No abstract

Recently, microRNAs (miRNA), small noncoding RNAs, have taken center stage in the field of human molecular oncology. However, their roles in tumor biology remain largely unknown. According to the assumption that miRNAs implicated in a specific tumor phenotype will show aberrant regulation of their target genes, we introduce an approach based on the miRNA target-dysregulated network (MTDN) to prioritize novel disease miRNAs. Target genes have predicted binding sites for any miRNA. The MTDN is constructed by combining computational target prediction with miRNA and mRNA expression profiles in tumor and nontumor tissues. Application of the proposed method to prostate cancer reveals that known prostate cancer miRNAs are characterized by a greater number of dysregulations and coregulators and the tendency to coregulate with each other and that they share a higher proportion of targets with other prostate cancer miRNAs. Support vector machine classifier, based on these features and changes in miRNA expression, is constructed and gives an average overall prediction accuracy of 0.8872 in cross-validation tests. The classifier is then applied to miRNAs in the MTDN. Functions enriched by dysregulated targets of novel predicted miRNAs are closely associated with oncogenesis. In addition, predicted cancer miRNAs within families or from different families show combinatorial dysregulation of target genes, as revealed by analysis of the MTDN modular organization. Finally, 3 miRNA target regulations are verified to hold in prostate cancer cells by transfection assays. These results show that the network-centric method could prioritize novel disease miRNAs and model how oncogenic lesions are mediated by miRNAs, providing important insights into tumorigenesis.

Three-dimensional macromolecular structures shed critical light on biological mechanism and facilitate development of small molecule inhibitors. Clinical success of raltegravir, a potent inhibitor of HIV-1 integrase, demonstrated the utility of this viral DNA recombinase as an antiviral target. A variety of partial integrase structures reported in the past 16 years have been instrumental and very informative to the field. Nonetheless, because integrase protein fragments are unable to functionally engage the viral DNA substrate critical for strand transfer inhibitor binding, the early structures did little to materially impact drug development efforts. However, recent results based on prototype foamy virus integrase have fully reversed this trend, as a number of X-ray crystal structures of active integrase-DNA complexes revealed key mechanistic details and moreover established the foundation of HIV-1 IN strand transfer inhibitor action. In this review we discuss the landmarks in the progress of IN structural biology during the past 17 years.

Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP450) inhibition is an important consideration in drug discovery. It is highly desirable to develop computational models that can predict the inhibitive effect of a compound against a specific CYP450 isoform. In this study, we developed a multitask model for concurrent inhibition prediction of five major CYP450 isoforms, namely, 1A2, 2C9, 2C19, 2D6, and 3A4. The model was built by training a multitask autoencoder deep neural network (DNN) on a large dataset containing more than 13 000 compounds, extracted from the PubChem BioAssay Database. We demonstrate that the multitask model gave better prediction results than that of single-task models, previous reported classifiers, and traditional machine learning methods on an average of five prediction tasks. Our multitask DNN model gave average prediction accuracies of 86.4% for the 10-fold cross-validation and 88.7% for the external test datasets. In addition, we built linear regression models to quantify how the other tasks contributed to the prediction difference of a given task between single-task and multitask models, and we explained under what conditions the multitask model will outperform the single-task model, which suggested how to use multitask DNN models more effectively. We applied sensitivity analysis to extract useful knowledge about CYP450 inhibition, which may shed light on the structural features of these isoforms and give hints about how to avoid side effects during drug development. Our models are freely available at http://repharma.pku.edu.cn/deepcyp/home.php or http://www.pkumdl.cn/deepcyp/home.php .