Literature DB >> 12761053

Integrative approach for computationally inferring protein domain interactions.

See-Kiong Ng1, Zhuo Zhang, Soon-Heng Tan.   

Abstract

MOTIVATION: The current need for high-throughput protein interaction detection has resulted in interaction data being generated en masse through such experimental methods as yeast-two-hybrids and protein chips. Such data can be erroneous and they often do not provide adequate functional information for the detected interactions. Therefore, it is useful to develop an in silico approach to further validate and annotate the detected protein interactions.
RESULTS: Given that protein-protein interactions involve physical interactions between protein domains, domain-domain interaction information can be useful for validating, annotating, and even predicting protein interactions. However, large-scale, experimentally determined domain-domain interaction data do not exist. Here, we describe an integrative approach to computationally derive putative domain interactions from multiple data sources, including protein interactions, protein complexes, and Rosetta Stone sequences. We further prove the usefulness of such an integrative approach by applying the derived domain interactions to predict and validate protein-protein interactions. AVAILABILITY: A database of putative protein domain interactions derived using the method described in this paper is available at http://interdom.lit.org.sg.

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Year:  2003        PMID: 12761053     DOI: 10.1093/bioinformatics/btg118

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


  55 in total

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2.  Identifying gene interaction networks.

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Journal:  Methods Mol Biol       Date:  2012

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Journal:  PLoS One       Date:  2009-12-07       Impact factor: 3.240

6.  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

7.  Assessing reliability of protein-protein interactions by integrative analysis of data in model organisms.

Authors:  Xiaotong Lin; Mei Liu; Xue-wen Chen
Journal:  BMC Bioinformatics       Date:  2009-04-29       Impact factor: 3.169

8.  Brief overview of bioinformatics activities in Singapore.

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Journal:  PLoS Comput Biol       Date:  2009-09-25       Impact factor: 4.475

9.  DASMIweb: online integration, analysis and assessment of distributed protein interaction data.

Authors:  Hagen Blankenburg; Fidel Ramírez; Joachim Büch; Mario Albrecht
Journal:  Nucleic Acids Res       Date:  2009-06-05       Impact factor: 16.971

10.  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

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