Angular Distribution of GRBs

We studied the complete randomness of the angular distribution of BATSE gamma-ray bursts (GRBs). Based on their durations and peak fluxes, we divided the BATSE sample into 5 subsamples (short1, short2, intermediate, long1, long2) and studied the angular distributions separately. We used three methods to search for non-randomness in the subsamples: Voronoi tesselation, minimal spanning tree, and multifractal spectra. To study any non-randomness in the subsamples we defined 13 test-variables (9 from Voronoi tesselation, 3 from the minimal spanning tree and one from the multifractal spectrum). We made Monte Carlo simulations taking into account the BATSE’s sky-exposure function. We tested therandomness by introducing squared Euclidean distances in the parameter space of the test-variables. We recognized that the short1, short2 groups deviate significantly (99.90%, 99.98%) from the fully random case in the distribution of the squared Euclidean distances but this is not true for the long samples. In the intermediate group, the squared Euclidean distances also give significant deviation (98.51%)

Sequential, structural and functional properties of protein complexes are defined by how folding and binding intertwine.

Intrinsically Disordered Proteins (IDPs) fulfill critical biological roles without having the potential to fold on their own. While lacking inherent structure, the majority of IDPs do reach a folded state via interaction with a protein partner, presenting a deep entanglement of the folding and binding process. Protein disorder has been recognized as a major determinant in several properties of proteins, such as sequence, adopted structure upon binding, and function. Yet, the way the binding process is reflected in these features in general lacks a detailed description. Here, we defined three categories of protein complexes depending on the unbound structural state of the interactors, and analyzed them in detail. We found that strikingly, the properties of interactors in terms of sequence and adopted structure are defined not only by the intrinsic structural state of the protein itself, but also to a comparable extent by the structural state of the binding partner. The three different types of interactions are also regulated through divergent molecular tactics of post-translational modifications. This not only widens the range of biologically relevant sequence and structure spaces defined by ordered proteins, but also presents distinct molecular mechanisms compatible with specific biological processes, separately for each interaction type. The distinct attributes of different binding modes identified in this study can help to understand how various types of interactions serve as building blocks for the assembly of tightly regulated and highly intertwined regulatory networks

The Membrane Protein Data Bank

The Membrane Protein Data Bank (MPDB) is an online, searchable, relational database of structural and functional information on integral, anchored and peripheral membrane proteins and peptides. Data originates from the Protein Data Bank and other databases, and from the literature. Structures are based on X-ray and electron diffraction, nuclear magnetic resonance and cryoelectron microscopy. The MPDB is searchable online by protein characteristic, structure determination method, crystallization technique, detergent, temperature, pH, author, etc. Record entries are hyperlinked to the PDB and Pfam for viewing sequence, three-dimensional structure and domain architecture, and for downloading coordinates. Links to PubMed are also provided. The MPDB is updated weekly in parallel with the Protein Data Bank. Statistical analysis of MPDB records can be performed and viewed online. A summary of the statistics as applied to entries in the MPDB is presented. The data suggest conditions appropriate for crystallization trials with novel membrane proteins

TMFoldRec: a statistical potential-based transmembrane protein fold recognition tool.

BACKGROUND: Transmembrane proteins (TMPs) are the key components of signal transduction, cell-cell adhesion and energy and material transport into and out from the cells. For the deep understanding of these processes, structure determination of transmembrane proteins is indispensable. However, due to technical difficulties, only a few transmembrane protein structures have been determined experimentally. Large-scale genomic sequencing provides increasing amounts of sequence information on the proteins and whole proteomes of living organisms resulting in the challenge of bioinformatics; how the structural information should be gained from a sequence. RESULTS: Here, we present a novel method, TMFoldRec, for fold prediction of membrane segments in transmembrane proteins. TMFoldRec based on statistical potentials was tested on a benchmark set containing 124 TMP chains from the PDBTM database. Using a 10-fold jackknife method, the native folds were correctly identified in 77 % of the cases. This accuracy overcomes the state-of-the-art methods. In addition, a key feature of TMFoldRec algorithm is the ability to estimate the reliability of the prediction and to decide with an accuracy of 70 %, whether the obtained, lowest energy structure is the native one. CONCLUSION: These results imply that the membrane embedded parts of TMPs dictate the TM structures rather than the soluble parts. Moreover, predictions with reliability scores make in this way our algorithm applicable for proteome-wide analyses. AVAILABILITY: The program is available upon request for academic use

Identification of Extracellular Segments by Mass Spectrometry Improves Topology Prediction of Transmembrane Proteins

Transmembrane proteins play crucial role in signaling, ion transport, nutrient uptake, as well as in maintaining the dynamic equilibrium between the internal and external environment of cells. Despite their important biological functions and abundance, less than 2% of all determined structures are transmembrane proteins. Given the persisting technical difficulties associated with high resolution structure determination of transmembrane proteins, additional methods, including computational and experimental techniques remain vital in promoting our understanding of their topologies, 3D structures, functions and interactions. Here we report a method for the high-throughput determination of extracellular segments of transmembrane proteins based on the identification of surface labeled and biotin captured peptide fragments by LC/MS/MS. We show that reliable identification of extracellular protein segments increases the accuracy and reliability of existing topology prediction algorithms. Using the experimental topology data as constraints, our improved prediction tool provides accurate and reliable topology models for hundreds of human transmembrane proteins

The Swift satellite and redshifts of long gamma-ray bursts

Until 6 October 2005 sixteen redshifts have been measured of long gamma-ray bursts discovered by the Swift satellite. Further 45 redshifts have been measured of the long gamma-ray bursts discovered by other satellites. Here we perform five statistical tests comparing the redshift distributions of these two samples assuming - as the null hypothesis - identical distribution for the two samples. Three tests (Student's $t$-test, Mann-Whitney test, Kolmogorov-Smirnov test) reject the null hypothesis on the significance levels between 97.19 and 98.55%. Two different comparisons of the medians show extreme $(99.78-99.99994)$% significance levels of rejection. This means that the redshifts of the Swift sample and the redshifts of the non-Swift sample are distributed differently - in the Swift sample the redshifts are on average larger. This statistical result suggests that the long GRBs should on average be at the higher redshifts of the Swift sample.Comment: 4 pages, accepted as an A&A Research Not

Transmembrane protein topology prediction using support vector machines

Background: Alpha-helical transmembrane (TM) proteins are involved in a wide range of important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion. Many are also prime drug targets, and it has been estimated that more than half of all drugs currently on the market target membrane proteins. However, due to the experimental difficulties involved in obtaining high quality crystals, this class of protein is severely under-represented in structural databases. In the absence of structural data, sequence-based prediction methods allow TM protein topology to be investigated.Results: We present a support vector machine-based (SVM) TM protein topology predictor that integrates both signal peptide and re-entrant helix prediction, benchmarked with full cross-validation on a novel data set of 131 sequences with known crystal structures. The method achieves topology prediction accuracy of 89%, while signal peptides and re-entrant helices are predicted with 93% and 44% accuracy respectively. An additional SVM trained to discriminate between globular and TM proteins detected zero false positives, with a low false negative rate of 0.4%. We present the results of applying these tools to a number of complete genomes. Source code, data sets and a web server are freely available from http://bioinf.cs.ucl.ac.uk/psipred/.Conclusion: The high accuracy of TM topology prediction which includes detection of both signal peptides and re-entrant helices, combined with the ability to effectively discriminate between TM and globular proteins, make this method ideally suited to whole genome annotation of alpha-helical transmembrane proteins