Blueprint for antimicrobial hit discovery targeting metabolic networks
Y. Shen, J. Liu, G. Estiu, B. Isin, Y-Y. Ahn, D-S. Lee, A-L. Barabási, V. Kapatral, O. Wiest, and Z. N. Oltvai
The authors use a flux balance analysis to identify reactions that essential for S. aureus growth. The enzymes required for the identified pathways were selected for in silico drug screening using both known structures and homology models. Inhibitors identified computationally where tend tested experimentally. I particularly liked the breath of different methods used in this study (FBA, homology modelling, ligand docking and experimental verification). It shows the usefulness of the increase in knowledge across these different areas (networks and structures).
Quantitative Phosphoproteomics Reveals Widespread Full Phosphorylation Site Occupancy During Mitosis
Jesper V. Olsen, Michiel Vermeulen, Anna Santamaria, Chanchal Kumar, Martin L. Miller, Lars J. Jensen, Florian Gnad, Jürgen Cox, Thomas S. Jensen, Erich A. Nigg, Søren Brunak, and Matthias Mann
In this study HeLa cells were synchronized in different stages of the cell cycle and their proteins and phosphorylation sites were quantified relative to asynchronous cells using a SILAC mass-spec approach. Changes in protein abundance and phosphorylation were combined with transcriptional changes and these were used to identify previously known and potentially novel complexes and kinase-substrate interactions important for cell-cycle progression. In addition, I thought it was pretty cool that the authors found a way to directly quantify the phosphorylation site occupancy from the mass-spec results. I was only slightly disappointed that the authors did not attempt to do a cross-species analysis given the available data from Liam Holt et al. on Cdk1 phosphorylation in S. cerevisiae. (Bonus- spot the blogger in the author list)
The Genetic Landscape of a Cell
Michael Costanzo et al.
This paper reports the large scale effort to quantify genetic interactions for approximately 1700 times 3800 pairs of genes in S. cerevisiae. As it is typically the case for these sort of "resource" papers describing a large dataset there is no way that a paper can do full justice to this work. They have mostly tried to show different ways to use this information: 1) predict gene function, 2) functional interactions between complexes and functional groups and 3) prediction of drug targets. Hopefully cell biology labs will pick up on this information to search for their genes of interested and bioinformatics groups will continue to find ways to make these resources easier to navigate (see STRING for a good example of this).