Abstract: ABSTRACT: The identification of molecular target and mechanism of action of compounds is a key hurdle in drug discovery. Multiplexed techniques for bead-based expression profiling allow the measurement of transcriptional signatures of compound-treated cells in high-throughput mode. Such profiles can be used to gain insight into compounds' mode of action and the protein targets they are modulating. Through the proxy of target prediction from such gene signatures we explored important aspects of the use of transcriptional profiles to capture biological variability of perturbed cellular assays. We found that signatures derived from expression data and signatures derived from biological interaction networks performed equally well, and we showed that gene signatures can be optimised using a genetic algorithm. Gene signatures of approximately 128 genes seemed to be most generic, capturing a maximum of the perturbation inflicted on cells through compound treatment. Moreover, we found evidence for oxidative phosphorylation to be one of the most general ways to capture compound perturbation.
Abstract: Neglected tropical disease drug discovery requires application of pragmatic and efficient methods for development of new therapeutic agents. In this report, we describe our target repurposing efforts for the essential phosphodiesterase (PDE) enzymes TbrPDEB1 and TbrPDEB2 of Trypanosoma brucei , the causative agent for human African trypanosomiasis (HAT). We describe protein expression and purification, assay development, and benchmark screening of a collection of 20 established human PDE inhibitors. We disclose that the human PDE4 inhibitor piclamilast, and some of its analogues, show modest inhibition of TbrPDEB1 and B2 and quickly kill the bloodstream form of the subspecies T. brucei brucei . We also report the development of a homology model of TbrPDEB1 that is useful for understanding the compound-enzyme interactions and for comparing the parasitic and human enzymes. Our profiling and early medicinal chemistry results strongly suggest that human PDE4 chemotypes represent a better starting point for optimization of TbrPDEB inhibitors than those that target any other human PDEs.
Abstract: Gene expression studies have been widely used in an effort to identify signatures that can predict clinical progression of cancer. In this study we focused instead on identifying gene expression differences between breast tumors and adjacent normal tissue, and between different subtypes of tumor classified by clinical marker status. We have collected a set of 20 breast cancer tissues, matched with the adjacent pathologically normal tissue from the same patient. The cancer samples representing each subtype of breast cancer identified by estrogen receptor ER(+/-) and Her2(+/-) status and divided into four subgroups (ER+/Her2+, ER+/Her2-, ER-/Her2+, and ER-/Her2-) were hybridized on Affymetrix HG-133 Plus 2.0 microarrays. By comparing cancer samples with their matched normal controls we have identified 3537 overall differentially expressed genes using data analysis methods from Bioconductor. When we looked at the genes in common of the four subgroups, we found 151 regulated genes, some of them encoding known targets for breast cancer treatment. Unique genes in the four subgroups instead suggested gene regulation dependent on the ER/Her2 markers selection. In conclusion, the results indicate that microarray studies using robust analysis of matched tumor and normal samples from the same patients can be used to identify genes differentially expressed in breast cancer tumor subtypes even when small numbers of samples are considered and can further elucidate molecular features of breast cancer.
Abstract: The increasing availability of genomic data for pathogens that cause tropical diseases has created new opportunities for drug discovery and development. However, if the potential of such data is to be fully exploited, the data must be effectively integrated and be easy to interrogate. Here, we discuss the development of the TDR Targets database (http://tdrtargets.org), which encompasses extensive genetic, biochemical and pharmacological data related to tropical disease pathogens, as well as computationally predicted druggability for potential targets and compound desirability information. By allowing the integration and weighting of this information, this database aims to facilitate the identification and prioritization of candidate drug targets for pathogens.
Abstract: We present a tool for the prediction of mRNA 3'-processing (cleavage and polyadenylation) sites in the yeast Saccharomyces cerevisiae, based on a discrete state-space model or hidden Markov model. Comparison of predicted sites with experimentally verified 3'-processing sites indicates good agreement. All predicted or known yeast genes were analyzed to find probable 3'-processing sites. Known alternative 3'-processing sites, both within the 3'-untranslated region and within the protein coding sequence were successfully identified, leading to the possibility of prediction of previously unknown alternative sites. The lack of an apparent 3'-processing site calls into question the validity of some predicted genes. This is specifically investigated for predicted genes with overlapping coding sequences.
Abstract: We have constructed, in a completely automated fashion, a new structure template library for threading that represents 358 distinct SCOP folds where each model is mathematically represented as a Hidden Markov model (HMM). Because the large number of models in the library can potentially dilute the prediction measure, a new triage method for fold prediction is employed. In the first step of the triage method, the most probable structural class is predicted using a set of manually constructed, high-level, generalized structural HMMs that represent seven general protein structural classes: all-alpha, all-beta, alpha/beta, alpha+beta, irregular small metal-binding, transmembrane beta-barrel, and transmembrane alpha-helical. In the second step, only those fold models belonging to the determined structural class are selected for the final fold prediction. This triage method gave more predictions as well as more correct predictions compared with a simple prediction method that lacks the initial classification step. Two different schemes of assigning Bayesian model priors are presented and discussed.
