Journal Articles

  • Potent and specific peptide inhibitors of human pro-survival protein Bcl-xL
    [Dec 2014]

    Publication date: Available online 14 November 2014
    Source:Journal of Molecular Biology

    Author(s): Sanjib Dutta , Jeremy Ryan , T. Scott Chen , Christos Kougentakis , Anthony Letai , Amy E. Keating

    The Bcl-2 family of proteins plays a critical role regulating apoptosis, and pro-survival Bcl-2 family members are important therapeutic targets due to their overexpression in different cancers. Pro-apoptotic BH3-only proteins antagonize pro-survival Bcl-2 protein functions by binding directly to them, and a sub-class of BH3-only proteins termed sensitizers can initiate apoptosis via this mechanism in response to diverse signals. The five pro-survival proteins Bcl-xL, Mcl-1, Bcl-2, Bcl-w and Bfl-1 differ in their binding preferences, with Bcl-xL, Bcl-2 and Bcl-w sharing similar interaction profiles for many natural sensitizers and small molecules. Peptides that bind selectively to just one or a subset of family members have shown utility in assays that diagnose apoptotic blockades in cancer cells and as reagents for dissecting apoptotic mechanism. Combining computational design, combinatorial library screening and rational mutagenesis, we designed a series of BH3 sensitizer peptides that bind Bcl-xL with sub-nanomolar affinity and selectivity up to 1000-fold over each of the four competing pro-survival proteins. We demonstrate the efficacy of our designed BH3 peptides in assays that differentiate between cancer cells that are dependent on different pro-survival proteins.
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    Categories: Journal Articles
  • Kiwi: a tool for integration and visualization of network topology and gene-set analysis
    [Dec 2014]

    Background: The analysis of high-throughput data in biology is aided by integrative approaches such as gene-set analysis. Gene-sets can represent well-defined biological entities (e.g. metabolites) that interact in networks (e.g. metabolic networks), to exert their function within the cell. Data interpretation can benefit from incorporating the underlying network, but there are currently no optimal methods that link gene-set analysis and network structures. Results: Here we present Kiwi, a new tool that processes output data from gene-set analysis and integrates them with a network structure such that the inherent connectivity between gene-sets, i.e. not simply the gene overlap, becomes apparent. In two case studies, we demonstrate that standard gene-set analysis points at metabolites regulated in the interrogated condition. Nevertheless, only the integration of the interactions between these metabolites provides an extra layer of information that highlights how they are tightly connected in the metabolic network. Conclusions: Kiwi is a tool that enhances interpretability of high-throughput data. It allows the users not only to discover a list of significant entities or processes as in gene-set analysis, but also to visualize whether these entities or processes are isolated or connected by means of their biological interaction. Kiwi is available as a Python package at http://www.sysbio.se/kiwi and an online tool in the BioMet Toolbox at http://www.biomet-toolbox.org.
    Categories: Journal Articles
  • CLAST: CUDA implemented large-scale alignment search tool
    [Dec 2014]

    Background: Metagenomics is a powerful methodology to study microbial communities, but it is highly dependent on nucleotide sequence similarity searching against sequence databases. Metagenomic analyses with next-generation sequencing technologies produce enormous numbers of reads from microbial communities, and many reads are derived from microbes whose genomes have not yet been sequenced, limiting the usefulness of existing sequence similarity search tools. Therefore, there is a clear need for a sequence similarity search tool that can rapidly detect weak similarity in large datasets. Results: We developed a tool, which we named CLAST (CUDA implemented large-scale alignment search tool), that enables analyses of millions of reads and thousands of reference genome sequences, and runs on NVIDIA Fermi architecture graphics processing units. CLAST has four main advantages over existing alignment tools. First, CLAST was capable of identifying sequence similarities ~80.8 times faster than BLAST and 9.6 times faster than BLAT. Second, CLAST executes global alignment as the default (local alignment is also an option), enabling CLAST to assign reads to taxonomic and functional groups based on evolutionarily distant nucleotide sequences with high accuracy. Third, CLAST does not need a preprocessed sequence database like Burrows?Wheeler Transform-based tools, and this enables CLAST to incorporate large, frequently updated sequence databases. Fourth, CLAST requires <2?GB of main memory, making it possible to run CLAST on a standard desktop computer or server node. Conclusions: CLAST achieved very high speed (similar to the Burrows?Wheeler Transform-based Bowtie 2 for long reads) and sensitivity (equal to BLAST, BLAT, and FR-HIT) without the need for extensive database preprocessing or a specialized computing platform. Our results demonstrate that CLAST has the potential to be one of the most powerful and realistic approaches to analyze the massive amount of sequence data from next-generation sequencing technologies.
    Categories: Journal Articles
  • QUDeX-MS: hydrogen/deuterium exchange calculation for mass spectra with resolved isotopic fine structure
    [Dec 2014]

