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2011 ACM Conference on bioinformatics, computational biology and Biomedicine, ACM-BCB 2011

作者： Xue, Li C. Jordan, Rafael A. El-Manzalawy, Yasser Dobbs, Drena Honavar, Vasant Bioinformatics and Computational Biology Program Iowa State University Ames IA 50011 United States Department of Genetics Development and Cell Biology Iowa State University Ames 50011 United States Department of Computer Science Iowa State University Ames IA 50011 United States Department of Computer Science Pontificia Universidad Javeriana Cali Colombia Department of Systems and Computer Engineering AI-Azhar University Cairo Egypt

ISBN: (纸本)9781450307963

computational protein-protein docking is a valuable tool for determining the conformation of complexes formed by interacting proteins. Selecting near-native conformations from the large number of possible models generated by docking software presents a significant challenge in practice. We introduce a novel method for ranking docked conformations based on the degree of overlap between the interface residues of a docked conformation formed by a pair of proteins with the set of predicted interface residues between them. Our approach relies on a method, called PS-HomPPI, for reliably predicting proteinprotein interface residues by taking into account information derived from both interacting proteins. PS-HomPPI infers the residues of a query protein that are likely to interact with a partner protein based on known interface residues of the homo-interologs of the query-partner protein pair, i.e., pairs of interacting proteins that are homologous to the query protein and partner protein. Our results on Docking Benchmark 3.0 show that the quality of the ranking of docked conformations using our method is consistently superior to that produced using ClusPro cluster-size-based and energy-based criteria for 61 out of the 64 docking complexes for which PS-HomPPI produces interface predictions. An implementation of our method for ranking docked models is freely available at: http://***/DockRank/. Copyright © 2011 ACM.

关键词： Proteins

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Free energies for coarse-grained proteins by integrating multibody statistical contact potentials with entropies from elastic network models

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Journal of Structural and Functional Genomics 2011年第2期12卷 137-147页

作者： Zimmermann, Michael T. Leelananda, Sumudu P. Gniewek, Pawel Feng, Yaping Jernigan, Robert L. Kloczkowski, Andrzej Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50011-0320 United States L.H.Baker Center for Bioinformatics and Biological Statistics Iowa State University Ames IA 50011-3020 United States Bioinformatics and Computational Biology Interdepartmental Graduate Program Iowa State University Ames IA 50011 United States Laboratory of Theory of Biopolymers Faculty of Chemistry University of Warsaw 02-093 Warsaw Pasteura 1 Poland Battelle Center for Mathematical Medicine Research Institute at Nationwide Children's Hospital Columbus OH 43205 United States Department of Pediatrics Ohio State University College of Medicine Columbus OH 43205 United States

We propose a novel method of calculation of free energy for coarse grained models of proteins by combining our newly developed multibody potentials with entropies computed from elastic network models of proteins. Multi-body potentials have been of much interest recently because they take into account three dimensional interactions related to residue packing and capture the cooperativity of these interactions in protein structures. Combining four-body non-sequential, four-body sequential and pairwise short range potentials with optimized weights for each term, our coarse-grained potential improved recognition of native structure among misfolded decoys, outperforming all other contact potentials for CASP8 decoy sets and performance comparable to the fully atomic empirical DFIRE potentials. By combing statistical contact potentials with entropies from elastic network models of the same structures we can compute free energy changes and improve coarse-grained modeling of protein structure and dynamics. The consideration of protein flexibility and dynamics should improve protein structure prediction and refinement of computational models. This work is the first to combine coarse-grained multibody potentials with an entropic model that takes into account contributions of the entire structure, investigating native-like decoy selection. © 2011 Springer Science+Business Media B.V.

