Premium
Assessment of prediction accuracy of protein function from protein–protein interaction data
Author(s) -
Hishigaki Haretsugu,
Nakai Kenta,
Ono Toshihide,
Tanigami Akira,
Takagi Toshihisa
Publication year - 2001
Publication title -
yeast
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.923
H-Index - 102
eISSN - 1097-0061
pISSN - 0749-503X
DOI - 10.1002/yea.706
Subject(s) - protein function prediction , biology , computational biology , function (biology) , protein–protein interaction , protein function , open reading frame , genome , genetics , gene , peptide sequence
Abstract Functional prediction of open reading frames coded in the genome is one of the most important tasks in yeast genomics. Among a number of large‐scale experiments for assigning certain functional classes to proteins, experiments determining protein–protein interaction are especially important because interacting proteins usually have the same function. Thus, it seems possible to predict the function of a protein when the function of its interacting partner is known. However, in vitro experiments often suffer from artifacts and a protein can often have multiple binding partners with different functions. We developed an objective prediction method that can systematically include the information of indirect interaction. Our method can predict the subcellular localization, the cellular role and the biochemical function of yeast proteins with accuracies of 72.7%, 63.6% and 52.7%, respectively. The prediction accuracy rises for proteins with more than three binding partners and thus we present the open prediction results for 16 such proteins. Copyright © 2001 John Wiley & Sons, Ltd.