    Background: Hydrogen/deuterium exchange (HDX) coupled to mass spectrometry permits analysis of structure, dynamics, and molecular interactions of proteins. HDX mass spectrometry is confounded by deuterium exchange-associated peaks overlapping with peaks of heavy, natural abundance isotopes, such as carbon-13. Recent studies demonstrated that high-performance mass spectrometers could resolve isotopic fine structure and eliminate this peak overlap, allowing direct detection and quantification of deuterium incorporation. Results: Here, we present a graphical tool that allows for a rapid and automated estimation of deuterium incorporation from a spectrum with isotopic fine structure. Given a peptide sequence (or elemental formula) and charge state, the mass-to-charge ratios of deuterium-associated peaks of the specified ion is determined. Intensities of peaks in an experimental mass spectrum within bins corresponding to these values are used to determine the distribution of deuterium incorporated. A theoretical spectrum can then be calculated based on the estimated distribution of deuterium exchange to confirm interpretation of the spectrum. Deuterium incorporation can also be detected for ion signals without a priori specification of an elemental formula, permitting detection of exchange in complex samples of unidentified material such as natural organic matter. A tool is also incorporated into QUDeX-MS to help in assigning ion signals from peptides arising from enzymatic digestion of proteins. MATLAB-deployable and standalone versions are available for academic use at qudex-ms.sourceforge.net and agarlabs.com. Conclusion: Isotopic fine structure HDX-MS offers the potential to increase sequence coverage of proteins being analyzed through mass accuracy and deconvolution of overlapping ion signals. As previously demonstrated, however, the data analysis workflow for HDX-MS data with resolved isotopic fine structure is distinct. QUDeX-MS we hope will aid in the adoption of isotopic fine structure HDX-MS by providing an intuitive workflow and interface for data analysis.
    Categories: Journal Articles
  • SPoRE: a mathematical model to predict double strand breaks and axis protein sites in meiosis
    [Dec 2014]

    Background: Meiotic recombination between homologous chromosomes provides natural combinations of genetic variations and is a main driving force of evolution. It is initiated via programmed DNA double-strand breaks (DSB) and involves a specific axial chromosomal structure. So far, recombination regions have been mainly determined by experiments, both expensive and time-consuming. Results: SPoRE is a mathematical model that describes the non-uniform localisation of DSB and axis proteins sites, and distinguishes high versus low protein density. It is based on a combination of genomic signals, based on what is known from wet-lab experiments, whose contribution is precisely quantified. It models axis proteins accumulation at gene 5?-ends with a discrete approximation of their diffusion and convection along genes. It models DSB accumulation at approximated gene promoter positions with intergenic region length and GC-content. SPoRE can be used for prediction and it is parameterised in an obvious way that makes it easy to understand from a biological viewpoint. Conclusions: When compared to Saccharomyces cerevisiae experimental data, SPoRE predicts axis protein and DSB positions with high sensitivity and precision, axis protein density with an average local correlation r=0.63 and DSB density with an average local correlation r=0.62. SPoRE outbreaks previous DSB predictors, which are based on nucleotide patterning, and it reaches 85% of success rate in DSB prediction compared to 54% obtained by available tools on a benchmarked dataset.SPoRE is available at the address http://www.lcqb.upmc.fr/SPoRE/.
    Categories: Journal Articles
  • Gene network inference using continuous time Bayesian networks: a comparative study and application to Th17 cell differentiation
    [Dec 2014]

