Quantum nondemolition measurement of mechanical motion quanta

The fields of opto- and electromechanics have facilitated numerous advances in the areas of precision measurement and sensing, ultimately driving the studies of mechanical systems into the quantum regime. To date, however, the quantization of the mechanical motion and the associated quantum jumps between phonon states remains elusive. For optomechanical systems, the coupling to the environment was shown to preclude the detection of the mechanical mode occupation, unless strong single photon optomechanical coupling is achieved. Here, we propose and analyse an electromechanical setup, which allows to overcome this limitation and resolve the energy levels of a mechanical oscillator. We find that the heating of the membrane, caused by the interaction with the environment and unwanted couplings, can be suppressed for carefully designed electromechanical systems. The results suggest that phonon number measurement is within reach for modern electromechanical setups.Comment: 8 pages, 5 figures plus 24 pages, 11 figures supplemental materia

Measurement of the Electric Form Factor of the Neutron at Q^2=0.5 and 1.0 (GeV/c)^2

The electric form factor of the neutron was determined from measurements of the \vec{d}(\vec{e},e' n)p reaction for quasielastic kinematics. Polarized electrons were scattered off a polarized deuterated ammonia target in which the deuteron polarization was perpendicular to the momentum transfer. The scattered electrons were detected in a magnetic spectrometer in coincidence with neutrons in a large solid angle detector. We find G_E^n = 0.0526 +/- 0.0033 (stat) +/- 0.0026 (sys) and 0.0454 +/- 0.0054 +/- 0.0037 at Q^2 = 0.5 and 1.0 (GeV/c)^2, respectively.Comment: 5 pages, 2 figures, as publishe

Unveiling Protein Functions through the Dynamics of the Interaction Network

Protein interaction networks have become a tool to study biological processes, either for predicting molecular functions or for designing proper new drugs to regulate the main biological interactions. Furthermore, such networks are known to be organized in sub-networks of proteins contributing to the same cellular function. However, the protein function prediction is not accurate and each protein has traditionally been assigned to only one function by the network formalism. By considering the network of the physical interactions between proteins of the yeast together with a manual and single functional classification scheme, we introduce a method able to reveal important information on protein function, at both micro- and macro-scale. In particular, the inspection of the properties of oscillatory dynamics on top of the protein interaction network leads to the identification of misclassification problems in protein function assignments, as well as to unveil correct identification of protein functions. We also demonstrate that our approach can give a network representation of the meta-organization of biological processes by unraveling the interactions between different functional classes

Neighbor Overlap Is Enriched in the Yeast Interaction Network: Analysis and Implications

The yeast protein-protein interaction network has been shown to have distinct topological features such as a scale free degree distribution and a high level of clustering. Here we analyze an additional feature which is called Neighbor Overlap. This feature reflects the number of shared neighbors between a pair of proteins. We show that Neighbor Overlap is enriched in the yeast protein-protein interaction network compared with control networks carefully designed to match the characteristics of the yeast network in terms of degree distribution and clustering coefficient. Our analysis also reveals that pairs of proteins with high Neighbor Overlap have higher sequence similarity, more similar GO annotations and stronger genetic interactions than pairs with low ones. Finally, we demonstrate that pairs of proteins with redundant functions tend to have high Neighbor Overlap. We suggest that a combination of three mechanisms is the basis for this feature: The abundance of protein complexes, selection for backup of function, and the need to allow functional variation

Exploring the Evolution of Novel Enzyme Functions within Structurally Defined Protein Superfamilies

