Literature DB >> 15509600

Statistical analysis of domains in interacting protein pairs.

Tom M W Nye1, Carlo Berzuini, Walter R Gilks, M Madan Babu, Sarah A Teichmann.   

Abstract

MOTIVATION: Several methods have recently been developed to analyse large-scale sets of physical interactions between proteins in terms of physical contacts between the constituent domains, often with a view to predicting new pairwise interactions. Our aim is to combine genomic interaction data, in which domain-domain contacts are not explicitly reported, with the domain-level structure of individual proteins, in order to learn about the structure of interacting protein pairs. Our approach is driven by the need to assess the evidence for physical contacts between domains in a statistically rigorous way.
RESULTS: We develop a statistical approach that assigns p-values to pairs of domain superfamilies, measuring the strength of evidence within a set of protein interactions that domains from these superfamilies form contacts. A set of p-values is calculated for SCOP superfamily pairs, based on a pooled data set of interactions from yeast. These p-values can be used to predict which domains come into contact in an interacting protein pair. This predictive scheme is tested against protein complexes in the Protein Quaternary Structure (PQS) database, and is used to predict domain-domain contacts within 705 interacting protein pairs taken from our pooled data set.

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Year:  2004        PMID: 15509600     DOI: 10.1093/bioinformatics/bti086

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  44 in total

1.  Identification of genomic features using microsyntenies of domains: domain teams.

Authors:  Sophie Pasek; Anne Bergeron; Jean-Loup Risler; Alexandra Louis; Emmanuelle Ollivier; Mathieu Raffinot
Journal:  Genome Res       Date:  2005-05-17       Impact factor: 9.043

2.  Co-evolutionary analysis of domains in interacting proteins reveals insights into domain-domain interactions mediating protein-protein interactions.

Authors:  Raja Jothi; Praveen F Cherukuri; Asba Tasneem; Teresa M Przytycka
Journal:  J Mol Biol       Date:  2006-08-01       Impact factor: 5.469

Review 3.  Embryonic stem cell interactomics: the beginning of a long road to biological function.

Authors:  Maram Yousefi; Vahid Hajihoseini; Woojin Jung; Batol Hosseinpour; Hassan Rassouli; Bonghee Lee; Hossein Baharvand; KiYoung Lee; Ghasem Hosseini Salekdeh
Journal:  Stem Cell Rev Rep       Date:  2012-12       Impact factor: 5.739

4.  Knowledge-guided inference of domain-domain interactions from incomplete protein-protein interaction networks.

Authors:  Mei Liu; Xue-Wen Chen; Raja Jothi
Journal:  Bioinformatics       Date:  2009-08-10       Impact factor: 6.937

5.  Evolutionary pressure on the topology of protein interface interaction networks.

Authors:  Margaret E Johnson; Gerhard Hummer
Journal:  J Phys Chem B       Date:  2013-06-14       Impact factor: 2.991

6.  Prediction of interactions between HIV-1 and human proteins by information integration.

Authors:  Oznur Tastan; Yanjun Qi; Jaime G Carbonell; Judith Klein-Seetharaman
Journal:  Pac Symp Biocomput       Date:  2009

7.  Multi-level learning: improving the prediction of protein, domain and residue interactions by allowing information flow between levels.

Authors:  Kevin Y Yip; Philip M Kim; Drew McDermott; Mark Gerstein
Journal:  BMC Bioinformatics       Date:  2009-08-05       Impact factor: 3.169

8.  Critical assessment of sequence-based protein-protein interaction prediction methods that do not require homologous protein sequences.

Authors:  Yungki Park
Journal:  BMC Bioinformatics       Date:  2009-12-14       Impact factor: 3.169

9.  Triangle network motifs predict complexes by complementing high-error interactomes with structural information.

Authors:  Bill Andreopoulos; Christof Winter; Dirk Labudde; Michael Schroeder
Journal:  BMC Bioinformatics       Date:  2009-06-27       Impact factor: 3.169

10.  Interface-resolved network of protein-protein interactions.

Authors:  Margaret E Johnson; Gerhard Hummer
Journal:  PLoS Comput Biol       Date:  2013-05-16       Impact factor: 4.475

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