    Background: Dynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous fashion at fixed points in time. However, nowadays omics data are being generated with increasing time course granularity. Thus, modellers now have the possibility to represent the system as evolving in continuous time and to improve the models? expressiveness. Results: Continuous time Bayesian networks are proposed as a new approach for gene network reconstruction from time course expression data. Their performance was compared to two state-of-the-art methods: dynamic Bayesian networks and Granger causality analysis. On simulated data, the methods comparison was carried out for networks of increasing size, for measurements taken at different time granularity densities and for measurements unevenly spaced over time. Continuous time Bayesian networks outperformed the other methods in terms of the accuracy of regulatory interactions learnt from data for all network sizes. Furthermore, their performance degraded smoothly as the size of the network increased. Continuous time Bayesian networks were significantly better than dynamic Bayesian networks for all time granularities tested and better than Granger causality for dense time series. Both continuous time Bayesian networks and Granger causality performed robustly for unevenly spaced time series, with no significant loss of performance compared to the evenly spaced case, while the same did not hold true for dynamic Bayesian networks. The comparison included the IRMA experimental datasets which confirmed the effectiveness of the proposed method. Continuous time Bayesian networks were then applied to elucidate the regulatory mechanisms controlling murine T helper 17 (Th17) cell differentiation and were found to be effective in discovering well-known regulatory mechanisms, as well as new plausible biological insights. Conclusions: Continuous time Bayesian networks were effective on networks of both small and large size and were particularly feasible when the measurements were not evenly distributed over time. Reconstruction of the murine Th17 cell differentiation network using continuous time Bayesian networks revealed several autocrine loops, suggesting that Th17 cells may be auto regulating their own differentiation process.
    Categories: Journal Articles
  • Integrating protein structural dynamics and evolutionary analysis with Bio3D
    [Dec 2014]

    Background: Popular bioinformatics approaches for studying protein functional dynamics include comparisons of crystallographic structures, molecular dynamics simulations and normal mode analysis. However, determining how observed displacements and predicted motions from these traditionally separate analyses relate to each other, as well as to the evolution of sequence, structure and function within large protein families, remains a considerable challenge. This is in part due to the general lack of tools that integrate information of molecular structure, dynamics and evolution. Results: Here, we describe the integration of new methodologies for evolutionary sequence, structure and simulation analysis into the Bio3D package. This major update includes unique high-throughput normal mode analysis for examining and contrasting the dynamics of related proteins with non-identical sequences and structures, as well as new methods for quantifying dynamical couplings and their residue-wise dissection from correlation network analysis. These new methodologies are integrated with major biomolecular databases as well as established methods for evolutionary sequence and comparative structural analysis. New functionality for directly comparing results derived from normal modes, molecular dynamics and principal component analysis of heterogeneous experimental structure distributions is also included. We demonstrate these integrated capabilities with example applications to dihydrofolate reductase and heterotrimeric G-protein families along with a discussion of the mechanistic insight provided in each case. Conclusions: The integration of structural dynamics and evolutionary analysis in Bio3D enables researchers to go beyond a prediction of single protein dynamics to investigate dynamical features across large protein families. The Bio3D package is distributed with full source code and extensive documentation as a platform independent R package under a GPL2 license from http://thegrantlab.org/bio3d/.
    Categories: Journal Articles
  • Finding gene regulatory network candidates using the gene expression knowledge base
    [Dec 2014]

    Background: Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of `omics? data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis. Results: We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions. Conclusions: Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.
    Categories: Journal Articles
  • Efficient prediction of human protein-protein interactions at a global scale
    [Dec 2014]

    Background: Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. Results: On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3?months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments. Conclusions: The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.
    Categories: Journal Articles
  • Identifying and quantifying metabolites by scoring peaks of GC-MS data
    [Dec 2014]

    Background: Metabolomics is one of most recent omics technologies. It has been applied on fields such as food science, nutrition, drug discovery and systems biology. For this, gas chromatography-mass spectrometry (GC-MS) has been largely applied and many computational tools have been developed to support the analysis of metabolomics data. Among them, AMDIS is perhaps the most used tool for identifying and quantifying metabolites. However, AMDIS generates a high number of false-positives and does not have an interface amenable for high-throughput data analysis. Although additional computational tools have been developed for processing AMDIS results and to perform normalisations and statistical analysis of metabolomics data, there is not yet a single free software or package able to reliably identify and quantify metabolites analysed by GC-MS. Results: Here we introduce a new algorithm, PScore, able to score peaks according to their likelihood of representing metabolites defined in a mass spectral library. We implemented PScore in a R package called MetaBox and evaluated the applicability and potential of MetaBox by comparing its performance against AMDIS results when analysing volatile organic compounds (VOC) from standard mixtures of metabolites and from female and male mice faecal samples. MetaBox reported lower percentages of false positives and false negatives, and was able to report a higher number of potential biomarkers associated to the metabolism of female and male mice. Conclusions: Identification and quantification of metabolites is among the most critical and time-consuming steps in GC-MS metabolome analysis. Here we present an algorithm implemented in an R package, which allows users to construct flexible pipelines and analyse metabolomics data in a high-throughput manner.
    Categories: Journal Articles
  • Comparative structural analysis of haemagglutinin proteins from type A influenza viruses: conserved and variable features
    [Dec 2014]

