International Journal of Computational Biology and Drug Design最新文献

Using a unique combination of visual, statistical, and data mining methods, we tested the hypothesis that an immune cell's movement pattern can convey key information about the cell's function, antigen specificity, and environment. We applied clustering, statistical tests, and a support vector machine (SVM) to assess our ability to classify different datasets of imaged flouresently labelled T cells in mouse liver. We additionally saw clusters of different movement patterns of T cells of identical antigenic specificity. We found that the movement patterns of T cells specific and non-specific for malaria parasites are differentiable with 72% accuracy, and that specific cells have a higher tendency to move towards the parasite than non-specific cells. Movements of antigen-specific T cells in uninfected mice vs. infected mice were differentiable with 69.8% accuracy. We additionally saw clusters of different movement patterns of T cells of identical antigenic specificity. We concluded that our combination of methods has the potential to advance the understanding of cell movements in vivo.

Differential expression has been a standard tool for analysing case-control transcriptomic data since the advent of microarray technology. It has proved invaluable in characterising the molecular mechanisms of disease. Nevertheless, the expression profile of a gene across samples can be perturbed in ways that leave the expression level unaltered, while a biological effect is nonetheless present. This paper describes and analyses differential Shannon entropy and differential coefficient of variation, two alternate techniques for identifying genes of interest. Ontological analysis across 16 human disease datasets demonstrates that these alternatives are effective at identifying disease-related genes not found by mere differential expression alone. Because the two alternate techniques are based on somewhat different mathematical formulations, they tend to produce somewhat different gene lists. Moreover, each may pinpoint genes completely overlooked by the other. Thus, measures of entropy and variation can be used to replace or better yet augment standard differential expression computations.

Transcription factors (TFs) and microRNAs (miRNAs), the two main gene regulators in the biological system, control the gene expression at the transcriptional and post-transcriptional level, respectively. However, little is known regarding whether the miRNATF co-regulatory mechanisms, predicted by several studies, truly reflect the molecular interactions in cellular systems. To tackle this important issue, we developed an integrative framework by utilising four independent miRNA and matched mRNA expression profiling datasets to identify reproducible regulations, and demonstrated this approach in non-small cell lung cancer (NSCLC). Our analyses pinpointed several reproducible miRNA-TF co-regulatory networks in NSCLC from which we systematically prioritised eight hub miRNAs that may have strong oncogenic characteristics. Here, we discussed the major findings of our study and explored the oncogenic and prognostic potential of eight prioritised miRNAs through literature-mining based analysis and patient survival analysis. The findings provide additional insights into the miRNA-TF co-regulation in lung cancer.

In the present study, three detour matrix-based topological indices (TIs) termed as adjacent path eccentric distance sum indices 1-3 (denoted by (A)ξ(1)(PDS), (A)ξ(2)(PDS) and (A)ξ(3)(PDS)) as well as their topochemical versions (denoted by (A)ξ(1c)(PDS), (A)ξ(2c)(PDS) and (A)ξ(3c)(PDS)) have been conceptualised. Values of the proposed TIs were computed for all possible cyclic and acyclic structures containing three, four, five vertices using an in-house computer programme. Proposed TIs were evaluated for discriminating power, degeneracy, intercorrelation and sensitivity towards branching as well relative position of substituent(s) in cyclic structures. Mathematical properties of one of the proposed TIs were also studied. Exceptionally high discriminating power, high sensitivity towards branching as well as relative position(s) of substituent(s) in cyclic structures and negligible degeneracy offer proposed indices a vast potential for use in characterisation of structures, similarity/dissimilarity studies, lead identification and optimisation, combinatorial library design and quantitative structure-activity/property/toxicity/pharmacokinetic relationship studies so as to facilitate drug design.

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) based on 3D-QSAR (3D-quantitative structure activity relationship) studies were carried out on 97 flavonoid derivatives as potent P56(lck) protein tyrosine kinase inhibitors. The best prediction was obtained with CoMFA standard model (q² = 0.838, r² = 0.948) using steric, electrostatic along with CoMSIA standard model (q² = 0.714, r² = 0.921) using steric, electrostatic, hydrophobic, hydrogen bond donor and acceptor fields. Of the 97 molecules a training set of 76 compounds and the predictive ability of the QSAR model were assessed employing a test set of 21 compounds. The resulting CoMFA and CoMSIA contour maps were used to identify the structural features relevant to the biological activity in this series of flavonoid derivatives, based upon which we identified and designed 10 novel molecules that showed superior inhibitory activity against P56(lck) protein which shed new light on effective therapeutic agents against these classes of enzymes.

MIA (Melanoma Inhibitory Activity) protein is over expressed in melanoma cells and binds to extracellular matrix proteins as well as to several integrins. These interactions were suggested to promote formation of metastasis. Therefore, abrogation of MIA interaction with other proteins using small molecules might show a diminishing effect on cancer cell invasion. The present study is aimed at the analysis of the integrin-binding site of MIA using molecular docking, followed by a virtual screening for drug-like compounds that show potential as putative inhibitors of MIA-integrin interaction. Results showed that at the proposed binding interface of the MIA-integrin complex, a druggable binding pocket is located. Therefore, the integrin-binding domain of MIA was used as a receptor to screen 2200 drug-like compounds. Next, we analysed the interactions of the identified hit compounds with the MIA binding pocket to find the most important features of the hit compounds.

High throughput bacterial RNA-Seq experiments can generate extremely high and imbalanced sequencing coverage. Over- or under-estimation of gene expression levels will hinder accurate gene differential expression analysis. Here we evaluated strategies to identify expression differences of genes with high coverage in bacterial transcriptome data using either raw sequence reads or unique reads with duplicate fragments removed. In addition, we proposed a generalised linear model (GLM) based approach to identify imbalance in read coverage based on sequence compositions. Our results show that analysis using raw reads identifies more differentially expressed genes with more accurate fold change than using unique reads. We also demonstrate the presence of sequence composition related biases that are independent of gene expression levels and experimental conditions. Finally, genes that still show strong coverage imbalance after correction were tagged using statistical approach.

In present study, adjacent path eccentric distance sum indices proposed in Part-I of the manuscript were successfully utilised for the development of models for cycloxygenase-2 (COX-2) inhibitory activity. Values of diverse molecular descriptors (MDs) for each of 38 indomethacin analogues involved in the dataset were computed. A total of 55 diverse MDs were ultimately shortlisted for further analysis. The suitable models were developed using decision tree (DT), random forest (RF) and moving average analysis (MAA). The DT identified the proposed topological index (TI)-(A)ξ(3)(PDS) as one of the important indices. The accuracy of prediction of DT, RF and MAA-based models varied from 81.58% to 97.37%. The statistical significance of proposed models was assessed through inter-correlation analysis, sensitivity, specificity, non-error rate and Mathews correlation coefficient. Proposed models offer vast potential for providing lead structures for the development of potent anti-inflammatory agents devoid of COX-1 side effects.

Microbes play an important role on human health, however, little is known on microbes in the past decades for the limitation of culture-based techniques. Recently, with the development of next-generation sequencing (NGS) technologies, it is now possible to sequence millions of sequences directly from environments samples, and thus it supplies us a sight to probe the hidden world of microbial communities and detect the associations between microbes and diseases. In the present work, we proposed a supervised learning-based method to mine the relationship between microbes and periodontitis with 16S rRNA sequences. The jackknife accuracy is 94.83% and it indicated the method can effectively predict disease status. These findings not only expand our understanding of the association between microbes and diseases but also provide a potential approach for disease diagnosis and forensics.