In previous years, scientists have begun understanding the significance of proteins and protein interactions. The direct connection of those with human diseases is now unquestionable and proteomics have become a scientific section of great research interest. In this chapter, we present a detailed description of the nature of protein interactions and describe the more important methodologies that are being used for their detection. Moreover, we review the mechanisms leading to diseases and involving protein interactions and refer to specific diseases such as Huntington’s disease and cancer. Lastly, we give an overview of the most popular computational methods that are used for the prediction or the healing of the diseases.
The recent completion of many genome-sequencing projects of various organisms, from viruses to mammals, is undoubtedly the greatest triumph of molecular biology since the discovery of the DNA double helix. After the complete genome sequencing of many organisms, including human, the focus of molecular biology has gradually shifted the interest from genomes to proteomes, in order to explore and discover the function of proteins (Eisenberg et al., 2000; Pandey et al., 2000).
One of the great challenges in the protein field is to reconstruct the complete protein interaction network within the cells, the so-called interactome. There is great difficulty in achieving this goal as the nature of the protein interactions varies depending on many environmental conditions that affect the cell (Nooren et al., 2003). However, due to the fact that protein interactions play a vital role in the basic functions of an organism’s cell, analysis of these networks will unravel the secrets of the pathways in which the under question interactions are detected and ultimately provide insights in how diseases are developed (Sam et al., 2007).
Several methods, which will be detailed within this book chapter, exist for the detection of protein interactions. During the last years, new high-throughput methodologies are being used to detect a great amount of protein interactions with a single experiment (Piehler 2005). Unfortunately, these methods are error-prone, therefore the generated data need further analysis (Droit et al., 2005). Today, large amounts of protein interactions of many organisms are stored in large on-line databases and are available for academic purposes.
These data are useful in order to better understand the connection between protein interactions and diseases (Chen 2006). In this chapter, we present a detailed description of protein interactions and a full overview of the approaches taking advantage of these, to better understand specific diseases.
The chapter is organized as follows: the first section reviews the nature of protein interactions and various experimental and computational methods for detecting and predicting those. The most important databases used for storing and integrating protein interactions and protein interactions associated with disease are described, whereas the recent information about the human interactome is mentioned. The second section describes mechanisms of protein interactions that have been shown to lead to disease and the third section describes the computational methods that are used for the holistic understanding of specific diseases.
Key Terms in this Chapter
Protein Interactions: Protein interactions refer to the association of protein molecules with proteins, DNA or any other molecule and the study of these associations from the perspective of biochemistry, network and signal transduction.
Protein Network: A protein network is a map of protein protein interaction. The network is usually presented as a graph where nodes indicate proteins and links between them indicate the interactions between the proteins.
ChIP: Abbreviation for Chromatin Immunoprecipitation. It refers to a procedure used to determine whether a given protein binds to or is localized to a specific DNA sequence in vivo.
Protein Complexes: Protein complex is a group of two or more associated proteins, formed by protein-protein interaction. It is usually stable over time and it is a form of quaternary structure.
Databases: A database is a collection of records or data that are stored in a computer in such a way that a computer program can easily select desired pieces of data.
SDS-PAGE: Abbreviation for sodium dodecyl sulfate polyacrylamide gel electrophoresis. This is a technique used in biochemistry, genetics and molecular biology to separate proteins according to their electrophoretic mobility.
TAP: Abbreviation for Tandem Affinity Purification. It involves the fusion of the TAP tag to the target protein of interest and the introduction of the construct into the cognate host cell or organism.
Diseases: The terms disease refers to the abnormal situation of a living organism that impairs function. It may include disabilities, disorders syndromes and infections.
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