Literature DB >> 17237047

Structure-based evaluation of in silico predictions of protein-protein interactions using Comparative Docking.

Simon J Cockell1, Baldo Oliva, Richard M Jackson.   

Abstract

MOTIVATION: Due to the limitations in experimental methods for determining binary interactions and structure determination of protein complexes, the need exists for computational models to fill the increasing gap between genome sequence information and protein annotation. Here we describe a novel method that uses structural models to reduce a large number of in silico predictions to a high confidence subset that is amenable to experimental validation.
RESULTS: A two-stage evaluation procedure was developed, first, a sequence-based method assessed the conservation of protein interface patches used in the original in silico prediction method, both in terms of position within the primary sequence, and in terms of sequence conservation. When applying the most stringent conditions it was found that 20.5% of the data set being assessed passed this test. Secondly, a high-throughput structure-based docking evaluation procedure assessed the soundness of three dimensional models produced for the putative interactions. Of the data set being assessed, 8264 interactions or over 70% could be modelled in this way, and 27% of these can be considered 'valid' by the applied criteria. In all, 6.9% of the interactions passed both the tests and can be considered to be a high confidence set of predicted interactions, several of which are described. AVAILABILITY: http://bioinformatics.leeds.ac.uk/~bmb4sjc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Substances:

Year:  2007        PMID: 17237047     DOI: 10.1093/bioinformatics/btl661

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


  4 in total

1.  SPPS: a sequence-based method for predicting probability of protein-protein interaction partners.

Authors:  Xinyi Liu; Bin Liu; Zhimin Huang; Ting Shi; Yingyi Chen; Jian Zhang
Journal:  PLoS One       Date:  2012-01-26       Impact factor: 3.240

2.  HOMCOS: a server to predict interacting protein pairs and interacting sites by homology modeling of complex structures.

Authors:  Naoshi Fukuhara; Takeshi Kawabata
Journal:  Nucleic Acids Res       Date:  2008-04-28       Impact factor: 16.971

3.  Virtual interactomics of proteins from biochemical standpoint.

Authors:  Jaroslav Kubrycht; Karel Sigler; Pavel Souček
Journal:  Mol Biol Int       Date:  2012-08-08

4.  Beyond tissueInfo: functional prediction using tissue expression profile similarity searches.

Authors:  Daniel Aguilar; Lucy Skrabanek; Steven S Gross; Baldo Oliva; Fabien Campagne
Journal:  Nucleic Acids Res       Date:  2008-05-15       Impact factor: 16.971

  4 in total

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