Le Grand-Bornand where can I buy cocaine

__________________________

📍 Verified store!

📍 Guarantees! Quality! Reviews!

__________________________

▼▼ ▼▼ ▼▼ ▼▼ ▼▼ ▼▼ ▼▼

▲▲ ▲▲ ▲▲ ▲▲ ▲▲ ▲▲ ▲▲

Le Grand-Bornand where can I buy cocaine

The show remains an independent, one-man operation. A former touring guitarist, Tyler lives in Nashville, Tennessee. Customer Reviews, including Product Star Ratings, help customers to learn more about the product and decide whether it is the right product for them. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. It also analyses reviews to verify trustworthiness. Purchase options and add-ons. By the early s nearly everybody paying attention to country music agreed that George Jones was the greatest country singer of all time. Only at this new level of fame did Jones realize he suffered from extreme stage fright. His method of dealing with that involved great quantities of alcohol, which his audience soon discovered as Jones more often than not showed up to concerts falling-down drunk or failed to show up at all. But the fans always forgave him because he just kept singing so damn good. After deciding to become a country singer herself, she went to Nashville, got a record deal, then met and married her hero. Many fans still believe that fairy tale today. The behind-the-scenes truth is very different from the images shown on album covers. Report an issue with this product. Previous slide of product details. Print length. Publication date. See all details. Next slide of product details. Customers who viewed this item also viewed. Page 1 of 1 Start again Page 1 of 1. Previous set of slides. Rich Kienzle. Next set of slides. Review 'Superb. A work of cultural anthropology with an uncommon sweep. Were you also hoping for entire chapters on pinball machines, bullfighting, drag shows and the evolution of cowboy boots—as well as the poetry of William Blake and the exploits of Buffalo Bill? A beautiful, barmy book. Fans of country music can now saddle up with Cocaine and Rhinestones for a ride through the shadowy past of shady recording-biz hawks, pinball addicts, and honky-tonk performers who were willing to risk everything for fame. Wayne White is an artist, art director, illustrator, and puppeteer. Born and raised in Chattanooga, Wayne has used his memories of the South to create inspired works for film, television, and the fine art world. Wayne is married to cartoonist and writer Mimi Pond. They live in Los Angeles. About the author Follow authors to get new release updates, plus improved recommendations. Tyler Mahan Coe. Brief content visible, double tap to read full content. Full content visible, double tap to read brief content. Read more about this author Read less about this author. Customer reviews. How customer reviews and ratings work Customer Reviews, including Product Star Ratings, help customers to learn more about the product and decide whether it is the right product for them. Learn more how customers reviews work on Amazon. Images in this review. Sort reviews by Top reviews Most recent Top reviews. Top reviews from United Kingdom. There are 0 reviews and 0 ratings from United Kingdom. Top reviews from other countries. Verified Purchase. This is a fun book where nearly every paragraph has me better understanding some country music artifact that I'd kind of understood before but after reading the book I now have a more complete picture. These discoveries are consistently interesting and fun. If you like his podcast, you'll love this book! Read it, Know it, Maybe don't live it. The illustrations are scary and you cover everything about country living before you even get to George. Do you ever get to George and Tammy? I gave up after quite a few chapters. I did learn a lot about moonshine and vinal records tho. See more reviews. Your recently viewed items and featured recommendations. Back to top. Get to Know Us. Make Money with Us. Amazon Payment Methods. Let Us Help You. Amazon Music Stream millions of songs. Veeqo Shipping Software Inventory Management. Audible Download Audiobooks. Shopbop Designer Fashion Brands. Amazon Business Service for business customers.

Your Amazon Prime 30-day FREE trial includes:

