| Accepted Posters |
| Category 'L'- Interactions' |
| Poster L01 |
| Finding Protein Sequence Signatures from Protein-Protein Interaction Data Using Gene Ontology Annotations |
| Osamu Maruyama- Kyushu University |
| Hideki Hirakawa (Kyushu University, Faculty of Agriculture); Takao Iwayanagi (National Institute of Genetics, Center for Information Biology and DNA Data Bank of Japan); Yoshiko Ishida (Hitachi, Ltd., Central Research Laboratory); Shizu Takeda (Hitachi, Ltd., Central Research Laboratory); Jun Otomo (Hitachi, Ltd., Central Research Laboratory); Satoru Kuhara (Kyushu University, Graduate School of Genetic Resources Technology); |
| Short Abstract: We propose a method for predicting protein sequence signatures ofinteracting partners with a particular protein called a host proteinusing gene ontology annotations. The method can also simultaneouslyfind potential interacting partners with host proteins.Our method was applied to our original human PPI data set. |
| Long Abstract: Click Here |
| Poster L02 |
| Discovering the carbohydrate binding properties of PA-1L lectin from Pseudomonas aeruginosa by molecular modeling |
| Alessandra Nurisso- CNRS |
| Anne Imberty (CNRS, CERMAV); |
| Short Abstract: Pseudomonas aeruginosa is a human pathogen whose virulence is based on the capability of adhesion to the surface of host cells through the production of two carbohydrate binding proteins, PA-1L and PA-IIL. In this work, the molecular bases of the carbohydrate–PA-IL interactions are elucidated through molecular mechanics techniques. |
| Long Abstract: Click Here |
| Poster L03 |
| NASCENT: An Automatic Protein Interaction Network Generation Tool for Non-Model Organisms |
| Dániel Bánky- Eötvös University |
| Vince Grolmusz (Eötvös University, Protein Information Technology Group); |
| Short Abstract: NASCENT is a tool capable of constructing protein-protein interaction networks for any chosen non-model organisms. Calculations are based on a model organism protein-protein interaction data, retrieved from several major biological databases. The mapping of the interactions is performed by corresponding the genes of the expressed proteins of the two species. |
| Long Abstract: Click Here |
| Poster L04 |
| PINA: an integrated network analysis platform for protein-protein interactions |
| Jianmin Wu- University of Helsinki |
| Tea Vallenius (University of Helsinki, Genome-Scale Biology Program and Institute of Biomedicine); Kristian Ovaska (University of Helsinki, Genome-Scale Biology Program and Institute of Biomedicine); Jukka Westermarck (University of Tampere and Tampere University Hospital, Institute of Medical Technology); Tomi Mäkelä (University of Helsinki, Genome-Scale Biology Program and Institute of Biomedicine); Sampsa Hautaniemi (University of Helsinki, Genome-Scale Biology Program and Institute of Biomedicine); |
| Short Abstract: We introduce a web-based Protein Interaction Network Analysis platform (PINA), which integrates protein-protein interaction data from six databases and provides network construction, filtering, analysis, and visualization tools. Its advantages have been demonstrated in analysis of two human PPI networks. PINA is freely available at http://csbi.ltdk.helsinki.fi/pina/. |
| Long Abstract: Click Here |
| Poster L05 |
| XIPPI: a merged database of protein-protein interactions |
| Yuri Vyatkin- Institute of Cytology and Genetics SB RAS |
| Dmitry Afonnikov (Institute of Cytology and Genetics, Laboratory of Theoretical Genetics); |
| Short Abstract: The information about protein-protein interactions spreads over a number of databases with different data formats, interaction descriptions and protein sequence referencing. We suggest a tool XIPPI to overcome a problem of protein sequence redundancy in PPI databases. The XIPPI allows searching for interactions available and building datasets of protein interactions. |
| Long Abstract: Click Here |
| Poster L06 |
| Analysis of Biomolecular Network in Structurome |
| Akinori Sarai- Kyushu Institute of Technology |
| Mitsuaki Ohtsuaka (KIT, Biosci. Bioinfo.); Satoshi Fujii (KIT, Biosci. Bioinfo.); |
| Short Abstract: We have developed a database/tool of biomolecular network, PDBnet, based on the structural information of structurome. PDBnet enables us to extract and visualize various kinds of information from the structurome. We have analyzed the relationship among various molecular, evolutionary and network properties, in a systematic way by using PDBnet. |
| Long Abstract: Click Here |
| Poster L07 |
| Identification of Computational Hot Spots in Protein Interfaces Using Solvent Accessibility and Inter-Residue Potentials |
| Nurcan Tuncbag- Koc University |
| Attila Gursoy (Koc University, Center for Computationa Biology and Bioinformatics and College of Engineering); Ozlem Keskin (Koc University, Center for Computationa Biology and Bioinformatics and College of Engineering); |
| Short Abstract: Hot spots are residues comprising only a small fraction of interfaces yet accounting for the majority of the binding energy. Here, we present a new efficient method to determine computational hot spots based on solvent accessibility and statistical pairwise potentials of the interface residues. Our method reaches 70% accuracy. |
| Long Abstract: Click Here |
| Poster L08 |
| Effective atomic interactions for the characterization of protein-ligand binding interfaces |
| Alex Slater- Pontificia Universidad Católica de Chile |
| Francisco Melo (Pontificia Universidad Católica de Chile, Genética Molecular y Microbiología); Evandro Ferrada (University of Zurich, Department of Biochemistry); |
