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2011, 12(12), 9440-9462; https://doi.org/10.3390/ijms12129440
Dynamic Structure-Based Pharmacophore Model Development: A New and Effective Addition in the Histone Deacetylase 8 (HDAC8) Inhibitor Discovery
Division of Applied Life Science (BK21 Program), Systems and Synthetic Agrobiotech Center (SSAC), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Gazwa-dong, Jinju 660-701, Korea
Author to whom correspondence should be addressed.
Received: 29 August 2011; in revised form: 15 November 2011 / Accepted: 8 December 2011 / Published: 19 December 2011
Histone deacetylase 8 (HDAC8) is an enzyme involved in deacetylating the amino groups of terminal lysine residues, thereby repressing the transcription of various genes including tumor suppressor gene. The over expression of HDAC8 was observed in many cancers and thus inhibition of this enzyme has emerged as an efficient cancer therapeutic strategy. In an effort to facilitate the future discovery of HDAC8 inhibitors, we developed two pharmacophore models containing six and five pharmacophoric features, respectively, using the representative structures from two molecular dynamic (MD) simulations performed in Gromacs 4.0.5 package. Various analyses of trajectories obtained from MD simulations have displayed the changes upon inhibitor binding. Thus utilization of the dynamically-responded protein structures in pharmacophore development has the added advantage of considering the conformational flexibility of protein. The MD trajectories were clustered based on single-linkage method and representative structures were taken to be used in the pharmacophore model development. Active site complimenting structure-based pharmacophore models were developed using Discovery Studio 2.5 program and validated using a dataset of known HDAC8 inhibitors. Virtual screening of chemical database coupled with drug-like filter has identified drug-like hit compounds that match the pharmacophore models. Molecular docking of these hits reduced the false positives and identified two potential compounds to be used in future HDAC8 inhibitor design.
Keywords:molecular dynamics simulation; pharmacophore; virtual screening; Lipinski's rule; molecular docking
1. Introduction
Histone deacetylase (HDAC) is a zinc-dependant enzyme involved in the deacetylation of terminal acetylated lysine residues of histone proteins [1–3]. Deacetylation is one of the post-translational chromatin modifications that regulate the epigenetic control of gene expression. This chromatin modification is indispensable as this prepares the chromatin accessible to a large number of chromatin interacting proteins [4]. The acetylation and deacetylation of lysine residues of histone protein are controlled by histone acetyl transferase (HAT) and HDAC enzymes, respectively [5,6]. The mechanism behind controlling the gene transcription is the extent of histone-DNA binding which is the effect of exposure of the positive charge of lysine residues on deacetylation. In contrast, HATs loosen the histone-DNA binding by acetylating the positively charged lysine residues back to their acetylated form and leads to the transcriptional activation [7]. The transcriptional repression of various pre-programmed set of genes including tumor suppressor gene leads to cancer. These repression and activation of transcription have fundamental regulatory roles in developmental processes and their deregulation has been linked to the progression of cancers and different human disorders [8]. HDAC inhibitors can induce cancer cell death whereas normal cells are relatively resistant to the inhibitor-induced cell death [9,10]. HDACs are present in almost all organisms and at least, to date, 18 types of HDACs were identified and classified into four broad classes. First 11 of these 18 types are named HDACs, which contain divalent Zn2+ cation as catalytic machinery and the rest of them are known as sirtuins, which are NAD-dependant enzymes due to their deacetylation mechanism. Class I HDACs include HDAC1-3 and 8 whereas Class II is made of HDACs 4–7, 9 and 10. All of the sirtuins are grouped into Class III HDACs as their mechanism of deacetylation process is different to that of other HDACs. HDAC11, which shares substantial homology with Class I and Class II HDACs, is the only member of Class IV HDACs. Because of the widespread biological effects of HDACs the inhibition of these enzymes has emerged as a new therapeutic approach to treat many diseases such as neurodegenerative, hereditary, inflammatory diseases and cancer [11–18]. HDAC inhibitors are a structurally distinct class of chemical compounds with unique structural elements to compliment the active site components. The generally known pharmacophoric elements present in HDAC inhibitors are: (i) a zinc-binding moiety (ZBM) to co-ordinate with the catalytic metal ion; (ii) a hydrophobic cap group (HCG) to bind at the surface of the tunnel-like active site; and (iii) a hydrophobic linker (HYL) that joins both ZBM and HCG. This hydrophobic linker is normally six carbon chains longer, which is almost equal to the length of the tunnel of the active site (Figure 1) [7].
To date, a number of potential HDAC inhibitors are in clinical trials. Suberoylanilide hydroxamic acid (SAHA) was the first HDAC inhibitor approved by FDA. In terms of diverse chemical structures, HDAC inhibitors include a wide range of scaffolds and can be classified into structural classes such as aliphatic acids, hydroxamic acids, cyclic peptides and benzamides. Design and evaluation of linkerless hydroxamic acid derivatives reported that these new linkerless scaffolds have shown >100 fold selectivity for HDAC8 over other Class I and II HDACs. In addition, it is also reported that the active site of HDAC8 is unconventionally malleable and can accommodate inhibitors of diverse structures with no conventional structural arrangement, which is "ZBM-linker-HCG" [19]. In terms of selectivity of inhibitors, modifications of the ZBM, linker, and HCG have individually contributed to the selective nature of the compounds. All these modifications at various positions have led to the class-selective rather than isoform-selective inhibitors [19,20]. Various experimental evidences reported that different subtypes have intrinsic differences in substrate selectivity [20–22]. A clear knowledge of the selectivity profile over various HDACs from the structural perspective is necessary to design isoform-selective HDAC inhibitors with elevated potency [20]. All Zn-dependant HDACs possess well-conserved deacetylase core domains of approximately 400 amino acids and identical catalytic machinery. This functionally important catalytic machinery includes Zn (a divalent cation) and a charge relay system formed by two histidine and two aspartate residues very close to the metal ion. The active site region of HDACs is highly conserved among the family members and making it very difficult to design isoform-selective inhibitors. A recent study reported that the exposed surface of the active site entrance is less conserved among HDAC isoforms and can be utilized in designing isoform selective inhibitors [20]. Functional HDACs, except HDAC8, are usually found as high molecular weight multiprotein complexes and their purified recombinant forms are also enzymatically inactive [23]. Therefore, HDAC8 has been considered as a best model among mammalian HDACs from a structural biology perspective. The first crystal structure of HDAC8 was determined with a bound inhibitor in 2004 followed by some other diverse inhibitors and with mutations [24–28]. A study observing the structural differences due to the bound inhibitor has reported that the active site topology showed large differences based on the nature of the co-crystallized inhibitors. It also concluded that the enzyme's inhibition is strongly regulated by the surface malleability [29]. Though the differences in the crystal structures showcase interesting information that they can be used in designing new class of selective inhibitors, the conformational flexibility of the enzyme also has become indispensable to be considered. Thus the utilization of molecular dynamic (MD) simulations to consider the flexibility of protein in the development of pharmacophore models can be a right choice and improve its reliability [30,31].
In this study pharmacophore models were developed using the representative structures of HDAC8 obtained from MD simulations. Two inhibitors showing potent inhibitory profile for HDAC8 were selected and docked into the active site of the enzyme to prepare enzyme-inhibitor complexes. The MD simulations of these complexes were performed and the representative structures from the highly clustered conformations of MD simulation trajectory were selected to be used in the development of pharmacophore models. These pharmacophore models, after validation, were used in virtual screening to identify new and potential hits for future drug design. Molecular docking study has reduced the probability of picking false positives as potential hits. Finally we obtained two potential compounds which can be used in future HDAC8 inhibitor design.