Le Grand-Bornand where can I buy cocaine

Official websites use. Share sensitive information only on official, secure websites. Most efforts to develop vaccines and drugs against this virus target the spike glycoprotein, particularly its S1 subunit, which is recognised by angiotensin-converting enzyme 2. Here we use the in-house developed tool CaverDock to perform virtual screening against spike glycoprotein using a cryogenic electron microscopy structure PDB-ID: 6VXX and the representative structures of five most populated clusters from a previously published molecular dynamics simulation. The dataset of ligands was obtained from the ZINC database and consists of drugs approved for clinical use worldwide. Multivariate statistical methods were then used to establish structure-activity relationships and select top candidate for movement inhibition. This new protocol for the rapid screening of globally approved drugs ligands in a multi-state protein structure 6 states showed high robustness in the rate of finished calculations. The protocol is universal and can be applied to any target protein with an experimental tertiary structure containing protein tunnels or channels. Drawing on established knowledge about the original virus, research groups worldwide have focused their efforts on two viral proteins: i the spike s -glycoprotein, with the aim of disrupting its recognition of the membrane-bound angiotensin-converting enzyme 2 ACE-2 ; and ii the main viral protease Mpro, 3CLpro \[5\] , \[6\] , to disrupt viral replication by hindering the processing of several polyproteins that are translated from the viral RNA. Another approach for tackling the spread of the new virus builds on work on the original SARS virus, which resulted in the development of a vaccine designed to induce the production of antibodies against the viral s-glycoprotein \[7\] , \[8\] , preventing it from recognising and binding to ACE Unfortunately, at the time \[9\] , work on this vaccine was discontinued because it had side effects in animal models that prevented its testing in humans \[10\] , \[11\]. These efforts to create a vaccine or a potent inhibitor that can be used as an a posteriori medical treatment with acceptable side-effects are being undertaken by both private companies and academic institutions. Both viral and host proteins are being targeted. While most efforts are focused on disrupting the viral protease or viral polymerase, the viral genome is also being targeted to disrupt its replication. In particular, the host enzymes involved in nucleotide synthesis are being studied to halt the final step in viral genome replication. However, most therapies in development target proteins acting upstream of replication; there are almost 40 preclinical and over 30 clinical trials targeting viral surface proteins including the s-glycoprotein. The s-glycoprotein is homo-trimer with three domains—the cytoplasmic tail, the transmembrane region, and the ectodomain \[31\]. The ectodomain is further divided into three areas: the proximal membrane region, the S2 subunit, and the S1 subunit. The receptor-binding domain is located in the S1 subunit. ACE-2 recognises the S1 subunit, and between 1 and 3s-glycoprotein monomers can bind to ACE-2 by opening and moving upwards. The viral membrane then fuses with that of the host cell via a series of substantial conformational changes. Blocking these conformational changes would be a way to taper the propagation of the virus \[32\]. The existence of visibly different conformations demonstrates that the viral s-glycoprotein can undergo conformational changes affecting not just its surface but also the gorge within the S1 subunit and the S2 subunit. Previous docking and virtual screening studies have focused on localised sites such as the active site of the viral Mpro protease \[38\] , \[39\] , \[40\] , \[41\] or the receptor-binding domain of the s-glycoprotein \[42\] , \[43\] , \[44\]. There were also studies aiming at drug repurposing targeting the gorge of the s-glycoprotein \[45\] , \[46\] , \[47\]. A long tunnel created by the formation of the s-glycoprotein trimer has received less attention. Therefore, we decided to search for the drugs that bind in the gorge as well as along the putative tunnel of the ectodomain up to the cleavage site. Studying drug interactions in such long tunnels would be laborious and computationally expensive if using alchemical \[48\] , \[49\] or ligand migration methods \[50\] , \[51\]. A long tunnel in a dynamical protein is a perfect target for study using the software tool CaverDock \[52\] , \[53\] , \[54\]. CaverDock is an in-house tool that uses Caver \[55\] , to identify tunnels in protein structures, and an optimised version of the well-established algorithm from AutoDock Vina to calculate possible ligand trajectories along those tunnels and the corresponding binding energies \[56\]. Once the conformation and binding energy have been calculated, the constrained atom is shifted to the next disc and the process is repeated until the ligand has moved through the full length of the tunnel. The tool is continuously maintained and is freely available as both a stand-alone program and a webtool named CaverWeb \[57\] , \[58\]. Since the start of the pandemic, the scientific community has recognized the need for collaboration and sharing of results by pledging to make data publicly available as soon as possible. Shaw Institute \[59\] , from which we