Predicting Disease Transmission from Geo-Tagged Micro-Blog Data

Researchers have begun to mine social network data in order to predict a variety of social, economic, and health related phenomena. While previous work has focused on predict- ing aggregate properties, such as the prevalence of seasonal influenza in a given country, researchers consider the task of fine- grained prediction of the health of specific people from noisy and incomplete data.

Structural and Biochemical Basis for Development of Influenza Virus Inhibitors Targeting the PA Endonuclease

Emerging influenza viruses are a serious threat to human health because of their pandemic potential. A promising target for the development of novel anti-influenza therapeutics is the PA protein, whose endonuclease activity is essential for viral replication. Translation of viral mRNAs by the host ribosome requires mRNA capping for recognition and binding, and the necessary mRNA caps are cleaved or “snatched” from host pre-mRNAs by the PA endonuclease. The structure-based development of inhibitors that target PA endonuclease is now possible with the recent crystal structure of the PA catalytic domain. In this study, authors sought to understand the molecular mechanism of inhibition by several compounds that are known or predicted to block endonuclease-dependent polymerase activity. Using an in vitro endonuclease activity assay, authors show that these compounds block the enzymatic activity of the isolated PA endonuclease domain. Using X-ray crystallography, authors show how these inhibitors coordinate the two-metal endonuclease active site and engage the active site residues. Two structures also reveal an induced-fit mode of inhibitor binding. The structures allow a molecular understanding of the structure-activity relationship of several known influenza inhibitors and the mechanism of drug resistance by a PA mutation. Taken together, study data reveal new strategies for structure-based design and optimization of PA endonuclease inhibitors.

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