Primer on Protein–Protein Interaction Maps


Interactions among proteins mediate regulatory, mechanical, structural and transport functions and are fundamental in the modulation of all cellular activities. About 15 years ago, it was first noted that physical interactions link most if not all proteins to each other in complex networks. The analysis of maps representing current knowledge about these molecular networks can provide insights that range from specific hypotheses on the function of a given protein to system‐level properties on the organisation or evolution of biological systems. Here, we provide an updated introduction to the numerous ways and available tools by which protein–protein interaction maps can be analysed, and caveats that need to be kept in mind.

Key Concepts

  • Protein–protein interaction maps are important tools to develop functional hypothesis, contextualise ‘omic’ gene lists, analyse system properties and systems evolution and provide an important basis for analysing system dynamics.
  • Networks can be assembled from small‐scale literature data, bioinformatic prediction and experimental high‐throughput technologies of which affinity‐purification followed by mass spectrometry or binary yeast‐based assays are most common.
  • Global and local properties of protein–protein interaction maps can be influenced by sociological, technological and experimental biases that need to be taken into account.
  • High‐throughput technologies can provide data that are in quality at least comparable to the high‐quality fraction from small‐scale studies in public databases.
  • Carefully benchmarked and appropriate quality controls must be implemented for all approaches.
  • Binary and co‐complex methodologies give rise to differential and complementary sets of protein interactions.
  • Bioinformatic tools are available that enable nonspecialists to analyse and use protein–protein interaction networks.

Keywords: protein–protein interactions; protein networks; molecular networks; interactome; yeast‐two‐hybrid; mass spectrometry; systems analysis; bioinformatics

Figure 1. Approaches to measure protein–protein interactions affect the type of detected interactions and bias the resulting network maps. Y2H assays identify predominantly direct binary interactions among two proteins X‐DB (DNA‐binding domain) and Y‐AD (activation domain) in an setting. Affinity purification followed by mass spectrometry (AP‐MS) allows selective capture of the target protein (e.g. Y‐TAP) and their directly (X) or indirectly associated partners (grey circles), which may or may not be in the same complex. In the constructed network, it is not possible to differentiate between direct or indirect associations. Literature‐curated networks contain interactions at different levels of documentation from both binary and protein complex‐derived methods. Popular proteins tend to be more studied and may have more interactions due to this social bias.
Figure 2. Strategies to analyse interaction network maps. Protein networks can be combined with additional information. Integration with functional annotations (left panel) may allow development of functional hypothesis for uncharacterised proteins (red nodes) interacting with several functionally characterised proteins (blue nodes). Integration of ‘omic’ datasets (middle panel), for example transcriptomic, phosphoproteomic or GWAS data, can result in the identification of putative functional modules. By analysing network topology using mathematical tools (right panel), it is possible to identify protein nodes that have a central network position, for example high connectivity or high betweenness, and relating this back to other biological features can provide insights into the systems‐level organisation of cells or organisms. Often several of these approaches are combined.


Akula N, Baranova A, Seto D, et al. (2011) A network‐based approach to prioritize results from genome‐wide association studies. PLoS One 6: e24220.

Albert R, Jeong H and Barabasi AL (2000) Error and attack tolerance of complex networks. Nature 406: 378–382.

Arabidopsis Interactome Mapping Consortium (2011) Evidence for network evolution in an Arabidopsis interactome map. Science 333: 601–607.

Beisser D, Klau GW, Dandekar T, Muller T and Dittrich MT (2010) BioNet: an R‐Package for the functional analysis of biological networks. Bioinformatics 26: 1129–1130.

Berggard T, Linse S and James P (2007) Methods for the detection and analysis of protein‐protein interactions. Proteomics 7: 2833–2842.

Betts MJ, Lu Q, Jiang Y, et al. (2015) Mechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions. Nucleic Acids Research 43: e10.

Bouwmeester T, Bauch A, Ruffner H, et al. (2004) A physical and functional map of the human TNF‐alpha/NF‐kappa B signal transduction pathway. Nature Cell Biology 6: 97–105.

Braun P, Tasan M, Dreze M, et al. (2009) An experimentally derived confidence score for binary protein‐protein interactions. Nature Methods 6: 91–97.

Braun P (2012) Interactome mapping for analysis of complex phenotypes: insights from benchmarking binary interaction assays. Proteomics 12: 1499–1518.