2. Results and Discussion
2.1. Molecular Dynamic Simulation Studies
Two most active HDAC8 inhibitors were selected from a set of inhibitory molecules with the experimental activity values determined using the same biological assay procedure collected from the literature. These two compounds are structurally very different except having the hydroxamic acid moiety as common zinc binding group and showing very similar HDAC8 inhibitory profile. The C1 has the modified linker portion when compared to trichostatin A and SAHA whereas the C2 is linkerless (Figure 2). Thus these two compounds were selected to be used in MD simulation studies and the dynamic structures of HDAC8 complexed with these two diverse inhibitors were used in structure-based pharmacophore modeling. Utilization of dynamic structures of the enzyme obtained upon binding of two diverse most active inhibitors in pharmacophore development has an advantage over using a single static conformation of the enzyme [31]. These compounds were docked into the active site of HDAC8 enzyme using GOLD (Genetic Optimization for Ligand Docking) 4.1 program [32]. The X-ray determined crystal structure (PDB code: 2V5X) was used in molecular docking study. The binding poses of the inhibitors were carefully chosen as it is very important that the metal binding part of the inhibitors should be located close to the divalent metal (Zn2+) ion (Figure S1). The best binding conformation among the poses predicted by GOLD program was selected and the protein-inhibitor complex is used in 5 ns MD simulations. Before selecting the crystal structure with the PDB code 2V5X for further study, another HDAC8 crystal structure (PDB code: 1T64) was considered in molecular docking as it is the high resolution structure resolved so far. The 1T64 was identified to have two adjacent active sites bound with two molecules of inhibitors but only one site contains the metal ion responsible for the catalytic activity [20]. The best docked poses of inhibitors bound at this metal containing active site were selected and subjected to MD simulation for 5 ns (data not shown).
The results of MD simulation have displayed only a single active site merging both the initial active sites. This behavior of 1T64 was considered unfavorable and thus 2V5X, which exhibits a single active site in its crystal structure, was selected for this study. Another MD simulation of HDAC8 without inhibitor (apo-form) was also performed to compare the changes upon the binding of inhibitor. Our simulation results were analyzed using tools bundled with GROMACS distribution package. The root mean square deviation (RMSD) of backbone atoms of the protein revealed that apo-form of HDAC8 was very stable with an average RMSD value of 0.14 nm throughout the simulation time whereas the inhibitor complexes were slightly unstable during the first half of the simulation time i.e., up to 2500 ps. During the second half of the simulation time (2500–5000 ps), the RMSD values for HDAC8-C1 and HDAC8-C2 complexes were maintained at an average value of 0.19 and 0.17 nm, respectively. The RMSD value for HDAC8-C2 complex was slightly lower than HDAC8-C1 complex. This difference can simply be explained upon the size and chemical differences between both of them. These changes in RMSD also conclude that the binding of inhibitor seems to increase the conformational flexibility of HDAC8. None of the three MD simulations resulted in very unstable structures which in turn confirmed the convergent behavior of the systems during the entire course of simulation and the results are reliable for further studies (Figure 3a).
The root mean square fluctuation (RMSF) plot was plotted to estimate the changes in dynamic flexibility of the regions of protein structure due to the inhibitor binding. It also has confirmed that all the catalytically important residues including charge relay system and most of the tunnel and surface forming residues except F209 and M274 were very stable throughout the simulation in all systems. The D101 which is present at the surface of the active site has shown increased flexibility in the C2 complex whereas the flexibility of F209 and M274 has reduced upon inhibitor binding (Figure 3b). The potential energy values of all the systems have shown a smooth decrease till 500 ps and later they stabilized around a constant value of −718,000 kcal/mol (Figure 3c).
The distance between the hydroxamic acid moieties of bound inhibitors and the catalytic divalent (Zn2+) metal ion present at the bottom of the tunnel like active site was measured throughout the simulations. The average distance between the hydroxamic acid moieties of both inhibitors and Zn2+ was 0.19 nm which was maintained very stable during the simulation (Figure S2). Both the inhibitors were in interacting distance from the divalent metal Zn2+ ion and the charge relay system. A charge relay system comprising a couple of histidine (H142 and 143) and aspartate (D178 and 183) residues was identified as very important tool in deacetylation process [33–35]. Some structural studies predicted that both histidine residues forming two independent hydrogen bonds are essential for the charge relay systems present in the active site [1,36]. In order to confirm the presence of the inhibitory molecules within interacting distance from the functionally important residues H142 and H143, we have calculated the distance between the hydroxamic acid moieties of the inhibitors and these histidine residues. From the distance plot, we observed that C1 stayed closer to both the histidine residues when compared to C2. In detail, hydroxamic acid moieties of both the compounds were in similar distance with the average values of 0.30 and 0.34 nm from H142 whereas the distance between hydroxamic acid moieties of both compounds and H143 was fluctuating. The center of mass of six atoms of hydroxamic acid moiety and the corresponding histidine residues were used in these distance calculations. The average distance values observed between the hydroxamic aid moieties of compounds (C1 and C2) and H143 (Figure 4a,b) were 0.34 and 0.43 nm, respectively. As the hydrogen bond interaction is important for any HDAC8 inhibitors we calculated the hydrogen bond interactions between the inhibitors and protein during the course of simulation. The C1 and C2 have formed 2 and 1 hydrogen bonds as average values throughout the simulation time with maximum of four and two hydrogen bonds with the active site residues, respectively (Figure S3). Based on the size of the compounds, it was assumed that C1 binds the whole stretch of the tunnel-like active site using its hydroxamic acid, thiophenyl, phenyl and four-membered alkylamino chain while positioning its indole moiety outside the surface of the active site for extended interactions. In the other hand, the C2 positions itself only within the tunnel-like active site approaching the hydrophobic surface with its phenylmethoxy end (Figure S1). An analysis over the orientation of C1 throughout the simulation time has shown that its phenyl ring was placed against the phenyl ring of F139 enabling π-π interaction during the simulation time (not shown in figure). The orientation analysis on C2 has shown that six-membered part of its indole ring was stacked between F139 and H180 enabling very strong hydrophobic interactions at the active site. Based on the results of these analyses both the compounds were predicted to be interacting well at the active site and thus their middle structures snapped from the clustering analysis were used in structure-based pharmacophore modeling.
The results of the simulation of HDAC8 enzyme with two different inhibitors were further analyzed using clustering method to cluster the snapshots based on the RMSDs of atom positions. Each structure is added to a cluster when its distance to any element is less than a given cutoff. This analysis will be useful to statistically characterize conformational families which are sampled during the simulations. Clusters were obtained with a cut off value of 0.077 nm and each cluster has a representative structure. The simulation with C1 and C2 were divided into 11 and 10 clusters, respectively, based on the RMSD values of the atom positions (Figure 5). This clustering process also calculates a middle structure for every single cluster obtained. In terms of simulation with C1, the ninth cluster containing 47% of simulated conformations was selected and its middle structure was obtained for further study. In the other hand from the simulation result of C2, tenth cluster containing 33% of the simulation structures was selected and its middle structure was taken to be used in further study. The representative middle structures from both the simulations with bound inhibitor were obtained and superimposed to observe the conformational changes occurred among active site catalytic residues including charge relay system upon different inhibitor binding. All of the catalytically important residues except H143, which is slightly tilted from its position, have behaved same for both the inhibitors whereas the surface forming hydrophobic residues such as Y100 and Y306 have fluctuated differently as response to the nature of the bound inhibitor (Figure 6).
