Membrane protein orientation and refinement using a knowledge-based statistical potential.

Background: Recent increases in the number of deposited membrane protein crystal structures necessitate the use of automated computational tools to position them within the lipid bilayer. Identifying the correct orientation allows us to study the complex relationship between sequence, structure and the lipid environment, which is otherwise challenging to investigate using experimental techniques due to the difficulty in crystallising membrane proteins embedded within intact membranes. Results: We have developed a knowledge-based membrane potential, calculated by the statistical analysis of transmembrane protein structures, coupled with a combination of genetic and direct search algorithms, and demonstrate its use in positioning proteins in membranes, refinement of membrane protein models and in decoy discrimination. Conclusions: Our method is able to quickly and accurately orientate both alpha-helical and beta-barrel membrane proteins within the lipid bilayer, showing closer agreement with experimentally determined values than existing approaches. We also demonstrate both consistent and significant refinement of membrane protein models and the effective discrimination between native and decoy structures. Source code is available under an open source license from http://bioinf.cs.ucl.ac.uk/downloads/memembed/ webcite

Scalable web services for the PSIPRED Protein Analysis Workbench

Here, we present the new UCL Bioinformatics Group’s PSIPRED Protein Analysis Workbench. The Workbench unites all of our previously available analysis methods into a single web-based framework. The new web portal provides a greatly streamlined user interface with a number of new features to allow users to better explore their results. We offer a number of additional services to enable computationally scalable execution of our prediction methods; these include SOAP and XML-RPC web server access and new HADOOP packages. All software and services are available via the UCL Bioinformatics Group website at http://bioinf.cs.ucl.ac.uk/

Evaluation of predictions in the CASP10 model refinement category.

Here we report on the assessment results of the third experiment to evaluate the state-of-the-art in protein model refinement, where participants were invited to improve the accuracy of initial protein models for twenty-seven targets. Using an array of complementary evaluation measures, we find that five groups performed better than the naïve (null) method - a marked improvement over CASP9, although only three were significantly better. The leading groups also demonstrated the ability to consistently improve both backbone and side-chain positioning, while other groups reliably enhanced other aspects of protein physicality. The top-ranked group succeeded in improving the backbone in almost 90% of targets, suggesting a strategy that for the first time in CASP refinement is successful in a clear majority of cases. A number of issues remain unsolved: the majority of groups still fail to improve the quality of the starting models; even successful groups were only able to make modest improvements; and no prediction was more similar to the native structure than to the starting model. Successful refinement attempts also often go unrecognized, as suggested by the relatively larger improvements when predictions not submitted as model 1 are also considered. © Proteins 2013;. © 2013 Wiley Periodicals, Inc

Patient Disease Perceptions and Coping Strategies for Arthritis in a Developing Nation: A Qualitative Study

<p>Abstract</p> <p>Background</p> <p>There is little prior research on the burden of arthritis in the developing world. We sought to document how patients with advanced arthritis living in the Dominican Republic are affected by and cope with their disease.</p> <p>Methods</p> <p>We conducted semi-structured, one-to-one interviews with economically disadvantaged Dominican patients with advanced knee and/or hip arthritis in the Dominican Republic. The interviews, conducted in Spanish, followed a moderator's guide that included topics such as the patients' understanding of disease etiology, their support networks, and their coping mechanisms. The interviews were audiotaped, transcribed verbatim in Spanish, and systematically analyzed using content analysis. We assessed agreement in coding between two investigators.</p> <p>Results</p> <p>18 patients were interviewed (mean age 60 years, median age 62 years, 72% women, 100% response rate). Patients invoked religious and environmental theories of disease etiology, stating that their illness had been caused by God's will or through contact with water. While all patients experienced pain and functional limitation, the social effects of arthritis were gender-specific: women noted interference with homemaking and churchgoing activities, while men experienced disruption with occupational roles. The coping strategies used by patients appeared to reflect their beliefs about disease causation and included prayer and avoidance of water.</p> <p>Conclusions</p> <p>Patients' explanatory models of arthritis influenced the psychosocial effects of the disease and coping mechanisms used. Given the increasing reach of global health programs, understanding these culturally influenced perceptions of disease will be crucial in successfully treating chronic diseases in the developing world.</p

