Functional organization of the yeast proteome by a yeast interactome map

被引:15
|
作者
Valente, Andre X. C. N. [1 ,2 ]
Roberts, Seth B. [3 ,4 ]
Buck, Gregory A. [3 ,4 ]
Gao, Yuan [5 ]
机构
[1] Biocant, Unidade Sistemas Biol, P-3060197 Cantanhede, Portugal
[2] Univ Coimbra, Ctr Neurociencias & Biol Celular, P-3030790 Coimbra, Portugal
[3] Virginia Commonwealth Univ, Ctr Study Biol Complex, Richmond, VA 23284 USA
[4] Virginia Commonwealth Univ, Dept Microbiol & Immunol, Richmond, VA 23284 USA
[5] Virginia Commonwealth Univ, Dept Comp Sci, Richmond, VA 23284 USA
基金
美国国家卫生研究院;
关键词
computational biology; protein interaction networks; systems biology; MYOCARDIAL-INFARCTION; KINASE PATHWAY; GENE ONTOLOGY; HOG1; KINASE; NETWORKS; IDENTIFICATION; KINETOCHORE; COMPLEXES; DATABASE; GENOMES;
D O I
10.1073/pnas.0808624106
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
It is hoped that comprehensive mapping of protein physical interactions will facilitate insights regarding both fundamental cell biology processes and the pathology of diseases. To fulfill this hope, good solutions to 2 issues will be essential: (i) how to obtain reliable interaction data in a high-throughput setting and (ii) how to structure interaction data in a meaningful form, amenable to and valuable for further biological research. In this article, we structure an interactome in terms of predicted permanent protein complexes and predicted transient, nongeneric interactions between these complexes. The interactome is generated by means of an associated computational algorithm, from raw high-throughput affinity purification/mass spectrometric interaction data. We apply our technique to the construction of an interactome for Saccharomyces cerevisiae, showing that it yields reliability typical of low-throughput experiments from high-throughput data. We discuss biological insights raised by this interactome including, via homology, a few related to human disease.
引用
收藏
页码:1490 / 1495
页数:6
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