2.2. Structure-Based Pharmacophore Models
Pharmacophore models were generated using the two representative structures obtained from the MD simulations of HDAC8 bound C1 and C2, respectively. The hydrogen bond acceptor (HA), hydrogen bond donor (HD), and hydrophobic (HY) chemical features were generated based on the interaction points available from the active site. Based on the mechanism of action and the general pharmacophore for HDAC8 inhibitors, residues were chosen and to trim down the generated pharmacophoric features we have selected only the features that are generated complementary to these residues. Seven pharmacophoric features containing three HD, three HY, and one HA were selected as a pharmacophore model (Pharm-A) from the dynamic structure resulted from HDAC8-C1 complex. Three HD features of Pharm-A were generated as complementary features to D101, H142 and D178 residues. Two features complementing H142 and D178 were located very close to each other and thus were merged to create a new average feature at their average position. The only HA feature of Pharm-A was generated as a complimentary to the catalytic metal (Zn2+) ion whereas the three HY features were picked to compliment the hydrophobic tunnel and surface residues. Thus the final Pharm-A contains one HA, two HD and three HY features was generated from HDAC8-C1 complex (Figure 7).
In addition to the Pharm-A, another pharmacophore model was developed using the middle structure obtained from the simulation of HDAC8 bound with C2. Similar to the generation of Pharm-A, the pharmacophoric features generated complimentary to the important active site components were selected and developed a pharmacophore model (Pharm-B). Initially this pharmacophore model was made of one HA, two HY, and four HD features. The HA and HY features of Pharm-B were the complimentary features to the metal ion and the tunnel as well as surface forming residues. Four HD features were the complimentary result to D101, D178, D267, and Y306 residues of the active site. Three of these four HD features were formed very close to each other and has the starting vector point at the same place. Thus these three HD features were merged together to form an average HD feature. The final Pharm-B contains five pharmacophoric features which include one HA, two HD, and two HY features (Figure 8). These generated pharmacophore models represent the flexibility of protein and ligands in its conformational space. Comparison of the generated pharmacophore models revealed that both the models contain same pharmacophoric features except an additional HY feature in Pharm-A. Although the models look similar in their chemical features they are different in their 3D conformational space and this conformational difference is considered as the major advantage of the dynamic structure-based pharmacophore models (Figure 9).
2.3. Validation of Generated Pharmacophore Models
The generated pharmacophore models were validated for their reliability to be used in database screening and prediction of new compounds. Two different methods with different information were employed in validating the pharmacophore models. The first method is based on the best binding orientation used in MD simulations while the second method is based on the collected dataset of HDAC8 inhibitors with the experimental inhibitory profile. In the first validation method, the best binding orientations of C1 and C2 were matched with generated pharmacophore models A and B, respectively. This study has shown interestingly that both the compounds could map the pharmacophoric features well except one HD feature generated complimentary to D101.
In terms of C1, its terminal indole, central phenyl, and thiophene rings mapped upon the HY features of Pharm-A, whereas the closely present functional groups of hydroxamic acid have mapped over the HA and HD features (Figure S4). Comparing to this, C2 has mapped the two HY features of Pharm-B with its phenyl and indole rings whereas the hydroxamic acid moiety mapped over the HA and HD features (Figure S5). In the second method, a dataset containing 100 HDAC8 inhibitors with experimental activity values was screened using the generated pharmacophore models as 3D structural queries. This dataset was divided into three categories: (i) active (<0.1 μM); (ii) moderately active (≥0.1 <1 μM); and (iii) inactive (>1 μM) to assess the predictive ability of the pharmacophore models. Compounds of this dataset were screened using the pharmacophore models. Of 100 compounds, Pharm-A and Pharm-B have screened 52 and 57 compounds, respectively. Pharm-A has identified 15 of 17 active compounds, 35 of 68 moderately active compounds, and only 2 of 15 inactive compounds with the percentage values of 88.24, 51.47, and 13.33, respectively. Pharm-B has identified 13 of 17 active compounds, 40 of 68 moderately active compounds, and 4 of 15 inactive compounds with the percentage values of 76.47, 58.82, and 26.66, respectively (Table 1). In addition, another pharmacophore model (Pharm-X) was developed from the X-ray structure (PDB code: 2V5X) directly to be kept as control to evaluate the results of Pharm-A and Pharm-B. This Pharm-X was used in the same way as other models in screening the test set of 100 known HDAC8 inhibitors. This control pharmacophore model has screened the known set of compounds with lesser predictions compared to that of Pharm-A and Pharm-B models (Table 1). These statistical results from the validation process proved that the generated pharmacophore models are good enough to be used in database screening to find new lead compounds with great potential to inhibit HDAC8.
2.4. Database Screening
Virtual database screening method using pharmacophore models as 3D queries has successfully been used in retrieving potential compounds that can confidently be used in novel drug discovery and development. As this approach serves an advantage over any de novo design methods by providing a set of compounds directly for the biological testing and it is very popular among drug discovery scientists. Both the validated pharmacophore models were used as 3D queries in database screening. A chemical database named Asinex containing 213,462 compounds was utilized in database screening procedure. The chemical compounds of the database fitting with all the pharmacophoric features of Pharm-A and Pharm-B were identified through ligand pharmacophore mapping process along with the Best/Flexible search option. During database screening Maximum Omitted Features option was set to "0" to screen the databases for the compounds those fit on all pharmacophoric features of Pharm-A and Pharm-B. The first pharmacophore model, Pharm-A, has identified 627 compounds mapping all of its pharmacophoric features. The hit compounds resulted from this step were considered in Lipinski's drug-like screening which resulted 515 compounds as Lipinski positives. These compounds were further filtered based on the fit value of the most active compound in the experimental dataset used in validation process. The most active compound (C1) has scored a fit value of 2.02 mapping five of six features of Pharm-A missing only the HD generated against D101. Thus 49 compounds mapping all the features and scoring a fit value greater than 2 were selected as hits from database screening using Pharm-A. Adding to these hits, the second pharmacophore model, Pharm-B, was also used in database screening to identify more hit compounds. Pharm-B containing five features has identified 2753 compounds mapping all of five features. These compounds were subjected to drug-like screening based on Lipinski's rule which has identified 2386 compounds as Lipinski positives. Based on the fit value of the most active compound (C1) for Pharm-B, which is 3.7, the hit compounds were filtered. Filter based on the fit value has identified 51 compounds which mapped all the features of Pharm-B and scored a fit value greater than C1. Totally 100 compounds were identified, 49 from Pharm-A and 51 from Pharm-B, respectively, through database screening and subsequently considered in molecular docking study. The 33 of these 100 compounds were identified by both the pharmacophore models and thus contains the characteristics of both C1 and C2 inhibitors.
