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Bioinformatics Vol. 17 no. 5 2001
Pages 455-460
© 2001 Oxford University Press

Predicting protein–protein interactions from primary structure

Joel R. Bock and David A. Gough *

Department of Bioengineering, 9500 Gilman Drive, University of California, San Diego, La Jolla, CA 92093-0412, USA

Received on August 22, 2000 ; revised on November 22, 2000 ; accepted on January 4, 2001

Motivation: An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. The expectation is that this will provide a fuller appreciation of cellular processes and networks at the protein level, ultimately leading to a better understanding of disease mechanisms and suggesting new means for intervention. This paper addresses the question: can protein–protein interactions be predicted directly from primary structure and associated data? Using a diverse database of known protein interactions, a Support Vector Machine (SVM) learning system was trained to recognize and predict interactions based solely on primary structure and associated physicochemical properties.

Results: Inductive accuracy of the trained system, defined here as the percentage of correct protein interaction predictions for previously unseen test sets, averaged 80% for the ensemble of statistical experiments. Future proteomics studies may benefit from this research by proceeding directly from the automated identification of a cell’s gene products to prediction of protein interaction pairs.

Contact: dgough{at}bioeng.ucsd.edu

* To whom correspondence should be addressed.


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