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The molecular clock, the circadian clock, and exploring protein surface pockets: the PLOS Comp Biol February Issue

Here are our highlights from February’s PLOS Computational Biology


The Molecular Clock of Neutral Evolution

Evolution is driven by genetic mutations. While some mutations affect an organism’s ability to survive and reproduce, most are neutral and have no effect. These neutral mutations can be used as a “molecular clock” to estimate, for example, how long ago humans diverged from chimpanzees and bonobos. Benjamin Allen and colleagues use mathematical modelling to study how the rates of these molecular clocks are affected by the spatial arrangement of a population in its habitat. The authors also apply their framework to the field of social network analysis, and show that the structure of sites such as Twitter affects the rate at which new ideas replace old ones.


The Interplay of the Cell Cycle and the Circadian Clock

The clustering of ZCOGs on zebrafish metabolic network. Image credit: Ying Li et al.
The clustering of ZCOGs on zebrafish metabolic network.
Image credit: Li et al.

The circadian timing system gates cell cycle progression in various organisms from unicellular microorganisms to mammals. Although a number of factors have been implicated in linking the circadian clock with the cell cycle, little is known about the contribution of metabolic processes to this interaction. Ying Li and colleagues show that cell cycle, metabolism and the circadian clock are intertwined through consistent circadian expression of genes in de novo purine synthesis in the zebrafish.


Exploring Protein Surface Pockets

Image credit: Johnson et al.
Image credit: Johnson et al.

Despite considerable effort, there are few examples of small molecules that directly inhibit protein-protein interactions. This presents a challenge for predicting ligand activity. John Karanicolas and colleagues describe a novel computational approach to exploring the ensemble of surface pockets accessible to each member of a protein family, and show that this can be used to predict those family members with which a given ligand will interact. This approach presents a new avenue for designing highly selective inhibitors of protein-protein interactions.

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