2.5. Molecular Docking
Final hit compounds along with the most active C1 and C2 were docked in to the active site of HDAC8. The prepared middle structures obtained from the MD simulations with both most active compounds C1 and C2 were used as target protein molecules. The molecular docking results were used as a post-docking filter to select the compounds those interact with the active site amino acids and to predict the binding orientations of the hit compounds. The docking program GOLD has generated several feasible binding conformations for each compound and ranked them according to their fitness scores. The bound conformation with the most favorable energies was considered as the best binding orientation. Hydroxamic acid moieties of C1 and C2 have shown interactions with functionally important metal ion and active site amino acids. The GOLD fitness scores for C1 and C2 at the active sites of two different inhibitor-induced conformations of HDAC8 were 65.658, 53.291 and 73.111, 56.362, respectively. Thus, compounds scoring GOLD fitness scores greater than 53 and 56 at C1 and C2 bound active site, respectively, were selected for further analysis on binding modes and detailed molecular interactions with the important amino acid residues. This analysis has shown that C1 has bound the active site well with its hydroxamic acid moiety contacting the catalytic machinery of HDAC8 enzyme. The carbonyl group of this hydroxamic acid which was mapped over the only HA feature of Pharm-A has formed a co-ordinate bond with metal ion. The NH and OH groups of hydroxamic acid part of C1 that mapped over the averaged HD feature have formed hydrogen bonds with H143 and D178, respectively. The interaction through the NH and OH group with H143 was not formed in C2-induced conformation of the enzyme as this H143 was a bit away from its position (Figure 10a). In terms of binding analysis of C2, a co-ordinate bond was formed between carbonyl group of the hydroxamic acid moiety which was mapped over the only HA feature of Pharm-B and metal ion whereas its hydroxyl group mapped over the average HD feature has formed a hydrogen bond with D178 (Figure 10b). The phenyl ring of C1 was placed parallel to the phenyl ring of Y306 enabling very strong π-π interaction at the surface of the tunnel-like active site. The five-membered ring of C1 was placed at the center of the tunnel to interact electrostatically with the tunnel forming hydrophobic residues. Based on the binding modes and molecular interactions observed at the active site for C1 and C2, two compounds from the database were selected as final hits of molecular docking study. Hit 1, a derivative of N-acetyl benzamide (Figure 11), has formed a strong hydrogen bond network with the catalytic machinery of the enzyme. The substituted amino group and fused oxygen atom of six-membered part of the fused ring of hit 1 has formed co-ordinate bond with metal ion and hydrogen bonds with D178 and H180 amino acids. The NH group of the five-membered part of the fused ring system has formed hydrogen bond with H143. The central and terminal phenyl rings were located at the middle and surface of the tunnel-like active site enabling strong hydrophobic interactions with tunnel and surface forming residues. As a part of these hydrophobic interactions, a π-π interaction was observed between the five-membered part of the fused ring of hit 1 and imidazole ring of H180 (Figure 10c). Hit 2, which is a derivative of acetamide (Figure 11), has also formed strong molecular interactions at the active site. The terminal amide moiety of hit 2 has formed a co-ordinate bond with metal ion and hydrogen bonds with H142 and D178 residues. In addition, the oxygen atom of substituted ethoxy group of a phenyl ring has formed a hydrogen bond with hydroxyl group of Y306. The three aromatic rings of this hit were placed in a way that they can strongly interact with tunnel and surface forming hydrophobic residues (Figure 10d). The fit values of hit 1 and 2 over Pharm-A and B, respectively, were 3.316, 3.245 and 2.817, 2.628 whereas the GOLD fitness scores were 72.095, 62.158 and 62.930, 53.898. Both of these compounds were identified by both the pharmacophore models and thus contain characteristics of both C1 and C2 inhibitors. Finally the novelty of these hit compounds was confirmed using SciFinder Scholar and PubChem search that these hits were not reported for HDAC8 inhibition elsewhere earlier [37,38].
3. Experimental Section
3.1. Selection of Protein and Inhibitor Structures
To date 18 crystal structures were determined by various research groups among which only 8 are native HDAC8 bound with different inhibitors [23,39–42]. Remaining structures were found to be mutant variants or bound with the same inhibitor but determined by different experimental methods or placed with different divalent metal ions at the active site (Table 2). From the 8 crystal structures available with bound inhibitors, 2V5X (PDB code) was selected based on the resolution and the topology of the active site. Despite of being determined at high resolutions, crystal structures 1T64 and 1 VKG were not used in this study because of the unusual binding characteristics of trichostatin observed in 1T64 and the gap due to the missing amino acids in 1VKG. An extensive literature search was performed over all scientific resources including patents to collect experimentally tested most potent HDAC8 inhibitors available so far. A hundred HDAC8 inhibitors tested using the same biological assay protocol were identified and two most active compounds (C1 and C2) with the IC50 values of 8 and 10 nM (Figure 2) were selected to be used in this study [43,44].
3.2. Molecular Docking and Dynamic Simulations
The selected crystal structure was checked for missing amino acids and bad valences. The water molecules and other molecular fragments present in the crystal structure were removed after checking for their catalytic importance. Prior to MD simulations, the selected most active inhibitors were docked into the active site of HDAC8 (PDB code: 2V5X) using GOLD program. The best docking pose was selected for each inhibitor based on the GOLD fitness score and molecular interactions observed with catalytic residues involved in deacetylation mechanism. The MD simulations were successfully used to verify or to generate the reliable binding mode of the docked small molecules [45]. Thus three 5 ns MD simulations were performed including an apo-form of HDAC8 and two HDAC8-inhibitor complexes using GROMACS 4.0.5 package running on a high performance linux cluster computer [46,47]. The GROMOS 96 forcefield was applied for all MD simulations [48]. The topology file of inhibitors consistent with GROMOS 96 force field was generated (supplementary file A1) by the public access PRODRG online server [49]. The molecular structures were solvated in a cubic water box of 1 nm from the surface of the structure with SPC3 water model followed by the addition of counter-ions to neutralize the system. The entire system was subjected to energy minimization using steepest descent algorithm for 10,000 steps. This energy minimization was followed by a 100 ps position restrained MD run where only the solvent molecules and counter-ions were allowed to move by fixing the protein backbone. Finally the equilibrated systems were subjected to 5 ns production runs with a time step of 2 fs at constant temperature (300 K) and pressure (1 atm). All simulations were run under periodic boundary conditions with NPT ensemble and V-rescale thermostat [50]. The structures from the dynamic trajectory were stored every 1 ps. The van der Walls (vdw) forces were treated by using a cutoff of 14 Å with the particle mesh Ewald method setting used for long-range electrostatic interactions [51]. All the post-dynamic analyses of the trajectories were done using the auxiliary analysis package available in GROMACS program.
3.3. Clustering of Snapshots
We used g_cluster analysis protocol available with GROMACS distribution package to find few representative snapshots from the bunch of 5000 frames saved after every 5 ns MD simulation. This clustering analysis was considered critical as the representative snapshots can represent the conformational flexibility of the protein throughout the simulation. The root mean square deviation (RMSD) after fitting or RMSD of atom-pair distance can be used to define the distance between structures. The g_cluster protocol clusters the structures by several different methods such as single linkage, Jarvis Patrick, Monte Carlo, diagonalization and gromos methods. In this study, single linkage method that adds a structure to a cluster when its distance to any element of the cluster is less than the cutoff value was used. Clusters of complete structures were generated using this method with a cutoff value of 0.077 nm.
3.4. Structure-Based Pharmacophore Generation and Validation
Two structural snapshots taken from two HDAC8-inhibitor complexes were considered in this step to generate structure-based pharmacophore models. Apo-form structure was not considered in pharmacophore generation as this study mainly accounts the conformational changes of protein structure due to the inhibitor binding. Interaction generation protocol available in Accelrys Discovery Studio 2.5 (DS) [52] was used to extract all the available hydrophilic and lipophilic interaction points that can be complemented by the inhibitor. The identified hydrogen bond acceptor (HBA), hydrogen bond donor (HBD) and hydrophobic (HY) features were clustered and edited using Edit and Cluster Pharmacophore tool which is available in DS. The final edited pharmacophore model comprises the necessary pharmacophoric features, which are expected to be present in the small molecule inhibitor. The final pharmacophore model was subjected to validation based on the active site orientation of inhibitors and using a data set containing 100 chemical compounds tested for HDAC8 inhibition using same biological assay protocol [53–59].
3.5. Database Screening
The generated structure-based pharmacophore models within DS were used as a 3D query in database searching. This virtual screening of chemical databases was conducted to find novel and diverse virtual leads suitable for further development. Database searching offers the advantage that the retrieved compounds are usually more easily available for testing than those based on de novo design methods [60–62]. All screening experiments were performed using the Ligand Pharmacophore Mapping protocol with the Best Flexible Search option as available in DS. A molecule must be able to map all of the features of the pharmacophore model to be listed as a hit. Geometric fit values are calculated for every database hit based on how well the chemical substructures of a compound map on to the pharmacophoric feature location constraints and their distance deviation from the feature centers. The hit compounds scoring a fit value higher than the most active compound are chosen for further studies. Hit compounds that scored fit values better than any of the most active compounds of this dataset were selected and checked for their drug-likeness properties based on Lipinski's rule of five using DS [63]. Hit compounds those passed all of these screening tests were selected and used in molecular docking. A Lipinski-positive compound has (i) a molecular weight less than 500; (ii) less than 10 hydrogen bond acceptor groups; (iii) less than 5 hydrogen bond donor groups; and iv) an octanol/water partition co-efficient (LogP) value less than 5.
