Literature DB >> 16204844

Prediction of physical protein-protein interactions.

András Szilágyi1, Vera Grimm, Adrián K Arakaki, Jeffrey Skolnick.   

Abstract

Many essential cellular processes such as signal transduction, transport, cellular motion and most regulatory mechanisms are mediated by protein-protein interactions. In recent years, new experimental techniques have been developed to discover the protein-protein interaction networks of several organisms. However, the accuracy and coverage of these techniques have proven to be limited, and computational approaches remain essential both to assist in the design and validation of experimental studies and for the prediction of interaction partners and detailed structures of protein complexes. Here, we provide a critical overview of existing structure-independent and structure-based computational methods. Although these techniques have significantly advanced in the past few years, we find that most of them are still in their infancy. We also provide an overview of experimental techniques for the detection of protein-protein interactions. Although the developments are promising, false positive and false negative results are common, and reliable detection is possible only by taking a consensus of different experimental approaches. The shortcomings of experimental techniques affect both the further development and the fair evaluation of computational prediction methods. For an adequate comparative evaluation of prediction and high-throughput experimental methods, an appropriately large benchmark set of biophysically characterized protein complexes would be needed, but is sorely lacking.

Mesh:

Year:  2005        PMID: 16204844     DOI: 10.1088/1478-3975/2/2/S01

Source DB:  PubMed          Journal:  Phys Biol        ISSN: 1478-3967            Impact factor:   2.583


  31 in total

1.  Community of protein complexes impacts disease association.

Authors:  Qianghu Wang; Weisha Liu; Shangwei Ning; Jingrun Ye; Teng Huang; Yan Li; Peng Wang; Hongbo Shi; Xia Li
Journal:  Eur J Hum Genet       Date:  2012-05-02       Impact factor: 4.246

Review 2.  Proteome-wide prediction of protein-protein interactions from high-throughput data.

Authors:  Zhi-Ping Liu; Luonan Chen
Journal:  Protein Cell       Date:  2012-06-22       Impact factor: 14.870

3.  Association of putative concave protein-binding sites with the fluctuation behavior of residues.

Authors:  Asli Ertekin; Ruth Nussinov; Turkan Haliloglu
Journal:  Protein Sci       Date:  2006-10       Impact factor: 6.725

4.  A Consensus Data Mining secondary structure prediction by combining GOR V and Fragment Database Mining.

Authors:  Taner Z Sen; Haitao Cheng; Andrzej Kloczkowski; Robert L Jernigan
Journal:  Protein Sci       Date:  2006-09-25       Impact factor: 6.725

5.  In silico modeling of pH-optimum of protein-protein binding.

Authors:  Rooplekha C Mitra; Zhe Zhang; Emil Alexov
Journal:  Proteins       Date:  2010-12-22

6.  M-TASSER: an algorithm for protein quaternary structure prediction.

Authors:  Huiling Chen; Jeffrey Skolnick
Journal:  Biophys J       Date:  2007-09-28       Impact factor: 4.033

7.  Structural templates for comparative protein docking.

Authors:  Ivan Anishchenko; Petras J Kundrotas; Alexander V Tuzikov; Ilya A Vakser
Journal:  Proteins       Date:  2015-06-13

8.  SpaK/SpaR two-component system characterized by a structure-driven domain-fusion method and in vitro phosphorylation studies.

Authors:  Anu Chakicherla; Carol L Ecale Zhou; Martha Ligon Dang; Virginia Rodriguez; J Norman Hansen; Adam Zemla
Journal:  PLoS Comput Biol       Date:  2009-06-05       Impact factor: 4.475

9.  Protein-protein docking using region-based 3D Zernike descriptors.

Authors:  Vishwesh Venkatraman; Yifeng D Yang; Lee Sael; Daisuke Kihara
Journal:  BMC Bioinformatics       Date:  2009-12-09       Impact factor: 3.169

10.  Protein-protein interaction based on pairwise similarity.

Authors:  Nazar Zaki; Sanja Lazarova-Molnar; Wassim El-Hajj; Piers Campbell
Journal:  BMC Bioinformatics       Date:  2009-05-17       Impact factor: 3.169

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