Predicting protein–protein interactions from primary structure

• DOI: 10.1093/bioinformatics/17.5.455
• Publication Date: 2002-07-26T22:49:38Z
• Authors: Joel R. Bock; David A. Gough
• Keywords: 0301 basic medicine; 03 medical and health sciences; Chemical Phenomena; Databases, Factual; Proteome; Artificial Intelligence; Chemistry, Physical; Computational Biology; Proteins; Software

Abstract 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: [e-mail] * To whom correspondence should be addressed.