531 research outputs found

    The Biomolecular Interaction Network Database in PSI-MI 2.5

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    The Biomolecular Interaction Network Database (BIND) is a major source of curated biomolecular interactions, which has been unmaintained for the last few years, a trend which will eventually result in the loss of a significant amount of unique biomolecular interaction information, mostly as database identifiers become out of date. To help reverse this trend, we converted BIND to a standard format, Proteomics Standard Initiative-Molecular Interaction 2.5, starting from the last curated data release (from 2005) available in a custom XML format and made the core components (interactions and complexes) plus additional valuable curated information available for download (http://download.baderlab.org/BINDTranslation/). Major work during the conversion process was required to update out of date molecule identifiers resulting in a more comprehensive conversion of BIND, by measures including number of species and interactor types covered, than what is currently accessible elsewhere. This work also highlights issues of data modeling, controlled vocabulary adoption and data cleaning that can serve as a general case study on the future compatibility of interaction databases

    The Biomolecular Interaction Network Database and related tools 2005 update

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    The Biomolecular Interaction Network Database (BIND) (http://bind.ca) archives biomolecular interaction, reaction, complex and pathway information. Our aim is to curate the details about molecular interactions that arise from published experimental research and to provide this information, as well as tools to enable data analysis, freely to researchers worldwide. BIND data are curated into a comprehensive machine-readable archive of computable information and provides users with methods to discover interactions and molecular mechanisms. BIND has worked to develop new methods for visualization that amplify the underlying annotation of genes and proteins to facilitate the study of molecular interaction networks. BIND has maintained an open database policy since its inception in 1999. Data growth has proceeded at a tremendous rate, approaching over 100 000 records. New services provided include a new BIND Query and Submission interface, a Standard Object Access Protocol service and the Small Molecule Interaction Database (http://smid.blueprint.org) that allows users to determine probable small molecule binding sites of new sequences and examine conserved binding residues

    The BioPAX community standard for pathway data sharing

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    Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery. © 2010 Nature America, Inc. All rights reserved

    CavBench: a benchmark for protein cavity detection methods

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    Extensive research has been applied to discover new techniques and methods to model protein-ligand interactions. In particular, considerable efforts focused on identifying candidate binding sites, which quite often are active sites that correspond to protein pockets or cavities. Thus, these cavities play an important role in molecular docking. However, there is no established benchmark to assess the accuracy of new cavity detection methods. In practice, each new technique is evaluated using a small set of proteins with known binding sites as ground-truth. However, studies supported by large datasets of known cavities and/or binding sites and statistical classification (i.e., false positives, false negatives, true positives, and true negatives) would yield much stronger and reliable assessments. To this end, we propose CavBench, a generic and extensible benchmark to compare different cavity detection methods relative to diverse ground truth datasets (e.g., PDBsum) using statistical classification methods.info:eu-repo/semantics/publishedVersio

    Systematic Approaches towards the Development of Host-Directed Antiviral Therapeutics

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    Since the onset of antiviral therapy, viral resistance has compromised the clinical value of small-molecule drugs targeting pathogen components. As intracellular parasites, viruses complete their life cycle by hijacking a multitude of host-factors. Aiming at the latter rather than the pathogen directly, host-directed antiviral therapy has emerged as a concept to counteract evolution of viral resistance and develop broad-spectrum drug classes. This approach is propelled by bioinformatics analysis of genome-wide screens that greatly enhance insights into the complex network of host-pathogen interactions and generate a shortlist of potential gene targets from a multitude of candidates, thus setting the stage for a new era of rational identification of drug targets for host-directed antiviral therapies. With particular emphasis on human immunodeficiency virus and influenza virus, two major human pathogens, we review screens employed to elucidate host-pathogen interactions and discuss the state of database ontology approaches applicable to defining a therapeutic endpoint. The value of this strategy for drug discovery is evaluated, and perspectives for bioinformatics-driven hit identification are outlined

    PutidaNET: Interactome database service and network analysis of Pseudomonas putida KT2440