3.6. Molecular Docking and Lead Identification
Compounds that were predicted to be positive in Lipinski drug-likeness screening were subjected to molecular docking studies. The GOLD uses a genetic algorithm to dock the small molecules into the protein active site [64]. GOLD allows for a full range of flexibility for the ligands and partial flexibility of the protein. The performance of GOLD program upon a huge diverse set of protein-ligand complexes has been evaluated in various studies, which proved that GOLD program performs better compared to many other currently available docking programs [65,66]. Protein coordinates from the structural snapshots taken from the MD simulation trajectories were used to define the active site. The active site was defined with 10 Å radius around the bound inhibitor. The ten top-scoring conformations of every ligand were saved at the end of the calculation. Early termination option was used to skip the genetic optimization calculation when any five conformations of a particular compound were predicted within an RMSD value of 1.5 Å. The GOLD fitness score is calculated from the contributions of hydrogen bond and van der Waals interactions between the protein and ligand, intramolecular hydrogen bonds and strains of the ligand [64,67]. Protein-ligand interactions including π-π interactions were analyzed using DS and Molegro Molecular Viewer programs [68].
4. Conclusions
In order to develop dynamic structure-based pharmacophore models, we selected two of the most active HDAC8 inhibitors with considerable chemical diversity and docked into the active site of the enzyme. The best binding conformations of these compounds were selected and their complexes with HDAC8 were used in 5 ns MD simulations. After examining the results of these MD simulations, middle structures from highly clustered conformational space were obtained and used in structure-based pharmacophore model development. Two pharmacophore models, namely, Pharm-A and Pharm-B were developed considering catalytically important amino acid residues as expected inhibitor-interacting points. These pharmacophore models, as developed using structures from MD simulations that provide information over 3D conformational changes, are highly reliable compared to that of single conformation structure-based pharmacophore models. These pharmacophore models were further validated using an experimentally tested HDAC8 inhibitors dataset, which additionally strengthened the quality of the pharmacophore models. The Asinex database containing huge sets of diverse chemical compounds was virtually screened for compounds that match the pharmacophoric features identified in the study. The hit compounds were further filtered for drug-like properties based on Lipinski's rule of five. The hits identified with drug-like properties were docked into the active site of HDAC8 enzyme to investigate the binding modes and molecular interactions formed with catalytic amino acid residues as a positive indication of enzyme inhibition. A post-docking analysis based on binding modes and molecular interactions produced two compounds as final hit compounds to be utilized in future HDAC8 inhibitor design.
Supplementary Materialijms-12-09440-s001.pdf
This research was supported by Basic Science Research Program (2009-0073267), Pioneer Research Center Program (2009-0081539), and Management of Climate Change Program (2010-0029084) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST) of Republic of Korea. And this work was also supported by the Next-Generation BioGreen 21 Program (PJ008038) from Rural Development Administration (RDA) of Republic of Korea.
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Figure 1. The 3D structure of human histone deacetylase 8 (HDAC8) bound with an inhibitor (PDB code 2V5X). A zoomed view of inhibitor binding region shows the tunnel-like active site in mesh form. The hydroxamic acid part of inhibitor binds close to the metal (Zn2+) ion whereas the aliphatic chain and hydrophobic cap groups occupy the tunnel and surface of the active site.
Figure 2. The 2D chemical structures of HDAC8 inhibitors used in this study are displayed with two clinically proven HDAC inhibitors for structural comparison.
Figure 3. Comparison of (a) root mean square deviation (RMSD) of backbone atoms; (b) root mean square fluctuation (RMSF); and (c) potential energy values of 5 ns molecular dynamic (MD) simulations for three systems.
Figure 4. Distance between the hydroxamic acid moieties of inhibitors and two catalytically important histidine residues (a) H142 and (b) H143 in the mechanism of HDAC8 enzyme.
Figure 5. The histogram obtained from the clustering analyses of last 4 of 5 ns conformational regions of HDAC8-inhibitor complex MD simulation trajectories. Dark and light gray color cylinders represent the clusters obtained from HDAC8-C1 and HDAC8-C2 complexes, respectively.
Figure 6. Overlay of the representative structures obtained from 5 ns MD simulations of HDAC8-C1 (white) and HDAC8-C2 (green) complexes. Important amino acid residues of HDAC enzyme and the inhibitors are shown in stick and ball-stick forms, respectively.
Figure 7. Structure-based pharmacophore model generated from the HDAC8-C1 complex. Secondary structure of protein is shown in cartoon and amino acid residues are shown in stick form. The C1 inhibitor is shown in ball-stick representation. The identified pharmacophoric features are shown in green, cyan and magenta for HA, HY and HD features, respectively.
Figure 8. Structure-based pharmacophore model generated from the HDAC8-C2 complex. Secondary structure of protein is shown in cartoon and amino acid residues are shown in stick form. The C2 inhibitor is shown in ball-stick representation. The identified pharmacophoric features are shown in green, cyan and magenta for HA, HY and HD features, respectively.
Figure 9. Generated pharmacophore models (a) Pharm-A and (b) Pharm-B are shown with their inter-feature distance constraints. Green, cyan, and magenta colors represent HA, HY and HD features, respectively.
Figure 10. Binding modes and molecular interactions of (a) inhibitor C1 (b) inhibitor C2 (c) hit 1 and (d) hit 2 at the active sites of two different inhibitor-induced conformations. White and green cartoons represent C1- and C2-induced conformations of HDAC8 enzyme. Amino acid residues are shown in stick form whereas the ligands are shown in ball-stick form. Hydrogen atoms are not shown for clear view.
Table 1. Results of validation process using HDAC8 dataset and generated pharmacophore models, Pharm-A and Pharm-B.
|HDAC8 Dataset
|Percentage (%)
|Percentage (%)
|Percentage (%)
|Active <0.1 μM
|Moderately active ≥0.1 <1 μM
|Inactive >1 μM
|Resolution (Å)
|Bound Ligand
|Metal Ion
|Trichostatin A
|Trichostatin A
© 2011 by the authors; licensee MDPI, Basel, Switzerland. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).


text:
I read. A lot. I'm usually going through 3 different books at a time, one physical book, one e-book, and one audio book. And I'd like to share my opinions about books that I read so that others can know whether the book is worthwhile. I do read books that aren't about programming, but I don't do reviews of them on here; it doesn't fit the theme.
Here's a list of all the book reviews I've put up.
My Top Programming Books Read in 2014
Book Review of Functional Python Programming
Book Review: Test-Driven Python Development
My Top Programming Books Read in 2015
Book Review: Effective Python
Book Review: Effective JavaScript

subset: commoncrawl


text:
This weekend I have been completely engrossed in watching The Crash Course, which is Chris Martensen's best attempt to explain exactly how we got into this economic crisis.
While The Crash Course is divided into small video sections of 3-18 minutes each, the whole thing runs just over 3 hours. We had it on DVD and watched the whole thing from beginning to end. While I already knew many of the themes in this presentation (Economic slump, Peak Oil, Environmental Problems, Baby Boomer Retirement, World Overpopulation), I found The Crash Course an excellent way of summarizing it all and showing how they all relate.
I've been dedicated to downshifting during the last few months, but somehow becoming aware of all this information has made the task more imperative and urgent for me. As I digest all of this more fully, I'm sure I'll share my thoughts over the coming weeks and months.
If any of you have already seen this presentation, I'd love to hear your thoughts.
