School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
Bioinformatics. 2011 Jan 1;27(1):111-7. doi: 10.1093/bioinformatics/btq652. Epub 2010 Nov 25.
Advanced technologies are producing large-scale protein-protein interaction data at an ever increasing pace. A fundamental challenge in analyzing these data is the inference of protein machineries. Previous methods for detecting protein complexes have been mainly based on analyzing binary protein-protein interaction data, ignoring the more involved co-complex relations obtained from co-immunoprecipitation experiments.
Here, we devise a novel framework for protein complex detection from co-immunoprecipitation data. The framework aims at identifying sets of preys that significantly co-associate with the same set of baits. In application to an array of datasets from yeast, our method identifies thousands of protein complexes. Comparing these complexes to manually curated ones, we show that our method attains very high specificity and sensitivity levels (∼ 80%), outperforming current approaches for protein complex inference.
Supplementary information and the program are available at http://www.cs.tau.ac.il/~roded/CODEC/main.html.
先进的技术正在以越来越快的速度产生大规模的蛋白质-蛋白质相互作用数据。分析这些数据的一个基本挑战是蛋白质机器的推断。以前检测蛋白质复合物的方法主要基于分析二进制蛋白质-蛋白质相互作用数据，而忽略了从免疫沉淀实验中获得的更复杂的共复合物关系。
在这里，我们设计了一种从免疫共沉淀数据中检测蛋白质复合物的新框架。该框架旨在识别与同一组诱饵显著共同关联的一组猎物。在应用于来自酵母的一系列数据集时，我们的方法可以识别数千个蛋白质复合物。将这些复合物与人工 curated 的复合物进行比较，我们表明我们的方法达到了非常高的特异性和灵敏度水平（约 80%），优于当前的蛋白质复合物推断方法。
补充信息和程序可在 http://www.cs.tau.ac.il/~roded/CODEC/main.html 获得。