关键词： 4-Body potentials Elastic network models Multibody potentials Protein dynamics Protein structure prediction Protein structure refinement

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Aberrant iron homeostasis, oxidative fiber enrichment, and activation of ketogenesis in muscle tissue of ISCU Myopathy patients

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BMC Proceedings 2012年第3期6卷 1-1页

作者： Daniel R Crooks Thanemozhi G Natarajan Cathy Wu Manik C Ghosh Wing-Hang Tong Tracey A Rouault Churning Chen Hongzhan Huang Ronald G Haller Protein Information Resource Department of Biochemistry Molecular and Cellular Biology Georgetown University Medical Center Washington DC 20057 USA Molecular Medicine Program Eunice Kennedy Shriver National Institute of Child Health and Human Development Bethesda MD 20892 USA Center for Bioinformatics and Computational Biology University of Delaware Newark DE 19711 USA Department of Neurology University of Texas Southwestern Medical Center and VA North Texas Medical Center and Neuromuscular Center Institute for Exercise and Environmental Medicine Dallas Texas 75231 USA

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Evolution and Medicine: An Inquiry-Based High School Curriculum Supplement

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Evolution: Education and Outreach 2011年第4期4卷 603-612页

作者： Beardsley, Paul M. Stuhlsatz, Molly A.M. Kruse, Rebecca A. Eckstrand, Irene A. Gordon, Shefa D. Odenwald, Ward F. BSCS (Biological Sciences Curriculum Study) 5415 Mark Dabling Blvd Colorado Springs 80918 CO United States National Institute of General Medical Sciences Division of Genetics and Developmental Biology Center for Bioinformatics and Computational Biology 45 Center Drive Bethesda 20892-6200 MD United States National Eye Institute Office of Program Planning and Analysis 9000 Rockville Pike Bethesda 20892-6200 MD United States National Institute of Neurological Disorders and Stroke Neural Cell-Fate Determinants Section Bethesda 20892-6200 MD United States Center for Excellence in Mathematics and Science Teaching and Department of Biological Sciences CEMaST 4-2-515 California State Polytechnic University 3801 W. Temple Ave. Pomona 91768 CA United States

Evolution and Medicine is a curriculum supplement designed by the National Institutes of Health (NIH) and the Biological Sciences Curriculum Study (BSCS) for high school students. The supplement is freely available from NIH's Office of Science Education (OSE) as a part of the NIH curriculum supplement series. Development of the supplement was a collaborative effort that included input from a panel of experts in medicine, evolution, education, and educational technology. In total, the curriculum supplement includes five inquiry-based lessons that are integrated into the BSCS 5E instructional model (based on constructivist learning theory). The goal was to develop a 2-week curriculum to help students understand major concepts of evolution using the dynamic, modern, and relevant context of medicine. A diverse group of students and teachers across the US participated in a formative evaluation of a field test version of the curriculum. High school students made significant learning gains from pretest to posttest, with a relatively large effect size for student understanding of common ancestry and a relatively small effect size for student understanding of natural selection. There was no statistically significant difference in achievement gains between white students and all other racial/ethnic categories. Overall, the evaluation suggests that a curriculum that emphasizes the role of evolution in medicine, uses a constructivist instructional model, and is grounded in inquiry is relatively well-received by teachers and students and shows promise for increasing student learning in evolution. © 2011 Springer Science+Business Media, LLC.

关键词： Common ancestry Evolution Evolution and medicine High school Inquiry Instructional model Natural selection

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MetNetAPI: A flexible method to access and manipulate biological network data from MetNet

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BMC Research Notes 2010年第1期3卷 1-9页

作者： Sucaet, Yves Wurtele, Eve Syrkin Department of Genetics Development and Cell Biology Iowa State University Ames IA 50011 United States Interdepartmental Program in Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States