Photo by: azrainman


text:
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text:
Anyone who has spent some time in the new Hard Modes operations will tell you one thing; melee is worthless, pointless, and it's best to reroll ranged. Maras, Jugs, Sins, and Ops are DPS of the past; subbed out and switched for their 30 meter brethren who can bring more to the table after balance and class changes. But, is this rush to pew pew over stab stab simply a trend based on what's being played at the top? Or, is the reign of the ranged something more?
So, which is better, RDPS or MDPS?
Ranged has always performed well in SWTOR. You could (and should currently) bring 4 ranged DPS to any fight. Mercs and Snipers consistently have topped charts and have brought raid utility since the beginning. Ballistic shield, self- cleanses, and off heals were all checks in the positive for these classes. Sorcs, who for long were the lowest ranged DPS, have also grown in popularity after the Madness buffs.
Ranged has the advantage of being able to be anywhere and hit the target. Switching to adds is easier for ranged, and ranged classes are more mobile now; even during transitions they have DPS uptime. Positioning wise, ranged is also superior. Being able to stack, or not stack, allows for many fight mechanics to be dealt with easier.
Melee has never been in a particularly good place throughout the history of the game. Operatives have seen more nerfs than any other class, and for a long time was not favored in PvE (or PvP). Juggs and Sins also haven't really stood out as the best DPS classes. For a long time these classes were weak defensively, and only exceptional players who magicianed the numbers were worth anything. Maras on the other hand were always the shining star melee class. Bloodthirst and multiple defensive cooldowns made them a needed and sought after DPS. Not to mention, Maras got the job done. It may just have been the Maras I played with, who were some of the best in this game, but they were always the last to die, the top of the charts, and if could DPS tank a boss longer than anyone else. The fact that each class can hypothetically parse around the same has very little impact when it comes to what is going to work best in a raid environment. DPS uptime is really the determining factor on how much damage you do.
Other than the raw numbers, cooldowns and utility are the deciding factors when it comes to who to play and who to bring. Most classes have seen an increase in the amount of damage they are able to mitigate through defensive cooldowns or by choosing certain Discipline Utilities. This aspect used to be a boon to melee, but is now shared amongst all the classes. Bloodthirst, and other utility spells, are also spread out since 3.0, Mercs, operatives, and Sorcs all have their own "bloodthirsts" now. Certain fight mechanics also can be avoided or cheesed by some classes and their particular abilities.
The fight designs in Ravagers and ToS have led to the "Ranged is Right" mentality. Sparky, Bulo, Torque, Cortanni, Walkers, and Revan all highly favor ranged. Mechanics in these fights cause long and constant DPS downtimes which ranged simply don't have to deal with. Minecarts, Load Lifters, and AoE fires require melee to move outside their 5-10m ranges, over and over and over again. Less DPS uptime means less DPS. Not to mention many of the AoE affects crowd up the real estate the Tanks need to use to tank. Many ranged DPS are also able to mitigate fight mechanics due to their CDs, something that melee could more exclusively do in the past. Mercs and Snipers can both avoid Magnetic Clamps on Torque, and their superior knockbacks made Revan far easier. If you're a mara or an op, good luck.
And that's part of my problem, classist mechanics. In DF and DP we saw this with self-cleanses, but in the new operations it has evolved. If a mechanic is in a fight, every class should be able to deal with it. Sure, someone can knock back for you, but then it shouldn't be a targeted mechanic. Another example is Sparky add management. While ranged can simply turn and shoot the adds, which can't be near the boss, Melee have to either move to them or stay on the boss. On Walkers, it's easier to avoid damage with players spread as well. Not to mention the loss of DPS having to run out of and back into range if you get the grenade.
Balance or FotM
It's not that you couldn't bring a Melee to a fight, but why would you? I don't exactly prescribe to the "everyone should play what they want" mentality. Sure, if you're just here to raid casually and not compete, play what you want. But if you care about numbers, progression, or bragging rights there really isn't a choice. This game, and most MMOs, have long had a FotM problem. Look at PvP stats from the last two Ranked Seasons and you will see Sorcs and Sins everywhere. <Zorz> cleared ToS with 8 bounty hunters, capitalizing on Merc healers and DPS putting out HUGE numbers. As well as Hydraulic Overrides being overpowered.
There is a cavernous gap in this game between what it good and what is not, with long spells of the status quo remaining the same. In SWTOR, unlike other MMOS, people have MANY alts. Most WoW players have a few characters, but most SWTOR players have 6-7. The fact that so many players have everything, or multiples of the same class, means people jump on the FotM bandwagon hard and fast. And due to the fact that balance happens in this game all at once across all the classes, the constant fine tuning to create true balance can't be accomplished. The demographics of what is being played and used, compared to not used, is a good indicator of what needs work or not.
So, do you play ranged or melee? Have you had to switch? Is one truly better than the other?
1/11/2015 04:56:39 am
Well Mae I would have to disagree with the ranged issue, for maras at least, while their experience must be high to get great numbers this tier they certainly can. My ol' melee buddy Stippling does great numbers in the new stuff, though sometimes we have to tweak more conventional strats a bit to allow him to shine fully. The triple bleeds that Anni can put up allow for high numbers in fights like Walkers....he does about 4k in that fight? I do about 3.7k as bomb runner AP PT in that fight. So we kinda? have always run with two melee (PT is still kinda melee). Stipp (Anni) and Myself, PT (both specs) have very similar numbers on training dummies, 4.6-4.9k but on fight where there are many adds stacked Stipp can do better than I depending on the layout of a given boss. When he runs carnage, for example, he can do better than myself on Commanders (sweeping slash burst AoE is out of control...easy on rage and can do about 25k on large trash packs)! I would agree that on fights such as HM Torque having 2 melee can get kinda crazy with all that fire. I would suggest that this tier, like most tiers before it, favor ranged and allow for a less experienced player to do better generally.
1/31/2015 10:58:51 am
I agree it CAN be done, but people have so many alts now that why not switch to what is best. When it comes to the progression race, wouldn't the fotm class with the best numbers be the best to bring? Do you lose time by not being on a fotm class?
2/13/2015 05:43:53 pm
I guess my point here is that not all melee dps are created equal. In this tier Maras and Juggs do very well (indeed as well as most ranged, pyro merc being the exception). In many fights, the damage profile of anni is terrific (as in walkers) as well as bringing solid utility to fights like The Underlurker, where liberal usage of predation makes the slow mechanic of the adds portion null and void. For Juggs, they may actually have the best damage of any AC in the game on Torque due to their reflect ability.
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- We have all winner! Fresh Linux Mint 17.1 – hands down the best
Beats Ubuntu, Fedora, openSUSE, Elementary (theregister.co.uk)
- Linux Mint 17.1 finally makes MATE's fancy Compiz graphics easy to use (pcworld.com)
- Systemd fallout: Debian fork Devuan set up (itwire.com)
- OS X 10.10 vs. Ubuntu 14.10 vs. Fedora 21 vs. openSUSE Factory (phoronix.com)
Main Topic: Linux Mint 17.1 "Rebecca" Released!
- Linux Mint 17.1 is a long term support release which will be supported until 2019. It comes with updated software and brings refinements and many new features to make your desktop even more comfortable to use.
- http://blog.linuxmint.com/?p=2700 — October 2014 News
- http://blog.linuxmint.com/?p=2702 — Mate
- http://blog.linuxmint.com/?p=2703 — Cinnamon
- Just a few more days before 17.1 (http://blog.linuxmint.com/?p=2711)
- Linux Mint 17.1 "Rebecca" MATE released! (blog.linuxmint.com)
- Linux Mint 17.1 "Rebecca" Cinnamon released! (blog.linuxmint.com)
Pre-Show Music:
- "C'est La Vie" by NaTtA <http://www.jamendo.com/en/list/a132671/c-est-la-vie>
- "Not Quite Connected" by Josh Woodward
- "Lightning" by Tamara Laurel <http://www.jamendo.com/en/list/a134086/lightning>
Web Site:
- 11 Useful Utilities To Supercharge Your Ubuntu Experience (omgubuntu.co.uk) Whether you're a relative novice or a seasoned pro, we all want to get the most from our operating system. Ubuntu, like most modern OSes, has more to offer than what is presented at first blush. From tweaking and refining the look, behavior and performance of the Unity desktop to performing system maintenance, there are a huge array of useful utilities and apps that can help tune Ubuntu to meet your needs in no time..