extracted the main representative conformations. We also used the original closed structure of the s-glycoprotein retrieved from the Protein Data Bank, giving a total of six structures to study \[60\]. Each structure was subjected to virtual screening using every drug in the globally approved drugs subset of the ZINC15 database \[61\]. This subset contained at the time of retrieval, unique drugs approved by the US Food and Drug Administration, European Medicines Agency, and other significant authorities. A single drug in the subset was not correctly handled by MGL tools for lack of parameters and the virtual screening was done with unique drugs. A better assessment of the full canonical ensemble could be obtained by performing several replicas in the simplest scenario. Even more complex and comprehensive sampling could be achieved by using enhanced sampling methods \[62\] , for example adaptive sampling \[63\] , umbrella sampling, \[64\] metadynamics, \[65\] replica exchange molecular dynamics \[66\] and others \[67\] , \[68\] , \[69\]. Although for smaller proteins this could be achievable in a reasonable amount of time, for proteins as large as the s-glycoprotein residues such task becomes very time demanding and computationally expensive. Binding energies along the s-glycoprotein tunnel were calculated for every drug and all six structures. We then compared the results obtained to identify the best ligands for each tunnel position in each conformation. We also analysed each drug to identify the contacts made with each monomeric unit of the s-glycoprotein trimer. This allowed us to select drugs that were predicted to interact with all three monomers and are thus likely to suppress the opening of the S1 subunits and thereby prevent the binding of the s-glycoprotein to ACE Quantitative structure—activity relationships analysis QSAR was carried out to correlate the binding energies of the drugs with their physicochemical properties using multivariate statistical methods, providing the top-scoring molecules based on their interactions with individual conformations of s-glycoprotein Fig. The computational workflow established within this study can be generalized and automated to make it applicable to other target proteins. Computational workflow showing the steps performed during the virtual screening with CaverDock using the full globally approved drug dataset and six protein states, along with the subsequent analytical steps. This workflow is currently being implemented on the web server CaverWeb \[29\] to allow the wider community to easily perform such virtual screens. Shaw group, which started from the same cryo-EM structure of s-glycoprotein. This trajectory was clustered using the cpptraj \[71\] module of AmberTools 16 \[72\] and a distance-based metric defined by the mass-weighted root-mean-square deviation RMSD of the backbone atoms of the residues surrounding the gorge of the S1 domain. The RMSD was calculated relative to the starting structure. The hierarchical agglomerative clustering algorithm was used with average-linkage, a minimum distance between clusters epsilon cut-off of 2. These segments were detached from the protein during the MD simulation and became unrealistically bound at the mouth of the s-glycoprotein tunnel see discussion below.. The tunnel extending through the s-glycoprotein trimer was characterized using HOLE v2. A sample rate of 0. However, the tunnel predicted by HOLE for the s-glycoprotein structure contained disconnections that made it undiscretisable. The probe radius, shell radius, and shell depth were set to 0. Finally, the selected tunnel parts were discretized into a series of discs using the discretiser tool with default settings \[53\]. The globally approved drug dataset was downloaded from the ZINC database \[61\] on the 26th of May in mol2 format. Only the first protonation state of each drug molecule was saved. Only the part of the tunnel in the S1 domain was considered in the CaverDock calculations. The ligand and receptor files were prepared using MGLtools 1. The grid box was generated around the relevant part of the tunnel using a script from the CaverDock package. The default drag atom i. Calculations were run in the inward direction only, in the lower-bound trajectory mode. The data matrix consisted of ligands objects docked into six different protein states obtained from the CaverDock trajectories. The data for each ligand consisted of its minimum binding energy along the CaverDock trajectory and three percentage values representing the proportion of the trajectory during which the ligand was in contact with one, two, or all three individual units of the s-glycoprotein trimer. The data were autoscaled to unit variance and centred before analysis. Partial Least Squares PLS analysis \[77\] was used to explore the relationships between the minimal binding energies of ligands objects docked to six different protein states dependent variables Y and molecular descriptors of individual ligands independent variables X. PLS reveals the correlation structure among variables X and Y by reweighting variables X with PLS weights and projecting them to a smaller number of new latent variables. Autoscaled and centred data were used in the PLS analysis. The importance of every molecular descriptor in the model was assessed using the variable importance in the projection VIP parameter \[78\] and plots of the PLS variable weights \[78\]. Internal validation was