Literature DB >> 18202838

Computational methods for predicting protein-protein interactions.

Sylvain Pitre1, Md Alamgir, James R Green, Michel Dumontier, Frank Dehne, Ashkan Golshani.   

Abstract

Protein-protein interactions (PPIs) play a critical role in many cellular functions. A number of experimental techniques have been applied to discover PPIs; however, these techniques are expensive in terms of time, money, and expertise. There are also large discrepancies between the PPI data collected by the same or different techniques in the same organism. We therefore turn to computational techniques for the prediction of PPIs. Computational techniques have been applied to the collection, indexing, validation, analysis, and extrapolation of PPI data. This chapter will focus on computational prediction of PPI, reviewing a number of techniques including PIPE, developed in our own laboratory. For comparison, the conventional large-scale approaches to predict PPIs are also briefly discussed. The chapter concludes with a discussion of the limitations of both experimental and computational methods of determining PPIs.

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Year:  2008        PMID: 18202838     DOI: 10.1007/10_2007_089

Source DB:  PubMed          Journal:  Adv Biochem Eng Biotechnol        ISSN: 0724-6145            Impact factor:   2.635


  20 in total

1.  Large-scale de novo prediction of physical protein-protein association.

Authors:  Antigoni Elefsinioti; Ömer Sinan Saraç; Anna Hegele; Conrad Plake; Nina C Hubner; Ina Poser; Mihail Sarov; Anthony Hyman; Matthias Mann; Michael Schroeder; Ulrich Stelzl; Andreas Beyer
Journal:  Mol Cell Proteomics       Date:  2011-08-11       Impact factor: 5.911

2.  Computational prediction of protein-protein interactions.

Authors:  Tobias Ehrenberger; Lewis C Cantley; Michael B Yaffe
Journal:  Methods Mol Biol       Date:  2015

Review 3.  Global approaches to study protein-protein interactions among viruses and hosts.

Authors:  Jorge Mendez-Rios; Peter Uetz
Journal:  Future Microbiol       Date:  2010-02       Impact factor: 3.165

4.  Recent advances in clustering methods for protein interaction networks.

Authors:  Jianxin Wang; Min Li; Youping Deng; Yi Pan
Journal:  BMC Genomics       Date:  2010-12-01       Impact factor: 3.969

5.  Open source tool for prediction of genome wide protein-protein interaction network based on ortholog information.

Authors:  Chandra Sekhar Pedamallu; Janos Posfai
Journal:  Source Code Biol Med       Date:  2010-08-04

6.  Mapping and identification of a potential candidate gene for a novel maturity locus, E10, in soybean.

Authors:  Bahram Samanfar; Stephen J Molnar; Martin Charette; Andrew Schoenrock; Frank Dehne; Ashkan Golshani; François Belzile; Elroy R Cober
Journal:  Theor Appl Genet       Date:  2016-11-10       Impact factor: 5.699

7.  The development of a universal in silico predictor of protein-protein interactions.

Authors:  Guilherme T Valente; Marcio L Acencio; Cesar Martins; Ney Lemke
Journal:  PLoS One       Date:  2013-05-31       Impact factor: 3.240

8.  Functional classification of proteins based on projection of amino acid sequences: application for prediction of protein kinase substrates.

Authors:  Boris Sobolev; Dmitry Filimonov; Alexey Lagunin; Alexey Zakharov; Olga Koborova; Alexander Kel; Vladimir Poroikov
Journal:  BMC Bioinformatics       Date:  2010-06-10       Impact factor: 3.169

9.  Short Co-occurring Polypeptide Regions Can Predict Global Protein Interaction Maps.

Authors:  Sylvain Pitre; Mohsen Hooshyar; Andrew Schoenrock; Bahram Samanfar; Matthew Jessulat; James R Green; Frank Dehne; Ashkan Golshani
Journal:  Sci Rep       Date:  2012-01-30       Impact factor: 4.379

10.  Reconstructing phylogenetic tree using a protein-protein interaction technique.

Authors:  Shamita Malik; Dolly Sharma; Sunil Kumar Khatri
Journal:  IET Nanobiotechnol       Date:  2017-12       Impact factor: 1.847

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