Current Issue : October - December Volume : 2011 Issue Number : 4 Articles : 6 Articles
Background\r\nThe analysis of protein structures provides fundamental insight into most biochemical functions and consequently into the cause and possible treatment of diseases. As the structures of most known proteins cannot be solved experimentally for technical or sometimes simply for time constraints, in silico protein structure prediction is expected to step in and generate a more complete picture of the protein structure universe. Molecular modeling of protein structures is a fast growing field and tremendous works have been done since the publication of the very first model. The growth of modeling techniques and more specifically of those that rely on the existing experimental knowledge of protein structures is intimately linked to the developments of high resolution, experimental techniques such as NMR, X-ray crystallography and electron microscopy. This strong connection between experimental and in silico methods is however not devoid of criticisms and concerns among modelers as well as among experimentalists.\r\nResults\r\nIn this paper, we focus on homology-modeling and more specifically, we review how it is perceived by the structural biology community and what can be done to impress on the experimentalists that it can be a valuable resource to them. We review the common practices and provide a set of guidelines for building better models. For that purpose, we introduce the H-factor, a new indicator for assessing the quality of homology models, mimicking the R-factor in X-ray crystallography. The methods for computing the H-factor is fully described and validated on a series of test cases.\r\nConclusions\r\nWe have developed a web service for computing the H-factor for models of a protein structure. This service is freely accessible at http://koehllab.genomecenter.ucdavis.edu/toolkit/h-factor...
Epilepsy is a disease of complex nature and of different etiology. A large number of populations of different age groups and sex are affected by this disease. Most antiepileptic drugs are associated with adverse effects, such as sedation, ataxia and weight loss (e.g. topiramate) or weight gain (e.g. valproate, tiagabine, and vigabatrin). Keeping in mind the demand of improving the drug for convulsion therapy, there is an urge for new drug synthesis of the same series. To get an insight about the right structural features needed to develop the AMPA receptor antagonist, as these drugs found to have good neuroprotective activity and also produce lesser side effect as compared to marketed drugs. 2D-QSAR and docking studies were carried out. BioMed CAChe 7.5 was used for QSAR study while Molegro Virtual Docker (MVD 2007) was used to carry out the docking studies. Total 29 molecules were considered in the study. Among them, 19 molecules activity was reported and 10 molecules were created doing bioisosteric changes in previous set. The first set was divided into training (15) and test set (4). The best QSAR model is obtained with an r2 value of 0.9703 and q2 value of 0.6994 with five descriptors. This study will give a deep insight to understand the major role played by the descriptors of AMPA receptor antagonist drugs and their binding efficiency in ligand drug interaction...
In present work, we have amalgamated computational methodologies viz. GUSAR, QSAR and molecular docking to identify pharmacophores and anti-pharmacophores for anti-inflammatory activity of some quinazoline derivatives. 1D, 2D and 3D descriptors were used for QSAR analysis. Lipophilicity and presence of halogen are the major factors that govern the anti-inflammatory activity. It was also observed that these compounds docked near the gate of COXs active site and might block the conversion of arachidonic acid to prostaglandin (PG) H2 at the active site of COXs. Further, we have carried out receptor based electrostatic analysis to confirm the electronic, steric and hydrophobic requirements for future modifications. The analyses provides substantial idea about the structural features responsible for their anti-inflammatory activity and provide guidelines for further modi?cations, with the aim of improving the activity and selectivity of designed drugs targeting COX-2 enzyme....
