Bioinformatics Analysis for Interactive Proteomics

互联网2013-12-31

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Abstract
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Abstract

High?throughput protein?protein interaction data are becoming a foundation for new biological discoveries. A major challenge is to manage, analyze, and model these data. In this unit several databases are described that are used to store, query, and visualize protein?protein interaction data. Comparison between experimental techniques reveals that each high?throughput technique has its limitations in detecting certain types of interactions; however, the techniques are generally complementary. In silico prediction methods for protein?protein interactions can expand the scope of experimental data and increase the confidence of certain interactions. Use of protein?protein interaction networks, preferably integrating them with other types of data, allows assignment of cellular functions to novel proteins and derivation of new biological pathways. As demonstrated in this unit, bioinformatics can be used to transform protein?protein interaction data from noisy information into knowledge of cellular mechanisms.

Keywords: protein?protein interaction; high?throughput data; yeast two hybrid; protein complex; proteome; bioinformatics

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Figure 25.1.1 (A ) Visualization of 8286 protein‐protein interactions from the BIND database generated with the Pajek visualization tool (http://vlado.fmf.uni‐lj.si/pub/networks/pajek/). (B ) Cytoscape (http://www.cytoscape.org/) graphic output for 359 protein‐protein interactions in yeast detected by two‐hybrid assays.

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Figure 25.1.2 The protein interaction map in yeast around Nup100p from PathCalling (http://curatools.curagen.com/pathcalling_portal/). A gene is represented as a node and a protein‐protein interaction is indicated as an edge.

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Figure 25.1.3 Three Cmd1p related protein complexes identified by mass spectrometry. The annotated Cmd1p interacting partners (i.e., proteins with known functions), shown in dark color, are derived from experimental results reviewed in Cyert ().

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Figure 25.1.4 Output generated by GENFAS (http://digbio.missouri.edu/genefas/) to predict the function of the hypothetical gene YER079W . “Index” indicates the gene ontology (GO) hierarchical level. “Reliability Score” is used to rank the predicted functions. “Probability” gives the likelihood of prediction accuracy. “GO identifier” is the entry ID for GO.

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Figure 25.1.5 Relationship between biological pathway and protein‐protein interaction data. (A ) The MAPK signaling pathway for filamentation (i.e., the connections between genes) taken from KEGG. (B ) A MAPK signal transduction pathway constructed using protein interaction data.

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Figure 25.1.6 Output generated by the Web server http://digbio.missouri.edu/genepath showing a predicted biological pathway using products of YBL062W and YNL119W as the two terminal proteins. “GO MF ID” indicates GO molecular function identification codes. The numbers in the Function row indicate GO hierarchical levels.

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Literature Cited

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