bond donor, red arrow: H-bond acceptor, yellow representation: hydrophobic site, and the dark red stars: negative-ionisable sites. TEN domain residues are represented in cyan and telomeric ssDNA and the ligands are presented in magneta. Figure 3. RMSD plots of, a. TEN domain backbone in the absence of the ligands (violet) and in complex with telomeric ssDNA (magenta), BIBR1532 (dark blue) and MST (cyan), b. the ligands telomeric ssDNA (magenta), BIBR1532 (dark blue) and MST (cyan). Figure 4. RMSD plots of TEN domain residues in the absence of the ligands (violet), and in complex with telomeric ssDNA (magenta), BIBR1532 (dark blue) and MST (cyan) for, a. residues 98-112, and b. residues 168-176, as well as RMSF plots of TEN domain residues in the absence of the ligands (violet), and in complex with telomeric ssDNA (magenta), BIBR1532 (dark blue) and MST (cyan) for, c. residues 98-112, and d. residues 168-176. Figure 5. Comparison of the ligands positions and the active pocket residues orientation deviations in TEN domain active groove through the average structures superimposition of TEN domain (green) over TEN domain in complex with , a. BIBR 1532, b. MST, and c. telomeric ssDNA. Figure 6. TEN domain in complex with telomeric ssDNA (magenta) superimposed over TEN domain in complex with a. BIBR1532 (cyan), and b. MST (green). Figure 7. Average structure of ssDNA-TEN interactions in the presence of BIBR1532 as the inhibitor after the system reached the equilibration state (the last 4ns of the simulation), TEN backbone residues are presented in blue, the inhibitor in magenta, and ssDNA bases in multicolor. Figure 8. RMSD plots of, a. TEN domain backbone in complex with telomeric ssDNA in the absence of BIBR1532 inhibitor (magenta) and in the presence of BIBR1532 inhibitor (green), b. telomeric ssDNA in complex with TEN domain in the absence of BIBR1532 inhibitor (magenta) and in the presence of BIBR1532 inhibitor (green), and c. Inhibitor BIBR1532 in complex with TEN domain in the absence of telomeric ssDNA (dark blue) and in the presence of telomeric ssDNA (green). ACS Paragon Plus Environment

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Figure 9. Number of hydrogen bonds formed between TEN domain residues and telomeric ssDNA, a. in the absence of BIBR1532, and b. in the presence of BIBR1532. Figure 10. Interactions of the extracted ligands with TEN domain observed employing docking methodology; from left to right, the pharmacophore model, the two dimensional representation of TEN-ligand interactions and the three dimensional representation of the corresponding TEN-ligand interactions; a. compound 1a from Model 1, b. compound 2b from Model 2, c. compound 3f from Model 3, d. compound 4c from Model 4, e. compound 5b from Model 5, and f. compound 6b from Model 6. Three dimensional representations; green dashes: H-bond donors, red dashes: H-bond acceptor, yellow sphere: hydrophobic site, green sphere: both hydrophobic and negative-ionisable sites. Two dimensional representations; green arrow: H-bond donor, red arrow: H-bond acceptor, yellow representation: hydrophobic site, the dark red stars: negative-ionisable sites and the dark blue lamp: electrostatic interactions. TEN domain residues are represented in cyan and the ligands are presented in magenta. The third column represents the designed model employed to elicit out the corresponding ligand from ZINC database. Figure 11. Final compounds extracted from ZINC library database and based on the applied pharmacophore models. The compounds are named after the applied model. Figure 12. Evaluation of telomerase inhibitory effects of the predicted telomerase inhibitors; a. 4c, b. 3f, c. 5b, d. 6b, e. BIBR1532, f. All of the tested compounds (4c, 3f, 5b, and 6b) and BIBR1532, g. Human telomerase direct assay results of the tested compounds in their 20 µ M concentrations.



