Protein-protein interaction

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.^[1] 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.^[2]

Structure

The structures of many protein complexes have been unlocked by the technique of X-ray crystallography.^[3] 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.^[4] 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.^[5] 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.^[3]

Visualization of networks

Visualization of protein–protein interaction networks is a popular application of scientific visualization techniques.^[6] 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.^[7]

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.^[8][9][10]

The Cytoscape software is a widely used application to visualise protein–protein interaction networks.^[11]

Database collections

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.^[12] Since that time a large number of further database collections have been created such as BioGRID, STRING and ConsensusPathDB.

See also

• Interactome
• Interactomics
• Fuzzy complex
• Complex systems biology
• Protein–protein interaction prediction
• Protein–protein interaction screening
• Types of protein–protein interactions

References

External links

• Calculating protein–DNA binding maps for chromatin
• DMOZ
• 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.