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There is growing interest in investigating the biochemical pathways involved in cellular responses to drugs. Here we propose new methods to explore the relationships between drugs, biochemical pathways and adverse drug reactions (ADRs) at a large scale. Using sparse canonical correlation analysis of 832 drugs characterized by 173 pathways and 1385 ADRs profiles, we identified 30 highly correlated sets of drugs, pathways and ADRs. This included known and potentially novel associations. To evaluate the predictive performance of our method, the extracted correlated components were used to predict known ADR profiles from drug pathway profiles. A relatively high prediction performance (AUC: 0.894) was achieved. To further investigate their association, we developed a network-based approach to extracting potentially significant modules of pathway-ADR associations. Five statistically significant modules were extracted. We found that most of the nodes contained in the modules are either pathways linked to a very limited number of drugs or rare ADRs. The work provides a foundation for future investigations of ADRs in the context of biochemical pathways under different clinical conditions. Our method and resulting datasets will aid in: a) the systematic prediction of ADRs, and b) the characterization of novel mechanisms of action for existing drugs. This merits additional research to further assess its potential in improving personalized drug safety monitoring, as well as for the repositioning of drugs in the longer term. [ABSTRACT FROM PUBLISHER]

It has been well recognized that the pace of the development of new drugs and therapeutic interventions lags far behind biological knowledge discovery. Network-based approaches have emerged as a promising alternative to accelerate the discovery of new safe and effective drugs. Based on the integration of several biological resources including two recently published datasets i.e., Drug-target interactions in myocardial infarction (My-DTome) and drug-domain interaction network, this paper reports the association between drugs and protein domains in the context of myocardial infarction (MI). A MI drug-domain interaction network, My-DDome, was firstly constructed, followed by topological analysis and functional characterization of the network. The results show that My-DDome has a very clear modular structure, where drugs interacting with the same domain(s) within each module tend to have similar therapeutic effects. Moreover it has been found that drugs acting on blood and blood forming organs (ATC code B) and sensory organs (ATC code S) are significantly enriched in My-DDome (p < 0.000001), indicating that by incorporating protein domain information into My-DTome, more detailed insights into the interplay between drugs, their known targets, and seemingly unrelated proteins can be revealed. [ABSTRACT FROM PUBLISHER]

The idea of the existence of date and party hubs in protein-protein interaction networks has been debated since it was proposed in 2004. Based on the incorporation of the information extracted from known three-dimensional structures of protein interactions, we revisited the properties associated with date and party hubs previously identified. The correlation of genomic essentiality, gene coexpression, and functional semantic similarity with date and party hubs were examined. The number of interaction interfaces associated with each hub was taken into account. The results suggested that the identification of date and party hubs based on their network connectivity and expression profiles with interaction partners may be incomplete. The number of interaction interfaces could play an important role in examining functional and topological properties associated with each hub protein. The observation is robust to the choice of degree cutoffs for hubs. Furthermore, we found that while singlish-interface hubs seem to correspond mostly to date hub, it appears that there is no significant difference between the proportions of multi-interface proteins categorized as date and as party hubs. [ABSTRACT FROM PUBLISHER]

Peptides in the skin secretion of frogs have been studied for some time now because they frequently possess important biological activity such as antibiotic, antimicrobial, or anticancer properties. In this paper, we present a computational approach for measuring the degree of similarity between the entire peptide complement of the skin secretion of specimens from the same or different species. The first step in the analysis is the generation of a mass spectral profile from an experimental high-performance liquid chromatography/electrosparay ionization analysis of the sample. An "overlap" between the mass spectral profiles of different specimens is then proposed as a measure of their similarity. Analysis of specimens from three species of the genus Litoria, viz., L. Aurea, L. Caerulea, and L. Infrafrenata, and Rana Capito of genus Rana shows that the degree of similarity is highest between specimens from the same species, lower for specimens from different species of the same genus, and lowest between specimens from different genera. This indicates that comparison of skin peptide profiles (i.e., mass spectral profiles of skin secretion) is potentially a useful aid in the taxonomic study of amphibian species.