International Journal of Computational Biology and Drug Design最新文献

Three-dimensional (3D) high resolution microscopic images have high potential for improving the understanding of both normal and disease processes where structural changes or spatial relationship of disease features are significant. In this paper, we develop a complete framework applicable to 3D pathology analytical imaging, with an application to whole slide images of sequential liver slices for 3D vessel structure analysis. The analysis workflow consists of image registration, segmentation, vessel cross-section association, interpolation, and volumetric rendering. To identify biologically-meaningful correspondence across adjacent slides, we formulate a similarity function for four association cases. The optimal solution is then obtained by constrained Integer Programming. We quantitatively and qualitatively compare our vessel reconstruction results with human annotations. Validation results indicate a satisfactory concordance as measured both by region-based and distance-based metrics. These results demonstrate a promising 3D vessel analysis framework for whole slide images of liver tissue sections.

Vibrio cholera, a gram-negative bacterium, has been categorised into clinical and environmental species. Phylogenetic studies have been performed to investigate the relationships of the V. cholerae populations in worldwide. In this study, phylogenetic relationship between V. cholerae isolates from Iran and other regions of the world was determined, based on three housekeeping genes analysis. Results for Iranian strains showed that congruency of asd and hlyA phylogenetic trees were remarkably higher than recA tree. Iranian strains displayed 2-3%, 1-14% and 3-5% deference in asd, hlyA and recA nucleotide sequences, respectively. Sequence similarity degrees were variable between Iranian and other region's strains. Furthermore, the non-congruence in the phylogeny of the pathogenic clones in cladograms is probably due to horizontal gene transfer. Finally, results of this study suggest that monitoring of surface waters for housekeeping genes of V. cholerae in the cholera endemic areas may be valuable for forecasting the expected cholera outbreaks.

The main goal of this study is to understand the molecular-level interactions of neuraminidase inhibitor. The molecular docking, molecular dynamics and binding energy calculation analyses were carried out and the results revealed that the 150-cavitiy in the active site may play an important role in binding of drugs. Free energy calculations revealed that electrostatic interaction is more favourable for Oseltamivir interaction with H1N1 and van der Waals interaction is more favourable for H5N1, whereas Zanamivir favours the electrostatic interaction in both the strains (H1N1 and H5N1). Energy-optimised pharmacophore mapping was created using Oseltamivir drug. The pharmacophore model contained two hydrogen-bond acceptor and two hydrogen bond donor sites. Using these pharmacophore features, we screened a compound database to find a potential ligand that could inhibit the H1N1 protein. The current research will pave the way to find potent neuraminidase inhibitors against H1N1 (2009) strain.

Calcitonin gene-related peptide (CGRP) is involved in triggering migraine. Many strategies for antimigraine drug designing have been employed using various CGRP antagonist/ligands but most of them have failed due to their inability to reach target CGRP receptor as they get metabolised before conferring their pharmacological action and they are also toxic to the liver. In the present study, we evaluated the binding of our active ligands present in real veggies with the CGRP receptor crystal structure and compared their binding energy and affinity with other reference anti-migraine drugs/ligands present in the market. A high-throughput screening comprising of molecular docking, Absorption, Distribution, Metabolism, Excretion and Toxicity predictions, logP values and % of human oral absorption value led to the identification of two potential compounds present in live green real veggies which could be considered for anti-migraine activity with better binding affinities than the reference drugs used and with liver-protective properties.

LXR (encoded by NR1H2 and 3) and FXR (known as bile acid receptor) encoded by NR1H4 (nuclear receptor subfamily 1, group H and member 4) are nuclear receptors in humans and are important regulators of bile acid production, cholesterol, fatty acid and glucose homeostasis hence responsible for liver detoxification. Several strategies for drug design with numerous ligands for this target have failed owing to the inability of the ligand to access the target/receptor or their early metabolisation. In this work, we have evaluated FXR and LXR structure bound with agonist and compared the binding energy affinity of active ligands present in live green-real veggies with reference drugs (ligands) present in the market. A high throughput screening combined with molecular docking, absorption, distribution, metabolism, excretion and toxicity (ADMET) predictions, log P values and percentage of human oral absorption value led to the identification of two compounds present in live green-real veggies with strong potential for liver detoxification.

Natural flavonoid derivatives against cancer for selective KB cell lines (oral human epidermoid carcinoma) are analysed to determine the relationship between biological activities and structural properties of these molecules. Molecular alignment was performed for 88 natural flavonoid derivatives; out of these 88 molecules, 69 molecules were taken into training set and rest of the 19 molecules were used in test set prediction. We describe our elucidation of their structure activity relation (SAR) using three-dimensional quantitative structure activity relationship (3D-QSAR) models. A predictive comparative molecular field analysis (CoMFA) model of q² = 0.888 and r² = 0.940 was obtained and a comparative molecular similarity indices analysis (CoMSIA) model q² = 0.778 and r² = 0.971 was used to describe the non-linearly combined affinity of each functional group in the inhibitors. The contour maps obtained from 3D-QSAR studies were evaluated for the activity trends of the molecules analysed.

MicroRNAseq (miRNAseq) is a form of RNAseq technology that has become an increasingly popular alternative to miRNA expression profiling. Unlike messenger RNA (mRNA), miRNA extraction can be difficult, and sequencing such small RNA can also be problematic. We designed a study to test the reproducibility of miRNAseq technology and the performance of the two popular miRNA isolation methods, mirVana and TRIzol, by sequencing replicated samples using microRNA isolated with each kit. Through careful analysis of our data, we found excellent repeatability of miRNAseq technology. The mirVana method performed better than TRIzol in terms of useful reads sequenced, number of miRNA identified, and reproducibility. Finally, we identified a baseline noise level for miRNAseq technology; this baseline noise level can be used as a filter in future miRNAseq studies.

Many types of clustering techniques for chemical structures have been used in the literature, but it is known that any single method will not always give the best results for all types of applications. Recent work on consensus clustering methods is motivated because of the successes of combining multiple classifiers in many areas and the ability of consensus clustering to improve the robustness, novelty, consistency and stability of individual clusterings. In this paper, the Cluster-based Similarity Partitioning Algorithm (CSPA) was examined for improving the quality of chemical structures clustering. The effectiveness of clustering was evaluated based on the ability to separate active from inactive molecules in each cluster and the results were compared with the Ward's clustering method. The chemical dataset MDL Drug Data Report (MDDR) database was used for experiments. The results, obtained by combining multiple clusterings, showed that the consensus clustering method can improve the robustness, novelty and stability of chemical structures clustering.