| Short Abstract: We describe a method to classify intermolecular interfaces using effective atomic interactions, and tested on 372 ATP-protein complexes. We found a high diversity in binding modes and illustrate specific cases where ligands in same conformation bind proteins with different interfaces, and also give examples where ligands in different conformations bind proteins with the same conformation. |
| Long Abstract: Click Here |
| Poster L09 |
| Identifying genetic interactions with sparse hidden factors |
| Leopold Parts- Wellcome Trust Sanger Institute |
| Oliver Stegle (University of Cambridge, Cavendish Laboratory); John Winn (Microsoft Research, Cambridge); Richard Durbin (Wellcome Trust Sanger Institute, ); |
| Short Abstract: We present a method for learning sparse, biologically informed hidden factors, and include them in a genetic interaction model.For the first time, we find biologically meaningful interactions between genotype and hidden determinants of gene expression, complementing and extending established results. |
| Long Abstract: Click Here |
| Poster L11 |
| Re-examining the connection between the network topology and essentiality |
| Elena Zotenko- Max-Planck Institute fuer Informatiks |
| Julian Mestre (Max Planck Institute for Informatics, Algorithms and Computational Complexity); Dianne O'Leary (University of Maryland, College Park, Computer Science Department); Teresa Przytycka (National Institutes of Health, National Center for Biotechnology Information); |
| Short Abstract: None On File |
| Long Abstract: Click Here |
| Poster L12 |
| Determinants of interaction specificity in the plant MADS transcription factor network |
| Aalt-Jan Van Dijk- PRI, Wageningen University And Research Centre |
| Roeland van Ham (PRI, Wageningen UR, Applied Bioinformatics); Richard Immink (PRI, Wageningen UR, Plant Developmental Systems); Gerco Angenent (PRI, Wageningen UR, Plant Developmental Systems); |
| Short Abstract: We obtain sequence-level determinants of protein interaction specificity for the Arabidopsis MADS proteins, which are involved in a wide range of important developmental processes (e.g. floral organ formation). Our predictions were experimentally validated using site-specific mutagenesis and yeast-two-hybrid screening. Not only loss-of-function was observed, but also more revealing gain-of-function. |
| Long Abstract: Click Here |
| Poster L13 |
| Discovery of correlated motifs in large protein-protein interaction networks |
| Peter Boyen- Hasselt University |
| Aalt-Jan Van Dijk (Wageningen UR, Applied Bioinformatics); Dries Van Dyck (Hasselt University, WNI); Roeland van Ham (Wageningen UR, Applied Bioinformatics); Frank Neven (Hasselt University, WNI); |
| Short Abstract: We present a local search algorithm to identify correlated motif pairs. We validate the algorithm both on artificial and biological datasets. Our predicted interaction motifs are overrepresented at protein interaction surfaces. For the first time we present the application of correlated motif search on large-scale interaction networks. |
| Long Abstract: Click Here |
| Poster L14 |
| Computational prediction of small non-coding RNA targets in bacteria |
| Andreas Richter- University of Freiburg |
| Anke Busch (University of Leipzig, Department of Computer Science); Rolf Backofen (University of Freiburg, Department of Computer Science); |
| Short Abstract: We present a general energy-based approach, IntaRNA, to the prediction of RNA-RNA interactions incorporating both interaction site accessibility and existence of an interaction seed. Its performance has been demonstrated on prediction of bacterial sRNA targets. We also successfully predicted the regulatory outcome of the sRNA-mRNA interaction on translation initiation. |
| Long Abstract: Click Here |
| Poster L15 |
| Increasing the reliability and coverage of protein-protein interaction data from tandem affinity purification experiments. |
| James Vlasblom- University of Toronto |
| Shuye Pu (Hospital for Sick Children, Molecular structure and function program); Shoshana Wodak (Hospital for Sick Children, Molecular Structure and Function program); |
| Short Abstract: High throughput purification methods are increasingly successful in identifying protein-protein interactions. However, to achieve acceptable accuracy, many of the observed interactions are discarded during data processing. Here, interactome coverage is extended by integrating additional biological evidence using supervised classification, and potentially novel protein complex components are identified with graph clustering. |
| Long Abstract: Click Here |
| Poster L16 |
| Classification and retrieval of protein interfaces based on interface similarity: Detecting homology and analogy in protein interactions |
| Dmitry Korkin- University of Missouri, Columbia |
| Nan Zhao (University of Missouri, Columbia, Informatics Institute and Dept. of Computer Science); Bin Pang (University of Missouri, Columbia, Informatics Institute and Dept. of Computer Science); Chi-Ren Shyu (University of Missouri, Columbia, Informatics Institute and Dept. of Computer Science); |
| Short Abstract: In this work, we (1) present a novel protein interface similarity measure, determined using a machine learning approach; (2) construct and compare two protein interface retrieval systems using the defined similarity; and (3) introduce a biologically sound hierarchical classification of protein interfaces applied to a set of ~2,800 protein-protein interactions. |
| Long Abstract: Click Here |
| Accepted Posters |
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