Accepted Posters
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Category 'L'- Interactions' |
| Poster L01 |
| Understanding Protein Evolutionary Rate by Integrating Gene Co-expression with Protein Interactions |
| Xiaotu Ma- University of Texas at Dallas |
| Kaifang Pang (Shanghai Jiao Tong University, Department of Computer Science and Engineering); Chao Cheng (Yale University, Program in Computational Biology and Bioinformatics); Zhenyu Xuan (University of Texas at Dallas, Biology); Huanye Sheng (Shanghai Jiao Tong University, Department of Computer Science and Engineering); |
| Short Abstract: To understand the controversial relationship between protein evolutionary rate and protein interaction degree (PPID), we proposed a new PPID measure by integrating co-expression information. This new measure is highly correlated with protein evolutionary rate. Our results highlight the importance of protein interaction degree and co-expression in affecting protein evolutionary rate. |
| Long Abstract:Click Here |
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| Poster L02 |
| Structural and Energetic insights into TEM-1 and SHV-1 beta-lactamase ligand binding |
| Pinar KANLIKILICER- Bogazici University |
| Nilay BUDEYRI (Marmara University, Bioengineering); Berna SARIYAR AKBULUT (Marmara University, Bioengineering); Elif OZKIRIMLI OLMEZ (Bogazici University, Chemical Engineering); |
| Short Abstract: One common antibiotic resistance mechanism utilized by bacteria is the production of beta-lactamase enzymes. Molecular dynamics simulations and binding free energy calculations were performed on TEM-1 andSHV-1 beta-lactamases in complex with Beta-lactamase inhibitor protein(BLIP) to elucidate the structural aspects that lead to a difference in binding affinity between these beta-lactamases. |
| Long Abstract:Click Here |
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| Poster L03 |
| Atomic motion knock-on effect and large scale anharmonic motion: Correlation of calmodulin-peptide binding entropy with intramolecular dynamics |
| Jeremy Harris- University of Cambridge |
| Guy Grant (Senior Researcher, Chemistry); |
| Short Abstract: We present molecular dynamic simulations of 21 different calmodulin-peptide interactions. Analyses of B Factors, computationally-derived NMR order parameters, normal modes, and side chain dihedral angles demonstrate the importance of the knock-on effect and large scale anharmonic motion in characterizing variation in binding entropy. |
| Long Abstract:Click Here |
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| Poster L04 |
| Comparative analysis of binding site residues and recognition mechanisms of protein-protein and protein-RNA complexes using energy based approach |
| Gromiha Michael- CBRC, AIST |
| Kiyonobu Yokota (AIST, CBRC); Selvaraj Samuel (Bharathidasan University, Bioinformatics); Kazuhiko Fukui (AIST, CBRC); |
| Short Abstract: We have developed a novel energy based approach for identifying the binding sites in protein-protein and protein-RNA complexes. The preferred binding site residues are related with physical interactions and the importance of them are verified with experimental observations. We proposed probable mechanisms for the recognition of protein-protein and protein-RNA complexes. |
| Long Abstract:Click Here |
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| Poster L05 |
| HotPOINT: Hot Spot Prediction Server for Protein Interfaces |
| Nurcan Tuncbag- Koc University |
| Ozlem Keskin (Koc University, Chemical and Biological Engineering); Attila Gursoy (Koc University, Computer Engineering); |
| Short Abstract: The residues contributing more to the binding are called hot spots. Here, we present a web server, HotPoint, which predicts hot spots in protein interfaces using a new empirical model. The model incorporates a few simple rules consisting of occlusion from solvent and total knowledge-based pair potentials of residues. |
| Long Abstract:Click Here |
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| Poster L06 |
| Computational redesign of Itk self-association: a tool to probe kinase regulation |
| Scott Boyken- Iowa State University |
| Amy Andreotti (Iowa State University, Biochemistry, Biophysics, and Molecular Biology); |
| Short Abstract: Itk is critical to T-cell receptor signaling. Preliminary results indicate that self-association down-regulates catalytic activity. Therefore, we seek mutants to incrementally alter the binding affinity of Itk self-association. I have computationally redesigned the intermolecular interfaces of Itk, successfully predicting mutants to both increase and decrease affinity. |