Background\r\nChanges in microRNA (miRNA) expression patterns have been extensively characterized in several cancers, including human colon cancer. However, how these miRNAs and their putative mRNA targets contribute to the etiology of cancer is poorly understood. In this work, a bioinformatics computational approach with miRNA and mRNA expression data was used to identify the putative targets of miRNAs and to construct association networks between miRNAs and mRNAs to gain some insights into the underlined molecular mechanisms of human colon cancer.\r\nMethod\r\nThe miRNA and mRNA microarray expression profiles from the same tissues including 7 human colon tumor tissues and 4 normal tissues, collected by the Broad Institute, were used to identify significant associations between miRNA and mRNA. We applied the partial least square (PLS) regression method and bootstrap based statistical tests to the joint expression profiles of differentially expressed miRNAs and mRNAs. From this analysis, we predicted putative miRNA targets and association networks between miRNAs and mRNAs. Pathway analysis was employed to identify biological processes related to these miRNAs and their associated predicted mRNA targets.\r\nResults\r\nMost significantly associated up-regulated mRNAs with a down-regulated miRNA identified by the proposed methodology were considered to be the miRNA targets. On average, approximately 16.5% and 11.0% of targets predicted by this approach were also predicted as targets by the common prediction algorithms TargetScan and miRanda, respectively. We demonstrated that our method detects more targets than a simple correlation based association. Integrative mRNA:miRNA predictive networks from our analysis were constructed with the aid of Cytoscape software. Pathway analysis validated the miRNAs through their predicted targets that may be involved in cancer-associated biological networks.\r\nConclusion\r\nWe have identified an alternative bioinformatics approach for predicting miRNA targets in human colon cancer and for reverse engineering the miRNA:mRNA network using inversely related mRNA and miRNA joint expression profiles. We demonstrated the superiority of our predictive method compared to the correlation based target prediction algorithm through a simulation study. We anticipate that the unique miRNA targets predicted by the proposed method will advance the understanding of the molecular mechanism of colon cancer and will suggest novel therapeutic targets after further experimental validations....
Background\r\nThe TlyA protein has a controversial function as a virulence factor in Mycobacterium tuberculosis (M. tuberculosis). At present, its dual activity as hemolysin and RNA methyltransferase in M. tuberculosis has been indirectly proposed based on in vitro results. There is no evidence however for TlyA relevance in the survival of tubercle bacilli inside host cells or whether both activities are functionally linked. A thorough analysis of structure prediction for this mycobacterial protein in this study shows the need for reevaluating TlyA's function in virulence.\r\nResults\r\nBioinformatics analysis of TlyA identified a ribosomal protein binding domain (S4 domain), located between residues 5 and 68 as well as an FtsJ-like methyltranferase domain encompassing residues 62 and 247, all of which have been previously described in translation machinery-associated proteins. Subcellular localization prediction showed that TlyA lacks a signal peptide and its hydrophobicity profile showed no evidence of transmembrane helices. These findings suggested that it may not be attached to the membrane, which is consistent with a cytoplasmic localization. Three-dimensional modeling of TlyA showed a consensus structure, having a common core formed by a six-stranded �Ÿ-sheet between two a-helix layers, which is consistent with an RNA methyltransferase structure. Phylogenetic analyses showed high conservation of the tlyA gene among Mycobacterium species. Additionally, the nucleotide substitution rates suggested purifying selection during tlyA gene evolution and the absence of a common ancestor between TlyA proteins and bacterial pore-forming proteins.\r\nConclusion\r\nAltogether, our manual in silico curation suggested that TlyA is involved in ribosomal biogenesis and that there is a functional annotation error regarding this protein family in several microbial and plant genomes, including the M. tuberculosis genome....
The means by which various microevolutionary processes have acted in the past to produce patterns of cranial variation that characterize modern humans is not thoroughly understood. Applying a microevolutionary framework, within- and among-population variance/covariance (V/CV) structure was compared for several functional and developmental modules of the skull across a worldwide sample of modern humans. V/CV patterns in the basicranium, temporal bone, and face are proportional within and among groups, which is consistent with a hypothesis of neutral evolution; however, mandibular morphology deviated from this pattern. Degree of intergroup similarity in facial, temporal bone, and mandibular morphology is significantly correlated with geographic distance; however, much of the variance remains unexplained. These findings provide insight into the evolutionary history of modern human cranial variation by identifying signatures of genetic drift, gene flow, and migration and set the stage for inferences regarding selective pressures that early humans encountered since their initial migrations around the world....
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