Magnetic fields in barred galaxies. IV. NGC 1097 and NGC 1365

We present 3.5cm and 6.2cm radio continuum maps in total and polarized intensity of the barred galaxies NGC 1097 and NGC 1365. Both galaxies exhibit radio ridges roughly overlapping with the massive dust lanes in the bar region. The contrast in total intensity across the radio ridges is compatible with compression and shear of an isotropic random magnetic field. The contrast in polarized intensity is significantly smaller than that expected from compression and shearing of the regular magnetic field; this could be the result of decoupling of the regular field from the dense molecular clouds. The regular field in the ridge is probably strong enough to reduce significantly shear in the diffuse gas (to which it is coupled) and hence to reduce magnetic field amplification by shearing. This contributes to the misalignment of the observed field orientation with respect to the velocity vectors of the dense gas. Our observations, for the first time, indicate that magnetic forces can control the flow of the diffuse interstellar gas at kiloparsec scales. The total radio intensity reaches its maximum in the circumnuclear starburst regions, where the equipartition field strength is about 60\mu G, amongst the strongest fields detected in spiral galaxies so far. The regular field in the inner region has a spiral shape with large pitch angle, indicating the action of a dynamo. Magnetic stress leads to mass inflow towards the centre, sufficient to feed the active nucleus in NGC 1097. We detected diffuse X-ray emission, possibly forming a halo of hot gas around NGC 1097.Comment: 32 pages with 45 PostScript figures. Accepted for publication in A&A; Typos corrected 12/10/200

Dark and luminous matter in the NGC 3992 group of galaxies, I. The large barred spiral NGC 3992

Detailed neutral hydrogen observations have been obtained of the large barred spiral galaxy NGC 3992 and its three small companion galaxies, UGC 6923, UGC 6940, and UGC 6969. For the main galaxy, the HI distribution is regular with a low level radial extension outside the stellar disc. However, at exactly the region of the bar, there is a pronounced central HI hole in the gas distribution. Likely gas has been transported inwards by the bar and because of the emptyness of the hole no large accretion events can have happened in recent galactic times. The gas kinematics is very regular and it is demonstrated that the influence of the bar potential on the velocity field is negligible. A precise and extended rotation curve has been derived showing some distinct features which can be explained by the non-exponential radial light distribution of NGC 3992. The decomposition of the rotation curve gives a slight preference for a sub maximal disc, though a range of disc contributions, up to a maximum disc situation fits nearly equally well. For such a maximum disc contribution, which might be expected in order to generate and maintain the bar, the required mass-to-light ratio is large but not exceptional.Comment: accepted for publication in Astronomy and Astrophysics. A copy with high resolution graphics will shortly become available at http://www.astro.rug.nl/Preprints/preprints.htm

Magnetic fields in barred galaxies I. The atlas

The total and polarized radio continuum emission of 20 barred galaxies was observed with the Very Large Array (VLA) at 3, 6, 18 and 22 cm and with the Australia Telescope Compact Array (ATCA) at 6 cm and 13 cm. Maps at 30 arcsec angular resolution are presented here. Polarized emission (and therefore a large-scale regular magnetic field) was detected in 17 galaxies. Most galaxies of our sample are similar to non-barred galaxies with respect to the radio/far-infrared flux correlation and equipartition strength of the total magnetic field. Galaxies with highly elongated bars are not always radio-bright. We discuss the correlation of radio properties with the aspect ratio of the bar and other measures of the bar strength. We introduce a new measure of the bar strength, \Lambda, related to the quadrupole moment of the bar's gravitational potential. The radio surface brightness I of the barred galaxies in our sample is correlated with \Lambda, I \propto \Lambda^0.4+/-0.1, and thus is highest in galaxies with a long bar where the velocity field is distorted by the bar over a large fraction of the disc. In these galaxies, the pattern of the regular field is significantly different from that in non-barred galaxies. In particular, field enhancements occur upstream of the dust lanes where the field lines are oriented at large angles to the bar's major axis. Polarized radio emission seems to be a good indicator of large-scale non-axisymmetric motions.Comment: 29 pages with 66 PostScript figures. Accepted for publication in A&A. Figures 5-24 also available at http://www.mpifr-bonn.mpg.d

A Structure-Based Approach for Detection of Thiol Oxidoreductases and Their Catalytic Redox-Active Cysteine Residues