In order to understand the evolution of enzyme reactions and to gain an overview of biological catalysis we have combined sequence and structural data to generate phylogenetic trees in an analysis of 276 structurally defined enzyme superfamilies, and used these to study how enzyme functions have evolved. We describe in detail the analysis of two superfamilies to illustrate different paradigms of enzyme evolution. Gathering together data from all the superfamilies supports and develops the observation that they have all evolved to act on a diverse set of substrates, whilst the evolution of new chemistry is much less common. Despite that, by bringing together so much data, we can provide a comprehensive overview of the most common and rare types of changes in function. Our analysis demonstrates on a larger scale than previously studied, that modifications in overall chemistry still occur, with all possible changes at the primary level of the Enzyme Commission (E.C.) classification observed to a greater or lesser extent. The phylogenetic trees map out the evolutionary route taken within a superfamily, as well as all the possible changes within a superfamily. This has been used to generate a matrix of observed exchanges from one enzyme function to another, revealing the scale and nature of enzyme evolution and that some types of exchanges between and within E.C. classes are more prevalent than others. Surprisingly a large proportion (71%) of all known enzyme functions are performed by this relatively small set of 276 superfamilies. This reinforces the hypothesis that relatively few ancient enzymatic domain superfamilies were progenitors for most of the chemistry required for life

Integrative Features of the Yeast Phosphoproteome and Protein–Protein Interaction Map

Following recent advances in high-throughput mass spectrometry (MS)–based proteomics, the numbers of identified phosphoproteins and their phosphosites have greatly increased in a wide variety of organisms. Although a critical role of phosphorylation is control of protein signaling, our understanding of the phosphoproteome remains limited. Here, we report unexpected, large-scale connections revealed between the phosphoproteome and protein interactome by integrative data-mining of yeast multi-omics data. First, new phosphoproteome data on yeast cells were obtained by MS-based proteomics and unified with publicly available yeast phosphoproteome data. This revealed that nearly 60% of ∼6,000 yeast genes encode phosphoproteins. We mapped these unified phosphoproteome data on a yeast protein–protein interaction (PPI) network with other yeast multi-omics datasets containing information about proteome abundance, proteome disorders, literature-derived signaling reactomes, and in vitro substratomes of kinases. In the phospho-PPI, phosphoproteins had more interacting partners than nonphosphoproteins, implying that a large fraction of intracellular protein interaction patterns (including those of protein complex formation) is affected by reversible and alternative phosphorylation reactions. Although highly abundant or unstructured proteins have a high chance of both interacting with other proteins and being phosphorylated within cells, the difference between the number counts of interacting partners of phosphoproteins and nonphosphoproteins was significant independently of protein abundance and disorder level. Moreover, analysis of the phospho-PPI and yeast signaling reactome data suggested that co-phosphorylation of interacting proteins by single kinases is common within cells. These multi-omics analyses illuminate how wide-ranging intracellular phosphorylation events and the diversity of physical protein interactions are largely affected by each other

The Ras Antagonist, Farnesylthiosalicylic Acid (FTS), Decreases Fibrosis and Improves Muscle Strength in dy2J/dy2J Mouse Model of Muscular Dystrophy

The Ras superfamily of guanosine-triphosphate (GTP)-binding proteins regulates a diverse spectrum of intracellular processes involved in inflammation and fibrosis. Farnesythiosalicylic acid (FTS) is a unique and potent Ras inhibitor which decreased inflammation and fibrosis in experimentally induced liver cirrhosis and ameliorated inflammatory processes in systemic lupus erythematosus, neuritis and nephritis animal models. FTS effect on Ras expression and activity, muscle strength and fibrosis was evaluated in the dy2J/dy2J mouse model of merosin deficient congenital muscular dystrophy. The dy2J/dy2J mice had significantly increased RAS expression and activity compared with the wild type mice. FTS treatment significantly decreased RAS expression and activity. In addition, phosphorylation of ERK, a Ras downstream protein, was significantly decreased following FTS treatment in the dy2J/dy2J mice. Clinically, FTS treated mice showed significant improvement in hind limb muscle strength measured by electronic grip strength meter. Significant reduction of fibrosis was demonstrated in the treated group by quantitative Sirius Red staining and lower muscle collagen content. FTS effect was associated with significantly inhibition of both MMP-2 and MMP-9 activities. We conclude that active RAS inhibition by FTS was associated with attenuated fibrosis and improved muscle strength in the dy2J/dy2J mouse model of congenital muscular dystrophy