Protein–protein interactions occur when two or more proteins bind together, often to carry out their biological function. Many of the most important molecular processes in the cell such as DNA replication are carried out by large molecular machines that are built from a large number of protein components organised by their protein–protein interactions. Protein interactions have been studied from the perspectives of biochemistry, quantum chemistry, molecular dynamics, chemical biology, signal transduction and other metabolic or genetic/epigenetic networks. Indeed, protein–protein interactions are at the core of the entire interactomics system of any living cell.
Interactions between proteins are important for the majority of biological functions. For example, signals from the exterior of a cell are mediated to the inside of that cell by protein–protein interactions of the signaling molecules. This process, called signal transduction, plays a fundamental role in many biological processes and in many diseases (e.g. cancers). Proteins might interact for a long time to form part of a protein complex, a protein may be carrying another protein (for example, from cytoplasm to nucleus or vice versa in the case of the nuclear pore importins), or a protein may interact briefly with another protein just to modify it (for example, a protein kinase will add a phosphate to a target protein). This modification of proteins can itself change protein–protein interactions. For example, some proteins with SH2 domains only bind to other proteins when they are phosphorylated on the amino acid tyrosine while bromodomains specifically recognise acetylated lysines. In conclusion, protein–protein interactions are of central importance for virtually every process in a living cell. Information about these interactions improves our understanding of diseases and can provide the basis for new therapeutic approaches.
Methods to investigate protein–protein interactions
As protein–protein interactions are so important there are a multitude of methods to detect them. Each of the approaches has its own strengths and weaknesses, especially with regard to the sensitivity and specificity of the method. A high sensitivity means that many of the interactions that occur in reality are detected by the screen. A high specificity indicates that most of the interactions detected by the screen are also occurring in reality.
High-throughput detection methods include yeast two-hybrid screening and affinity capture mass spectrometry. Yeast two hybrid screens allow for interactions between proteins that are never expressed in the same time and place, lowering the specificity of this method; affinity capture mass spectrometry does not have this drawback. Yeast two-hybrid data better indicates non-specific tendencies towards sticky interactions rather while affinity capture mass spectrometry better indicates functional in vivo protein–protein interactions.
The structures of many protein complexes have been unlocked by the technique of X-ray crystallography. Whereas many high throughput techniques to investigate protein interactions can only tell you which protein interacts with which other proteins, molecular structure can give fine details about which specific parts are interacting and what kinds of chemical bonds mediate that interaction. One of the earliest protein structures to be solved was that of haemoglobin by Perutz and colleagues, which is a complex of four proteins: two alpha chains and two beta chains. As the number of structures available for protein complexes grew researchers began to investigate the underlying principles of protein–protein interactions. Based on just three structures of the insulin dimer, trypsin-pancreatic trypsin inhibitor complex and oxyhaemoglobin, Cyrus Chothia and Joel Janin found that between 1,130 and 1,720 Angstroms2 of surface area was removed from contact with water indicating that hydrophobicity was the major factor stabilising protein–protein interactions. Later studies refined the buried surface area of the majority of interactions to be 1,600±350 Angstroms2. However, much larger interaction interfaces were observed that were associated with large changes in conformation of one of the interaction partners.
Visualization of networks
Visualization of protein–protein interaction networks is a popular application of scientific visualization techniques. Although protein interaction diagrams are common in textbooks, diagrams of whole cell protein interaction networks were not as common since the level of complexity made them difficult to generate. One example of a manually produced molecular interaction map is Kurt Kohn's 1999 map of cell cycle control.
Drawing on Kohn's map, in 2000 Schwikowski et al. published a paper on protein–protein interactions in yeast, linking together 1,548 interacting proteins determined by two-hybrid testing. They used a layered graph drawing method to find an initial placement of the nodes and then improved the layout using a force-based algorithm.
