From experimental approaches to computational techniques: a review on the prediction of protein-protein interactions

Authors:
Fiona Browne;Huiru Zheng;Haiying Wang;Francisco Azuaje
Affiliations:
Faculty of Computing and Engineering, University of Ulster Jordanstown Campus, Co. Antrim, UK;Faculty of Computing and Engineering, University of Ulster Jordanstown Campus, Co. Antrim, UK;Faculty of Computing and Engineering, University of Ulster Jordanstown Campus, Co. Antrim, UK;Laboratory of Cardiovascular Research, Public Research Centre for Health, Luxembourg
Venue:
Advances in Artificial Intelligence - Special issue on artificial intelligence in neuroscience and systems biology: lessons learnt, open problems, and the road ahead
Year:
2010

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Abstract

A crucial step towards understanding the properties of cellular systems in organisms is to map their network of protein-protein interactions (PPIs) on a proteomic-wide scale completely and as accurately as possible. Uncovering the diverse function of proteins and their interactions within the cell may improve our understanding of disease and provide a basis for the development of novel therapeutic approaches. The development of large-scale high-throughput experiments has resulted in the production of a large volume of data which has aided in the uncovering of PPIs. However, these data are often erroneous and limited in interactome coverage. Therefore, additional experimental and computational methods are required to accelerate the discovery of PPIs. This paper provides a review on the prediction of PPIs addressing key prediction principles and highlighting the common experimental and computational techniques currently employed to infer PPI networks along with relevant studies in the area.