Interaction Networks of Proteins

Shailesh V Date, University of Pennsylvania School of Medicine, Philadelphia, USA
Guang Chen, University of Pennsylvania, Philadelphia, USA

Published online: September 2007

DOI: 10.1002/9780470015902.a0020203

Full Article on Wiley Online Library

Proteins are often the ultimate expression of information encoded in the DNA (deoxyribonucleic acid). Proteins interact with other proteins, nucleic acids, and a variety of small molecules to affect cellular function. Such interactions assume the form of a vast network, portions of which are activated in a spatial and temporal manner. Understanding the makeup, properties and behaviour of this network promises to reveal many important details of the biology of organisms, ultimately helping better our knowledge of molecular and biochemical events that take place at the cellular level.

Keywords: protein-protein interactions; functional interactions; protein interaction networks; protein-DNA regulation networks; transcriptional regulatory networks; COGRIM

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Article Title:	Interaction Networks of Proteins
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	Figure 1. Computational and experimental methods for generating protein–protein interaction data. Both direct and indirect methods of reporting interactions, as described in the text, are illustrated.
	Figure 2. (a) A network of functional protein–protein interactions in the human malarial parasite Plasmodium falciparum. Information from three different sources was combined within a Bayesian framework, and the resulting interaction set was further filtered for false positives using extraneous gene expression data (Date and Stoeckert, 2006; for data, see http://cbil.upenn.edu/plasmoMAP/). The map illustrates interactions between the top 1000 highest scoring interacting pairs, visualized using Cytoscape (Shannon et al., 2003). Orange nodes in the figure indicate uncharacterized proteins; red edges indicate that the interaction was observed in all three sources, while blue links indicate presence in two sources. Edges in the graph are unweighted, i.e. strength of the link is not correlated with edge length. (b) A sub-network of ‘helicases’ (MAL7P1.113, PF14_0436, PF14_0437, PFL0100c, PF10_0209, PFE1085w, PFI0910w, PFL2010c, PF14_0429) is highlighted, suggesting that hypothetical proteins PF11_0077, PFC0465c and PF11_0207 may also interact with DNA during events that require separation of DNA strands, such as during replication or protein synthesis.
	Figure 3. An illustration of a typical transcriptional regulatory network or a regulatory circuit. The network was generated based on data available for interactions between 16 different genes and the serum response factor (SRF) (Chen et al., 2007). Three genes participating in the network are co-factors of SRF (GATA4 , NIKX25 and MYOD1, indicated in the light blue box). Target genes are clustered based on functional annotation. Information used in this figure is located at: http://www.cbil.upenn.edu/COGRIM.

Interaction Networks of Proteins

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