performed to assess the quality of the developed PLS models \[79\] by cross-validation and permutation testing. During cross-validation \[77\] , a portion of the Y data are excluded during model development, and the resulting model is used to predict the missing data. The predictions are then compared to the original data to obtain a Q 2 value. During permutation testing, the model was recalculated times by randomly re-ordering the dependent variable y. The topology and input files were prepared for each complex for performing an energy-minimization cycle and the energy calculations. The solvent accessible surface area was computed with the LCPO algorithm \[87\]. We analysed the 10 best binders in order to identify which common structural features could be used as a basis for searching similar drugs in the future. The parameter ringMatchesRingOnly was switched to True, so that the aliphatic carbon chains would not be matched with aromatic rings. We analysed all ten molecules and every pair combination. Furthermore, we calculated the Tanimoto similarity with the DataStructs. FingerprintSimilarity module to quantify the similarity of molecules in each pair. Despite missing some loops on the surface, the cryo-EM structure had a sufficiently high resolution and structural integrity inside the tunnel for virtual screening with CaverDock. Because the goal was to block large conformational changes of the s-glycoprotein trimer, we ranked the best binding drugs based on both their overall binding energies and the extent of their contacts with all three monomeric units. Three distinct clusters of drugs with binding profiles showing clear energy minima were identified, each binding to a different region of the tunnel Fig. Visualization of the tunnel in the cryo-EM structure with the top ten inhibitors bound to the positions corresponding to their lowest binding energy. The drugs were ranked by multivariate analyses presented below Fig. Inhibitors are shown using all-atom models, coloured by atom type. For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. The second and smallest cluster of drugs binds in the middle of the tunnel. The final region of the tunnel is also the most populated; All the top ten drugs identified in this study Fig. The profile of the tunnel in this region is narrower than in the other tunnel regions. Tunnels in the six protein states showing the regions where the drugs bind with the lowest binding energy. Top: Visualization of the tunnel used for virtual screening in the six protein states analysed with CaverDock. These states are the cryo-EM structure red and 5 representative structures s1 in orange, s2 in green, s3 in blue, s4 in purple and s5 in pink obtained by clustering the results of an MD simulation. Yellow spheres in the tunnels indicate the centre of mass of each drug when bound at the location where it binds most strongly. The D. These events are responsible for the two spikes seen in the RMSD plots at around 2. These unrealistically dynamical fragments, which were originally located on the outer surface of the s-glycoprotein were excluded from all subsequent analyses in this work. We clustered the MD snapshots based on the RMSD of the gorge residues to obtain diverse but biologically relevant conformations of the s-glycoprotein. The obtained clusters are ranked in terms of their populations. The most populated cluster, s1, dominated almost the entire second half of the trajectory SI-Fig. The mean RMSD of the gorge residues in this cluster was 3. Conversely, the least populated cluster s7 had RMSD values indicating that it remained close to its starting structure 1. Representative structures of the clusters SI-Fig. CaverDock calculations were performed using representative structures of the 5 most populated clusters in the same way as described for the cryo-EM structure Fig. Each tunnel had a unique profile, but in all cases, the narrowest section was in the deepest region of the tunnel, close to the S2 subunit. The vast majority of the ligands have their lowest binding energies in this region Fig. This was expected given that this region resembles a binding pocket with many possible molecular interactions. The sole exception is the most populated state, s1, for which the majority of the ligands have their lowest binding energies in the middle of the tunnel Fig. The tunnel in this state is slightly wider than in the other states, making it difficult for ligands to form contacts with all three monomers. The tendency for the binding energies of drugs to be lowest immediately before or after a bottleneck was seen for all states. Multivariate statistical analyses were used to: i comprehend the large data sets obtained from the CaverDock calculations, ii establish structure—activity relationships, and iii select the best potential drug candidates. Two statistically significant models were generated by PCA using the CaverDock results obtained using the set of ligands and six protein states. The data used in the PCA were the minimum binding energies for each drug along the trajectory and the proportions of the trajectory during which the docked ligand was in contact with one, two, and all three individual subunits of the s-glycoprotein trimer, expressed as percentages. The first PCA model PCA-1 used 24 variables: 3 related to the minimum binding energies for each protein state, and 3 quantifying the percentages of the trajectory during which the drug was in contact