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

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

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

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

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&

ResBoost: characterizing and predicting catalytic residues in enzymes

Abstract Background Identifying the catalytic residues in enzymes can aid in understanding the molecular basis of an enzyme's function and has significant implications for designing new drugs, identifying genetic disorders, and engineering proteins with novel functions. Since experimentally determining catalytic sites is expensive, better computational methods for identifying catalytic residues are needed. Results We propose ResBoost, a new computational method to learn characteristics of catalytic residues. The method effectively selects and combines rules of thumb into a simple, easily interpretable logical expression that can be used for prediction. We formally define the rules of thumb that are often used to narrow the list of candidate residues, including residue evolutionary conservation, 3D clustering, solvent accessibility, and hydrophilicity. ResBoost builds on two methods from machine learning, the AdaBoost algorithm and Alternating Decision Trees, and provides precise control over the inherent trade-off between sensitivity and specificity. We evaluated ResBoost using cross-validation on a dataset of 100 enzymes from the hand-curated Catalytic Site Atlas (CSA). Conclusion ResBoost achieved 85% sensitivity for a 9.8% false positive rate and 73% sensitivity for a 5.7% false positive rate. ResBoost reduces the number of false positives by up to 56% compared to the use of evolutionary conservation scoring alone. We also illustrate the ability of ResBoost to identify recently validated catalytic residues not listed in the CSA

Catalytic residues in hydrolases: analysis of methods designed for ligand-binding site prediction

The comparison of eight tools applicable to ligand-binding site prediction is presented. The methods examined cover three types of approaches: the geometrical (CASTp, PASS, Pocket-Finder), the physicochemical (Q-SiteFinder, FOD) and the knowledge-based (ConSurf, SuMo, WebFEATURE). The accuracy of predictions was measured in reference to the catalytic residues documented in the Catalytic Site Atlas. The test was performed on a set comprising selected chains of hydrolases. The results were analysed with regard to size, polarity, secondary structure, accessible solvent area of predicted sites as well as parameters commonly used in machine learning (F-measure, MCC). The relative accuracies of predictions are presented in the ROC space, allowing determination of the optimal methods by means of the ROC convex hull. Additionally the minimum expected cost analysis was performed. Both advantages and disadvantages of the eight methods are presented. Characterization of protein chains in respect to the level of difficulty in the active site prediction is introduced. The main reasons for failures are discussed. Overall, the best performance offers SuMo followed by FOD, while Pocket-Finder is the best method among the geometrical approaches

Novel Feature for Catalytic Protein Residues Reflecting Interactions with Other Residues

Owing to their potential for systematic analysis, complex networks have been widely used in proteomics. Representing a protein structure as a topology network provides novel insight into understanding protein folding mechanisms, stability and function. Here, we develop a new feature to reveal correlations between residues using a protein structure network. In an original attempt to quantify the effects of several key residues on catalytic residues, a power function was used to model interactions between residues. The results indicate that focusing on a few residues is a feasible approach to identifying catalytic residues. The spatial environment surrounding a catalytic residue was analyzed in a layered manner. We present evidence that correlation between residues is related to their distance apart most environmental parameters of the outer layer make a smaller contribution to prediction and ii catalytic residues tend to be located near key positions in enzyme folds. Feature analysis revealed satisfactory performance for our features, which were combined with several conventional features in a prediction model for catalytic residues using a comprehensive data set from the Catalytic Site Atlas. Values of 88.6 for sensitivity and 88.4 for specificity were obtained by 10fold crossvalidation. These results suggest that these features reveal the mutual dependence of residues and are promising for further study of structurefunction relationship

Automated functional classification of experimental and predicted protein structures

BACKGROUND: Proteins that are similar in sequence or structure may perform different functions in nature. In such cases, function cannot be inferred from sequence or structural similarity. RESULTS: We analyzed experimental structures belonging to the Structural Classification of Proteins (SCOP) database and showed that about half of them belong to multi-functional fold families for which protein similarity alone is not adequate to assign function. We also analyzed predicted structures from the LiveBench and the PDB-CAFASP experiments and showed that accurate homology-based functional assignments cannot be achieved approximately one third of the time, when the protein is a member of a multi-functional fold family. We then conducted extended performance evaluation and comparisons on both experimental and predicted structures using our Functional Signatures from Structural Alignments (FSSA) algorithm that we previously developed to handle the problem of classifying proteins belonging to multi-functional fold families. CONCLUSION: The results indicate that the FSSA algorithm has better accuracy when compared to homology-based approaches for functional classification of both experimental and predicted protein structures, in part due to its use of local, as opposed to global, information for classifying function. The FSSA algorithm has also been implemented as a webserver and is available at