Tip of the Podcast:
- 10 Sed (Stream Editor) Command Examples (nextstep4it.com) Sed is a stream editor in UNIX like operating system which is used for filtering and transforming text. Sed is derived originally from the basic line editor 'ed', an editor you will find on every unix system but one that is rarely used because of its difficult user interface.
- Xfce window borders missing (keheliya.blogspot.com) On one bad day if you find your nice, slick Xfce desktop environment is all messed up and there are no window decorations or title bar in the application windows.. it can be because your window manager has crashed.
Podcast Announcements:
- SCaLE 13x: The 13th annual Southern California Linux Expo (February 19-22, 2015) Hilton Los Angeles Airport, Los Angeles, CA
- Kansas Linux Fest (March 21-22, 2015) Lawrence, KS
- LinuxCon North America (August 17-19, 2015) in Seattle, WA
More Information:
Hosts: Rob, Scott, Joe
Live Stream every other Sunday 2:00 p.m.(Central): mintcast.org/livestream
Contact Us:
- Forum: forums.linuxmint.com
- Email: email@example.com
- Twitter: @mintCast @Linux_Mint @3dbeef @txhawkins @JoeRessington
- IRC: irc.spotchat.org – #mintcast, or irc.freenode.net – #mintcast
- Google+: mintCast
More Linux Mint info: website, blog, forums, community
Podcast Entry and exit music provided by Mark Blasco (podcastthemes.com). Podcast bumpers provided by Oscar.


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Rafting in the Smokies and The United Way of Sevier County present "Rafting for Reading"The United Way of Sevier County in cooperation with the Dollywood Foundation and Head Start programs in Sevier County are building a volunteer-based reading initiative that provides an enthusiastic adult reader for 20 minutes per day to the 4 year old children in each of the ten Head Start programs in Sevier County. The goal is to increase the vocabulary and love of reading among the 4 year olds before they reach kindergarten. Rafting in the Smokies is proud to partner with United Way to benefit this new program. On Saturday August 6, you can raft for a special discounted price of $10 off ($29 + tax). Rafting in the Smokies will donate $5 from every rafting reservation to the Early Reading Initiative. Rafting in the Smokies is happy to offer our community and yearly visitors a quality whitewater rafting experience. For 33 years Rafting in the Smokies has focused on providing a fun, "Family Adventure" to all who visit us. This year we want to focus on the Family portion in a new way! This United Way benefit day is one small way we choose to support our wonderful community in return for the loyal support we have been given for so many years. While you are there on August 6, don't forget to check our new Family Adventure Island where we have ziplines, a ropes course, a nature trail, swings and picnic tables. Make plans to join us for a great day at the river.
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They are so quick to make you could make heaps and give them as gifts or use them to sell at fund raisers and fêtes. If you don't sew.... and here is a plug for my store... i make these and sell them in my etsy store and via my facebook page and am happy to take custom orders.
Here is how to make a tissue holder for your tissue stash!
Cut two rectangles of fabric, you can use matching or contrasting fabric whatever you want to! I used a bright yellow and orange floral print as my lining fabric, and a blue polka dotted fabric as my outer fabric.
The rectangles need to be about 16.5 cm * 14 cm(you need to alter this according to the size of your mini tissue pack as some are larger than others!).
- With face side in, sew all around the rectangle with about 0.5cm seam leaving 2 cm un-stitched.
- Turn right side around, and iron flat,. Ironing helps to keep the project neat and makes it easier to sew as there are no bubbles or creases in the fabric to mess up your straight lines!
- Stitch around the rectangle, to close off the small opening, and to make all the sides uniform. In this example I chose a decorative stitch, however a straight stitch or hand stitching is fine as well.
- with long sides horizontal, fold the fabric in half short sides together to provide a half way line, then unfold it, and fold the left side and right side up to the midway line. Iron and pin to make it easier to stitch.
- Stitch across the short sides
- Then turn the fabric the right way around, and put your small packet of tissues, or some loose tissues you have folded into the holder, and you are ready to use it!
Ideal to make for fund raisers, fêtes, or as a small gift! These tissue holders help to organise your tissues in your bag, and you could make other accessories, such as diary cover and draw string bags for makeup, in matching or coordinating fabric and have a set of items!


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May is National Stroke Awareness Month
Jenifer S.
Tuesday, May 1, 2012
By: Jenifer Schuerman
As an active, healthy and well adjusted 21-year old, I was looking forward to my first "grown up" trip. I was going to celebrate my 21st birthday with my mom and brother in Las Vegas. The trip was a long time in the planning and we had a ton of things we were going to do and see.
After landing in Las Vegas we headed to the hotel. While I had a low-grade headache, I chalked that off to the excitement and perhaps a little lack of sleep. After all, I'd been working, going to school and doing homework. My days AND nights were always busy.
Once at the hotel, mom and I ate a quiet dinner and headed for bed. So much to see and do the next day! Mom and I took a few pictures of ourselves together at the hotel relaxed, happy and healthy. Those were the last pictures I would have of myself in that condition.
The following morning, May 29, 2004, at 10:00 a.m. I awoke a little disoriented and with a splintering headache. After talking to my mom a bit, I got up to get dressed. I don't remember anything much after that.
The story goes that I dropped to the ground, right in front of my mom. I do remember a panic stricken mother and brother, and my mother frantically calling for help. After that everything was pretty much a blank.
I was rushed to the nearest hospital fortunately for me it was a certified stroke center and the doctors on duty rushed to save my life. Mom was told that it wasn't likely I would survive the night I had suffered a massive ischemic stroke but the doctors were going to try the TPA "Clot buster." I now know this was instrumental in my recovery.
In and out of consciousness, days turned into weeks in intensive care. Brain swelling had caused a real scare and the drugs used to combat the swelling were playing havoc with my system.
Eventually, the brain swelling was reduced and I was transferred to the Rehab Unit of the same Hospital. That was where the work began. I had no idea that my body wasn't willing to move no idea that I might not be able to swallow or talk or walk. I was angry, sad and depressed. But each step of the way I had my family, who by now were all by my side. I guess they'd been there since the day of my stroke, but I really didn't notice I gained my strength watching my mom; dad, brother and sister reassure me that we'll all get through this together.
I began the testing process to see whether I could swallow, eat, talk and move. I managed to swallow, was slow to eat, talked (although somewhat incoherently) and moved on one side only. It was as if one side of my body had now been disconnected from the rest. But within days, I was in a rehab unit and I was being raised to my feet to begin the process of relearning how to walk. I was fitted with a leg brace to assist my left leg and to keep me from falling. I was working with speech therapists and with occupational therapists.
Then the day came I was ready to go home! So I thought. I was transported by ambulance from Las Vegas to Barrows Neurological in Phoenix—a mere 10 minutes from my house. I was so homesick I cried when I knew we were in my neighborhood because all I really wanted to do was go home with my mom and see my dogs.
Once at Barrows, the therapists and doctors continued the strict regiments of rehab and little by little I adapted to the loss of most of my left side. We worked with numbers tested my memory, read, solved puzzles—all as I found out later in an attempt to re-engage my brain.
