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In the present investigation, we study the influence of conventual foundations on the origin of the urban layout of two of the first cities in the Spanish colonization of America: Santo Domingo (1502) and Panama Viejo (1519), examples of early colonial urbanism. Starting with cartography and historical bibliography, as well as recent studies on their urban evolution and using Geographic Information Systems (GIS) for graphic representation, a comparative study is carried out between both cities divided into stages of urban expansion until their consolidation (or disappearance in the case of Panama in 1671). We have been able to verify how, in Santo Domingo, the religious orders settled on the outskirts of the city, marking the axes of expansion as an instrument of control and consolidation of an urban layout closer to the idea of a grid, which will materialize more precisely in later cities. Meanwhile, in Panama Viejo, the city was formed longitudinally on the streets occupied by the convents, which served as the main axes that defined the urban design of the city. This article aims to demonstrate the importance of the role of religious power in the formation of the cities presented here.

Metabolic diseases are characterized by a decreased action of insulin. During the course of the disease, usual treatments frequently fail and patients are finally submitted to insulinotherapy. There is thus a need for innovative therapeutic strategies to improve insulin action. Growth factor receptor-bound protein 14 (Grb14) is a molecular adapter that specifically binds to the activated insulin receptor (IR) and inhibits its tyrosine kinase activity. Molecules disrupting Grb14-IR binding are therefore potential insulin-sensitizing agents. We used Structure-Based Virtual Ligand Screening to generate a list of 1000 molecules predicted to hinder Grb14-IR binding. Using an acellular bioluminescence resonance energy transfer (BRET) assay, we identified, out of these 1000 molecules, 3 compounds that inhibited Grb14-IR interaction. Their inhibitory effect on insulin-induced Grb14-IR interaction was confirmed in co-immunoprecipitation experiments. The more efficient molecule (C8) was further characterized. C8 increased downstream Ras-Raf and PI3-kinase insulin signaling, as shown by BRET experiments in living cells. Moreover, C8 regulated the expression of insulin target genes in mouse primary hepatocytes. These results indicate that C8, by reducing Grb14-IR interaction, increases insulin signalling. The use of C8 as a lead compound should allow for the development of new molecules of potential therapeutic interest for the treatment of diabetes.

Today, computational methods are commonly used in all areas of health science research. Among these methods, virtual ligand screening has become an established technique for hit discovery and optimization. In this review, we first introduce structure-based virtual ligand screening and briefly comment on compound collections and target preparations. We also provide the readers with a list of resources, from chemoinformatics packages to compound collections, which could be helpful to implement a structure-based virtual screening platform. Then we discuss seventeen recent success stories obtained with various receptor-based in silico methods, performed on experimental structures (X-ray crystallography, 12 cases) or homology models (5 cases) and concerning different target classes, from the design of catalytic site inhibitors to drug-like compounds impeding macromolecular interactions. In light of these results, some suggestions are made about areas that present opportunities for improvements.

Background: Hydrophobic Cluster Analysis (HCA) is an efficient way to compare highly divergent sequences through the implicit secondary structure information directly derived from hydrophobic clusters. However, its efficiency and application are currently limited by the need of user expertise. In order to help the analysis of HCA plots, we report here the structural preferences of hydrophobic cluster species, which are frequently encountered in globular domains of proteins. These species are characterized only by their hydrophobic/non-hydrophobic dichotomy. This analysis has been extended to loop-forming clusters, using an appropriate loop alphabet., Results: The structural behavior of hydrophobic cluster species, which are typical of protein globular domains, was investigated within banks of experimental structures, considered at different levels of sequence redundancy. The 294 more frequent hydrophobic cluster species were analyzed with regard to their association with the different secondary structures (frequencies of association with secondary structures and secondary structure propensities). Hydrophobic cluster species are predominantly associated with regular secondary structures, and a large part (60 %) reveals preferences for alpha-helices or beta-strands. Moreover, the analysis of the hydrophobic cluster amino acid composition generally allows for finer prediction of the regular secondary structure associated with the considered cluster within a cluster species. We also investigated the behavior of loop forming clusters, using a "PGDNS" alphabet. These loop clusters do not overlap with hydrophobic clusters and are highly associated with coils. Finally, the structural information contained in the hydrophobic structural words, as deduced from experimental structures, was compared to the PSI-PRED predictions, revealing that beta-strands and especially alpha-helices are generally over-predicted within the limits of typical beta and alpha hydrophobic clusters., Conclusion: The dictionary of hydrophobic clusters described here can help the HCA user to interpret and compare the HCA plots of globular protein sequences, as well as provides an original fundamental insight into the structural bricks of protein folds. Moreover, the novel loop cluster analysis brings additional information for secondary structure prediction on the whole sequence through a generalized cluster analysis (GCA), and not only on regular secondary structures. Such information lays the foundations for developing a new and original tool for secondary structure prediction.