1,721,002 research outputs found
Integrated Bioinformatics analysis of structural differences in metabolic pathways. An application to Mycobacterium Leprae
No full textComparative analysis of Rac1 binding efficiency with different classes of ligands: morpholines, flavonoids and imidazoles
No full textThe focal adhesion pathway has a great impact on cellular growth and survival. Its disregulation is correlated with the loss of cellular mechanical properties. Such modifications are, in many cases, associated with pathologies such as cancer and cardiovascular diseases. Actin remodeling is a critical reaction cascade embedded in focal adhesion pathway, and Rac1 is one of the proteins involved in actin remodeling. In order to design highly selective pharmacophores against this target, it is necessary to maximize the binding affinity of chemical entities against Rac1. To this purpose we propose an integrative chemo-bioinformatics tool to screen ligand specificity for a target protein. Our integrative workflow includes chemo-informatics data mining (Chemical System), structural bioinformatics and combined exploratory data analysis. We have applied this integrative chemo-bioinformatics workflow to a comparative analysis of three different classes of ligands (morpholines, flavonoids and imidazoles) against the Rac1 protein. Our analysis emphasizes the presence of several ligands that preferentially dock Rac1 in the domain that seems to be responsible for Rac1-phospholipase C gamma 1 interaction. Recent studies have highlighted the Rac1 and PLC interactions in platelet adhesion. Our study has highlighted the role of Rac1-PLC gamma1 interaction in cytoskeleton remodeling associated with cardiovascular diseases. Rac1 PLC interaction is Calcium dependent. This suggest that some of the analysed ligands, could be used to control the Calcium dependent cytoskeleton remodeling since they dock Rac1 in the switch 2 domain. Our results, in a nanotechnology perspective, also endorse the use Rac1's switch 2 domain suitable for new highly specific biosensors
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Drug design for cardiovascular disease: The effect of solvation energy on Rac1-ligand interactions
No full text'OMICS' techniques have deeply changed the drug discovery process. The availability of many different potential druggable genes, generated by these new techniques, have exploited the complexity of new lead compounds screening. 'Virtual screening', based on the integration of different analytical tools on high performance hardware platforms, has speeded up the search for new chemical entities suitable for experimental validation. Docking is a key step in the screening process. The aim of this paper is the evaluation of binding differences due to solvation. We have compared two commonly used software, one of which takes into account solvation, on a set of small molecules (Morpholines, flavonoids and imidazoles) which are able to target the RAC1 protein--a cardiovascular target. We have evaluated the degree of agreement between the two different programs using a machine learning approach combined with statistical test. Our analysis, on a sample of small molecules, has pointed out that 35% of the molecules seem to be sensitive to solvation. This result, even though quite preliminary, stresses the need to combine different algorithms to obtain a more reliable filtered set of ligands
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