Background. Convenient programmatic access to different biological databases allows automated integration of scientific knowledge. Many databases support a function to download files or data snapshots, or a webservice that offers "live" data. However, the functionality that a database offers cannot be represented in a static data download file, and webservices may consume considerable computational resources from the host server. Results. MetNetAPI is a versatile Application programming Interface (API) to the MetNetDB database. It abstracts, captures and retains operations away from a biological network repository and website. A range of database functions, previously only available online, can be immediately (and independently from the website) applied to a dataset of interest. Data is available in four layers: molecular entities, localized entities (linked to a specific organelle), interactions, and pathways. Navigation between these layers is intuitive (e.g. one can request the molecular entities in a pathway, as well as request in what pathways a specific entity participates). Data retrieval can be customized: Network objects allow the construction of new and integration of existing pathways and interactions, which can be uploaded back to our server. In contrast to webservices, the computational demand on the host server is limited to processing data-related queries only. Conclusions. An API provides several advantages to a systems biology software platform. MetNetAPI illustrates an interface with a central repository of data that represents the complex interrelationships of a metabolic and regulatory network. As an alternative to data-dumps and webservices, it allows access to a current and "live" database and exposes analytical functions to application developers. Yet it only requires limited resources on the server-side (thin server/fat client setup). The API is available for Java, *** and R programming environments and offers flexible query and bro

关键词： Application program Interface Molecular Entity Biological Database Command Line Interface Network Object

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PS2-108. Infection with Hepatitis C down-regulates the expression of cytokine genes in peripheral blood

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Cytokine 2011年第1期56卷 94-94页

作者： Milton W. Taylor Christopher R. Bolen Steven H. Kleinstein Leonid Brodsky Department of Biology Indiana University Bloomington IN 47401 USA 1Interdepartmental Program in Computational Biology and Bioinformatics Yale University 300 George St. Suite 501 New Haven CT 06511 USA Department of Pathology Yale University School of Medicine New Haven CT USA Institute of Evolution University of Haifa Mount Carmel Haifa 31905 Israel

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Characterizing the tumor microenvironmental changes in a network perspective

Characterizing the tumor microenvironmental changes in a net...

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第四届全国生物信息学与系统生物学学术大会

作者：李文婷王瑞孙之荣 Institute of Bioinformatics School of Life SciencesTsinghua University Computational Biology and Bioinformatics Program Institute for Genome Science and PolicyDuke University Medical CenterDurhamNC27705

Alternations in tumor microenvironment are critical in driving tumor development. However,it proves difficult to characterize their molecular components and further relate to important pathological *** using a network approach,we characterized systematic expression changes at multi-gene module levels between colorectal tumors with and without recurrence after surgery and confirmed their association with two critical

关键词： Characterizing the tumor microenvironmental changes in a network perspective

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Dynamic and static analysis of transcriptional regulatory networks in a hierarchical context

Dynamic and static analysis of transcriptional regulatory ne...

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IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

作者： Nitin Bhardwaj Mark Gerstein Program in Computational Biology and Bioinformatics Yale University New Heaven CT USA Department of Computer Science Yale University New Heaven CT USA

Gene regulatory networks have been shown to share some common aspects with commonplace social governance structures such as hierarchies. Thus, we can get some intuition into their organization by arranging them into well-known hierarchical layouts. Here we study a wide range of regulatory networks (transcriptional, modification and phosphorylation) in a hierarchical context for five evolutionarily diverse species. We specify three levels of regulators - top, middle and bottom - which collectively regulate the non-regulator targets lying in the lowest fourth level, and we define quantities for nodes, levels and entire networks that measure their degree of collaboration and autocratic or democratic character. Overall we show that in all the networks studied, the middle level has the highest collaborative propensity and that co-regulatory partnerships occur most frequently amongst mid-level regulators, an observation that has parallels in efficient corporate settings where middle managers need to interact most to ensure organizational effectiveness. Then to study dynamic effects, superimpose the phenotypic effects of tampering with nodes and edges directly onto the hierarchies. We reconstruct modified hierarchies reflecting changes in the level of regulators within the hierarchy upon deletions or insertions of nodes or edges. Overall, we find that rewiring events that affect upper levels have a more dramatic effect on cell proliferation rate and survival than do those involving lower levels. We also investigate other features connected to the importance of upper-level regulators: expression divergence, back-up copies and expression level.