Cysteine (Cys) residues often play critical roles in proteins, for example, in the formation of structural disulfide bonds, metal binding, targeting proteins to the membranes, and various catalytic functions. However, the structural determinants for various Cys functions are not clear. Thiol oxidoreductases, which are enzymes containing catalytic redox-active Cys residues, have been extensively studied, but even for these proteins there is little understanding of what distinguishes their catalytic redox Cys from other Cys functions. Herein, we characterized thiol oxidoreductases at a structural level and developed an algorithm that can recognize these enzymes by (i) analyzing amino acid and secondary structure composition of the active site and its similarity to known active sites containing redox Cys and (ii) calculating accessibility, active site location, and reactivity of Cys. For proteins with known or modeled structures, this method can identify proteins with catalytic Cys residues and distinguish thiol oxidoreductases from the enzymes containing other catalytic Cys types. Furthermore, by applying this procedure to Saccharomyces cerevisiae proteins containing conserved Cys, we could identify the majority of known yeast thiol oxidoreductases. This study provides insights into the structural properties of catalytic redox-active Cys and should further help to recognize thiol oxidoreductases in protein sequence and structure databases

Partial Order Optimum Likelihood (POOL): Maximum Likelihood Prediction of Protein Active Site Residues Using 3D Structure and Sequence Properties

A new monotonicity-constrained maximum likelihood approach, called Partial Order Optimum Likelihood (POOL), is presented and applied to the problem of functional site prediction in protein 3D structures, an important current challenge in genomics. The input consists of electrostatic and geometric properties derived from the 3D structure of the query protein alone. Sequence-based conservation information, where available, may also be incorporated. Electrostatics features from THEMATICS are combined with multidimensional isotonic regression to form maximum likelihood estimates of probabilities that specific residues belong to an active site. This allows likelihood ranking of all ionizable residues in a given protein based on THEMATICS features. The corresponding ROC curves and statistical significance tests demonstrate that this method outperforms prior THEMATICS-based methods, which in turn have been shown previously to outperform other 3D-structure-based methods for identifying active site residues. Then it is shown that the addition of one simple geometric property, the size rank of the cleft in which a given residue is contained, yields improved performance. Extension of the method to include predictions of non-ionizable residues is achieved through the introduction of environment variables. This extension results in even better performance than THEMATICS alone and constitutes to date the best functional site predictor based on 3D structure only, achieving nearly the same level of performance as methods that use both 3D structure and sequence alignment data. Finally, the method also easily incorporates such sequence alignment data, and when this information is included, the resulting method is shown to outperform the best current methods using any combination of sequence alignments and 3D structures. Included is an analysis demonstrating that when THEMATICS features, cleft size rank, and alignment-based conservation scores are used individually or in combination THEMATICS features represent the single most important component of such classifiers

Rings and spirals in barred galaxies. III. Further comparisons and links to observations

In a series of papers, we propose a theory to explain the formation and properties of rings and spirals in barred galaxies. The building blocks of these structures are orbits guided by the manifolds emanating from the unstable Lagrangian points located near the ends of the bar. In this paper, the last of the series, we present more comparisons of our theoretical results to observations and also give new predictions for further comparisons. Our theory provides the right building blocks for the rectangular-like bar outline and for ansae. We consider how our results can be used to give estimates for the pattern speed values, as well as their effect on abundance gradients in barred galaxies. We present the kinematics along the manifold loci, to allow comparisons with the observed kinematics along the ring and spiral loci. We consider gaseous arms and their relations to stellar ones. We discuss several theoretical aspects and stress that the orbits that constitute the building blocks of the spirals and rings are chaotic. They are, nevertheless, spatially well confined by the manifolds and are thus able to outline the relevant structures. Such chaos can be termed `confined chaos' and can play a very important role in understanding the formation and evolution of galaxy structures and in galactic dynamics in general. This work, in agreement with several others, argues convincingly that galactic dynamic studies should not be limited to the study of regular motions and orbits.Comment: 17 pages, 12 figures; accepted in MNRA

Probing the Dust Properties of Galaxies at Submillimetre Wavelengths II. Dust-to-gas mass ratio trends with metallicity and the submm excess in dwarf galaxies

We are studying the effects of submm observations on the total dust mass and thus dust-to-gas mass ratio measurements. We gather a wide sample of galaxies that have been observed at submm wavelengths to model their Spectral Energy Distributions using submm observations and then without submm observational constraints in order to quantify the error on the dust mass when submm data are not available. Our model does not make strong assumptions on the dust temperature distribution to precisely avoid submm biaises in the study. Our sample includes 52 galaxies observed at submm wavelengths. Out of these, 9 galaxies show an excess in submm which is not accounted for in our fiducial model, most of these galaxies being low- metallicity dwarfs. We chose to add an independant very cold dust component (T=10K) to account for this excess. We find that metal-rich galaxies modelled with submm data often show lower dust masses than when modelled without submm data. Indeed, these galaxies usually have dust SEDs that peaks at longer wavelengths and require constraints above 160 um to correctly position the peak and sample the submillimeter slope of their SEDs and thus correctly cover the dust temperature distribution. On the other hand, some metal-poor dwarf galaxies modelled with submm data show higher dust masses than when modelled without submm data. Using submm constraints for the dust mass estimates, we find a tightened correlation of the dust-to-gas mass ratio with the metallicity of the galaxies. We also often find that when there is a submm excess present, it occurs preferentially in low-metallicity galaxies. We analyse the conditions for the presence of this excess and find a relation between the 160/850 um ratio and the submm excess.Comment: 19 pages, 10 figures, 1 table, accepted for publication in A&

