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Structure-based prediction of protein-protein interactions on a genome-wide scale.

Authors
  • D, Accili
  • Tony Hunter
  • T, Maniatis
  • A, Califano
  • B, Honig
  • Qc, Zhang
  • D, Petrey
  • L, Deng
  • L, Qiang
  • Yu Shi
  • Ca, Thu
  • B, Bisikirska
  • C, Lefebvre
Type
Published Article
Journal
Nature
Publisher
Springer Nature
Volume
490
Issue
7421
Pages
556–560
Identifiers
DOI: 10.1038/nature11503
Source
Hunter Lab
License
Unknown

Abstract

The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms. Much of our present knowledge derives from high-throughput techniques such as the yeast two-hybrid assay and affinity purification, as well as from manual curation of experiments on individual systems. A variety of computational approaches based, for example, on sequence homology, gene co-expression and phylogenetic profiles, have also been developed for the genome-wide inference of protein-protein interactions (PPIs). Yet comparative studies suggest that the development of accurate and complete repertoires of PPIs is still in its early stages. Here we show that three-dimensional structural information can be used to predict PPIs with an accuracy and coverage that are superior to predictions based on non-structural evidence. Moreover, an algorithm, termed PrePPI, which combines structural information with other functional clues, is comparable in accuracy to high-throughput experiments, yielding over 30,000 high-confidence interactions for yeast and over 300,000 for human. Experimental tests of a number of predictions demonstrate the ability of the PrePPI algorithm to identify unexpected PPIs of considerable biological interest. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models combined with the exploitation of both close and remote geometric relationships between proteins.

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