title: Structure-based prediction of protein-protein interactions on a genome-wide scale.
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.
authors.affiliation: Howard Hughes Medical Institute, Columbia University, New York, New York 10032, USA.
authors.forename: Qiangfeng Cliff
authors.initials: QC
authors.lastname: Zhang
authors.forename: Donald
authors.initials: D
authors.lastname: Petrey
authors.forename: Lei
authors.initials: L
authors.lastname: Deng
authors.forename: Li
authors.initials: L
authors.lastname: Qiang
authors.forename: Yu
authors.initials: Y
authors.lastname: Shi
authors.forename: Chan Aye
authors.initials: CA
authors.lastname: Thu
authors.forename: Brygida
authors.initials: B
authors.lastname: Bisikirska
authors.forename: Celine
authors.initials: C
authors.lastname: Lefebvre
authors.forename: Domenico
authors.initials: D
authors.lastname: Accili
authors.forename: Tony
authors.initials: T
authors.lastname: Hunter
authors.forename: Tom
authors.initials: T
authors.lastname: Maniatis
authors.forename: Andrea
authors.initials: A
authors.lastname: Califano
authors.forename: Barry
authors.initials: B
authors.lastname: Honig
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001921:Brain / Q000378:metabolism; D015820:Cadherins / Q000378:metabolism; D057166:High-Throughput Screening Assays; D006801:Humans; D036961:Matrix Attachment Region Binding Proteins / Q000378:metabolism; D051379:Mice; D008958:Models, Molecular; D047495:PPAR gamma / Q000378:metabolism; D010802:Phylogeny; D011485:Protein Binding; D011487:Protein Conformation; D025941:Protein Interaction Mapping / Q000379:methods*; D060066:Protein Interaction Maps; D011494:Protein Kinases / Q000737:chemistry / Q000378:metabolism; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D020543:Proteome / Q000737:chemistry / Q000378:metabolism; D040901:Proteomics / Q000379:methods*; D012372:ROC Curve; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000737:chemistry / Q000378:metabolism; D050826:Suppressor of Cytokine Signaling Proteins / Q000378:metabolism; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D015820:Cadherins; D036961:Matrix Attachment Region Binding Proteins; D047495:PPAR gamma; D011506:Proteins; D020543:Proteome; C521297:SATB2 protein, human; D050826:Suppressor of Cytokine Signaling Proteins; D014157:Transcription Factors; D011494:Protein Kinases