| Long Abstract:Click Here |
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| Poster L07 |
| Pattern of Physical-Chemical and Structural Properties in Protein-Protein Interfaces |
| Fabio de Moraes- State University of Campinas |
| Ivan Mazoni (Brazilian Agricultural Research Corporation, Computational Biology Laboratory); Adauto Mancini (Brazilian Agricultural Research Corporation, Computational Biology Laboratory); Izabella Neshich (State University of Campinas, Biology Institute); José Jardine (Brazilian Agricultural Research Corporation, Computational Biology Laboratory); Goran Neshich (Brazilian Agricultural Research Corporation, Computational Biology Laboratory); |
| Short Abstract: We studied the pattern of physical-chemical and structural properties of amino acids in protein-protein interfaces, using STING_DB as the source of all the characteristics values. Multivariate statistical analysis was performed to check the difference between interface and non interface residues. |
| Long Abstract:Click Here |
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| Poster L08 |
| i-Patch: inter-protein contact prediction using local network information |
| Rebecca Hamer- University of Oxford |
| Qiang Luo (National University of Defence Technology, Mathematics and Systems Science); Judith Armitage (University of Oxford, Oxford Centre for Integrative Systems Biology); Gesine Reinert (University of Oxford, Statistics); Charlotte Deane (University of Oxford, Statistics); |
| Short Abstract: i-Patch is a new tool which predicts inter-protein contact sites by considering two proteins as a network, with residues as nodes and contacts as edges. i-Patch requires a reference structure and a multiple sequence alignment for each protein, then uses interaction propensities of residue patches to predict contact sites. |
| Long Abstract:Click Here |
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| Poster L09 |
| Investigating the Specificity of the Histidine Kinase-Response Regulator Complexes using Mutual Information |
| Mireille Gomes- University of Oxford |
| Rebecca Hamer (University of Oxford, Statistics); Charlotte Deane (University of Oxford, Statistics); Gesine Reinert (University of Oxford, Statistics); |
| Short Abstract: A review of the current state of predicting specificity, with special emphasis on the use of MI-based scores. |
| Long Abstract:Click Here |
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| Poster L10 |
| Discovering the recognition code of RNA-Protein Interactions |
| Aditi Gupta- Purdue University |
| Michael Gribskov (Purdue University, Biological Sciences); |
| Short Abstract: RNAs and proteins recognize each other to form ribonucleoprotein complexes. We find that RNA sequence and structure properties at protein binding regions are distinguishable from overall RNA in sequence and structure. We present a simple computational model based on these properties to predict protein binding regions in RNA. |
| Long Abstract:Click Here |
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| Poster L11 |
| HomPPI: Homologous Sequence-Based Protein-Protein Interface Prediction |
| Li Xue- Iowa State University |
| Vasant Honavar (Iowa State Universtiy, Computer Science); Drena Dobbs (Iowa State University, Genetics, Development & Cell Biology Dept.); |
| Short Abstract: We examined the degree of conservation of protein-protein interfaces (PPIs) using a large non-redundant dataset. We propose a simple sequence homology based Protein-Protein Interface (HOMPPI) prediction method. We present results using a benchmark dataset of protein complexes which suggest that HOMPPI is competitive with existing PPI prediction methods. |
| Long Abstract:Click Here |
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| Poster L12 |
| Modeling Interactions of Lentiviral Rev Proteins with Viral RNA and Cellular Proteins |
| Usha Muppirala- Iowa State University |
| Yungok Ihm (RIKEN, SPring-8 Center); Jae-Hyung Lee (UCLA, Dept of Integrative Biology and Physiolog); Susan Carpenter (Iowa State University, Dept of Animal Science); Drena Dobbs (Iowa State University, Dept Genetics, Development and Cell Biology); |
| Short Abstract: Rev, a regulatory protein encoded by HIV-1 and other lentiviruses, is essential for viral replication and thus is a potential target for antiviral therapies. Rev interacts with viral RNAs and several cellular proteins. We modeled these interactions to investigate Rev recognition mechanisms and identify structural motifs critical for Rev activity. |
| Long Abstract:Click Here |
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| Poster L13 |
