Valente André X C N, Roberts Seth B, Buck Gregory A, Gao Yuan
Unidade de Sistemas Biológicos, Biocant, 3060-197 Cantanhede, Portugal.
Proc Natl Acad Sci U S A. 2009 Feb 3;106(5):1490-5. doi: 10.1073/pnas.0808624106. Epub 2009 Jan 21.
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.
人们希望蛋白质物理相互作用的全面图谱将有助于深入了解基本的细胞生物学过程和疾病病理学。为实现这一希望，解决两个问题的良好方案至关重要：（i）如何在高通量环境中获得可靠的相互作用数据，以及（ii）如何以有意义的形式构建相互作用数据，使其适合并有利于进一步的生物学研究。在本文中，我们根据预测的永久性蛋白质复合物以及这些复合物之间预测的瞬时、非通用相互作用来构建一个相互作用组。该相互作用组是通过一种相关的计算算法从原始的高通量亲和纯化/质谱相互作用数据生成的。我们将我们的技术应用于酿酒酵母相互作用组的构建，表明它从高通量数据中产生了低通量实验典型的可靠性。我们讨论了这个相互作用组所引发的生物学见解，包括通过同源性，一些与人类疾病相关的见解。
