Distinctive communication networks in inactive states of β2-adrenergic receptor: Mutual information and entropy transfer analysis

Mutual information and entropy transfer analysis employed on two inactive states of human beta-2 adrenergic receptor (β₂-AR) unraveled distinct communication pathways. Previously, a so-called “highly” inactive state of the receptor was observed during 1.5 microsecond long molecular dynamics simulation where the largest intracellular loop (ICL3) was swiftly packed onto the G-protein binding cavity, becoming entirely inaccessible. Mutual information quantifying the degree of correspondence between backbone-C_α fluctuations was mostly shared between intra- and extra-cellular loop regions in the original inactive state, but shifted to entirely different regions in this latest inactive state. Interestingly, the largest amount of mutual information was always shared among the mobile regions. Irrespective of the conformational state, polar residues always contributed more to mutual information than hydrophobic residues, and also the number of polar-polar residue pairs shared the highest degree of mutual information compared to those incorporating hydrophobic residues. Entropy transfer, quantifying the correspondence between backbone-C_α fluctuations at different timesteps, revealed a distinctive pathway directed from the extracellular site toward intracellular portions in this recently exposed inactive state for which the direction of information flow was the reverse of that observed in the original inactive state where the mobile ICL3 and its intracellular surroundings drove the future fluctuations of extracellular regions.     

Spatial distribution of phytochromes

Phytochromes are chromoproteins which mediate several light responses in plants. Phytochrome proteins are encoded by a gene family which is currently being characterized in several plant species. Analysis of type-specific mutants of two well-characterized members of the family, PhyA and PhyB, indicates that these proteins have distinct functions. Much remains to be learned about the mechanisms by which the phytochromes carry out their distinct and diverse functions. It is hoped that information concerning the localization of phytochromes, at the whole plant and subcellular levels, will aid in elucidating the mechanism of phytochrome function. This review, which summarizes information about phytochrome distribution, has an emphasis on recent reports in which the molecular species of phytochrome are differentiated. However, classical data are also included and reinterpreted using knowledge of the phytochrome family.     

Nitrogen-source-induced activation of neutral trehalase in Schizosaccharomyces pombe and Pachysolen tannophilus: role of cAMP as second messenger

Abstract Resting cells of the fission yeast Schizosaccharomyces pombe , suspended in buffer with glucose, responded to the addition of asparagine by increasing trehalase activity. This response was preceded by a peak in cAMP concentration. The addition of the nitrogen source to resting cells, devoid of the catalytic subunit of cAMP-dependent protein kinase, produced the transient increase in cAMP but did not promote any change in trehalase activity. In the budding yeast Pachysolen iannophilus , the activation of trehalase by nitrogen source was also accompanied by a sharp peak in cAMP. These results suggest that in the two yeasts cAMP acts as a second messenger in the transduction of the nitrogen-source-induced signal causing the activation of trehalase.     

Quantitative proteomic profiling identifies global protein network dynamics in murine embryonic heart development

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Detecting early-warning signals for influenza A pandemic based on protein dynamical network biomarkers

The outbreak of influenza A comes from a relatively stable state is a critical phenomenon on epidemic. In this paper, influenza A varying from different states is studied in the method of dynamical network biomarkers (DNB). Through studying DNB of influenza A virus protein, we can detect the warning signals of outbreak for influenza A and obtain a composite index. The composite index varies along with the state of pandemic influenza, which gives a clue showing the turn point of outbreak. The low value (<1) steady state of the composite index means influenza A is normally in the relatively steady stage. Meanwhile, if the composite index of a certain year increases by more than 0.8 relative to the previous year and it is less than 1 and it increases sharply and reaches a peak being larger than 1 in next year, it means the year is normal in the critical state before outbreak and the next year is normally in the outbreak state. Therefore, we can predict the outbreak of influenza A and identify the critical state before influenza A outbreak or outbreak state by observing the variation of index value.