Literature DB >> 12603024

Assessment of the reliability of protein-protein interactions and protein function prediction.

Minghua Deng1, Fengzhu Sun, Ting Chen.   

Abstract

As more and more high-throughput protein-protein interaction data are collected, the task of estimating the reliability of different data sets becomes increasingly important. In this paper, we present our study of two groups of protein-protein interaction data, the physical interaction data and the protein complex data, and estimate the reliability of these data sets using three different measurements: (1) the distribution of gene expression correlation coefficients, (2) the reliability based on gene expression correlation coefficients, and (3) the accuracy of protein function predictions. We develop a maximum likelihood method to estimate the reliability of protein interaction data sets according to the distribution of correlation coefficients of gene expression profiles of putative interacting protein pairs. The results of the three measurements are consistent with each other. The MIPS protein complex data have the highest mean gene expression correlation coefficients (0.256) and the highest accuracy in predicting protein functions (70% sensitivity and specificity), while Ito's Yeast two-hybrid data have the lowest mean (0.041) and the lowest accuracy (15% sensitivity and specificity). Uetz's data are more reliable than Ito's data in all three measurements, and the TAP protein complex data are more reliable than the HMS-PCI data in all three measurements as well. The complex data sets generally perform better in function predictions than do the physical interaction data sets. Proteins in complexes are shown to be more highly correlated in gene expression. The results confirm that the components of a protein complex can be assigned to functions that the complex carries out within a cell. There are three interaction data sets different from the above two groups: the genetic interaction data, the in-silico data and the syn-express data. Their capability of predicting protein functions generally falls between that of the Y2H data and that of the MIPS protein complex data. The supplementary information is available at the following Web site: http://www-hto.usc.edu/-msms/AssessInteraction/.

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Year:  2003        PMID: 12603024

Source DB:  PubMed          Journal:  Pac Symp Biocomput        ISSN: 2335-6928


  40 in total

1.  Protein complexes and functional modules in molecular networks.

Authors:  Victor Spirin; Leonid A Mirny
Journal:  Proc Natl Acad Sci U S A       Date:  2003-09-29       Impact factor: 11.205

2.  Predicting protein complex membership using probabilistic network reliability.

Authors:  Saurabh Asthana; Oliver D King; Francis D Gibbons; Frederick P Roth
Journal:  Genome Res       Date:  2004-05-12       Impact factor: 9.043

3.  Genome wide prediction of protein function via a generic knowledge discovery approach based on evidence integration.

Authors:  Jianghui Xiong; Simon Rayner; Kunyi Luo; Yinghui Li; Shanguang Chen
Journal:  BMC Bioinformatics       Date:  2006-05-25       Impact factor: 3.169

Review 4.  Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments.

Authors:  Alexey I Nesvizhskii
Journal:  Proteomics       Date:  2012-05       Impact factor: 3.984

5.  Gene networks in Drosophila melanogaster: integrating experimental data to predict gene function.

Authors:  James C Costello; Mehmet M Dalkilic; Scott M Beason; Jeff R Gehlhausen; Rupali Patwardhan; Sumit Middha; Brian D Eads; Justen R Andrews
Journal:  Genome Biol       Date:  2009-09-16       Impact factor: 13.583

6.  Identifying protein complexes directly from high-throughput TAP data with Markov random fields.

Authors:  Wasinee Rungsarityotin; Roland Krause; Arno Schödl; Alexander Schliep
Journal:  BMC Bioinformatics       Date:  2007-12-19       Impact factor: 3.169

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.  Systems integration of biodefense omics data for analysis of pathogen-host interactions and identification of potential targets.

Authors:  Peter B McGarvey; Hongzhan Huang; Raja Mazumder; Jian Zhang; Yongxing Chen; Chengdong Zhang; Stephen Cammer; Rebecca Will; Margie Odle; Bruno Sobral; Margaret Moore; Cathy H Wu
Journal:  PLoS One       Date:  2009-09-25       Impact factor: 3.240

9.  Improved homology-driven computational validation of protein-protein interactions motivated by the evolutionary gene duplication and divergence hypothesis.

Authors:  Christian Frech; Michael Kommenda; Viktoria Dorfer; Thomas Kern; Helmut Hintner; Johann W Bauer; Kamil Onder
Journal:  BMC Bioinformatics       Date:  2009-01-19       Impact factor: 3.169

10.  Chromatin regulation and gene centrality are essential for controlling fitness pleiotropy in yeast.

Authors:  Linqi Zhou; Xiaotu Ma; Michelle N Arbeitman; Fengzhu Sun
Journal:  PLoS One       Date:  2009-11-30       Impact factor: 3.240

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