Interaction Networks of Proteins


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

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, ; for data, see The map illustrates interactions between the top 1000 highest scoring interacting pairs, visualized using Cytoscape (Shannon et al., ). 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., ). 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:



Aloy P and Russel RB (2002) Interrogating protein interaction networks through structural biology. Proceedings of the National Academy of Sciences of the USA 99: 5896–5901.

Altschul SF, Gish W, Miller W et al. (1990) Basic local alignment search tool. Journal of Molecular Biology 215: 403–410.

Bar‐Joseph Z, Gerber GK, Lee TI et al. (2003) Computational discovery of gene modules and regulatory networks. Nature Biotechnology 21: 1337–1342.

Beer M and Tavazoie S (2004) Predicting gene expression from sequence. Cell 117: 185–198.

Bergmann S, Ihmels J and Barkai N (2004) Similarities and differences in genome‐wide expression data of six organisms. Public Library of Science Computational Biology 2(1): e9.

Boulesteix AL and Strimmer K (2005) Predicting transcription factor activities from combined analysis of microarray and chip data: a partial least squares approach. Theoretical Biology and Medical Modelling 2: 23.

Bussemaker H, Li H and Siggia E (2001) Regulatory element detection using correlation with expression. Nature Genetics 27: 167–171.

Chen G, Jensen ST and Stoeckert CJ Jr (2007) Clustering of genes into regulons using integrated modeling‐COGRIM. Genome Biology 8: R4.

Dandekar T, Snel B, Huynen M et al. (1998) Conservation of gene order: a fingerprint of proteins that physically interact. Trends in Biochemical Sciences 23: 324–328.

Date SV and Marcotte EM (2003) Discovery of uncharacterized cellular systems by genome‐wide analysis of functional linkages. Nature Biotechnology 21: 1055–1062.

Date SV and Stoeckert CJ Jr (2006) Computational modeling of the Plasmodium falciparum interactome reveals protein function on a genome‐wide scale. Genome Research 16: 542–549.

Fields S and Song O (1989) A novel genetic system to detect protein–protein interactions. Nature 340: 245–246.

Gao F, Foat B and Bussemaker H (2004) Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data. BMC Bioinformatics 5: 1.

Gavin AC, Bosche M, Krause R et al. (2002) Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415: 141–147.

Giot L, Bader JS, Brouwer C et al. (2003) A protein interaction map of Drosophila melanogaster. Science 302: 1727–1736.

Ho Y, Gruhler A, Heilbut A et al. (2002) Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415: 180–183.

Ito T, Chiba T, Ozawa R et al. (2001) A comprehensive two‐hybrid analysis to explore the yeast protein interactome. Proceedings of the National Academy of Sciences of the USA 98: 4569–4574.

Jansen R, Yu H, Greenbaum D et al. (2003) A Bayesian networks approach for predicting protein–protein interactions from genomic data. Science 302: 449–453.

Jenssen TK, Laegreid A, Komorowski J et al. (2001) A literature network of human genes for high‐throughput analysis of gene expression. Nature Genetics 28: 21–28.

Jeong H, Tombor B, Albert R et al. (2000) The large‐scale organization of metabolic networks. Nature 407: 651–654.

Jiang R, Tu Z, Chen T et al. (2006) Network motif identification in stochastic networks. Proceedings of the National Academy of Sciences of the USA 103: 9404–9409.

LaCount DJ, Vignali M, Chettier R et al. (2005) A protein interaction network of the malaria parasite Plasmodium falciparum. Nature 438: 103–107.

Lee I, Date SV, Adai AT et al. (2004) A probabilistic functional network of yeast genes. Science 306: 1555–1558.

Lee T, Rinaldi N, Robert F et al. (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298: 763–764.

Lemmens K, Dhollander T, Bie TD et al. (2006) Inferring transcriptional modules from chip‐chip, motif and microarray data. Genome Biology 7 doi:10.1186/gb–2006–7–5–r37.

Li S, Armstrong CM, Bertin N et al. (2004) A map of the interactome network of the metazoan C. elegans. Science 303: 540–543.

Liao JC, Boscolo R, Yang YL et al. (2003) Network component analysis: Reconstruction of regulatory signals in biological systems. Proceedings of the National Academy of Sciences of the USA 100: 15522–15527.

Marcotte EM, Pellegrini M, Ng H‐L et al. (1999) Detecting protein function and protein–protein interactions from genome sequences. Science 285: 751–753.

Matthews LR, Vaglio P, Reboul J et al. (2001) Identification of potential interaction networks using sequence‐based searches for conserved protein–protein interactions or ‘interologs’. Genome Research 11: 2120–2126.

Milo R, Shen‐Orr S, Itzkovitz S et al. (2002) Network motifs: simple building blocks of complex networks. Science 298: 824–827.