Automatic prediction of catalytic residues by modeling residue structural neighborhood

Background: Prediction of catalytic residues is a major step in characterizing the function of enzymes. In its simpler formulation, the problem can be cast into a binary classification task at the residue level, by predicting whether the residue is directly involved in the catalytic process. The task is quite hard also when structural information is available, due to the rather wide range of roles a functional residue can play and to the large imbalance between the number of catalytic and non-catalytic residues.Results: We developed an effective representation of structural information by modeling spherical regions around candidate residues, and extracting statistics on the properties of their content such as physico-chemical properties, atomic density, flexibility, presence of water molecules. We trained an SVM classifier combining our features with sequence-based information and previously developed 3D features, and compared its performance with the most recent state-of-the-art approaches on different benchmark datasets. We further analyzed the discriminant power of the information provided by the presence of heterogens in the residue neighborhood.Conclusions: Our structure-based method achieves consistent improvements on all tested datasets over both sequence-based and structure-based state-of-the-art approaches. Structural neighborhood information is shown to be responsible for such results, and predicting the presence of nearby heterogens seems to be a promising direction for further improvements.Journal ArticleResearch Support, N.I.H. Extramuralinfo:eu-repo/semantics/publishe

Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties

BACKGROUND: The number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for functional prediction. Knowledge of catalytic sites provides a valuable insight into protein function. Although many computational methods have been developed to predict catalytic residues and active sites, their accuracy remains low, with a significant number of false positives. In this paper, we present a novel method for the prediction of catalytic sites, using a carefully selected, supervised machine learning algorithm coupled with an optimal discriminative set of protein sequence conservation and structural properties. RESULTS: To determine the best machine learning algorithm, 26 classifiers in the WEKA software package were compared using a benchmarking dataset of 79 enzymes with 254 catalytic residues in a 10-fold cross-validation analysis. Each residue of the dataset was represented by a set of 24 residue properties previously shown to be of functional relevance, as well as a label {+1/-1} to indicate catalytic/non-catalytic residue. The best-performing algorithm was the Sequential Minimal Optimization (SMO) algorithm, which is a Support Vector Machine (SVM). The Wrapper Subset Selection algorithm further selected seven of the 24 attributes as an optimal subset of residue properties, with sequence conservation, catalytic propensities of amino acids, and relative position on protein surface being the most important features. CONCLUSION: The SMO algorithm with 7 selected attributes correctly predicted 228 of the 254 catalytic residues, with an overall predictive accuracy of more than 86%. Missing only 10.2% of the catalytic residues, the method captures the fundamental features of catalytic residues and can be used as a "catalytic residue filter" to facilitate experimental identification of catalytic residues for proteins with known structure but unknown function

How accurate and statistically robust are catalytic site predictions based on closeness centrality?

<p>Abstract</p> <p>Background</p> <p>We examine the accuracy of enzyme catalytic residue predictions from a network representation of protein structure. In this model, amino acid α-carbons specify vertices within a graph and edges connect vertices that are proximal in structure. Closeness centrality, which has shown promise in previous investigations, is used to identify important positions within the network. Closeness centrality, a global measure of network centrality, is calculated as the reciprocal of the average distance between vertex <it>i </it>and all other vertices.</p> <p>Results</p> <p>We benchmark the approach against 283 structurally unique proteins within the Catalytic Site Atlas. Our results, which are inline with previous investigations of smaller datasets, indicate closeness centrality predictions are statistically significant. However, unlike previous approaches, we specifically focus on residues with the very best scores. Over the top five closeness centrality scores, we observe an average true to false positive rate ratio of 6.8 to 1. As demonstrated previously, adding a solvent accessibility filter significantly improves predictive power; the average ratio is increased to 15.3 to 1. We also demonstrate (for the first time) that filtering the predictions by residue identity improves the results even more than accessibility filtering. Here, we simply eliminate residues with physiochemical properties unlikely to be compatible with catalytic requirements from consideration. Residue identity filtering improves the average true to false positive rate ratio to 26.3 to 1. Combining the two filters together has little affect on the results. Calculated p-values for the three prediction schemes range from 2.7E-9 to less than 8.8E-134. Finally, the sensitivity of the predictions to structure choice and slight perturbations is examined.</p> <p>Conclusion</p> <p>Our results resolutely confirm that closeness centrality is a viable prediction scheme whose predictions are statistically significant. Simple filtering schemes substantially improve the method's predicted power. Moreover, no clear effect on performance is observed when comparing ligated and unligated structures. Similarly, the CC prediction results are robust to slight structural perturbations from molecular dynamics simulation.</p