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Figure 1

Figure 2


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Figure 4



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Figure 5


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Figure 8


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Figure 10 ACS Paragon Plus Environment

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Figure 11



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Figure 12


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Figure 1. Tunnel (green sphere) identified by CAVER with probe radius as 0.9 Å, shell depth 4 Å, and clustering threshold 3.5 Å, and bottleneck 1.6 Å. 80x42mm (300 x 300 DPI)



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Figure 2. Proposed interactions of the ligands with TEN domain observed employing docking methodology; a. telomeric ssDNA, b. BIBR1532, and c. MST. Three dimensional representations; green dashes: H-bond donors, red dashes: H-bond acceptor, yellow sphere: hydrophobic site, green sphere: both hydrophobic and negative-ionisable sites. Two dimensional representations; green arrow: H-bond donor, red arrow: H-bond acceptor, yellow representation: hydrophobic site, and the dark red stars: negative-ionisable sites. TEN domain residues are represented in cyan and telomeric ssDNA and the ligands are presented in magneta. 105x63mm (600 x 600 DPI)


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Figure 3. RMSD plots of, a. TEN domain backbone in the absence of the ligands (violet) and in complex with telomeric ssDNA (magenta), BIBR1532 (dark blue) and MST (cyan), b. the ligands telomeric ssDNA (magenta), BIBR1532 (dark blue) and MST (cyan). 89x91mm (600 x 600 DPI)



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Figure 4. RMSD plots of TEN domain residues in the absence of the ligands (violet), and in complex with telomeric ssDNA (magenta), BIBR1532 (dark blue) and MST (cyan) for, a. residues 98-112, and b. residues 168-176, as well as RMSF plots of TEN domain residues in the absence of the ligands (violet), and in complex with telomeric ssDNA (magenta), BIBR1532 (dark blue) and MST (cyan) for, c. residues 98-112, and d. residues 168-176. 99x58mm (300 x 300 DPI)


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Figure 5. Comparison of the ligands positions and the active pocket residues orientation deviations in TEN domain active groove through the average structures superimposition of TEN domain (green) over TEN domain in complex with , a. BIBR 1532, b. MST, and c. telomeric ssDNA. 185x467mm (600 x 600 DPI)



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Figure 6. TEN domain in complex with telomeric ssDNA (magenta) superimposed over TEN complex with a. BIBR1532 (cyan), and b. MST (green). 64x27mm (600 x 600 DPI)


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domain in

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Figure 7. Average structure of ssDNA-TEN interactions in the presence of BIBR1532 as the inhibitor after the system reached the equilibration state (the last 4ns of the simulation), TEN backbone residues are presented in blue, the inhibitor in magenta, and ssDNA bases in multicolor. 76x40mm (600 x 600 DPI)



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Figure 8. RMSD plots of, a. TEN domain backbone in complex with telomeric ssDNA in the absence of BIBR1532 inhibitor (magenta) and in the presence of BIBR1532 inhibitor (green), b. telomeric ssDNA in complex with TEN domain in the absence of BIBR1532 inhibitor (magenta) and in the presence of BIBR1532 inhibitor (green), and c. Inhibitor BIBR1532 in complex with TEN domain in the absence of telomeric ssDNA (dark blue) and in the presence of telomeric ssDNA (green). 134x207mm (600 x 600 DPI)


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Figure 9. Number of hydrogen bonds formed between TEN domain residues and telomeric ssDNA, a. in the absence of BIBR1532, and b. in the presence of BIBR1532. 50x30mm (600 x 600 DPI)



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Figure 10. Interactions of the extracted ligands with TEN domain observed employing docking methodology; from left to right, the pharmacophore model, the two dimensional representation of TEN-ligand interactions and the three dimensional representation of the corresponding TEN-ligand interactions; a. compound 1a from Model 1, b. compound 2b from Model 2, c. compound 3f from Model 3, d. compound 4c from Model 4, e. compound 5b from Model 5, and f. compound 6b from Model 6. Three dimensional representations; green dashes: H-bond donors, red dashes: H-bond acceptor, yellow sphere: hydrophobic site, green sphere: both hydrophobic and negative-ionisable sites. Two dimensional representations; green arrow: H-bond donor, red arrow: H-bond acceptor, yellow representation: hydrophobic site, the dark red stars: negative-ionisable sites and the dark blue lamp: electrostatic interactions. TEN domain residues are represented in cyan and the ligands are presented in magenta. The third column represents the designed model employed to elicit out the corresponding ligand from ZINC database. 239x330mm (300 x 300 DPI)


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Figure 11. Final compounds extracted from ZINC library database and based on the applied pharmacophore models. The compounds are named after the applied model. 204x262mm (300 x 300 DPI)


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Figure 12. Evaluation of telomerase inhibitory effects of the predicted telomerase inhibitors; a. 4c, b. 3f, c. 5b, d. 6b, e. BIBR1532, f. All of the tested compounds (4c, 3f, 5b, and 6b) and BIBR1532, g. Human telomerase direct assay results of the tested compounds in their 20 µM concentrations.


Potent Human Telomerase Inhibitors: Molecular ... - ACS Publications

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