with 1, 2, and 3 units of the trimeric s-glycoprotein. The second model, PCA-2, was generated using 12 variables representing the energy minima and the percentages of each trajectory during which the drug was in contact with all three monomeric units of the s-glycoprotein trimer for each of the six studied protein states. Because it had only two principal components, this model was easier to interpret than the first. The top hits predicted by the two models were very similar, so only the results obtained with the simpler model 2 will be discussed further. By inspecting the distribution of the docked compounds in the 2D space spanned by the first two principal components Fig. Such compounds are most likely to modify the conformational behaviour of the s-glycoprotein and thus affect its biological function. The distribution of the 12 variables used to cluster the ligands is shown at the bottom of Fig. Scores and loadings plots of the first two principal components of the second PCA model. Top: Scores plot of the first two principal components showing the distributions of all studied compounds based on their minimal binding energies and number of contacts with the three subunits of the spike glycoprotein. The top hits were selected from this plot. The positions of the compounds in the 2D space are determined by the locations of variables in the loadings plot bottom. Compounds showing the strongest binding to all three units in the different states of the spike protein are located on the left of the plot red box. Bottom: Loadings plot of the first two principal components showing the distribution of the variables in the 2D space. This plot corresponds to the scores plot presented above. The variables describing the minimal binding energies calculated for the six different s-glycoprotein states are on the right, while those describing the contact percentage with the three individual subunits of the spike protein trimer are located on the left. A PLS analysis was performed to correlate the minimum binding energies for each ligand from the CaverDock calculations with the molecular descriptors of the docked ligands. Binding energies calculated for all six states of the s-glycoprotein were considered simultaneously using a single PLS model. To simplify the model, the variable selection was performed. Specifically, independent variables were selected based on their position in the loadings plot and variable importance in the projection VIP plot. In this way, the number of variables was reduced from to A new model generated with these variables, PLS-2, had three principal components, with an R 2 of 0. Validation by permutation testing - scrambling the Y variables while keeping the X-matrix unchanged — indicated that this correlation would be very unlikely to be observed by chance, as expected given the large number of observations on which the model is based. The established PLS models are applicable for predictive purposes. The predictions can be made even for extensive sets of compounds and can guide selection of suitable candidates for experimental testing. The PLS models allow prediction of minimum binding energies solely from the molecular structure of the ligands. Molecular descriptors can be generated using the on-line version of MORDRED and inserted as the variables to the model for fast prediction of binding energies. The observed minimal binding energies were plotted against the corresponding predicted values for the starting structure 6VXX and state s4, for which the worst and best fits were obtained, respectively SI-Fig. VIP values were computed to quantify the relative importance of the chosen molecular descriptors in explaining the differences in the minimum binding energies for all six states SI-Fig. We obtained a ranking of the best binders from the PCA and selected the top ten for further analysis Fig. These ligands had consistently low binding energies in all of the studied protein structures and exhibited a high percentage of contacts with all three monomeric units of the s-glycoprotein trimer during the CaverDock simulations. We also found that multivariate statistical methods were needed to rank the drugs meaningfully. For example, a simple ranking of the drugs based on their minimum binding energies would not have placed Daclatasvir Fig. It was thus clear that interaction with all three monomers was weighted strongly in the ranking of the drugs; for three of the studied protein states, Daclatasvir was observed in contact with two and three subunits of the s-glycoprotein trimer, and in the remaining three states clusters s1, s3 and s5 it was in contact with two or three subunits for at least Top ten inhibitors predicted using CaverDock simulations and machine learning. Drug names and labels are shown in the first column; respective chemical structures are shown below the table. The bar plots under each binding energy represent the percentage of the corresponding trajectory during which these compounds formed contacts with one monomer red , two monomers yellow , and three monomers green. The top ten ranked drugs were analysed further. The results showed that most of these drugs can interact with the protein with very strong and favourable energies SI-Tables 3 and 4. This suggests the need for larger conformational changes on the protein with respect to the 6VXX structure to accommodate this molecule in such a position. If we observe Fig. There were other studies tackling the