Bultinck J, Lievens S and Tavernier J (2012) Protein‐protein interactions: network analysis and applications in drug discovery. Current Pharmaceutical Design 18: 4619–4629.

Caufield JH, Sakhawalkar N and Uetz P (2012) A comparison and optimization of yeast two‐hybrid systems. Methods 58: 317–324.

Cline MS, Smoot M, Cerami E, et al. (2007) Integration of biological networks and gene expression data using Cytoscape. Nature Protocols 2: 2366–2382.

Collins SR (2007) Towards a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae. Molecular and Cellular Proteomics 6: 439–450.

Collins BC, Gillet LC, Rosenberger G, et al. (2013) Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14‐3‐3 system. Nature Methods 10: 1246–1253.

Costanzo M, Baryshnikova A, Bellay J, et al. (2010) The genetic landscape of a cell. Science 327: 425–431.

Csardi G and Nepusz T (2006) The igraph software package for complex network research. InterJournal, Complex Systems 1695.

Cusick ME, Yu H, Smolyar A, et al. (2009) Literature‐curated protein interaction datasets. Nature Methods 6: 39–46.

Choi H, Larsen B, Lin ZY, et al. (2011) SAINT: probabilistic scoring of affinity purification‐mass spectrometry data. Nature Methods 8: 70–73.

Dreze M, Monachello D, Lurin C, et al. (2010) High‐quality binary interactome mapping. Methods in Enzymology 470: 281–315.

Edwards AM, Isserlin R, Bader GD, et al. (2011) Too many roads not taken. Nature 470: 163–165.

Eyckerman S, Verhee A, Der Heyden JV, et al. (2001) Design and application of a cytokine‐receptor‐based interaction trap. Nature Cell Biology 3: 1114–1119.

Garbutt CC, Bangalore PV, Kannar P and Mukhtar MS (2014) Getting to the edge: protein dynamical networks as a new frontier in plant‐microbe interactions. Frontiers in Plant Science 5: 312.

Gavin AC (2002) Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415: 141–147.

Gavin AC (2006) Proteome survey reveals modularity of the yeast cell machinery. Nature 440: 631–636.

Gentleman RC, Carey VJ, Bates DM, et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biology 5: R80.

Gillis J, Ballouz S and Pavlidis P (2014) Bias tradeoffs in the creation and analysis of protein‐protein interaction networks. Journal of Proteomics 100: 44–54.

Grossmann A, Benlasfer N, Birth P, et al. (2015) Phospho‐tyrosine dependent protein‐protein interaction network. Molecular Systems Biology 11: 794.

Han JD, Bertin N, Hao T, et al. (2004) Evidence for dynamically organized modularity in the yeast protein‐protein interaction network. Nature 430: 88–93.

Ideker T and Sharan R (2008) Protein networks in disease. Genome Research 18: 644–652.

Jia P and Zhao Z (2014) Network.assisted analysis to prioritize GWAS results: principles, methods and perspectives. Human Genetics 133: 125–138.

Jin G, Zhang S, Zhang XS and Chen L (2007) Hubs with network motifs organize modularity dynamically in the protein‐protein interaction network of yeast. PLoS One 2: e1207.

Kiran M and Nagarajaram HA (2013) Global versus local hubs in human protein‐protein interaction network. Journal of Proteome Research 12: 5436–5446.

Krogan NJ, Cagney G, Yu H, et al. (2006) Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440: 637–643.

Lievens S, Eyckerman S, Lemmens I and Tavernier J (2010) Large‐scale protein interactome mapping: strategies and opportunities. Expert Review of Proteomics 7: 679–690.

Mellacheruvu D, Wright Z, Couzens AL, et al. (2013) The CRAPome: a contaminant repository for affinity purification‐mass spectrometry data. Nature Methods 10: 730–736.

Michnick SW, Ear PH, Landry C, Malleshaiah MK and Messier V (2011) Protein‐fragment complementation assays for large‐scale analysis, functional dissection and dynamic studies of protein‐protein interactions in living cells. Methods in Molecular Biology 756: 395–425.

Mukhtar MS, Carvunis AR, Dreze M, et al. (2011) Independently evolved virulence effectors converge onto hubs in a plant immune system network. Science 333: 596–601.

Nesvizhskii AI (2012) Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments. Proteomics 12: 1639–1655.

Oliver S (2000) Guilt‐by‐association goes global. Nature 403: 601–603.

