Discovery of Protein Interaction Sites

Discovery of Protein Interaction Sites

Haiquan Li (The Samuel Roberts Noble Foundation, Inc., USA), Jinyan Li (Nanyang Technological University, Singapore) and Xuechun Zhao (The Samuel Roberts Noble Foundation, Inc., USA)
Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-60566-010-3.ch106
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Physical interactions between proteins are important for many cellular functions. Since protein-protein interactions are mediated via their interaction sites, identifying these interaction sites can therefore help to discover genome-scale protein interaction map, thereby leading to a better understanding of the organization of living cell. To date, the experimentally solved protein interaction sites constitute only a tiny proportion among the whole population due to the high cost and low-throughput of currently available techniques. Computational methods, including many biological data mining methods, are considered as the major approaches in discovering protein interaction sites in practical applications. This chapter reviews both traditional and recent computational methods such as protein-protein docking and motif discovery, as well as new methods on machine learning approaches, for example, interaction classification, domain-domain interactions, and binding motif pair discovery.
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Proteins carry out most biological functions within living cells. They interact with each other to regulate cellular processes. Examples of these processes include gene expression, enzymatic reactions, signal transduction, inter-cellular communications and immunoreactions.

Protein-protein interactions are mediated by short sequence of residues among the long stretches of interacting sequences, which are referred to as interaction sites (or binding sites in some contexts). Protein interaction sites have unique features that distinguish them from other residues (amino acids) in protein surface. These interfacial residues are often highly favorable to the counterpart residues so that they can bind together. The favored combinations have been repeatedly applied during evolution (Keskin and Nussinov, 2005), which limits the total number of types of interaction sites. By estimation, about 10,000 types of interaction sites exist in various biological systems (Aloy and Russell, 2004).

To determine the interaction sites, many biotechnological techniques have been applied, such as phage display and site-directed mutagenesis. Despite all these techniques available, the current amount of experimentally determined interaction sites is still very small, less than 10% in total. It should take decades to determine major types of interaction sites using present techniques (Dziembowski and Seraphin, 2004).

Due to the limitation of contemporary experimental techniques, computational methods, especially biological data mining methods play a dominated role in the discovery of protein interaction sites, for example, in the docking-based drug design. Computational methods can be categorized into simulation methods and biological data mining methods. By name, simulation methods use biological, biochemical or biophysical mechanisms to model protein-protein interactions and their interaction sites. They usually take individual proteins as input, as done in protein-protein docking. Recently, data mining methods such as classification and clustering of candidate solutions contributed the accuracy of the approach. Data mining methods learn from large training set of interaction data to induce rules for prediction of the interaction sites. These methods can be further divided into classification methods and pattern mining methods, depending on whether negative data is required. Classification methods require both positive and negative data to develop discriminative features for interaction sites. In comparison, pattern mining methods learn from a set of related proteins or interactions for over-presented patterns, as negative data are not always available or accurate. Many homologous methods and binding motif pair discovery fall into this category.

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