关键词： Regulators Collaboration Context bioinformatics Biochemistry Molecular biophysics Performance analysis

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Characterization of the retinal proteome during rod photoreceptor genesis

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BMC Research Notes 2010年第1期3卷 1-8页

作者： Barnhill, Alison E Hecker, Laura A Kohutyuk, Oksana Buss, Janice E Honavar, Vasant G Greenlee, Heather West Interdepartmental Neuroscience Program Iowa State University Ames IA United States Department of Biomedical Sciences Iowa State University Ames IA United States Bioinformatics and Computational Biology Program Iowa State University Ames IA United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA United States Department of Computer Science Iowa State University Ames IA United States National Animal Disease Center Ames IA 50010 United States Mayo Clinic Department of Ophthalmology Rochester MN 55905 United States

Background. The process of rod photoreceptor genesis, cell fate determination and differentiation is complex and multi-factorial. Previous studies have defined a model of photoreceptor differentiation that relies on intrinsic changes within the presumptive photoreceptor cells as well as changes in surrounding tissue that are extrinsic to the cell. We have used a proteomics approach to identify proteins that are dynamically expressed in the mouse retina during rod genesis and differentiation. Findings. A series of six developmental ages from E13 to P5 were used to define changes in retinal protein expression during rod photoreceptor genesis and early differentiation. Retinal proteins were separated by isoelectric focus point and molecular weight. Gels were analyzed for changes in protein spot intensity across developmental time. Protein spots that peaked in expression at E17, P0 and P5 were picked from gels for identification. There were 239 spots that were picked for identification based on their dynamic expression during the developmental period of maximal rod photoreceptor genesis and differentiation. Of the 239 spots, 60 of them were reliably identified and represented a single protein. Ten proteins were represented by multiple spots, suggesting they were post-translationally modified. Of the 42 unique dynamically expressed proteins identified, 16 had been previously reported to be associated with the developing retina. Conclusions. Our results represent the first proteomics study of the developing mouse retina that includes prenatal development. We identified 26 dynamically expressed proteins in the developing mouse retina whose expression had not been previously associated with retinal development. © 2010 Greenlee et al.;licensee BioMed Central Ltd.

关键词： Protein Spot Mouse Retina Retinal Development Retinal Protein Probability Base Mowse Score

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Analysis of cancer omics data in a semantic web framework

Analysis of cancer omics data in a semantic web framework

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Workshop on Semantic Web Applications and Tools for Life Sciences 2010, SWAT4LS 2010

作者： Holford, Matthew E. McCusker, James P. Cheung, Kei-Hoi Krauthammer, Michael Interdepartmental Program in Computational Biology and Bioinformatics Yale University New Haven CT United States Department of Pathology Yale University New Haven CT United States Department of Computer Science Yale University New Haven CT United States Center for Medical Informatics Yale University New Haven CT United States Department of Genetics Yale University New Haven CT United States

Our work concerns the elucidation of the cancer (epi)genome, transcriptome and proteome to better understand the complex interplay between a cancer cell's molecular state and its response to anti-cancer therapy. To study the problem, we have previously focused on data warehousing technologies and statistical data integration. In this paper, we present recent work on extending our analytical capabilities using Semantic Web technology. A key new component presented here is a SPARQL endpoint to our existing data warehouse. This endpoint allows the merging of observed quantitative data with existing data from semantic knowledge sources such as Gene Ontology (GO). We show how such variegated quantitative and functional data can be integrated and accessed in a universal manner using Semantic Web tools. We also demonstrate how Description Logic (DL) reasoning can be used to infer previously unstated conclusions from existing knowledge bases. As proof of concept, we illustrate the ability of our setup to answer complex queries on resistance of cancer cells to Decitabine, a demethylating agent.

关键词： Semantic Web

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