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    <p>Abstract</p> <p>Background</p> <p><it>Pseudomonas putida </it>KT2440 (<it>P. putida </it>KT2440) is a highly versatile saprophytic soil bacterium. It is a certified bio-safety host for transferring foreign genes. Therefore, the bacterium is used as a model organism for genetic and physiological studies and for the development of biotechnological applications. In order to provide a more systematic application of the organism, we have constructed a protein-protein interaction (PPI) network analysis system of <it>P. putida </it>KT2440.</p> <p>Results</p> <p>PutidaNET is a comprehensive interaction database and server of <it>P. putida </it>KT2440 which is generated from three protein-protein interaction (PPI) methods. We used PSIMAP (Protein Structural Interactome MAP), PEIMAP (Protein Experimental Interactome MAP), and Domain-domain interactions using iPfam. PutidaNET contains 3,254 proteins, and 82,019 possible interactions consisting of 61,011 (PSIMAP), 4,293 (PEIMAP), and 30,043 (iPfam) interaction pairs except for self interaction. Also, we performed a case study by integrating a protein interaction network and experimental 1-DE/MS-MS analysis data <it>P. putida</it>. We found that 1) major functional modules are involved in various metabolic pathways and ribosomes, and 2) existing PPI sub-networks that are specific to succinate or benzoate metabolism are not in the center as predicted.</p> <p>Conclusion</p> <p>We introduce the PutidaNET which provides predicted interaction partners and functional analyses such as physicochemical properties, KEGG pathway assignment, and Gene Ontology mapping of <it>P. putida </it>KT2440 PutidaNET is freely available at <url>http://sequenceome.kobic.kr/PutidaNET</url>.</p

    Carbon Sequestration in Synechococcus Sp.: From Molecular Machines to Hierarchical Modeling

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    The U.S. Department of Energy recently announced the first five grants for the Genomes to Life (GTL) Program. The goal of this program is to "achieve the most far-reaching of all biological goals: a fundamental, comprehensive, and systematic understanding of life." While more information about the program can be found at the GTL website (www.doegenomestolife.org), this paper provides an overview of one of the five GTL projects funded, "Carbon Sequestration in Synechococcus Sp.: From Molecular Machines to Hierarchical Modeling." This project is a combined experimental and computational effort emphasizing developing, prototyping, and applying new computational tools and methods to ellucidate the biochemical mechanisms of the carbon sequestration of Synechococcus Sp., an abundant marine cyanobacteria known to play an important role in the global carbon cycle. Understanding, predicting, and perhaps manipulating carbon fixation in the oceans has long been a major focus of biological oceanography and has more recently been of interest to a broader audience of scientists and policy makers. It is clear that the oceanic sinks and sources of CO2 are important terms in the global environmental response to anthropogenic atmospheric inputs of CO2 and that oceanic microorganisms play a key role in this response. However, the relationship between this global phenomenon and the biochemical mechanisms of carbon fixation in these microorganisms is poorly understood. The project includes five subprojects: an experimental investigation, three computational biology efforts, and a fifth which deals with addressing computational infrastructure challenges of relevance to this project and the Genomes to Life program as a whole. Our experimental effort is designed to provide biology and data to drive the computational efforts and includes significant investment in developing new experimental methods for uncovering protein partners, characterizing protein complexes, identifying new binding domains. We will also develop and apply new data measurement and statistical methods for analyzing microarray experiments. Our computational efforts include coupling molecular simulation methods with knowledge discovery from diverse biological data sets for high-throughput discovery and characterization of protein-protein complexes and developing a set of novel capabilities for inference of regulatory pathways in microbial genomes across multiple sources of information through the integration of computational and experimental technologies. These capabilities will be applied to Synechococcus regulatory pathways to characterize their interaction map and identify component proteins in these pathways. We will also investigate methods for combining experimental and computational results with visualization and natural language tools to accelerate discovery of regulatory pathways. Furthermore, given that the ultimate goal of this effort is to develop a systems-level of understanding of how the Synechococcus genome affects carbon fixation at the global scale, we will develop and apply a set of tools for capturing the carbon fixation behavior of complex of Synechococcus at different levels of resolution. Finally, because the explosion of data being produced by high-throughput experiments requires data analysis and models which are more computationally complex, more heterogeneous, and require coupling to ever increasing amounts of experimentally obtained data in varying formats, we have also established a companion computational infrastructure to support this effort as well as the Genomes to Life program as a whole.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63164/1/153623102321112746.pd
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