Nguyen DH and D'haeseleer P (2006) Deciphering principles of transcription regulation in eukaryotic genomes. Molecular Systems Biology 2 doi:10.1038/msb4100054.

Overbeek R, Fonstein M, D'Souza M et al. (1999a) Use of contiguity on the chromosome to predict functional coupling. In Silico Biology 1: 93–108.

Overbeek R, Fonstein M, D'Souza M et al. (1999b) The use of gene clusters to infer functional coupling. Proceedings of the National Academy of Sciences of the USA 96: 2896–2901.

Pellegrini M, Marcotte EM, Thompson MJ et al. (1999) Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proceedings of the National Academy of Sciences of the USA 96: 4285–4288.

Pennacchio L and Rubin E (2001) Genomic strategies to identify mammalian regulatory sequences. Nature Review Genetics 2: 100–109.

Puig O, Caspary F, Rigaut G et al. (2001) The tandem affinity purification (TAP) method: a general procedure of protein complex purification. Methods 24: 218–229.

Remenyi A, Good MC and Lim WA (2006) Docking interactions in protein kinase and phosphatase networks. Current Opinion in Structural Biology 16: 676–685.

Ren B, Robert F, Wyrick JJ et al. (2000) Genome‐wide location and function of DNA binding proteins. Science 290: 2306–2309.

Rhee SY, Beavis W, Berardini TZ et al. (2003) The Arabidopsis Information Resource (TAIR): a model organism database providing a centralized, curated gateway to Arabidopsis biology, research materials and community. Nucleic Acids Research 31: 224–228.

Segal E, Shapira M, Regev A et al. (2003) Module networks: identifying regulatory modules and their condition‐specific regulators from gene expression data. Nature Genetics 34: 166–176.

Segal E, Yelensky R and Koller D (2001) Genome‐wide discovery of transcriptional modules from DNA sequence and gene expression. Bioinformatics 19(Suppl.1): 273–282.

Shannon P, Markiel A, Ozier O et al. (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research 13: 2498–2504.

Smith GR and Sternberg MJE (2002) Prediction of protein–protein interactions by docking methods. Current Opinion in Structural Biology 12: 28–35.

Stuart JM, Segal E, Koller D et al. (2003) A gene‐coexpression network for global discovery of conserved genetic modules. Science 302: 249–255.

Sun Q, Chen G, Streb JW et al. (2006) Defining the mammalian cargome. Genome Research 16: 197–207.

Uetz P, Giot L, Cagney G et al. (2000) A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae. Nature 403: 623–627.

Walhout AJM, Sordella R, Lu X et al. (2000) Protein interaction mapping in C. elegans using proteins involved in vulval development. Science 287: 116–122.

Wuchty S (2004) Evolution and topology in the yeast protein interaction network. Genome Research 14: 1310–1314.

Xing B and Van Der Laan MJ (2005) A statistical method for constructing transcriptional regulatory networks using gene expression and sequence data. Journal of Computational Biology 12: 229–246.

Yang YL, Suen J, Brynildsen MP et al. (2005) Inferring yeast cell cycle regulators and interactions using transcription factor activities. BMC Genomics 6: 90.

Yu H, Luscombe NM, Lu HX et al. (2004) Annotation transfer between genomes: protein–protein interologs and protein–DNA regulogs. Genome Research 14: 1107–1118.

Further Reading

Blais A and Dynlacht BD (2005) Constructing transcriptional regulatory networks. Genes & Development 19: 1499–1511.

Davidson EH, Rast JP, Oliveri P et al. (2002) A genomic regulatory network for development. Science 295: 1669–1678.

Harbison CT, Gordon DB, Lee TI et al. (2004) Transcriptional regulatory code of a eukaryotic genome. Nature 431: 99–104.

Marcotte EM, Pellegrini M, Thompson MJ et al. (1999) A combined algorithm for genome‐wide prediction of protein function. Nature 402: 83–86.

von Mering C, Krause R, Snel B et al. (2002) Comparative assessment of large‐scale data sets of protein–protein interactions. Nature 417: 399–403.

Stagljar I (2003) Finding partners: emerging protein interaction technologies applied to signaling networks. Science Signal Transduction Knowledge Environment 213: 56.

Watts DJ and Strogatz SH (1998) Collective dynamics of ‘small‐world’ networks. Nature 393: 440–442.

Contact Editor close
Submit a note to the editor about this article by filling in the form below.

* Required Field

How to Cite close
Date, Shailesh V, and Chen, Guang(Sep 2007) Interaction Networks of Proteins. In: eLS. John Wiley & Sons Ltd, Chichester. [doi: 10.1002/9780470015902.a0020203]