Methods for identifying interacting proteins have defined hundreds of thousands of interactions. These interactions are collected together in specialised biological databases that allow the interactions to be assembled and studied further. The first of these databases was DIP, the database of interacting proteins. Since that time a large number of further database collections have been created such as BioGRID, STRING and ConsensusPathDB.
- Fuzzy complex
- Complex systems biology
- Protein–protein interaction prediction
- Protein–protein interaction screening
- Types of protein–protein interactions
- Phizicky, E. M.; Fields, S. (1995). "Protein-protein interactions: Methods for detection and analysis". Microbiological reviews 59 (1): 94–123. PMC 239356. PMID 7708014.
- Brettner, L. M.; Masel, J. (2012). "Protein stickiness, rather than number of functional protein-protein interactions, predicts expression noise and plasticity in yeast". BMC Systems Biology 6: 128. doi:10.1186/1752-0509-6-128. PMC 3527306. PMID 23017156.
- Janin, J.; Chothia, C. (1990). "The structure of protein-protein recognition sites". The Journal of biological chemistry 265 (27): 16027–16030. PMID 2204619.
- Perutz, M. F.; Rossmann, M. G.; Cullis, A. F.; Muirhead, H.; Will, G.; North, A. C. (1960). "Structure of haemoglobin: A three-dimensional Fourier synthesis at 5.5-A. Resolution, obtained by X-ray analysis". Nature 185 (4711): 416–422. doi:10.1038/185416a0. PMID 18990801.
- Chothia, C.; Janin, J. (1975). "Principles of protein-protein recognition". Nature 256 (5520): 705–708. doi:10.1038/256705a0. PMID 1153006.
- De Las Rivas, J.; Fontanillo, C. (2010). "Protein–Protein Interactions Essentials: Key Concepts to Building and Analyzing Interactome Networks". In Lewitter, Fran. PLoS Computational Biology 6 (6): e1000807. doi:10.1371/journal.pcbi.1000807. PMC 2891586. PMID 20589078.
- Kohn, K. W. (1999). "Molecular interaction map of the mammalian cell cycle control and DNA repair systems". Molecular biology of the cell 10 (8): 2703–2734. PMC 25504. PMID 10436023.
- Schwikowski, B.; Uetz, P.; Fields, S. (2000). "A network of protein-protein interactions in yeast". Nature Biotechnology 18 (12): 1257–1261. doi:10.1038/82360. PMID 11101803.
- Rigaut, G.; Shevchenko, A.; Rutz, B.; Wilm, M.; Mann, M.; Séraphin, B. (1999). "A generic protein purification method for protein complex characterization and proteome exploration". Nature Biotechnology 17 (10): 1030–1032. doi:10.1038/13732. PMID 10504710.
- Prieto, C.; De Las Rivas, J. (2006). "APID: Agile Protein Interaction DataAnalyzer". Nucleic Acids Research 34 (Web Server issue): W298–W302. doi:10.1093/nar/gkl128. PMC 1538863. PMID 16845013.
- Michael Kohl, Sebastian Wiese, and Bettina Warscheid (2011) Cytoscape: Software for Visualization and Analysis of Biological Networks. In: Michael Hamacher et al. (eds.), Data Mining in Proteomics: From Standards to Applications, Methods in Molecular Biology, vol. 696, DOI 10.1007/978-1-60761-987-1_18
- Xenarios, I.; Rice, D. W.; Salwinski, L.; Baron, M. K.; Marcotte, E. M.; Eisenberg, D. (2000). "DIP: The database of interacting proteins". Nucleic acids research 28 (1): 289–291. doi:10.1093/nar/28.1.289. PMC 102387. PMID 10592249.
|About Protein–protein interaction|
- Calculating protein–DNA binding maps for chromatin
- Proteins and Enzymes at the Open Directory Project
- Casado-Vela J, Matthiesen R, Sellés S, Naranjo, JR Protein-Protein Interactions: Gene Acronym Redundancies and Current Limitations Precluding Automated Data Integration Proteomes 2013, 1(1), 3-24.