Orchard S, Kerrien S, Abbani S, et al. (2012) Protein interaction data curation: the International Molecular Exchange (IMEx) consortium. Nature Methods 9: 345–350.

Orchard S, Ammari M, Aranda B, et al. (2014) The MIntAct project – IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Research 42: D358–D363.

Petschnigg J, Wong V, Snider J and Stagljar I (2012) Investigation of membrane protein interactions using the split‐ubiquitin membrane yeast two‐hybrid system. Methods in Molecular Biology 812: 225–244.

Rolland T, Tasan M, Charloteaux B, et al. (2014) A proteome‐scale map of the human interactome network. Cell 159: 1212–1226.

Ryan DP and Matthews JM (2005) Protein‐protein interactions in human disease. Current Opinion in Structural Biology 15: 441–446.

Saito R, Smoot ME, Ono K, et al. (2012) A travel guide to Cytoscape plugins. Nature Methods 9: 1069–1076.

Salwinski L, Licata L, Winter A, et al. (2009) Recurated protein interaction datasets. Nature Methods 6: 860–861.

Sevimoglu T and Arga KY (2014) The role of protein interaction networks in systems biomedicine. Computational and Structural Biotechnology Journal 11: 22–27.

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.

Smoot ME, Ono K, Ruscheinski J, Wang PL and Ideker T (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27: 431–432.

Stark C, Breitkreutz BJ, Reguly T, et al. (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Research 34: D535–D539.

Teo G, Liu G, Zhang J, et al. (2014) SAINTexpress: improvements and additional features in Significance Analysis of INTeractome software. Journal of Proteomics 100: 37–43.

Varjosalo M, Sacco R, Stukalov A, et al. (2013) Interlaboratory reproducibility of large‐scale human protein‐complex analysis by standardized AP‐MS. Nature Methods 10: 307–314.

Venkatesan K, Rual JF, Vazquez A, et al. (2009) An empirical framework for binary interactome mapping. Nature Methods 6: 83–90.

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

Wessling R, Epple P, Altmann S, et al. (2014) Convergent targeting of a common host protein‐network by pathogen effectors from three kingdoms of life. Cell Host & Microbe 16: 364–375.

Woodsmith J and Stelzl U (2014) Studying post‐translational modifications with protein interaction networks. Current Opinion in Structural Biology 24: 34–44.

Wuchty S and Uetz P (2014) Protein‐protein Interaction Networks of E. coli and S. cerevisiae are similar. Scientific Reports 4: 7187.

Xin X, Gfeller D, Cheng J, et al. (2013) SH3 interactome conserves general function over specific form. Molecular Systems Biology 9: 652.

Yamada T and Bork P (2009) Evolution of biomolecular networks: lessons from metabolic and protein interactions. Nature Reviews. Molecular Cell Biology 10: 791–803.

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

Yu H, Zhu X, Greenbaum D, Karro J and Gerstein M (2004b) TopNet: a tool for comparing biological sub‐networks, correlating protein properties with topological statistics. Nucleic Acids Research 32: 328–337.

Yu H, Braun P, Yildirim MA, et al. (2008) High‐quality binary protein interaction map of the yeast interactome network. Science 322: 104–110.

Further Reading

Barabasi AL, Gulbahce N and Loscalzo J (2011) Network medicine: a network‐based approach to human disease. Nature Reviews Genetics 12: 56–68.

Braun P and Gingras AC (2012) History of protein‐protein interactions: from egg‐white to complex networks. Proteomics 12: 1478–1498.

Braun P, Aubourg S, Van Leene J, De Jaeger G and Lurin C (2013) Plant protein interactomes. Annual Review of Plant Biology 64: 161–187.

Han JD (2008) Understanding biological functions through molecular networks. Cell Research 18: 224–237.

Mitra K, Carvunis AR, Ramesh SK and Ideker T (2013) Integrative approaches for finding modular structure in biological networks. Nature Reviews Genetics 14: 719–732.

Zhang QC, Petrey D, Deng L, et al. (2012) Structure‐based prediction of protein‐protein interactions on a genome‐wide scale. Nature 490: 556–560.

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Marín‐de la Rosa, Nora, and Braun, Pascal(Sep 2015) Primer on Protein–Protein Interaction Maps. In: eLS. John Wiley & Sons Ltd, Chichester. [doi: 10.1002/9780470015902.a0006205.pub2]