    Background: Genome variation is very high in influenza A viruses. However, viral evolution and spreading is strongly influenced by immunogenic features and capacity to bind host cells, depending in turn on the two major capsidic proteins. Therefore, such viruses are classified based on haemagglutinin and neuraminidase types, e.g. H5N1. Current analyses of viral evolution are based on serological and primary sequence comparison; however, comparative structural analysis of capsidic proteins can provide functional insights on surface regions possibly crucial to antigenicity and cell binding. Results: We performed extensive structural comparison of influenza virus haemagglutinins and of their domains and subregions to investigate type- and/or domain-specific variation. We found that structural closeness and primary sequence similarity are not always tightly related; moreover, type-specific features could be inferred when comparing surface properties of haemagglutinin subregions, monomers and trimers, in terms of electrostatics and hydropathy. Focusing on H5N1, we found that variation at the receptor binding domain surface intriguingly relates to branching of still circulating clades from those ones that are no longer circulating. Conclusions: Evidence from this work suggests that integrating phylogenetic and serological analyses by extensive structural comparison can help in understanding the `functional evolution? of viral surface determinants. In particular, variation in electrostatic and hydropathy patches can provide molecular evolution markers: intriguing surface charge redistribution characterizing the haemagglutinin receptor binding domains from circulating H5N1 clades 2 and 7 might have contributed to antigenic escape hence to their evolutionary success and spreading.
    Categories: Journal Articles
  • Room for growth
    [Dec 2014]

    Nature -

    Room for growth

    Nature 516, 7530 (2014). doi:10.1038/516143a

    The European Commission’s plans to allow individual countries a veto on the farming of genetically modified crops, although a compromise, should enable the technology to move forward.

    Categories: Journal Articles
  • Assess the real cost of research assessment
    [Dec 2014]

    Nature -

    Assess the real cost of research assessment

    Nature 516, 7530 (2014). http://www.nature.com/doifinder/10.1038/516145a

    Author: Peter M. Atkinson

    The Research Excellence Framework keeps UK science sharp, but the process is overly burdensome for institutions, says Peter M. Atkinson.

    Categories: Journal Articles
  • Animal behaviour: Cockroach night-vision
    [Dec 2014]

    Nature -

    Animal behaviour: Cockroach night-vision

    Nature 516, 7530 (2014). doi:10.1038/516146a

    Cockroaches can see in near-darkness thanks to the many light-sensing cells in their eyes that pool a tiny number of light signals over space and time.Matti Weckström and his colleagues at the University of Oulu, Finland, tested the behavioural responses of the American cockroach

    Categories: Journal Articles
  • Engineering: Smartphones sniff gases
    [Dec 2014]

    Nature -

    Engineering: Smartphones sniff gases

    Nature 516, 7530 (2014). doi:10.1038/516146b

    A common technology that enables short-range communication in smartphones could be used to detect airborne chemicals.Near-field communication chips are found in half a billion mobile devices worldwide. They communicate wirelessly with small external tags and are used in contactless payment systems, for instance. A

    Categories: Journal Articles
  • Molecular evolution: Ancient apes digested ethanol
    [Dec 2014]

    Nature -

    Molecular evolution: Ancient apes digested ethanol

    Nature 516, 7530 (2014). doi:10.1038/516146c

    Human ancestors were able to metabolize ethanol 10 million years ago, around the time that they came down from the trees.Matthew Carrigan at Santa Fe College in Gainesville, Florida, and his co-workers analysed the gene encoding the enzyme ADH4, which is made in the

    Categories: Journal Articles
  • Glaciology: Antarctic ice loss accelerates
    [Dec 2014]

    Nature -

    Glaciology: Antarctic ice loss accelerates

    Nature 516, 7530 (2014). doi:10.1038/516146d

    Glaciers flowing into Antarctica's Amundsen Sea are some of the fastest melting on the continent — and in recent years have lost ice at an ever-quicker rate.Different remote-sensing techniques have yielded slightly different estimates for the amount of ice melting from the Amundsen glaciers.

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