Genomic Analysis of Pseudomonas putida Phage tf with Localized Single-Strand DNA Interruptions

The complete sequence of the 46,267 bp genome of the lytic bacteriophage tf specific to Pseudomonas putida PpG1 has been determined. The phage genome has two sets of convergently transcribed genes and 186 bp long direct terminal repeats. The overall genomic architecture of the tf phage is similar to that of the previously described Pseudomonas aeruginosa phages PaP3, LUZ24 and phiMR299-2, and 39 out of the 72 products of predicted tf open reading frames have orthologs in these phages. Accordingly, tf was classified as belonging to the LUZ24-like bacteriophage group. However, taking into account very low homology levels between tf DNA and that of the other phages, tf should be considered as an evolutionary divergent member of the group. Two distinguishing features not reported for other members of the group were found in the tf genome. Firstly, a unique end structure - a blunt right end and a 4-nucleotide 3'-protruding left end - was observed. Secondly, 14 single-chain interruptions (nicks) were found in the top strand of the tf DNA. All nicks were mapped within a consensus sequence 5'-TACT/RTGMC-3'. Two nicks were analyzed in detail and were shown to be present in more than 90% of the phage population. Although localized nicks were previously found only in the DNA of T5-like and phiKMV-like phages, it seems increasingly likely that this enigmatic structural feature is common to various other bacteriophages.

Design of composite stiffener run-outs for damage tolerance

A major concern in stiffener run-out regions, where the stiffener is terminated due to a cut-out, intersecting rib, or some other structural feature which interrupts the load path, is the relatively weak skin–stiffener interface in the absence of mechanical fasteners. More damage tolerant stiffener run-outs are clearly required and these are investigated in this paper. Using a parametric finite element analysis, the run-out region was optimised for stable debonding crack growth. The modified run-out, as well as a baseline configuration, were manufactured and tested. Damage initiation and propagation was investigated in detail using state-of-the-art monitoring equipment including Acoustic Emission and Digital Image Correlation. As expected, the baseline configuration failed catastrophically. The modified run-out showed improved crack-growth stability, but subsequent delamination failure in the stiffener promptly led to catastrophic failure.

The Role of Arg228 in the phosphorylation of galactokinase: The mechanism of GHMP kinases by QM/MM studies

GHMP kinases are a group of structurally-related small molecule kinases. They have been found in all kingdoms of life and are mostly responsible for catalysing the ATP-dependent phosphorylation of intermediary metabolites. Although the GHMP kinases are of clinical, pharmaceutical and biotechnological importance, the mechanism of GHMP-kinases is controversial. A catalytic base mechanism was suggested for mevalonate kinase that has a structural feature of the ?-phosphate of ATP close to an aspartate residue; however, for one GHMP member, homoserine kinase, where the residue acting as general base is absent, a direct phosphorylation mechanism was suggested. Furthermore, it has been proposed by some authors that all the GHMP kinases function via the direct phosphorylation mechanism. This controversy in mechanism has limited our ability to exploit these enzymes as drug targets and in biotechnology. Here the phosphorylation reaction mechanism of the human galactokinase, a member of GHMP kinase was investigated using molecular dynamics simulations and density functional theory-based QM/MM calculations (B3LYP-D/AMBER99). The reaction coordinates were localized by potential energy scan using adiabatic mapping method. Our results indicate that a highly conserved Glu174 captures Arg105 to the proximity of the a-phosphate of ATP forming a H-bond network, therefore the mobility of ATP in the large oxyanion hole is restricted. Arg228 functions to stabilize the negative charge developed at the ß,?-bridging oxygen of the ATP during bond cleavage. The reaction occurs via direct phosphorylation mechanism and the Asp186 in proximity of ATP does not directly participate in the reaction pathway. Since Arg228 is not conserved among GHMP kinases, reagents which form interactions with Arg228, and therefore can interrupt its function in phosphorylation may be developed into potential selective inhibitors for galactokinase.

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

Comparing large graphs efficiently by margins of feature vectors

Measuring the structural similarity of graphs is a challenging and outstanding problem. Most of the classical approaches of the so-called exact graph matching methods are based on graph or subgraph isomorphic relations of the underlying graphs. In contrast to these methods in this paper we introduce a novel approach to measure the structural similarity of directed and undirected graphs that is mainly based on margins of feature vectors representing graphs. We introduce novel graph similarity and dissimilarity measures, provide some properties and analyze their algorithmic complexity. We find that the computational complexity of our measures is polynomial in the graph size and, hence, significantly better than classical methods from, e.g. exact graph matching which are NP-complete. Numerically, we provide some examples of our measure and compare the results with the well-known graph edit distance. (c) 2006 Elsevier Inc. All rights reserved.