After many weeks at Barrows, I was READY to go home! What a relief—a little worse for the wear but home nonetheless. My mom took me home and my first request was to take a bath. Something I hadn't done since that fateful day. But the bathtub ordeal was just the beginning. Mom got me in there. and we couldn't get out! We realized that there wasn't any manual for this type of thing. We didn't know whether to laugh or cry, so mom and I laughed until she got in the water with me and carried me out. While we may have cried later, that was a turning point. We knew at that time that life wasn't going to be easy and that the work had just begun. What followed was an intensive 9-month rehabilitation day program, every weekday all day. Speech therapy, occupational therapy, neuropsychological, group meetings, specialists—the list goes on and on. Some days I was so exhausted I would beg my mom not to make me go. More often than not that fell on deaf ears as mom pushed me in my wheelchair from the parking lot all the way through to the hospital's day rehab program. Those are bittersweet days exhausting both mentally and physically. But I graduated from their program and was ready for the next step.
After being released from the Intensive Outpatient Clinic, I began at SWAN REHAB under the care of Dr. Kay Wing. I immediately felt like I had an angel at my side. Kay was kind and firm. Just like I needed. Since my first visit to SWAN, my life has continued to improve.
Two summers ago I finished my Associate's degree at Phoenix College and today I'm back in school at ASU working on my Bachelors. I'm driving, doing a little housework, reading, engaging and I have high hopes for the future and a renewed sense of strength, hope and appreciation for life. My family and I are closer than ever, and my priorities are now to continue to improve each day and to live my life in tribute to my survival.
Display of the Faces of Stroke stories does not imply National Stroke Association's endorsement of any product, treatment, service or entity. National Stroke Association strongly recommends that people ask a healthcare professional about diagnosis and treatment questions before using any product, treatment or service. The views expressed through the stories reflect those of the authors and do not reflect the opinion of National Stroke Association.
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Measure a highball glass full of ice and add to a cocktail shaker. Add all ingredients except ginger ale and shake well. Pour mix back into highball glass and fill with ginger ale. Garnish with a maraschino cherry.
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Over on our regular blog, Red String PaperCuts, I wrote about our marketing plan for this year. The vanguard of that plan is our email newsletter, The Evening Satellite!
In an effort to start gaining newsletter subscribers, we're offering some exclusive content in the run-up to my first novel's publication! Check it out…
The Evening Satellite
The first 10 people to sign up for our newsletter will receive:
- A free digital copy of The Warden of Everfeld: Memento, to be published early 2018 (giveaway date to be announced later)
The first 10 + anyone else who signs up before February 28 will receive:
- Previously unpublished WoEM character bios
- And the earliest possible updates on the book's publication date, before anyone else.
We'll send you an introductory welcome email in the first week. Then, you'll receive updates and new content about once per month after that.
You can sign up here: http://eepurl.com/cFLJdX
Steve D


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Volcano erupting. From mynews.in
From Wired magazine, august 2007, p. 36, this is the ranking selected for ¨best dangerous science jobs¨:
ASTRONAUT: Since manned spaceflight began in 1961, 24 US astronauts have died in astro-action. Plus more from Russia.
BIOSAFETY LEVEL 4 LAB RESEARCHER. They handle the deadliest diseases on Earth.
HURRICANE HUNTER: Just remember, even on the ground, they are not safe. Hurricane Katrina, for example, destroyed the squad´s home base.
DOCTORS WITHOUT BORDERS: from the mobile lab tech. Testing blood for sleeping sickness, an infectious disease, is dangerous enough, imagine it on the ongoing genocide in Sudan´s Darfur region.
PROPULSION ENGINEER: People who ground-test rockets engines, they could completely ignite.
GRAD STUDENTS: Do not forget, they are in constant danger all around the world, jars of chemicals could explode unexpectedly.
VOLCANOLOGISTS: Many volcanologists do not come back from their helicopter visits to hell.
BIOLOGISTS: Animal research can lead to much more than allergic reaction; being bitten, scratched, or exposed to ¨secretions¨ can be deadly.


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It's been made. Making it newer elsewhere: Instagram: @meaganlikereagan Twitter: @sayitlikereagan
Friday, December 14, 2007
Wednesday, December 05, 2007
there is no "i" in team
I rushed into the office at five after seven, pealed off a layer or three, and joined my coworkers in the conference room. A few minutes later, my boss escorted a black, double-breasted power suit into the room. Chris (my boss) introduced him as "Eric" and said he'd be the host of our "experience", then elaborated further by saying that Eric's company was our newest client. They had engineered a program guaranteed to build group unity. There was only one other program like it in the world. Based in Virgina, it had a two-year waiting list. We were to be the guinea pigs in its sister Salt Lake program. He then handed the proverbial conch shell to Eric the Suit.
Eric introduced himself as having grown up in South Jordan and graduating from the U. After graduation, he was profiled by the FBI and had spent the last ten years working for them as a field agent primarily based in Russia where he had worked undercover with the Russian mafia. He had recently taken a leave of absence as his partner had been shot. Funds from this program would go towards supporting his disabled partner and his partner's family.
Eric then proceeded to tell us that the vast majority of crime in the world stems from mistrust. For example, you can't trust where your money has been. "Seventy percent of paper money has traces of cocaine on it. SEVENTY PERCENT," he said. Therefore, to create unity, trust was the essential element. We would be spending all day and much of the evening participating in activities to build trust. Our experience would include trusting each other as we:
- Ate exotic and possible dangerous foods
- Shot a variety of weapons
- Practiced team formation
- Drove vehicles on a closed course
- Participated in an urban warfare simulation
- Thorougly exhausted ourselves
He then directed us all to a waiting car and told us we would be traveling to west West Valley (the best "ghetto" he could find in the Salt Lake area) to await further instructions. He would meet up with us later.
So into the white van we got (very 24), and drove to a designated parking lot in the warehouse district where we waited for intructions. After about ten minutes, all our cell phones went off at the same time with text messages that read, "Mission compromised! Abort." My boss, Mike, then got a text telling us to check the trunk. In it, we found a large cardboard that read, "TOP SECRET. OPEN ONLY WHEN INSTRUCTED", he didn't wait to be instructed, but tore the lid right off. Inside, there were t-shirts that read, "TEAM BUILDING EXERCISE 2007" and hoodies with our company logo on them (surprise, surprise). There was also a white envelope with our names printed on the outside for each of us. Inside, was $50 and a boarding pass...
... to Long Beach Airport.
"Psyche!" yelled the bosses in unison. "We're really going to Disneyland. Now hurry, get in the car, we're gonna miss our flight!"
We raced to the airport amidst a flurry of questions and answers:
- "Wait, we're really going to Disneyland?" "Yes."
- "Who was that guy then?" "Some guy from my ward."
- "Does he really work for the F.B.I.?" "No."
- "Good. I was really worried about the US Government." "Uh...that's not a question."
- "Do you think there's cocaine on this $50 bill?" "..."
So off to Disneyland we went, one cameraman/women and the rest of us walking three by three in perfect syzygy with our matching shirts. Of course, it rained almost the entire time, so we also wore matching ponchos ): But there were negative percent lines (: Of course, the rain delayed our flight an hour-and-a-half so I got home at 3 a.m. ): But we ate at a fantastic Korean restaurant with a grill right at our table for hand warming (:
Oh yeah, and did I mention we spent the day at DISNEYLAND!?!?!
And wouldn't you know it, we still did everything Eric said we would.


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Hello December! I can't believe you are already here! Before we know it we will be saying....2015! That's another topic and I will save that for another time.
I just finished 30 days of Thankful and I am now I am prepping to do December Daily with a sweet group of ladies. In November we did an December Daily gift exchange. I really got some great stuff to add to my album. It was very exciting!!
Through the month of December I will be doing some prompts for the group and sharing them. And at the end of the month I should have a beautiful December Daily album.
Today it is a rainy day in California. Perfect day to get out my Christmas decorations and craft!
What are doing today? Will you be doing a December Daily album? I would love to know!!
See you next time!!
4 months ago