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

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.

• Publication year

  2012

• Venue

  Nature

• Publication date

  2012-08-21

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1038/nature11503PMID 23023127PMCID 3482288
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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