| Efficient Retrieval of Protein Interfaces with a Machine Learning-based Similarity Measure |
| Bin Pang- Univ. of Missouri |
| Nan Zhao (Univ. of Missouri, Informatics Institute and Dpt. Computer Science); Dmitry Korkin (Univ. of Missouri, Informatics Institute, Dpt. Computer Science, Bond Life Science Center); Chi-Ren Shyu (Univ. of Missouri, Informatics Institute and Dpt. Computer Science); |
| Short Abstract: We propose an efficient retrieval system to search a large-scale protein interface database using a metric tree structure. The system is based on a similarity measure determined by a machine learning approach. It allows for on-the-fly retrieval of a new interface with high accuracy. |
| Long Abstract:Click Here |
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| Poster L14 |
| Similarity in protein-protein interactions: Disappearing and reappearing act by key interface residues |
| Nan Zhao- Univ. of Missouri |
| Bin Pang (Univ. of Missouri, Informatics Institute and Dpt. Computer Science); Chi-Ren Shyu (Univ. of Missouri, Informatics Institute and Dpt. Computer Science); Dmitry Korkin (Univ. of Missouri, Informatics Institute, Dpt. Computer Science, Bond Life Science Center); |
| Short Abstract: First, we developed an interface similarity measure that does not require superposing structures of either entire subunits or interfaces, which to the best of our knowledge, was done for the first time. Second, we detected interface similarity from homologous and analogous interactions; analyzing the results, we observed an intriguing phenomenon. |
| Long Abstract:Click Here |
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| Poster L15 |
| A Novel Scoring Approach for Protein Co-Purification Data Reveals High Interaction Specificity |
| Jaques Reifman- US Army Medical Research and Materiel Command |
| Joseph Ivanic (SAIC-Frederick, Advanced Biomedical Computing Center); Anders Wallqvist (U.S. Army Medical Research and Materiel Command, 1Biotechnology HPC Software Applications Institute); Xueping Yu (U.S. Army Medical Research and Materiel Command, 1Biotechnology HPC Software Applications Institute); |
| Short Abstract: In this talk, we will present a novel but conceptually simple statistical method to score and extract high-confidence (HC) protein interactions generated by high-throughput affinity purification followed by mass spectrometry (AP/MS) techniques. We will show how this method derives HC protein interaction networks (PINs) for a user-selected false-discovery rate, and overcomes limitations of more complex schemes by bypassing the requirement of a training dataset, making it applicable to any AP/MS dataset for any species. We will present results obtained when we applied our scoring method to two recent yeast AP/MS datasets, which show that the derived HC PINs are enriched with specific protein associations compared with random profiles. Finally, we will show that, when we compared our scoring method using four diverse high-quality reference datasets, we found that the accuracies of our scored interaction sets were manifestly higher than those of previously proposed schemes. |
| Long Abstract:Click Here |
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| Poster L16 |
| Prediction of Protein-Protein Interactions in the Apoptosis Pathway |
| Ece Ozbabacan- Koc University |
| Ozlem Keskin (Koc University, Chemical and Biological Engineering); Attila Gursoy (Koc University, Computer Engineering); |
| Short Abstract: This project focuses on prediction of protein-protein interactions in the apoptosis pathway, by structurally comparing the genes/proteins in that pathway with the template interfaces of non-obligate complexes obtained from the PDB. This is achieved by using the PRISM server and this approach adds a structural dimension to the apoptosis pathway. |
| Long Abstract:Click Here |
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| Poster L17 |
| Properties and evolution of specificity-determining residues in yeast protein-protein interactions |
| David Talavera- University of Manchester |
| David Robertson (University of Manchester, Faculty of Life Sciences); Simon Lovell (University of Manchester, Faculty of Life Sciences); |
| Short Abstract: Protein interaction interfaces are composed both of residues that determine specificity and some that do not. Our analyses show that just a 40% of the inter-protein contacts are sequence specific. Conversely more than a half of the interface residues are involved in non-conservative changes during evolution or after gene duplication. |
| Long Abstract:Click Here |