s-glycoprotein as a whole or its receptor-binding domain RBD Table 1. Trezza et al. Additionally, the authors performed supervised MD simulations to study the binding of the s-glycoprotein RBD to the human angiotensin-converting enzyme 2, and steered MD simulations with two drugs complexed with the RBD of the s-glycoprotein. We notice that three of these drugs Dihydroergotamine, Trypan blue and Simeprevir were included among top ten hits in our study. Panda et al. The authors then performed MD simulations to validate their best binding drug, pc, which is still in the clinical trials. On the other hand, following top-ten drugs were selected for the whole s-glycoprotein: pc, Lorecivivint, Tegavivint, Maraviroc, Itraconazole, Dolutegravir, Troglitazone, Elvitegravir, Danirixin, and Linagliptin. Examples of other previously published virtual screening studies that targeted the s-glycoprotein or its receptor-binding domain. Another virtual screening study targeting the RBD by Kalathiya et al. Wei et al. They performed virtual screening of these two datasets on RBD and ran short MD simulations on the best-binding drugs. Awad et al. The methodology developed by the authors was however slightly different and yielded different top ten candidates: Silodosin, Ebastine, Salazosulfadimidine, Indacaterol, Chidamide, Regorafenib, Tasosartan, Bagrosin, Lumacaftor, and Risperidone. The authors target the RBD only with classical docking and implemented absorption, distribution, metabolism, and excretion values in their workflow. Romeo et al. Another experimental study analysed in depth 17 hits for drug repurposing screening for Covid19 from a library of 43 FDA-approved compounds and clinical candidates. Three of these drugs were not part of our study, as they were investigational, pre-clinical and a dietary supplement. We conclude that there is an overlap of three drugs Dihydroergotamine, Trypan blue and Simeprevir from one study and one drug Dihydroergotamine from another study with our results, even though different protocols were employed. Most importantly, there is an overlap of one drug Lomitapide with the experimental work done by Mirabelli et al. We stress that the results from theoretical calculations provide prioritization of the potential drugs for the experimental testing but should not be seen as the replacement for the laboratory tests by any means. The analysis revealed that all ten molecules share no common sub-structure. Analysis of all molecule pairs from the data set revealed that the largest common substructure is between Dihydroergotamine and Dihydroergocristine 43 atoms; Tanimoto similarity score 0. Other pairs showed the common substructures of less than 15 atoms. Here we describe a computational workflow that was used to perform virtual screening based on CaverDock trajectories for drug molecules and six conformational states of the s-glycoprotein of SARS-CoV This analysis involved a total of 26, calculations. Each calculation took a real-time average of 37 min to complete on 8 CPUs, making the method sufficiently fast for thorough virtual screening. However, this long tunnel can serve as a good representative of the structural features present in transmembrane proteins. We used a machine learning to identify the most promising drug candidates based on their strength of binding inside the tunnel and their likely ability to prevent the s-glycoprotein trimer from undergoing functionally necessary conformational change. Although we only selected 10 inhibitors here for the sake of brevity, this number could easily be increased. Importantly, this workflow is currently being made available on the CaverWeb tool to enable automated virtual screenings of the ZINC globally approved drugs dataset. This will enable researchers around the world to perform virtual screening and data analysis in the same way as reported here, in a user-friendly manner. The procedure will be applicable to any protein with an available tertiary structure containing tunnels or channels and should thus find diverse applications in drug design, protein engineering, and metabolic engineering. We are currently implementing this virtual screening platform into CaverWeb \[57\] to allow the community to perform similar automated calculations against other target proteins using the approved drug datasets. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This section collects any data citations, data availability statements, or supplementary materials included in this article. As a library, NLM provides access to scientific literature. Comput Struct Biotechnol J. Find articles by Gaspar P Pinto. Find articles by Ondrej Vavra. Find articles by Sergio M Marques. Find articles by Jiri Filipovic. Find articles by David Bednar. Find articles by Jiri Damborsky. Open in a new tab. Virtual screening study Methods Targets Trezza et al. Similar articles. Add to Collections. Create a new collection. Add to an existing collection. Choose a collection Unable to load your collection due to an error Please try again. Add Cancel. Kalathiya et al. Mirabelli et al.

Le Grand-Bornand where can I buy cocaine

Buying coke online in Vina del Mar

Le Grand-Bornand where can I buy cocaine

Geilo buy coke

Le Grand-Bornand where can I buy cocaine

Buying cocaine online in Manila

Le Grand-Bornand where can I buy cocaine

Buy coke Rancagua

Buy Cocaine Alicante

Le Grand-Bornand where can I buy cocaine

Buying cocaine online in Brasilia

Taipei buy coke