Publication Date:
2012-10-02
Description:
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.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3482288/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉 〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3482288/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Zhang, Qiangfeng Cliff -- Petrey, Donald -- Deng, Lei -- Qiang, Li -- Shi, Yu -- Thu, Chan Aye -- Bisikirska, Brygida -- Lefebvre, Celine -- Accili, Domenico -- Hunter, Tony -- Maniatis, Tom -- Califano, Andrea -- Honig, Barry -- CA082683/CA/NCI NIH HHS/ -- CA121852/CA/NCI NIH HHS/ -- DK057539/DK/NIDDK NIH HHS/ -- GM030518/GM/NIGMS NIH HHS/ -- GM094597/GM/NIGMS NIH HHS/ -- R01 CA082683/CA/NCI NIH HHS/ -- R01 DK057539/DK/NIDDK NIH HHS/ -- R01 GM030518/GM/NIGMS NIH HHS/ -- R01 NS043915/NS/NINDS NIH HHS/ -- R01NS043915/NS/NINDS NIH HHS/ -- U54 CA121852/CA/NCI NIH HHS/ -- U54 GM094597/GM/NIGMS NIH HHS/ -- Howard Hughes Medical Institute/ -- England -- Nature. 2012 Oct 25;490(7421):556-60. doi: 10.1038/nature11503. Epub 2012 Sep 30.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Howard Hughes Medical Institute, Columbia University, New York, New York 10032, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/23023127" target="_blank"〉PubMed〈/a〉
Keywords:
*Algorithms
;
Animals
;
Bayes Theorem
;
Brain/metabolism
;
Cadherins/metabolism
;
High-Throughput Screening Assays
;
Humans
;
Matrix Attachment Region Binding Proteins/metabolism
;
Mice
;
Models, Molecular
;
PPAR gamma/metabolism
;
Phylogeny
;
Protein Binding
;
Protein Conformation
;
Protein Interaction Mapping/*methods
;
*Protein Interaction Maps
;
Protein Kinases/chemistry/metabolism
;
Proteins/*chemistry/*metabolism
;
Proteome/chemistry/metabolism
;
Proteomics/*methods
;
ROC Curve
;
Reproducibility of Results
;
Saccharomyces cerevisiae/chemistry/metabolism
;
Suppressor of Cytokine Signaling Proteins/metabolism
;
Transcription Factors/metabolism
Print ISSN:
0028-0836
Electronic ISSN:
1476-4687
Topics:
Biology
,
Chemistry and Pharmacology
,
Medicine
,
Natural Sciences in General
,
Physics
Permalink
