title: Predicting protein--protein interactions from primary structure.
abstract: BACKGROUND
  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.
authors.affiliation: Department of Bioengineering, 9500 Gilman Drive, University of California, San Diego, La Jolla, CA 92093-0412, USA.
authors.forename: J R
authors.initials: JR
authors.lastname: Bock
authors.forename: D A
authors.initials: DA
authors.lastname: Gough
mesh_terms: D001185:Artificial Intelligence; D055598:Chemical Phenomena; D002627:Chemistry, Physical; D019295:Computational Biology; D016208:Databases, Factual; D011506:Proteins / Q000737:chemistry* / Q000502:physiology*; D020543:Proteome; D012984:Software
chemical_list: D011506:Proteins; D020543:Proteome