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| Poster L18 |
| Prediction of Domain-Peptide Binding Affinity from Primary Sequence |
| Xiaojian Shao- University of Toronto |
| Chris Tan (University of Toronto, Banting and Best Department of Medical Research); Courtney Voss (University of Western Ontario, Department of Biochemistry); Shawn Li (University of Western Ontario, Department of Biochemistry); Naiyang Deng (China Agricultural University, College of Science); Gary Bader (University of Toronto, Banting and Best Department of Medical Research); |
| Short Abstract: Ideally, quantitative protein interactions could be predicted based on sequence. We developed computational methods to accurately predict quantitative domain-peptide interactions using published and in-house quantitative data. We present preliminary work on predicting binding affinity of PDZ domains and WW domains. |
| Long Abstract:Click Here |
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| Poster L19 |
| Machine learning approaches for prediction of SH2-peptide interactions |
| Kousik Kundu- BIOSS, University of Freiburg |
| Rileen Sinha (University of Freiburg, Computer Science); Michael Reth (BIOSS, University of Freiburg, MPI of Immunobiology, Molecular Immunology); Michael Huber (RWTH Aachen University, Biochemistry and Molecular Immunology); Rolf Backofen (BIOSS, University of Freiburg, Computer Science); |
| Short Abstract: Computational identification of SH2-peptide interactions is an open problem with high relevance. In this study we applied combined machine learning approaches to identify interacting and non-interacting pairs of SH2 domains and phosphotyrosine containing peptides. We achieved an acceptable area under the ROC curve score of 0.89 with 10-fold cross-validation. |
| Long Abstract:Click Here |
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| Poster L20 |
| PIPs - Human Protein-Protein Interaction Prediction |
| Mark McDowall- University of Dundee |
| Michelle S. Scott (University of Dundee, Biochemistry and Drug Discovery); Geoffrey J. Barton (University of Dundee, Biochemistry and Drug Discovery); |
| Short Abstract: The PIPs framework is a method for predicting human protein-protein interactions via a naïve Bayesian method that is able to integrate numerous predictive features. In this poster we present new developments to the predictor including analysis of Gene Ontology terms, a new network analysis module and the PIPs webserver. |
| Long Abstract:Click Here |
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| Poster L21 |
| Interface & Interaction Networks |
| Billur Engin- Koc University |
| Billur Engin (Koc University, Computer Engineering); Attila Gursoy (Koc University, Center for Computational Biology and Bioinformatics ); |
| Short Abstract: This study focuses on "Interface & Interaction Networks", resulting from integration of binding site information into PPI networks. Here proteins are depicted as nodes, interactions as edges and interfaces as a different kind of node. Network attacks are performed to interfaces(deleting all similar interfaces to a chosen target interface). |
| Long Abstract:Click Here |
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| Poster L22 |
| Understanding how functional modules are organized from S. cerevisiae network data |
| Jimin Song- Princeton University |
| Mona Singh (Princeton University, Department of Computer Science & Lewis-Sigler Institute for Integrative Genomics); |
| Short Abstract: Genetic interactions have been recently determined in high-throughput manner. It has been shown that these interactions tend to occur between different functional modules. We consider genetic interactions in the context of other types of network data, and infer significant relationships between functional modules and show how these modules are organized within the cell. |
| Long Abstract:Click Here |
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| Poster L23 |
| Protein interface residues amenable to small molecule modulation predicted using structure |
| Fred Davis- HHMI/Janelia Farm Research Campus |
| No additional authors |
| Short Abstract: Small molecules that modulate protein-protein interactions are of great interest for chemical biology and therapeutics. I present a method to predict protein interface residues amenable to small molecule modulation by identifying residues with significant similarities to both protein and ligand binding sites of known structure. HOMOLOBIND is available at http://pibase.janelia.org/homolobind |
| Long Abstract:Click Here |
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| Poster L24 |
