2009Proceedings of the National Academy of SciencesOpen access

Functional organization of the yeast proteome by a yeast interactome map

André Valente, Seth B. Roberts, Gregory A. Buck, Yuan Gao

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Abstract

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.

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What this paper is about

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.

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Available abstract

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.

Key concepts: Interactome, Computational biology, Throughput, Proteome, Saccharomyces cerevisiae, Computer science, Yeast, Protein–protein interaction

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