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

A membrane-inserted structural model of the yeast mitofusin Fzo1

Mitofusins are large transmembrane GTPases of the dynamin-related protein family, and are required for the tethering and fusion of mitochondrial outer membranes. Their full-length structures remain unknown, which is a limiting factor in the study of outer membrane fusion. We investigated the structure and dynamics of the yeast mitofusin Fzo1 through a hybrid computational and experimental approach, combining molecular modelling and all-atom molecular dynamics simulations in a lipid bilayer with site-directed mutagenesis and in vivo functional assays. The predicted architecture of Fzo1 improves upon the current domain annotation, with a precise description of the helical spans linked by flexible hinges, which are likely of functional significance. In vivo site-directed mutagenesis validates salient aspects of this model, notably, the long-distance contacts and residues participating in hinges. GDP is predicted to interact with Fzo1 through the G1 and G4 motifs of the GTPase domain. The model reveals structural determinants critical for protein function, including regions that may be involved in GTPase domain-dependent rearrangements

Search for R-parity-violating supersymmetry in events with four or more leptons in sqrt(s) =7 TeV pp collisions with the ATLAS detector

A search for new phenomena in final states with four or more leptons (electrons or muons) is presented. The analysis is based on 4.7 fb−1 of $\sqrt{s}=7\;\mathrm{TeV}$ proton-proton collisions delivered by the Large Hadron Collider and recorded with the ATLAS detector. Observations are consistent with Standard Model expectations in two signal regions: one that requires moderate values of missing transverse momentum and another that requires large effective mass. The results are interpreted in a simplified model of R-parity-violating supersymmetry in which a 95% CL exclusion region is set for charged wino masses up to 540 GeV. In an R-parity-violating MSUGRA/CMSSM model, values of m 1/2 up to 820 GeV are excluded for 10 < tan β < 40

Search for high-mass resonances decaying to dilepton final states in pp collisions at s√=7 TeV with the ATLAS detector

The ATLAS detector at the Large Hadron Collider is used to search for high-mass resonances decaying to an electron-positron pair or a muon-antimuon pair. The search is sensitive to heavy neutral Z′ gauge bosons, Randall-Sundrum gravitons, Z * bosons, techni-mesons, Kaluza-Klein Z/γ bosons, and bosons predicted by Torsion models. Results are presented based on an analysis of pp collisions at a center-of-mass energy of 7 TeV corresponding to an integrated luminosity of 4.9 fb−1 in the e + e − channel and 5.0 fb−1 in the μ + μ −channel. A Z ′ boson with Standard Model-like couplings is excluded at 95 % confidence level for masses below 2.22 TeV. A Randall-Sundrum graviton with coupling k/MPl=0.1 is excluded at 95 % confidence level for masses below 2.16 TeV. Limits on the other models are also presented, including Technicolor and Minimal Z′ Models

Enhancing coevolution-based contact prediction by imposing structural self-consistency of the contacts

Based on the development of new algorithms and growth of sequence databases, it has recently become possible to build robust higher-order sequence models based on sets of aligned protein sequences. Such models have proven useful in de novo structure prediction, where the sequence models are used to find pairs of residues that co-vary during evolution, and hence are likely to be in spatial proximity in the native protein. The accuracy of these algorithms, however, drop dramatically when the number of sequences in the alignment is small. We have developed a method that we termed CE-YAPP (CoEvolution-YAPP), that is based on YAPP (Yet Another Peak Processor), which has been shown to solve a similar problem in NMR spectroscopy. By simultaneously performing structure prediction and contact assignment, CE-YAPP uses structural self-consistency as a filter to remove false positive contacts. Furthermore, CE-YAPP solves another problem, namely how many contacts to choose from the ordered list of covarying amino acid pairs. We show that CE-YAPP consistently improves contact prediction from multiple sequence alignments, in particular for proteins that are difficult targets. We further show that the structures determined from CE- YAPP are also in better agreement with those determined using traditional methods in structural biology