Abstract
Protein-protein interaction networks (PPI) are one of the vital resources that are available for understanding the processes in a living cell. Protein associations are studied in different perspectives, including biochemistry, quantum chemistry, molecular dynamics, metabolic or genetic/epigenetic networks and so on. There are several experimental methods designed to probe these interactions, such as co-immunoprecipitation or affinity chromatography, mass spectrometry, yeast to hybrid and so on. These experimental techniques can be further categorized into low-throughput and high-throughput methods. All these techniques are labor-intensive methods. However, there are several computational tools developed to predict the protein-protein interaction network from experiment verified protein-protein interactions. There are several complementing efforts made to centralize protein-protein interaction data through the construction of databases which play a vital role in the prediction of protein-protein interactions from sequence and structural features. These PPI databases can be grouped under general databases (contains wide variety of organisms) and specialized databases (meant for specific organism). In this paper, we attempt to provide a summary of these recent and most widely used protein-protein interactions databases. However, computational approaches developed for PPI and their performance is out of the scope of this paper.
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Pedamallu, C.S., Ozdamar, L. (2014). A Review on Protein-Protein Interaction Network Databases. In: Pinto, A., Zilberman, D. (eds) Modeling, Dynamics, Optimization and Bioeconomics I. Springer Proceedings in Mathematics & Statistics, vol 73. Springer, Cham. https://doi.org/10.1007/978-3-319-04849-9_30
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