| SVM methods for prediction of Calmodulin binders and binding sites |
| Asa Ben-Hur- Colorado State University |
| Michael Hamilton (Colorado State University, Computer Science); Anireddy SN Reddy (Colorado State University, Biology); |
| Short Abstract: We present methods for predicting Calmodulin binding and binding sites based on support vector machines (SVMs) and structured SVMs. Using our methods to rank potential Calmodulin binding proteins in Arabidopsis gave a list of over 600 proteins that interact with Calmodulin at an expected false positive rate around 2%. |
| Long Abstract:Click Here |
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| Poster L25 |
| A GO-Based Search Engine for Protein-Protein Interactions |
| Kyungsook Han- Inha University |
| Byungkyu Park (Inha University, Computer Science and Engineering); Guangyu Cui (Inha University, Computer Science and Engineering); Chao Fang (Inha University, Computer Science and Engineering); Hyunjin Lee (Inha University, Computer Science and Engineering); |
| Short Abstract: We developed a new method for representing protein interactions and the Gene Ontology (GO) using modified Göodel numbers. Each GO term is assigned a prime number and the relation between the terms is represented by the product of the prime numbers. This representation is hidden from users but enables to efficiently find protein interactions. |
| Long Abstract:Click Here |
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| Poster L26 |
| Similarity in protein-protein interactions: Disappearing and reappearing act by key interface residues |
| Dmitry Korkin- University of Missouri |
| Dmitry Korkin (University of Missouri, Informatics Institute and Dept. of Computer Science); Nan Zhao (University of Missouri, Informatics Institute and Dept. of Computer Science); |
Short Abstract: Interactions between proteins play a key role in many cellular processes. Studying protein-protein interactions that share similar interaction interfaces may shed light on their evolution and could be helpful in elucidating the mechanisms behind stability and dynamics of the protein complexes. When two complexes share structurally similar subunits, the similarity of the interaction interfaces can be found through a structural superposition of the subunits. However, an accurate detection of similarity between the protein complexes containing subunits of unrelated structure remains an open problem.
Here, we present a machine learning approach to measuring interface similarity that does not depend on the superposition of the interacting subunit pairs. Specifically, two measures are determined: one, which is optimized to compare an artificial interface obtained by docking with a biological interface, and another optimized to compare two biological interfaces. Our analysis finds that the critical features allowing to distinguish between similar and non-similar interfaces include the relative difference of contact pair numbers, accessible surface area, and interface planarity. Next, the similarity measure is applied to 2,806 binary complexes to develop a hierarchical classification of protein-protein interactions followed by the analysis of similar interfaces, when the corresponding subunits are either homologous or structurally unrelated. The analysis has revealed an intriguing phenomenon where the relative positions of charged residues in similar interfaces are either swapped between the interacting binding sites or appear in different regions of the interfaces. The obtained results can be useful when studying convergent and divergent evolution of protein-protein interactions. |
| Long Abstract:Click Here |
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| Poster L27 |
| Towards the prediction of protein interaction partners using physical docking |
| Mark Wass- Imperial College London |
| Mark Wass (Imperial College London, Division of Molecular Biosciences); Gloria Fuentes (CNIO, Structral Biology and Biocomputing); Florencio Pazos (National Centre for Biotechnology (CNB-CSIC). Madrid. Spain , 3. Computational Systems Biology Group); Alfonso Valencia (CNIO, 1. Structural Biology and Biocomputing); |
| Short Abstract: We demonstrate that it is possible to use protein docking algorithms to detect interaction partners; something previously thought beyond their scope. This is done by comparing the docking models of known interactors with those of non-interacting proteins. Our approach can be developed into methods for interactome prediction. |
| Long Abstract:Click Here |
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Accepted Posters
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